The ecology and silviculture of oaks [3rd edition] 978-1-78064-708-1, 1780647085, 9781780647739, 9781780647746

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The Ecology and Silviculture of Oaks 3rd Edition

The Ecology and Silviculture of Oaks 3rd Edition Paul S. Johnson US Department of Agriculture, Retired Forest Service Northern Research Station, USA

Stephen R. Shifley US Department of Agriculture Forest Service Northern Research Station, USA

Robert Rogers College of Natural Resources, Emeritus Professor of Forestry University of Wisconsin/Stevens Point, USA

Daniel C. Dey US Department of Agriculture Forest Service Northern Research Station, USA and

John M. Kabrick US Department of Agriculture Forest Service Northern Research Station, USA

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© CAB International 2019. All rights reserved. No part of this publication may be reproduced in any form or by any means, electronically, mechanically, by photocopying, recording or otherwise, without the prior permission of the copyright owners. A catalogue record for this book is available from the British Library, London, UK. Library of Congress Cataloging-in-Publication Data Names: Johnson, Paul S., author. | Shifley, Stephen R., author. | Rogers, Robert, 1941- author. | Dey, Daniel C., author. | Kabrick, John M., author. Title: The ecology and silviculture of oaks / Paul S. Johnson, Stephen R. Shifley, Robert Rogers, Daniel C. Dey, and John M. Kabrick. Description: 3rd edition. | Boston, MA : CABI, [2019] | Includes bibliographical references and index. Identifiers: LCCN 2018043937 (print) | LCCN 2018047020 (ebook) | ISBN 9781780647739 (ePDF) | ISBN 9781780647746 (ePub) | ISBN 9781780647081 (alk. paper) Subjects: LCSH: Oak--United States. | Oak--Ecology--United States. Classification: LCC SD397.O12 (ebook) | LCC SD397.O12 J64 2019 (print) | DDC 634.9/721--dc23 LC record available at https://lccn.loc.gov/2018043937 ISBN-13: 978 1 78064 708 1 (hardback) 978 1 78064 773 9 (ePDF) 978 1 78064 774 6 (ePub) Commissioning editor: Ward Cooper Editorial assistant: Emma McCann Production editor: Shankari Wilford Typeset by SPi, Pondicherry, India Printed and bound in the UK by Bell & Bain Ltd, Glasgow

Contents

Preface to the Third Edition

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Acknowledgementsxiii Introduction1 Conflicting Environmental Philosophies 1 Silviculture: a Consilient Discipline 4 References6 1  Oak-dominated Ecosystems 8 Introduction8 The Taxonomy of Oaks 8 The Geographic Distribution of US Oaks 11 Species ranges and groupings 11 Distribution of oaks by hierarchically classified ecoregions 14 Eastern Oak Forests 17 The Northern Hardwood Region 17 The Central Hardwood Region 24 The Southern Pine–Hardwood Region 31 The Forest–Prairie Transition Region 35 Western Oak Forests 39 The Southwestern Desert–Steppe Region 39 The Pacific Mediterranean–Marine Region 41 The Influence of Climate Change 45 Notes46 References46 2 Regeneration Ecology I: Flowering, Fruiting and Reproduction Characteristics 53 Introduction53 Flowering53 Male flowers 54 Female flowers 57 Factors Affecting Acorn Production 59 Weather60 Premature abscission 60 Variation in acorn production 61 Acorn Predation and Dispersal 66 Insects: destroyers of acorns 66 The significance of acorn dispersal by animals 73 Oak Seedling Establishment 83 Germination and initial establishment 83 Early growth 84 Seedling Sprouts 89 Shoot dieback and root:shoot ratio 89 Occurrence of shoot dieback 91 v

Stump Sprouts and Related Growth Forms 94 Definitions and origins 94 Sprouting probability 97 Sprout growth and mortality 100 Dominance probability 101 Notes104 References106 3  Regeneration Ecology II: Population Dynamics 121 Introduction121 Regeneration Strategy 122 Reproductive mechanisms: seeding and sprouting 122 Accumulation of oak reproduction 124 Fluctuation in population density 143 Regeneration Potential 147 Regeneration mode 148 Modelling theory and objectives 154 Stand-level regeneration models: purpose, problems and limitations 156 Notes158 References159 4  Site Quality and Productivity 169 Introduction169 Measures of Site Quality and Productivity 169 The National Cooperative Soil Survey and Site Productivity 172 Relation of Site Productivity to Ecological Classification 172 Productivity and Related Self-sustaining Properties of Oak Forests 174 Effects of harvesting on site productivity 175 Modifying site productivity through fertilization 177 Methods of Evaluating Site Quality 178 Site index 178 Site evaluation alternatives to site index 185 Notes189 References189 5  Development of Natural Stands 195 Introduction195 Forest Canopy Layers 195 Disturbance196 Disturbance type 196 Disturbance size and frequency of occurrence 197 Response to disturbance 198 Development of Even-aged Stands 199 The stand initiation stage 201 The stem exclusion stage 203 The understorey reinitiation stage 208 The complex stage 213 Development of Uneven-aged Stands 215 Disturbance–Recovery Cycles 217 References221 6  Self-thinning and Stand Density 225 Introduction225

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Self-thinning225 Reineke’s model 225 The −3/2 rule 226 Stand Density and Stocking 231 Terminology231 Maximum and minimum growing space 233 Stand density diagrams 237 Note245 References246 7  Fire and Oak Forests 249 Introduction249 Attributes of Oak Fire Regimes 249 Fire intensity 250 Fire frequency 250 Fire season 252 Fire extent 253 Fire type 253 Fire severity 254 The History of Fire and Oaks 255 Paleo-history255 Historical to modern era 256 Changes over time in fire attributes 257 Fire and the Oak Regeneration Problem 260 Fire in the Life Cycle of Oaks 263 Period of flowering, pollination and acorn production 263 Period of acorn germination and seedling establishment 264 Period of seedling development 265 Period of recruitment into the overstorey 270 Fire Effects on Ground Flora 270 Native ground flora 272 Invasive species 274 Fire Effects on Tree Quality, Volume and Value 276 Overstorey mortality 276 Timber quality and value 277 Notes280 References281 8  Even-aged Silvicultural Methods 296 Introduction296 Natural Regeneration Methods 296 The clearcutting method 297 The shelterwood method 311 The seed tree method 314 Artificial Regeneration Methods 314 Intermediate Cuttings 315 Definitions and theory 315 Application316 Economic, Environmental and Social Considerations 324 The clearcutting method 324 The shelterwood and seed tree methods 326 References326

Contents

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  9  Uneven-aged Silvicultural Methods 335 Introduction335 The Single-tree Selection Method 335 Principles of application 335 Applicability to oak forests 347 The Group Selection Method 361 Economic, Environmental and Social Considerations 367 Notes369 References369 10  Artificial Regeneration 377 Introduction377 Site Evaluation and Species Selection 378 Uplands378 Bottomlands378 Artificial Regeneration Methods 379 Direct seeding 380 Planting seedlings 382 Note399 References399 11  Managing Forest Health 409 Introduction409 Gypsy Moth 409 Oak Decline 417 Symptoms418 Treatment419 Oak Wilt 420 Symptoms and spread 421 Treatment and prevention 421 Rapid White Oak Mortality 423 Symptoms424 Spread424 Treatment and prevention 424 Sudden Oak Death 424 Symptoms424 Spread425 Treatment and prevention 425 Deer426 Note427 References427 12  Silvicultural Methods for Oak Savannahs and Woodlands 432 Introduction432 Characteristics and Extent 432 Disturbance Processes 434 Silvicultural Concepts and Methods 439 Estimating light intensity 439 Restoration and Maintenance 444 Restoration445 Maintenance447 Note449 References449

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13  Silvicultural Methods for Selected Ecosystem Services 455 Introduction455 Managing Stands for Acorn Production 455 Assessing and predicting acorn crops 455 Guidelines for sustaining acorn production 462 Managing Oak Forests for Wildlife 466 Managing stand structure for wildlife 467 Oaks as wildlife food 471 Managing snags and coarse woody debris 473 Managing tree cavities 475 Managing Stands for Biomass Production and Carbon Sequestration 480 Sequestering carbon in trees 480 Estimating biomass and carbon 481 Managing for biomass production 484 Managing for carbon sequestration 485 Old-growth Oak Forests 488 Extent and characteristics 488 Silvicultural options 492 Old-growth oak forests at the landscape scale 493 Aesthetics495 Stand-level aesthetics 495 Landscape-level aesthetics 498 Notes500 References500 14  Managing Oak Forests in a Changing Climate 511 Introduction511 Climate Change: When, Where and How Much 512 Climate Change and the Distribution of Oaks 512 Managing Oak Forests in a Changing Climate 513 Mitigation strategies 514 Adaptation strategies 515 Metrics for Assessing Climate Change Vulnerability 518 Metrics for national climate change vulnerability assessments 518 Metrics for regional- and stand-scale vulnerability assessments 520 Practical Management Considerations 523 Perspective on Managing Oaks in a Changing Climate 525 References526 15  Growth and Yield 530 Introduction530 Growth of an Oak 530 Annual phenology 530 Diameter growth 531 Height growth 537 Survival rates 542 Stand Growth 544 Growth and yield in even-aged stands 544 Growth and yield in uneven-aged stands 548 Growth and Yield Models 549 Modelling methods 549 Stand-level models for oaks 550

Contents ix

Stand table projection models 553 Individual-tree-level models for oaks 553 Forest landscape models 557 Estimating ingrowth 559 Model evaluation 561 Volume Equations 562 Regional Patterns in Yield and Productivity 562 Note564 References564 Appendix 1  Common and Scientific Names of Species Mentioned

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Appendix 2 Forest Cover Types of Eastern USA Dominated by Oaks or Oaks Mixed with Other Species

584

Appendix 3 Forest Cover Types of Western USA Dominated by Oaks or Oaks Mixed with Other Species

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Appendix 4 Formulae for Converting Site Index of One Species to Another in Unglaciated Regions of Indiana, Kentucky, Ohio and West Virginia

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Appendix 5 Formulae for Converting Site Indexes for Oaks and Associated Species from One Species to Another in Three Regions

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Appendix 6 Formulae for Converting Yellow-poplar Site Index to Oak Site Indexes in the Virginia-Carolina Piedmont

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Appendix 7 Parameter Estimates for Site Index Asymptotes (S) and Species Coefficients (b) for Deriving Height/dbh Site Index Curves from Equation 4.1

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Appendix 8  Common Conversions

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Index597

x Contents

Preface to the Third Edition

The third edition of The Ecology and Silviculture of Oaks is a reorganization and expansion of earlier editions. It has been expanded to 15 chapters from the earlier 10. The new chapters emphasize topics previously given briefer treatment. Expanded topics include artificial regeneration, fire effects and prescribed burning, silvicultural methods for woodlands and savannahs, forest health, and managing oaks in a changing climate. As in earlier editions, the book is intended for forest and wildlife managers, ecologists, silviculturists, environmentalists, students of those fields, and others interested in sustaining oak forests for commodities and other ecosystem services. Our goal is to provide an accessible approach to thinking about oak forests as responsive ecosystems. We assume readers have some prior background in forest and forestry literature, but we also recognize that everyone differs in their knowledge of those subjects. We have thus used Helm’s The Dictionary of Forestry1 to standardize definitions. Readers may find it a useful companion to this book. We gratefully acknowledge the contributions of the many scientists and practitioners who have contributed to the collective knowledge of oaks. Published information on oak forests continues to rapidly accumulate, now made apparent through widely available online access. A search of the US Forest Service online publication repository reveals more than 1200 oak-related manuscripts were published by agency scientists and their collaborators since the second edition of this book was printed in 2009. That accounts for nearly one-third of the 3900 oak-related publications in the Forest Service repository. For the period 2009–2018, Google Scholar reports there are more than 17,000 new publications with the phrase ‘oak forest’ in the title or keywords. Despite the prodigious number of recent oak publications, some of the older publications continue to grow in importance and relevance, although they are not prominent in online searches. Examples include G.  Luther Schnur’s 1937 ‘Yield, stand, and volume tables for even-aged upland oak forests’2 and Samuel Gingrich’s 1967 landmark ‘Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States’.3 The continuing importance of these and other older works might easily be overlooked by a younger, contemporary generation of forest managers and scientists. The nature of forestry information delivery is changing rapidly. In the past, the information on oak silviculture in the USA has been organized primarily by forest types and ecoregions: bottomland oaks, upland oaks in the Ozark Highlands, northern pine–oak woodlands, northern hardwoods, mixed mesophytic woodlands, Allegheny hardwoods, central hardwoods, southern hardwoods, southern pine–oak forests, western oak woodlands, and more. Now, silviculturally relevant information increasingly is delivered in the form of online maps that present site-specific information about locations and impacts of factors such as invasive species, insect pests, diseases, hazardous fuels, historic fire regimes, soils, site quality, climate change, transportation networks, endangered species, sensitive areas, and even sites with a high (or low) propensity for successful oak regeneration. In the future, map-based interfaces and databases will be essential in synthesizing basic information to support silvicultural decision making within the context of the surrounding landscape. This will be a particularly important way to accumulate site-specific knowledge in an era where timber is no longer the primary ecosystem service expected from oak forests and forest managers have diverse educational and experiential backgrounds. Such developments will not eliminate the need for traditional stand-based guides or for books such as this one, but they will provide the next generation of silviculturists and forest planners with better access to detailed, site-specific information for preparing silvicultural prescriptions. The five authors of the new edition collectively bring nearly two centuries of experience to the subject. Included are two new authors: Drs Daniel Dey and John Kabrick, both of the Northern Research Station of the US Forest Service. They provide a combined 60 years of silvicultural research and management experience in upland and bottomland oak forests of the USA and southern Canada. We hope the infusion of their talent will provide the continuity and momentum to carry the book forward into new realms lying beyond this edition.

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The encouragement, advice, critiques and suggestions from our colleagues and collaborators have been enormous gifts to the preparation of this edition. Although the Acknowledgements section lists many contributors, it cannot fully capture the magnitude of the help, kindness and patience we have received. Paul S. Johnson Stephen R. Shifley Robert Rogers Daniel C. Dey John M. Kabrick July 2018

Notes 1

  Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. 2   Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc.nal.usda.gov/download/ CAT86200555/PDF (accessed 21 January 2019). 3   Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi.org/10.1093/forestscience/13.1.38

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Preface to the Third Edition

Acknowledgements

We appreciate the encouraging comments and helpful suggestions from the many readers that provided much of the motivation for this third edition. We gratefully acknowledge the invaluable assistance of many others, without which this book would not have been possible. For their technical assistance and for creating a productive and congenial work environment, we are indebted to Forest Service Northern Research Station employees Tim Bray, Dianne Brooks, Kenneth Davidson, Laura Herbeck, Kevin Huen, Marilyn Magruder, Allison Ramsey, Hoyt Richards and Neal Sullivan. Special thanks are accorded to William Dijak for his diligence and talent in preparing maps for Chapter 1, and to Kevin McCullough for the new colour plate showing the distribution of US oaks. We thank Lynn Roovers for exceptional help with graphics and countless other details, and Dacoda Maddox for adding digital links to thousands of literature citations in the book. Jim Lootens-White and colleagues created the Treesearch portal to all US Forest Service publications (https:// www.fs.usda.gov/treesearch/), and we have found it enormously helpful. For selflessly contributing their ideas, artwork, data, photographs, reviews, and technical and scientific knowledge, we are deeply indebted to the following current and former foresters and scientists of the United States Department of Agriculture (USDA) Forest Service: Leslie Brandt, Pat Brose, Robert Cecich, Stacy Clark, Jeffrey Goelz, Gerald Gottfried, Kurt Gottschalk, David Graney, James Guldin, Coeli Hoover, Jay Law, Patricia Leopold, David Loftis, Robert McQuilkin, Steve Meadows, Ross Melick, Bruce Moltzan, Paul Murphy, Greg Nowacki, Felix Ponder Jr, Ivan Sander, Callie Schweitzer, Martin Spetich, Kyle Steele, Richard Teck, Frank Thompson III, Gary Z. Wang, Dale Weigel, Daniel Yaussy and John Zasada. Even in retirement, Dr Susan Stout continues to be an inspiration on how to conduct and disseminate silviculture research. Drs Louis Iverson, Matthew Peters and Anantha Prasad of the US Forest Service generously provided current information and graphics that were of great help while preparing the new sections on climate change and carbon sequestration. We also thank all our partners in the University of Missouri School of Natural Resources and Auburn University for their valuable reviews, advice and support. These include Drs Francisco Aguilar, John Dwyer, H.E. Gene Garrett, Richard Guyette, Hong He, Benjamin Knapp, David Larsen, Bernie Lewis, Edward Loewenstein, Nancy Loewenstein, Rose-Marie Muzika, Stephen Pallardy, Sharon Reed, Michael Stambaugh and Dustin Walter. We are similarly indebted to Drs Jeffrey Ward (Connecticut Agricultural Experiment Station), Carl Ramm (Michigan State University), William Parker (Ontario Forestry Research Institute) and Willard Carmean (Lakehead University). We owe special thanks to Dr W. Carter Johnson, University of South Dakota at Brookings, for allowing us to reprint his incredible blue jay photos, and to Dr Bob Mosier, University of Wisconsin/Stevens Point, for his spectacular squirrel photograph. Drs W. Keith Moser (US Forest Service), Zoltan Somogyi (Hungarian Forest Research Institute) and Ronald Lanner (Placerville, California) were kind enough to write favourable reviews of the first edition of the book that increased its visibility and distribution. We thank the Missouri Department of Conservation, and especially Randy Jensen, for sharing inventory data on forest dynamics and coarse woody debris. Likewise, we thank the Pioneer Forest of Salem, Missouri, and especially Clint Trammel and Terry Cunningham for providing data describing outcomes of uneven-aged oak silviculture. We thank the USDA Forest Service and Northern Research Station Directors Michael Rains and Tony Ferguson; Deputy Director Lon Yeary; past and current Assistant Directors Donald Boelter, David Shriner, Thomas Schmidt and Ralph Crawford; and past and current Project Leaders Frank Thompson and Lynne Westphal for collectively allowing us the freedom and resources to complete three editions of this book. Perhaps it goes without saying that this book could not have been written without the many scientists and practitioners, past and present, who have contributed to the rich and growing body of literature in oak ecology and silviculture. We do not take their dedication and contributions to forest science for granted. The

xiii

value of long-term research programmes is evident in the literature cited in this book which spans 80 years and is sourced predominantly from work sponsored by the USDA, Forest Service. We are most grateful to our partners at CABI who have been gracious, patient and accommodating through all editions of this book. Ward Cooper, Emma McCann and Shankari Wilford, in particular, have been wonderful guides and mentors. We are deeply indebted to Priscilla Sharland who has amazed us with her skill and patience while editing this book. Finally, we thank our families for their encouragement and enduring support.

xiv Acknowledgements

Introduction

Conflicting Environmental Philosophies Ecology is the scientific study of the interrelations among living things and their environment. Ecological knowledge effects an awareness of precarious interdependencies among the myriad organisms, large and minuscule, between organisms and non-living components of ecosystems, and the pervasive human impacts that affect these relations. Ecology thus obviates our dependency on, and our relation to, natural processes and systems. Perhaps no science more so than ecology has generated more knowledge with implications relating to ethics, morality and human behaviour. In contrast, silviculture is the art and science of tending forests to meet human needs. Because silviculture is usually directly involved in the extraction of biomass, it produces disturbances along with associated ecological side effects. Silviculture is thus based on the planned use of controlled and directed disturbances to achieve defined human objectives. Ideally, it should be based on scientific principles that assure that specified silvicultural goals are consistent with preserving or improving a forest’s ecological qualities, are compatible with its natural dynamic, and thereby provide reasonable assurance of the forest’s sustainability. Like its parent discipline, forestry, silviculture evolved out of 17th-century Europe in response to purely utilitarian needs, especially for the timber required for sustaining the large naval armadas required for projecting colonial power in the late 18th century. Paramount among these concerns in Britain and France was a ready supply of pine and oak for ship masts and hulls. However, in the USA, serious concern over a declining forest resource did not occur until the late 19th century. By then exploitive logging had decimated the forests of eastern USA. A small but politically influential group of conservationists feared the same would happen to the western forests. This prompted the setting aside of forest reserves in the early 1890s from what

remained of the public domain in the west. In 1897, the Organic Act was passed, which specified that the purpose of the reserves was ‘to improve and protect the forest within the reservation, or for the purpose of securing favourable conditions of water flows, and to furnish a continuous supply of timber for the use and necessities of citizens of the United States’ (United States Congress, 1897). This landmark legislation specified that the forest reserves were intended for managed use, not for wilderness preservation. Following the recommendations of the American Forest Congress of 1905, the reserves were transferred from the Department of Interior to the Department of Agriculture. Known as the Transfer Act, it provided that funds from the sale of products or the use of land in the reserves be used for managing and developing the forest reserve system. This change heralded the implementation of an ambitious programme of scientific forest management under the direction of Gifford Pinchot, the first Chief of the United States Department of Agriculture (USDA) Forest Service. At that time, forestry was virtually an unknown discipline in the USA and forestry curricula in US universities were just emerging. Although politically controversial in its day, the conservation movement was hailed by its founders as not only environmentally wise, but also economically beneficial (Pinchot, 1987). Pinchot and the founders of the early forest conservation movement envisioned a scientifically based forestry that would not only provide conservation benefits but would also result in the economic stability of rural communities in forested regions. Such benefits would accrue, they argued, from the application of scientifically derived sustained yield principles, which would assure to perpetuity the even flow of timber and other commodities originating from the forest (Pinchot, 1987). Because the scientific underpinnings of sustained yield were largely invested in silviculture, and because silviculture has historically been justified on economic grounds, silviculture

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

1

philosophically straddled agronomy (i.e. growing trees as crops) and economics. However, modern silviculture has been broadened to include not only sustaining timber yields, but also sustaining noncommodity values and ecosystem services including old-growth forests, biodiversity, wildlife habitat, clean water, clean air, aesthetics, carbon sequestration and climate regulation. In this wider context, silviculture assumes application to a panoply of values that transcend economic utilitarianism. Despite the differences between the two disciplines, contemporary silviculture as it has been applied to most North American forests, remains naturally allied with and dependent upon ecology for much of its scientific underpinnings. The schism between silviculturists and some ecologists nevertheless runs deep. One source of this disunion emanates from the ecologists’ traditional focus on studying ecological processes in ecosystems largely unaffected or minimally affected by humans and drawing conclusions therefrom. In contrast, silviculturists depend on scientifically based knowledge of disturbance-mediated mechanisms to control and direct forest ecosystem processes for human benefit. Recovery from such disturbances is predicated on the assumption that forests are inherently resilient, that is capable of rapidly returning to their previous or other silviculturally directed state. The silviculturist’s anthropocentric view of the forest is often in conflict with the biocentric view, which elevates nature to a position superior to human selfinterest (Devall and Sessions, 1985; Chase, 1995; Ferry, 1995; Fox, 1995). Ecologists have begun to grapple with the rise of the Anthropocene, a proposed epoch characterized by significant, pervasive human influence on earth’s ecosystems and by the creation of novel ecosystems without historical precedent (Hobbs et al., 2009; Smith and Zeder, 2013). Despite the growing recognition of the role of anthropogenic influences in shaping contemporary oak forests, the biocentric viewpoint still dominates some segments of ecological thinking. The biocentrist’s agenda is centred on maintaining ‘natural’ ecosystems, including forest, in states free from human interference, and the need for establishing the preeminence of those states. From that perspective, human-mediated disturbance is seen as a disrupter of fragile ecosystems and the intended order of things. Moreover, such disruptions can potentially produce species extinctions and other irreversible environmental effects. The biocentric view therefore holds that the best way to preserve nature, wherever some

vestige of it remains, is to leave it alone (Devall and Sessions, 1985; Chase, 1995). Humans are viewed as just one of many organisms in the biosphere no more important than any other – and like all component organisms should be subordinate to the healthy functioning of the interactive whole (i.e. the ecosystem). Biocentrism is therefore egalitarian among organisms and premised on an inherent right to life of all species and life forms. By extension, maintaining ecosystems in their ‘natural’ state becomes a social imperative. A biocentrist thus may view silviculture, along with other human interferences in the development of forests, as ecologically threatening, if not ruinous. The biocentric interpretation of the ‘message’ from ecology is thus at irreconcilable odds with the interpretation from silviculture. Biocentrism nevertheless now occupies a position of social and political prominence (Chase, 1995). The connections between ecology and silviculture none the less are apparent and important, especially when silviculture is applied to forests of natural origin. In that setting, silviculture by itself may not introduce new species or populations (i.e. new genetic material) from outside the forest. Human energy expenditures are often limited only to those required in cutting and removing trees. Such relatively non-intensive practices have characterized the silviculture applied to oak forests of the USA. There, oak silviculture has largely followed an ecological model whereby forests are managed by directing their continually changing states, or ecological successions, through manipulation of existing on-site vegetation and propagules. This approach relies on periodic timber harvesting and usually natural regeneration to maintain or periodically recreate desired ecological states. It contrasts with the more intensive agronomic model used in growing pine plantations and other monotypes. The latter approach usually depends on artificial regeneration, the introduction of new and ‘improved’ genotypes, exotic species and other intensive and energy-expensive cultural methods like those used in agriculture, horticulture and agroforestry (growing trees intermixed with agricultural or horticultural crops). Nevertheless, the silvicultural methods that have been applied to oaks span the entire range of approaches from ecological to agronomic. In the public’s view, silviculture is an often confusing and controversial subject exacerbated by the claims of some environmentalists that it is an ecologically damaging enterprise that ‘seeks to accept “tree farms” in place of natural forests . . . The usual

2Introduction

approach . . . is to seek ever more intensive management, which spawns even more problems’ (Devall and Sessions, 1985: p. 146). By comparison, there is seemingly little controversy and confusion over the reason to preserve something in its natural state free from human interference if it is otherwise threatened with extinction – even though the method or means of preservation may be debatable. Likewise, the reason for the cultivation and harvest of a maize field is easily understood and accepted because of its purely utilitarian value, and its physical origins borne of human endeavour. Socially, silviculture is a more complicated issue. It is vulnerable in appearance, conceptually and often physically, seen as conforming to neither preservation nor agronomy. It is neither fish nor fowl, yet is often identified as disruptive if not exploitive of nature. To the non-silviculturist, application of the ecological model to silviculture may sometimes be difficult to distinguish from purely exploitive and environmentally damaging practices. However, such exploitation is not the intent of, nor does it constitute, silviculture. Silviculture is not synonymous with timber harvesting, yet is dependent upon it. The objective of modern silviculture is to create and maintain forests by design that produce material and non-material benefits to humans without sacrificing their sustainability. Silvicultural intentions nevertheless are not ecologically infallible. A given silvicultural application, despite best intentions, may be inconsistent with ecological realities because of our incomplete knowledge and understanding of ecosystems. Poorly applied silviculture therefore can produce unintended and undesirable long-term ecological consequences. The possibilities for such outcomes impose serious responsibilities on silviculturists in the practice of their art and science. When silviculture is applied to ‘natural’ ecosystems, the intent, some would say, is to improve on nature by tinkering with it. But the biocentrist would argue that humans cannot improve upon nature – a notion consistent with the theological view that ‘man cannot improve upon God’s handiwork’. And much ecological knowledge and theory is purported to support that perception. Perhaps it is the proximity of the existing ‘near-natural’ state to the intended silviculturally created state that concerns those whose sentiments might be to ‘leave well enough alone’. The biocentrist might argue that silviculture promises only a kinder, gentler assault on the forest. These views may be further bolstered by an awareness

of the shrinkage of natural ecosystems globally and its consequences. The fragmentation of today’s landscape into discrete blocks of forests spatially detached from human development may further reinforce the perception of the separation of humans and forest. This outlook is reflected in the Latin origin of the word forest, foris, which means outside. This etymology suggests a human view of forests evolving from deep historical and psychological roots, and one in which forests are functionally disconnected from humans. Even as late as the 18th century, the forest was perceived as something ‘beyond’ the boundary of European culture (Bonney, 1996). Today, most of the US population resides in urban areas. There, sources of basic human-sustaining forest resources are physically distanced from and foreign to everyday experience, except as consumable products afforded scant thought about their origin. A perception of forests as functionally and spatially distant from humans may be further reinforced by the common acknowledgement that, in some cases, the physical separation of humans from nature is necessary to preserve rare or endangered species and habitats. The results are commonly and favourably experienced annually by millions of visitors to national and state parks, wildlife refuges and designated wilderness areas in national forests and other federal lands. It is generally perceived that the role of humans there is restricted to that of protector and spectator, but not interloper. However, human management intervention is increasingly required even in ‘protected’ areas for outcomes such as restoration of fire-adapted communities, managing wildfire risk, suppression of invasive species or preventing damage by unchecked ungulate grazing. Contrasting with such models of the separation of humans and nature is the historical relation between humans and oaks, which are characterized by connectedness. From the oak’s perspective, those connections have produced both beneficial and harmful effects. The ecological evidence, as later discussed, nevertheless indicates that sustaining and thus preserving many oak-dominated ecosystems will require human intervention. Humans and oaks have been closely associated throughout history. Before the arrival of Europeans, Native Americans set fires, both accidentally and intentionally, which were recurrent and often spread unencumbered over enormous areas (Pyne, 1982, 1997; Grimm, 1983; Guyette et al., 1999). These fire regimes persisted for centuries in regions indigenous to the oaks, which

Introduction3

includes much of North America. The result was extensive areas of open-grown forests and woodlands favourable to the survival of the relatively light-demanding but fire-tolerant oaks. It was a disturbance cycle that, in time and space, is unlikely to be repeated. Humans thus have had a prominent effect in shaping the nature and extent of the oak’s habitat, and perhaps even its evolution. But those events have been largely relegated to history. Much of what today remains of the oak forests of the USA is a legacy of an earlier disturbance history that was partially, if not largely, dependent on fire. Unlike the ecologist, the silviculturist has traditionally viewed forests from a utilitarian perspective that emphasized sustainable timber production. Accordingly, failure to harvest forests at their inherent sustainable capacity to produce wood (sustained timber yield) was deemed wasteful. A theological counterpart was seemingly expressed by the biblical admonition for man to exert dominion over the earth (Genesis 1: 28). An economic analogue was expressed in Adam Smith’s 1776 treatise on the inherent value of the individual pursuit of economic self-interest (Smith, 1776). Collectively, these beliefs and values, largely borne of the Enlighten­ ment, have dominated the thinking and institutions of Western civilization for over 200 years. The American conservationist, Aldo Leopold (1966: p. 251) expressed concern for this philosophy by asserting that ‘a system of conservation based solely on economic self-interest is hopelessly lopsided. It tends to ignore, and thus eventually to eliminate, many elements in the land community that lack commercial value, but that are (as far as we know) essential to its healthy functioning’. The practice of silviculture has evolved in response to that philosophy and to increased expectations for a wider array of amenities, services and commodities from forests. Consequently, the practice of silviculture continues to expand to encompass more nontimber objectives such as: (i) restoring desirable understory vegetation communities; (ii) improving wildlife habitat; (iii) enhancing biodiversity; (iv) increasing water yield and quality; (v) controlling invasive species; (vi) increasing renewable energy production; and (vii) increasing carbon sequestration.

Silviculture: a Consilient Discipline The practice of silviculture therefore is caught in a web of competing values arising from different philosophies ranging from biocentrism to economic

utilitarianism. Unlike ecology, silviculture is directly connected to social institutions and conventions apart from science. Lying within its parent discipline, forest management, it is subject to the legal and social constraints of environmental law and policy operating within democratic processes (at least in the USA and other democratic countries where silviculture is practised). Within the context of democracy, silviculture is therefore socially integrative, that is in its application it must consider values borne of diverse social and political interests. As a consilient discipline, silviculture fits nicely into the contemporary framework of sustainable forest management. Strong sustainability requires management actions that are simultaneously ecologically sound, socially acceptable and economically viable (Robertson et al., 2011: p. I-2). The remainder of this book addresses ecologically sound silvicultural practices for oak forests. How­ ever, there are countless examples where ecologically sound silvicultural prescriptions were never implemented because they lacked social license or economic viability. Social acceptability of a silvicultural practice requires at a minimum that the practice is: (i) legal; and (ii) consistent with the desires of the property owner or manager. However, other social constraints can materialize in the form of voluntary best management practices, community aesthetic preferences, tax incentives, tax penalties, public comments solicited during land management planning, neighbours’ opinions, or local norms and customs. Economic viability implies some desired return on the cost of preparing and implementing a silvicultural prescription. Historically that return usually came as current or anticipated timber value. Rightfully there is growing appreciation for the economic and intrinsic values associated with the broad suite of ecosystem services that forests provide. Ecologically sound and socially acceptable silvicultural prescriptions can be written specifically to increase ecosystem services such as biodiversity, habitat for non-­game wildlife species, water quality, or carbon sequestration. But that does not guarantee financing to implement the prescription. The suite of management practices most often used by silviculturists – regardless of the silvicultural objectives – includes planning, prescribing, cutting, burning, scarifying, herbiciding, planting, fertilizing, fencing, monitoring, waiting, or encouraging hunting of herbivores such as white-tailed deer. All but the last two require money to

4Introduction

implement, and therefore economic considerations are an explicit or implicit part of every viable silvicultural prescription. Because so much of the labour to implement silvicultural prescriptions is supplied by forest-associated contractors (e.g. loggers, tree planters, herbicide applicators, equipment operators), a cadre of woods workers and forest-associated businesses are necessary partners in the successful practice of silviculture. Silviculture lies at the core of forest resource management because its application results in direct physical action on the forest. This is also where fundamental scientific analysis is most needed. However, silviculture does not stand firmly by itself as a scientific discipline. This results in part from its strong connections to social and political institutions, and in part from its interdisciplinary qualities as a science. Within the biological domain of science, silviculture is most closely allied to ecology. However, it is also heavily dependent on plant physiology and genetics, plant pathology, entomology, and applied mathematics and statistics. Among the physical sciences, it borrows knowledge from geology, climatology, hydrology and soil science. It is also closely allied to other resource management

disciplines including wildlife, fisheries, water and air quality management. Silviculture therefore is inherently scientifically integrative. Silviculture consequently depends on linking knowledge and theories across many disciplines, both scientific and non-scientific, to form what Wilson (1998) terms ‘a common groundwork of explanation’. If we accept that such linkages comprise consilience, we might consider that silviculture fits Wilson’s context, in that it comprises a hybrid domain of knowledge in which consilience is implicit. Because of silviculture’s socio-economic connections, this consilience extends to other branches of learning including the social sciences and humanities. These connections can be represented by a series of concentric circles representing the social hierarchies within which silviculture exists. With silviculture at its centre, each ring of the social hierarchy bounds all the great areas of knowledge, including the biological, physical and social sciences as well as the humanities (Fig. A). This representation emphasizes the consilient nature of silviculture by placing it at the locus of all knowledge comprising its context. It represents an ideal, a unity of learning in which subjects that

Humanities

st nt re e Fo gem a an

m

cy ra oc l m ta De en y m lic on po vir nd En w a la

Physical sciences

Silviculture

st nt re e Fo e m l ag ta an en y m m lic on po vir d n En a law cy ra oc

m

De Social sciences

Biological sciences

Fig. A.  Silviculture’s relation to other disciplines. Concentric circles represent the social hierarchy within which silviculture exists in a democratic society. With silviculture at its centre, each ring of the hierarchy bounds all the major areas of knowledge, including the biological, physical and social sciences, and the humanities.

Introduction5

have been traditionally compartmentalized are breached in Wilson’s words, to: provide a balanced clearer view of the world as it really is . . . A balanced perspective cannot be acquired by studying disciplines in pieces but through pursuit of the consilience among them . . . The enterprise is important for yet another reason: it gives ultimate purpose to intellect. (Wilson, 1998: p. 13)

Despite the complexities of silviculture’s complete context, our intent in the following pages is to present a synthesis of the ecological and silvicultural knowledge of oak forests in the USA. It is not to resolve the environmental issues surrounding oak forests, which fall in the social, legal, political and managerial domains represented by the concentric circles surrounding silviculture in Fig. A. The silvicultural context is nevertheless broad, and not limited to narrowly defined economic or commodityproduction objectives. Consistent with the view of silviculture as a consilient discipline, we view the role of the silviculturist as just one of many possible players in the management of oak forests. Unlike the biocentrist, we infer no moral imperative to create or maintain oak forests in specified states other than those that are perceived, as best we can discern, as sustainable, beneficial and pleasing to humankind, and that provide habitat for the many plant and animal species naturally associated with oaks. We believe these goals are consistent with the philosophy of land stewardship and wise use as proposed by earlier generations of conservationists, from which the more recent philosophy of ecosystem management has evolved. Our intention is to present information that can lead to an understanding of, and solutions to, silvicultural problems related to oak forests. Moreover, we hope that this information fosters an informed and amiable dialogue and trust among foresters, land managers and owners, environmentalists, students and others interested in oak forests. The subject therefore is presented from a silvicultural perspective. The approach comprises a comprehensive view of forests as providing important social, spiritual and economic needs. Such an approach requires anticipating and managing for change, both predictable and unpredictable. This notion is consistent with Botkin’s (1990) call for a ‘new management’, wherein conservation and utilization of forest resources are compatible parts of an integrated ecosystem approach. It contrasts

with the ‘old management’ in which conservation was too often subordinate to timber and other commodity production. The central concern of the new forest management, or ecosystem management (Salwasser, 1994), is the sustainability of forested ecosystems and associated human values in a continually changing mosaic of landscape patterns. The resulting management and silviculture therefore must accommodate the complexities of the inevitably and continually changing ecological states that comprise a forested landscape. It also recognizes that such changes occur with or without human interference, and that we have both potentialities and limitations in controlling these changes.

References Bonney, W. (1996) Troping trees. In: Schultz, K.L. and Calhoon, K.S. (eds) The Idea of the Forest. Peter Lang, New York, pp. 119–146. Botkin, D.B. (1990) Discordant Harmonies. Oxford University Press, New York. Chase, A. (1995) In a Dark Wood: the Fight Over Forests and the Rising Tyranny of Ecology. Houghton Miflin, Boston, Massachusetts. Devall, B. and Sessions, G. (1985) Deep Ecology. Gibbs M. Smith, Layton, Utah. Ferry, L. (1995) The New Ecological Order. University of Chicago Press, Chicago. Fox, W. (1995) Toward a Transpersonal Ecology. State University New York Press, Albany, New York. Grimm, E.C. (1983) Chronology and dynamics of vegetation changes in the prairie-woodland region of southern Minnesota, USA. New Phytologist 93, 311–350. https://doi.org//10.1111/j.1469-8137.1983.tb03434.x Guyette, R., Dey, M. and Dey, D.C. (1999) An Ozark fire history. Missouri Conservationist 60, 4–7. Hobbs, R.J., Higgs, E. and Harris, J.A. (2009) Novel ecosystems: implications for conservation and restoration. Trends in Ecology & Evolution 24, 599–605. https:// doi.org/10.1016/j.tree.2009.05.012 Leopold, A. (1966) A Sand County Almanac. Ballantine, New York. Pinchot, G. ([1947] 1987) Breaking New Ground. Island Press, Washington, DC. Pyne, S.J. (1982) Fire in America. Princeton University Press, Princeton, New Jersey. Pyne, S.J. (1997) America’s Fires: Management on Wildlands and Forests. Forest History Society, Durham, North Carolina. Robertson, G., Gualke, P., McWilliams, R., LaPlante, S. and Guldin, R. (eds) (2011) National report on sustainable forests – 2010. USDA Forest Service Report

6Introduction

FS-979. USDA Forest Service, Washington, DC. Available at: https://www.fs.usda.gov/treesearch/ pubs/54685 (accessed 30 August 2018). Salwasser, H. (1994) Ecosystem management: can it sustain diversity and productivity? Journal of Forestry 92(8), 6–10. https://doi.org//10.1093/jof/92.8.6 Smith, A. (1776) An Inquiry Into the Nature and Causes of the Wealth of Nations, Volume 1. W. Strahan and T. Cadell, London.

Smith, B.D. and Zeder, M.A. (2013) The onset of the Anthropocene. Anthropocene 4, 8–13. https://doi. org/10.1016/j.ancene.2013.05.001 United States Congress (1897) Surveying the public lands. US Statutes at Large 30 (Chapter 2). USDA Forest Service, United States Government Printing Office, Washington, DC, pp. 32–36. Wilson, E.O. (1998) Consilience: the Unity of Knowledge. Knopf, New York.

Introduction7

1



Oak-dominated Ecosystems

Introduction We are still in transition from the notion of man as master of the earth to the notion of man as part of it. (Wallace Stegner, 1995) A truly ecological perspective recognizes that humans and their activities are part of nature, and that enhancing all aspects of their lives – including their surroundings – begins with cooperation between individuals, based on mutual trust … (Alston Chase, 1995)

From earliest times, oaks have held a prominent place in human culture. Their uses have included wood for fuel, acorns for hog fodder and flour meal for human consumption, bark for tanning, wood strips for weaving baskets, charcoal for smelting ore, timbers for shipbuilding, mining timbers, railroad ties, pulpwood for paper, and lumber and laminates for furniture, panelling and flooring. Through the mid-19th century, oak was the wood of choice for shipbuilding in Europe and America. For that reason, oak forests and even individual trees were treated as critical national assets. During the Revolutionary War, the poor condition of the British fleet, which lacked replacements and repairs due to shortages of suitable oak timbers, may have contributed to the war’s outcome (Thirgood, 1971). In the 17th century, alarm over the depletion of timber supplies, especially oak, prompted passage and enforcement of laws mandating the protection, culture and establishment of forests in several European countries. In turn, those events influenced the development of scientific silviculture, as we know it today. Modern as well as ancient man has benefited from the oak’s relation to wildlife. Wherever oaks occur as a prominent feature of the landscape, wildlife populations rise and fall with the cyclic production of acorns. Numerous species of birds and mammals are dependent on acorns during the foodscarce autumn and winter months. Even human cultures have relied on oaks as a staple food. Acorns

8

were an important part of the diet of Native Americans in California before the 20th century (Kroeber, 1925) (Fig. 1.1). Today, the ecological role of oaks in sustaining wildlife, biodiversity and landscape aesthetics directly affects the quality of human life. But while all that is known, putting our knowledge of oaks into a comprehensive value framework that is understandable and acceptable to most people has remained elusive (Starrs, 2002). The demand for wood products from oaks nevertheless continues to increase and compete with other less tangible values. Some have proposed that forests, including those dominated by oaks, are best allowed to develop naturally, free from human disturbance. What should the balance be among timber, wildlife, water, recreation and other forest values? Is there some middle ground that adequately sustains multiple goals? Informed answers and perspectives require an understanding of the ecology of oaks and the historical role that humans have had in that ecology, especially the comparatively recent role of humans in the ‘protection’ of oak forests from fire. A prerequisite to such understanding is a general knowledge of the oak’s geographical occurrence, taxonomic diversity, adaptations to diverse environments, and the historical changes in its environment.

The Taxonomy of Oaks Taxonomically, the oaks are in the genus Quercus in the family Fagaceae (beech family). Members migrated and diverged into the current living genera by the late Cretaceous period (about 60 million years ago). By that time, mammals and birds had only recently evolved. Rapid speciation of oaks commenced in the middle Eocene epoch (40–60 million years ago). This was in response to the expansion of drier and colder climates, and subsequently to increased topographic diversity in the late Cenozoic era (< 20 million years ago) and fluctuating climates

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

Fig. 1.1.  Native American collecting acorns as shown in Hutchings’ California Magazine in 1859. Acorns were a staple food of most California tribes before the end of the 19th century. They were gathered in conical woven baskets, which could hold a bushel or two of the nuts. Although the acorns of many species were eaten, favoured species were California black and California live oaks (Pavlik et al., 1991). After removing the shell (pericarp), acorns were ground into a flour, leached of tannins by soaking in running water, and then used to make a variety of foods including porridge and bread. Acorns were so highly valued that they sometimes provoked intertribal acorn wars. They were also widely utilized as food by Native Americans in Eastern USA. (From the Bancroft Library, University of California, Berkeley, California.)

during the Quaternary period (< 2 million years ago) (Axelrod, 1983). Their fruit, the acorn, distinguishes the oaks from other members of the beech family (e.g. the beeches and chestnuts). With one exception, all plants that produce acorns are oaks. The exception is the genus Lithocarpus, which includes the tanoak of Oregon and California. Although represented by only one North American species, Lithocarpus is represented by 100–200 species in Asia (Nixon, 1997a). Lithocarpus may be an evolutionary link between the chestnut and the oak (McMinn, 1964; cf. Miller and Lamb, 1985: p. 200). Worldwide there are about 400 species of oaks, and they are taxonomically divided into three

Oak-dominated Ecosystems

groups: (i) the red oak group (Quercus section Lobatae1); (ii) the white oak group (Quercus section Quercus2); and (iii) the intermediate group (Quercus section Protobalanus3) (Tucker, 1980; Jensen, 1997; Manos, 1997; Nixon, 1997b; Nixon and Muller, 1997). All three groups include tree and shrub species. The red oaks and white oaks include evergreen and deciduous species, whereas the intermediate oaks are all evergreen. The red oaks are found only in the western hemisphere where their north–south range extends from Canada to Colombia. In contrast, the white oaks are widely distributed across the northern hemisphere. The intermediate group comprises only five species, all of which occur within south-western

9

Jensen, 1997; Manos, 1997; Nixon and Muller, 1997; Hardin et al., 2001) and field identification guides (e.g. Miller and Lamb, 1985; Petrides, 1988; Petrides and Petrides, 1992; Stein et al., 2003). These sources also include range maps. In addition, the Silvics of North America, Vol. 2 (Burns and Honkala, 1990) provides information on the silvics and geographic ranges of 25 oaks. See also online references: Burns and Honkala (1990), USDA Natural Resources Conservation Service (2008) and USDA Forest Service (2008). United States Department of Agriculture (USDA) Forest Service publications can be accessed online via http://www. treesearch.fs.fed.us/ Of the more than 250 oak species occurring in the western hemisphere, the largest number occurs in Mexico and Central America, with only about ten species in Canada. In the USA and Mexico, the oaks collectively have greater species richness and greater biomass than any other genus of trees. Oak species comprise 20% of the woody forest biomass in the USA and nearly 30% in Mexico. Oak species richness increases from north to south across North America. Although northern oak species are fewer in number, they have greater biomass per species than the more numerous oak species with smaller geographic ranges found in Mexico and Central America (Cavender-Bares, 2016). Recent genetic studies have concluded that the red oak and white oak groups (or clades) in North America evolved from common ancestors in northern Canada some 45 million years ago. Ten to 20 million years ago in western North America the two oak groups gradually migrated south into what is now

USA and north-western Mexico. Many of the world’s oaks occur in regions with arid climates, including Mexico, North Africa and Eurasia, where they are often limited in stature to shrubs and small trees. About 80% of the world’s oaks occur below 35° north latitude and fewer than 2% (six or seven species) reach 50° (Axelrod, 1983). The most reliable distinction between the white oaks and red oaks is the inner surface of the acorn shell. In the white oaks it is glabrous (hairless) or nearly so, whereas in the red oaks it is conspicuously tomentose (hairy or velvety) (Tucker, 1980) (Fig. 1.2). In the intermediate group, this characteristic is not consistent among species. The leaves of the white oaks are usually rounded and without bristle tips whereas the leaf lobes of the red oaks are usually pointed and often bristle tipped. To many silviculturists, ecologists and wildlife biologists, the most important difference between the white oaks and red oaks is the length of the acorn maturation period. Acorns of species in the white oak group require one season to mature whereas species in the intermediate and most of the red oak group require two seasons. The white oaks and intermediate oaks are characterized by the presence of tyloses (occlusions) in the latewood vessels (water-conducting cells) whereas tyloses are usually absent in the red oaks. These vessel-plugging materials confer greater decay resistance to the wood of the white and intermediate oaks than the red oaks, and make them uniquely suited for production of tight cooperage. Other morphological features that differentiate the three groups and species within them are presented in various taxonomic treatments (e.g. Tucker, 1980; (A)

(B)

Fig. 1.2.  Comparison of the interior of acorn shells for red oaks versus white oaks. (A) The tomentose (velvety) interior of a Shumard oak (red oak group); (B) the glabrous (hairless) interior of a bur oak (white oak group).

10

Chapter 1

California, while in the east the two oak groups also migrated south, spreading throughout the eastern USA and then into Mexico and Central America. There the rate of speciation accelerated along moisture gradients leading to the high species richness in those locales (Hipp et al., 2018). The exceptionally large, overlapping, spatial extents and the resultant widespread coexistence of the white and red oak groups in North America have been attributed to differences in their niches, including different susceptibilities to insects and diseases (Cavender-Bares, 2016). For the US species, the most complete and authoritative taxonomic treatment of the oaks is in the Flora of North America North of Mexico, Vol. 3 (Flora of North America Editorial Committee, 1997), which lists 90 species of oaks native or naturalized to the continental USA. Although we follow the scientific names from that reference, we use the longaccepted common names of trees cited in Little’s (1979) Checklist of United States Trees (Native and Naturalized) because of its widespread use in North American forestry literature. Little’s (1979) checklist recognizes 58 native oak species plus nine varieties. Of these, about ten species are shrubs or shrub-like forms. More than 80 hybrids also have been described (Little, 1979; Tucker, 1980).

The Geographic Distribution of US Oaks Species ranges and groupings The oaks are widely distributed across the USA (Plate 1). According to Little (1979), about 40 species and varieties occur east of the 100th meridian and about 30 species and varieties occur to the west. Only two species, chinkapin oak and bur oak, are common to both regions. Bur oak extends to the north-west whereas chinkapin extends to the south-west beyond the 100th meridian. The western oaks fall into three geographically distinct groups. One group is comprised of the west Texas oaks (nine species and varieties), and a second includes the south-western oaks (16 species) that occur in New Mexico, Arizona, Utah, Colorado and Nevada. A third group is comprised of the Pacific Coast oaks (about 13 tree species plus several shrubby species) occurring largely in California, Oregon and Washington. Within the USA, numbers of oak species vary regionally. Based on a count of the number of oak species that occur within 6000 square mile areas, oak species ‘richness’ reaches a maximum of 20

Oak-dominated Ecosystems

species in the south-east (Aizen and Patterson, 1990) (Fig. 1.3). There, the ranges of several narrowly distributed North American oak species overlap with the ranges of several widely distributed species. Although the range of an oak species is positively correlated with its acorn size, the reason for this is unknown (Aizen and Patterson, 1990). Forest cover types (or simply cover types) are combinations of tree species that tend to spatially reoccur at stand-level scales (e.g. < 100 acres). The resulting categories are thus silviculturally useful in differentiating among different kinds of oak stands. Categorization of US forests based on defined cover types was begun by the Society of American Foresters in 1929. There are 145 defined cover types in the USA and Canada (Eyre, 1980). These include 31 with ‘oak’ in the cover type name or in the list of species that define the type (see Appendices 2 and 3, this volume). Of these, 23 oak types occur east and eight occur west of the 100th meridian. In addition, many of the non-oak cover types include one or more oak species as common associates. The geographic extent of individual cover types ranges from tens of millions of acres (e.g. the white oak–black oak–northern red oak cover type of the eastern USA) to relatively restricted areas (e.g. the northern pin oak cover type of the upper Lake States and the Mohr oak cover type of Texas and Oklahoma). Other types such as the live oak type of the South and the bur oak cover type in the Great Plains occur within long narrow belts associated with coastal plains and river corridors, respectively. Many of the western oak cover types, especially those in California, form belts that follow the Coastal and Sierra Nevada mountain ranges and foothills surrounding the Central Valley. Oaks occur in environments ranging from extremely wet and humid (e.g. the overcup oak– water hickory cover type of southern flood plains), to mesic (moist) upland forests receiving 50 or more inches of precipitation/year (e.g. the yellow-poplar– white oak–northern red oak cover type), to Mediterranean climates that receive 10 inches or less precipitation/year (e.g. the blue oak–digger pine cover type). Oaks occur in even drier climates where they form shrub vegetation such as the chaparral of southern California and the semidesert scrub woodland vegetation of the interior southwest. Western cover types such as the canyon live oak cover type include closed-canopy stands in the northern part of their range and savannah-like woodlands in the south. Oak forests therefore range

11

1

5 10

15 5 10

1

15

15

20

10

15 10

10 5

5

Fig. 1.3.  The geographic distribution of numbers of oak species in eastern USA and Canada (redrawn from Aizen and Patterson, 1990). The isolines were drawn from a grid comprised of 78 × 78 square mile cells within which the number of oak species were counted based on Little’s (1971, 1977) range maps. The greatest concentration of oak species (15–20) occurs in south-eastern USA where the ranges of several narrowly distributed species overlap the ranges of several widely distributed species.

from closed canopy upland and lowland forests with trees greater than 120 ft tall to xeric (droughty) scrublands dominated by dwarf trees and shrubs. Some oaks, such as Georgia oak and McDonald oak are confined to very small geographical ranges and a narrow range of habitat conditions. Others such as white oak are widely distributed and occur over a broad range of climates and habitat conditions. A species’ flexibility in occupying different habitats is implicit in the definition of species niche. The term denotes the specific set of environmental and habitat conditions that permit the full development and completion of the life cycle of an organism (Helms, 1998). The oaks occupy many niches because of the wide range of environmental

12

conditions within which they can collectively occur. However, the niche of an individual species is more limited. Niche differentiation among the oaks and associated species is often evident from the way species segregate along environmental gradients such as the soil moisture gradient (Fig. 1.4). Oaks also differ in their ecological amplitude (i.e. the range of habitat conditions that a species can tolerate) (Allaby, 1994). The ecological amplitude of a species often forms a bell-shaped curve when illustrated diagrammatically (Fig. 1.4). However, some species, such as bur oak, occur in both bottomlands and dry uplands but are nearly absent at intermediate points along the moisture gradient (Curtis, 1959; Johnson, 1990).

Chapter 1

Importance value

Black oak

White oak

Northern red oak

Sugar maple American beech

Bur oak

Xeric

Mesic Compositional index

Fig. 1.4.  Changes in the relative importance of six tree species in the upland forests of southern Wisconsin in relation to the regional soil moisture gradient (adapted from Curtis, 1959, with permission from the University of Wisconsin Press). Species’ importance is quantitatively expressed by an importance value, which is an index of species’ importance based on its frequency of occurrence, density and basal area relative to other species within a stand. Although there is much overlap among species’ importance value curves, no two species behave exactly the same way with respect to the moisture gradient. The length of the gradient spanned by a species’ range of importance values together with the shape of its importance value curve reflects its niche with respect to the gradient. Importance value curves also define the ecological amplitude of a species, that is the range of conditions it can tolerate and the magnitude of its importance in relation to the gradient under the prevailing (i.e. relatively undisturbed) stand conditions. The moisture gradient shown is inferred from the species composition of a series of relatively undisturbed stands (see Curtis, 1959).

The species composition of forests is continually changing as a result of forces both internal (autogenic) and external (allogenic) to the forest. Changes are often gradual and frequently result in the replacement of one tree species by another in the process of ecological succession. The vegetation and other organisms within the forest thus effect autogenic change. For example, shade-tolerant species growing beneath the main forest canopy may gradually replace dominant species of lesser shade tolerance that are unable to regenerate under their own shade. In contrast, allogenic change occurs as a result of changes in climate, defoliation by exotic insects and pathogens, the movement of soil by wind and water, or from other forces originating outside the forest. Autogenic and allogenic factors sometimes jointly affect the direction and rate of succession. Moreover, disturbances such as windthrow, insect and disease outbreaks, and timber harvesting can accelerate succession or alter its direction. Although the oaks are relatively intolerant of shade, species vary substantially in this attribute. In some habitats, oaks are vulnerable to successional replacement by more shade-tolerant species. Compared to many of their competitors, oak seedlings grow more slowly during their first few years

Oak-dominated Ecosystems

after initial establishment. When young oaks are overtopped and heavily shaded by other vegetation, few survive for very long. On the other hand, the oaks tend to be relatively drought tolerant, and often survive in habitats that limit the development of species of lesser drought tolerance. Oaks also can produce vigorous sprouts that often outgrow competitors. The combination of these factors relative to competitors determines the relative permanence of oaks within a given cover type. In the eastern half of the USA, oaks are often relatively permanent members of cover types on drier sites. In the absence of disturbance, many of the pine and oak–pine cover types occurring on dry habitats are successional to oaks because the oaks are somewhat more shade tolerant than the pines. This successional pattern creates silvicultural problems in maintaining pure pine stands in the south and other regions where oaks and pines co-occur (Burns and Barber, 1989). In bottomlands and mesic uplands, shade-tolerant or faster growing species often successionally displace the oaks. Such displacement creates silvicultural problems in perpetuating oaks in these forests. The relative permanence of an oak species within a given cover type (i.e. its resistance to successional

13

replacement by other species) is likely to be highly variable if the cover type spans a broad range of environments. For example, the white oak cover type occurs across a wide range of site conditions from dry to moist. Whereas the type tends to be relatively permanent on dry sites, it is successional to other types on the more mesic sites. Cover type designations, although useful, largely fail to consider these and other ecological factors that determine changes in species composition and how those changes vary spatially (e.g. in relation to climate and site quality), and temporally (e.g. in relation to plant succession and disturbance). Consequently, two or more stands representing a single cover type may represent quite different ecologies with respect to the successional status of oaks, physical environment, understorey vegetation, forest regeneration, fauna and other factors. Forest inventories and satellite imagery have been used to describe the geographic distribution of forest types in the USA (e.g. Fig. 1.5). These maps identify broad cover type groups that are aggregates of the stand cover types described above. Four groupings widely used to delineate oak forests at the regional scale are: (i) the oak–hickory group; (ii) the oak–pine forest group; (iii) the oak–gum–cypress group (bottomland forests); and (iv) the western hardwood group that includes the western oaks as a subset (Fig. 1.5). However, the names commonly applied to the resulting species aggregations can be misleading. For example, hickory is absent throughout much of the northern part of the range delineated as oak–hickory (Fig. 1.5). Moreover, other forest cover types dominated by oaks are also included within the delineated oak– hickory area. The term ‘oak–hickory’ nevertheless is widely used in reporting forest resource statistics at the regional level even though it is an ecologically imprecise term. Oaks also occur as ecologically and silviculturally important components of many non-oak forests (e.g. pine forests and maple– beech–birch forests). In the eastern USA the oak–hickory, oak–pine and the oak–gum–cypress cover type groups collectively covered 179 million acres or 34% of all US timberland4 in 2012. That is an increase from 162 million acres and 32% of USA timberland in 1953 (Smith et al. 2009; Oswalt et al., 2014). At 128 million acres, the oak–hickory forest type is the largest forest cover type in the USA. In the eastern USA oak forest types cover nearly half of all timberland (oak–hickory 34%, oak–pine 7% and

14

oak–gum–cypress 6%). The western oaks are also significant geographically and ecologically. Western oak forests cover 2 million acres or about 2% of western timberland (Oswalt et al., 2014; Miles, 2016). Collectively, oak species comprise 15% of the growing stock volume (cubic measure) on timberland in the conterminous USA. Nearly one-quarter of all growing stock volume on eastern timberland consists of oak species. Oaks comprise 1% of western growing stock volume on timberland. Since 1953, total oak volume in the USA has increased by 76%, from 75 to 133 billion ft3 (Smith et al., 2009; Miles, 2016). Distribution of oaks by hierarchically classified ecoregions Climate and landform strongly influence the distribution of oaks. Locally, the distribution of oaks is influenced by factors such as physiography, soil moisture and geology. These and other factors have been used to structure a hierarchical ecological classification system (McNab and Avers, 1994; Bailey, 1995, 1997, 1998). This system recognizes the increasing detail necessary to explain the spatial arrangement of forests at increasingly smaller spatial scales (Table 1.1). It thus provides an objective basis for the regional delineation of ecosystems into successively smaller and more homogeneous units. The hierarchical ecological units range in size from continents to a few acres. The larger units are often referred to as ecoregions; the smallest units are often equivalent to forest stands. Domains, divisions and provinces form the larger ecoregions (Table 1.1). These are climatic and climatic–physiographic regions that cover millions to tens of thousands of square miles. Provinces are further subdivided into smaller units termed sections, subsections, landtype associations (LTAs), ecological landtypes (ELTs) and ecological landtype phases (ELTPs). These units range in size from thousands of square miles for sections to less than 10 acres for some ELTPs. ELTs and ELTPs are important silviculturally because they often correspond to individual stands, which are the objects of silviculture. The oaks occur in all three domains (major climatic regions) of the 48 contiguous states: Humid Temperate, Dry and Humid Tropical (Bailey, 1997). The latter occurs only in the southern tip of Florida. The three domains are further subdivided into 11 climatic divisions. Within each division,

Chapter 1

(A)

240

Long. 100 210

210 330 340 250

220

320 260

230

310

Oak–hickory

(B)

410

240 210

210 330 340 250

220

320 260

230

310

Oak–gum–cypress (C)

410

240 210

210 330 340 250

220

320 260

Western hardwoods

230

310

Oak–pine

410

Fig. 1.5.  The major areas of oak–hickory, oak–pine, oak–gum–cypress and western hardwoods (shaded areas) by state and ecoregion divisions (map compiled by W.D. Dijak, USDA Forest Service, North Central Research Station, Columbia, Missouri). In the western USA, the map shows the composite western hardwood group that includes oaks, tanoak, red alder, cottonwood and aspen. Numbered ecoregion boundaries on the map are from Bailey (1997) and are summarized in Tables 1.2 and 1.3. Generated from Advanced Very High Resolution Radiometer satellite images at a scale of 1 km2 (1990) and an associated system of land cover classification (USDA Forest Service, 1993; Powell et al., 1994).

Oak-dominated Ecosystems

15

Table 1.1.  Hierarchy of ecological units used to classify forest ecosystems in the USA. (Adapted from Cleland et al., 1993; McNab and Avers, 1994; Bailey, 1995.)a Ecological unit

Scale (reference size)b

Delineating factorsc

Domain

Macroclimate, ocean temperature and currents, geomorphology Geomorphology, climate

Landtype association

Millions to tens of thousands of square miles (subcontinent) Millions to tens of thousands of square miles (multi-state) Millions to tens of thousands of square miles (multi-state, state) 1000s of square miles (state, multi-county, National Forest) 10s to 100s of square miles (multiple counties, National Forest Ranger District) 10s to 1000s of acres (landscape, watershed)

Ecological landtype

10s to 100s of acres (stand, multiple stands)

Ecological landtype phase

1 to 10s of acres (stand)

Division Province Section Subsection

Geomorphology, climate Geomorphology, climate, vegetation Geomorphology, climate, vegetation Landforms, species composition of overstorey, soil associations Landform, natural vegetative communities, soils Soils, landscape position, natural vegetative communities

a

See also Plate 1; Figs 1.5 and 1.6. Indicates a familiar unit of comparable size for reference purposes. This reference unit is not used to delineate the ecological unit. c Some of the factors used to distinguish among ecological units at a given level. Classification complexity typically increases with decreasing unit size. b

mountainous areas with elevational zonation of vegetation are also identified. Although oaks naturally occur in all 11 of the divisions, within each division the distribution of the four major oak forest types is closely related to division boundaries (Fig. 1.5; Tables 1.2 and 1.3). The 11 ecoregion divisions within the conterminous USA are further subdivided into 44 provinces (Plate 1). Provinces are delineated based on broad vegetation groups and related regional landforms. Oak forests and woodlands commonly occur in 23 provinces (Tables 1.2 and 1.3). Province boundaries are useful in delineating oak distributions in some parts of the USA. For example, province boundaries correspond with the spatial distribution of the oak forests and woodlands encircling California’s Central Valley. Province boundaries also separate the oak–pine forests of the Piedmont (Province 232) from the wetter oak habitats of the Coastal Plain and the lower Mississippi flood plain (Province 231 and Riverine forest). Province boundaries are also useful in separating the regions where oaks occur from those where they do not. In contrast to the coarser levels of the classification hierarchy (domains through subsections), which have been delineated nationally, classification of the ELT and ELTP levels is incomplete across much of the oak range. Even though classification

16

systems down to the ELT or ELTP have been developed for millions of acres, they include only a small fraction of the total area of oak forests. ELTs or ELTPs are usually mapped in the field based on differences in soils, physiography and vegetation (including herbs and shrubs). The species composition of the herbaceous layer is often used to distinguish among different ELT or ELTP units because of the fidelity of some herbaceous species (‘indicator’ species) to specific biophysical conditions. Accordingly, the presence or absence of one or more indicator species can be used to differentiate among otherwise similar ELTs or ELTPs. Shrubs are also sometimes used as indicator species. Compared with the herbaceous layer, the composition of the tree layer often recovers slowly from disturbances. Moreover, the tree component may not recover to its pre-disturbance composition. Joint consideration of physical and biological factors and their interactions provide a basis for identifying ecologically homogeneous land units that are silviculturally relevant and useful in delineating management units (Barnes et al., 1982). Ecological classification provides a broader ecological context for understanding why oaks occur where they do, and how those occurrences change with time, disturbance and other factors. At the broadest scale the oak forests of the USA can be

Chapter 1

Table 1.2.  The ecoregion domains, divisions and provinces in the eastern conterminous USA where oaks are found and the principal oak species occurring in each. Ecoregions from Bailey (1995). Division and province boundaries are shown in Plate 1. Division 200 Humid Temperate Domain 210 Warm Continental M210 Warm Continental Mountains Ten oak species: 220 Hot Continental M220 Hot Continental Mountains 22 oak species:

230 Subtropical M230 Subtropical Mountains 31 oak species:

250 Prairie 20 oak species:

400 Humid Tropical Domain 410 Savannah

Four oak species: Intrazonal Regions

Province 211 Mixed deciduous coniferous forests M211a Mixed forest–coniferous forest–tundra, medium M211b Mixed forest–coniferous forest–tundra, high Bear, black, bur, chestnut, chinkapin, northern pin, northern red, scarlet, swamp white, white 221a Broadleaved forests, oceanic 221b Broadleaved forests, continental M221 Deciduous or mixed forest–coniferous forest–meadow M222 Broadleaf forest–meadow Basket, bear, black, blackjack, bur, cherrybark, chestnut, chinkapin, northern pin, northern red, overcup, pin, post, scarlet, shingle, Shumard, southern red, swamp chestnut, swamp white, water, willow, white 231 Broadleaved–coniferous evergreen forests 232 Coniferous–broadleaved semi-evergreen forests M231 Mixed forest–meadow province Arkansas, bear, black, blackjack, bluejack, bur, Chapman, cherrybark, chestnut, chinkapin, Durand, Georgia, laurel, live, myrtle, Ogelthorpe, northern red, Nuttall, overcup, pin, post, scarlet, shingle, Shumard, southern red, swamp chestnut, swamp white, turkey, water, white, willow 251 Forest–steppes and prairies province 252 Prairies and savannahs province Black, blackjack, bluejack, bur, chinkapin, Durand, live, northern pin, northern red, overcup, laurel, pin, post, southern red, shingle, Shumard, swamp chestnut, swamp white, water, white 411 Open woodlands, shrubs and savannah 412 Semi-evergreen forests 413 Deciduous forests province Chapman, live, laurel, myrtle R Riverine forest

divided into four eastern and two western groups based on species associations, ecological conditions and successional relations (Fig. 1.6). Boundaries between regions follow division boundaries in the hierarchical ecological classification system. These regional groupings are useful ecologically and silviculturally because they identify areas with broadly similar macroclimates and species associations. Regional differences in the application of silvicultural methods are closely related to corresponding differences in species composition, environmental factors and other ecological conditions. The six forest regions are described below in relation to the domains, divisions and provinces of the hierarchical ecological classification system described in the preceding

Oak-dominated Ecosystems

section. However, the regional designations do not explicitly identify the lowland and riparian forests occurring within them. There, along the major rivers and streams within the Southern Pine– Hardwood Region, the oaks attain their greatest size and growth.

Eastern Oak Forests The Northern Hardwood Region Geographic extent The Northern Hardwood Region includes the northern halves of Minnesota, Wisconsin, Michigan and much of the north-eastern USA including New Hampshire, Vermont and Maine in their entirety.

17

Table 1.3.  The ecoregion domains, divisions and provinces in the western conterminous USA where oaks are found and the principal oak species occurring in each. Ecoregions from Bailey (1995). Division and province boundaries are shown in Plate 1. Division 200 Humid Temperate Domain 240 Marine M240 Marine Mountains

3 oak species: 260 Mediterranean

M260 Mediterranean Mountains

13 oak species: 300 Dry Domain 310 Tropical/Subtropical Steppe

M310 Tropical/Subtropical Steppe Mountains 16 oak species:

Province 241 Mixed forests M241 Deciduous or mixed forest–coniferous forest–meadow M242a Forest–meadow, medium M242b Forest–meadow, high Oregon white, California black, canyon live 261 Dry steppe 262 Mediterranean hardleaved evergreen forests, open woodlands and shrubs 263 Redwood forests M261 Mixed forest–coniferous forest–alpine meadow M262 Mediterranean woodland or shrub–mixed or coniferous forest–steppe or meadow M263Shrub or woodland–steppe–meadow Blue, California black, California scrub, canyon live, coast live, Dunn, Engelmann, interior live, island live, McDonald, Oregon white, turbinella, valley 311 Coniferous open woodland and semideserts 312 Steppes 313 Steppes and shrubs 314 Shortgrass steppes M311 Steppe or semidesert–mixed forest–alpine meadow or steppe

Arizona, canyon live, Chisos, Dunn, Emory, Gambel, grey, Havard, Lacey, lateleaf, Mohr, sandpaper, silverleaf, Toumey, wavyleaf, turbinella 320 Tropical/Subtropical Desert 321 Semideserts 322 Oceanic semideserts 323 Deserts on sand M320 Tropical/Subtropical Desert M321 Semidesert–shrub–open woodland–steppe or Mountains alpine meadow M322 Desert or semidesert–open woodland or shrub–desert or steppe 22 oak species: Arizona, chinkapin, Chisos, Dunn, Durand, Emory, Gambel, Graves, grey, Havard, Lacey, lateleaf, live, Mexican blue, Mohr, netleaf, post, sandpaper, silverleaf, Toumey, turbinella, wavyleaf 330 Temperate Steppe 331 Steppes 332 Dry steppes M330 Temperate Steppe Mountains M331 Forest–steppe–coniferous forest–meadow–tundra M332 Steppe–coniferous forest–tundra M333 Steppe–coniferous forest M334 Steppe–open woodland–coniferous forest–alpine meadow 2 oak species: Bur, Gambel 340 Temperate Desert 341 Semideserts 342 Semideserts and deserts M340 Temperate Desert Mountains M341 Semidesert–open woodland–coniferous forest–alpine meadow 3 oak species: Gambel, turbinella, wavyleaf

It  includes two ecoregion provinces: Mixed Deciduous–Coniferous Forests Province (211) and Mixed Forest–Coniferous Forest–Tundra, High Province (M211b) within the Warm Continental

18

Division (Plate 1; Fig. 1.6; Table 1.2). The region extends 1300 miles from west to east and covers 123 million acres, about three-quarters of which are forested.

Chapter 1

Pacific Mediterranean–Marine Region Driftless Area

Coast Range

340 260 Sierra Nevada

Central Valley

320 310 Southwestern Desert–Steppe Region

Edwards Plateau Cross Timbers

210 Ohio Valley Forest–Prairie Transition Region

330

240

Northern Hardwood Region 210

250

220 Central Hardwood Region

Southern Pine–Hardwood Region 230 Lower Ozark Mississippi Highlands Valley

Allegheny Plateau Appalachian Mountains Coastal Plain Piedmont Cumberland Plateau Highland Rim Piedmont Coastal Plain 410

Boston Mountains

Fig. 1.6.  The six regions where oaks commonly occur: Northern Hardwood Region; Central Hardwood Region; Southern Pine–Hardwood Region; Forest–Prairie Transition Region; Southwestern Desert–Steppe Region; and Pacific Mediterranean–Marine Region. Numbers correspond to ecoregion divisions (Fig. 1.5) (Bailey, 1997). Not considered by the regional groupings are the ranges of Gambel and bur oak, which extend into Division 330, and the ranges of Gambel, turbinella and wavyleaf oaks, which extend into Division 340. The shading shows the distribution of oaks from Plate 1.

Braun (1972) called this area the Hemlock–White Pine–Northern Hardwood Region. She recognized two major subsections, the Great Lakes–St Lawrence and the Northern Appalachian Highlands. The western and eastern portions of the Northern Hardwood Region share many of the same species, but they differ ecologically and silviculturally (Godman, 1985). Those differences are due in part to the influence of the Appalachian Mountains in the eastern part of the Northern Hardwood Region. More than 1.5 million non-industrial private forest owners own approximately half of the forests in the Northern Hardwood Region. Corporate and other private owners hold an additional 25% (Birch, 1996; USDA Forest Service, 2018). There are 11 national forests in the region (primarily in the Lake States) that cover 6.5 million acres. Climate, physiography and soil Precipitation typically ranges from 24 to 45 inches/ year although as much as 70 inches occurs in some mountainous areas in the eastern part of the region.

Oak-dominated Ecosystems

Snowfall of 60–100 inches/year is common throughout the region. More than 100 inches of snowfall occurs at some of the higher elevations, and snowfall exceeds 400 inches in some locales near the Great Lakes. Mean annual temperature ranges from 35° to 52°F (2–11°C) and the growing season lasts from 100 to 160 days (Fig. 1.7) (McNab and Avers, 1994). The region is characterized by low relief with numerous lakes, depressions, morainic hills, drumlins, eskers, outwash plains and other glacial landforms. Variation in the depth and type of glacial deposits and associated heights of water tables are important factors in the identification of silviculturally relevant ELTPs (Fig. 1.8). Elevations in the mountainous areas range from 1000 to 4000 ft with individual peaks exceeding 5000 ft. Valleys in the mountainous areas include outwash plains and lakes resulting from glaciation (Bailey, 1995). Soils have formed in diverse organic and mineral materials including peat, muck, marl, clay, silt, sand, gravel and boulders in various combinations. At lower elevations in New England and along the

19

100

3

80

2 1 0

20

5

120

4

100

3

80

40

J FMAMJ J ASOND Month

100

100

3

80

80

2 1

40

Degrees F

4

60

250 Prairie

1

40

0

20

0 J FMAMJ J ASOND Month

J FMAM J J A SOND

Boone, NC 52°F 55 in.

Key West, FL 78°F 40 in.

Month

410 Savannah 100

80

2

60

Inches

3

1

40 J FMAMJ J ASOND Month

0

Degrees F

4 100

3 2

60

M220 Hot Continental

Degrees F

J FMAM J J A SOND

Fargo, ND 41°F 19 in.

Inches

Degrees F

220 Hot Continental

1

Month

Fort Wayne, IN 50°F 49 in. 120

2

60

Inches

40

140

4

90 3 80

Inches

60

230 Subtropical

4

Degrees F

210 Warm Continental

Inches

Degrees F

120

Atlanta, GA 62°F 49 in.

Inches

Iron Mountain, MI 42°F 30 in.

2

70 J FMAM J J A SOND Month

Fig. 1.7.  Representative climates for selected ecoregion divisions where oaks occur in the eastern USA. Mean monthly precipitation is shown by the solid lines (right axis) and temperature by dashed lines (left axis). Mean annual values are given above each graph. Division boundaries are shown in Plate 1 and Fig. 1.5. (Ecoregion and climatic data from Bailey, 1995.)

Great Lakes, Spodosols are common. Inceptisols and Alfisols dominate at lower elevations elsewhere. In the mountainous zones the soils are primarily Spodosols (Bailey, 1995). Forest history The forests of the Northern Hardwood Region were strongly influenced by the aboriginal people

20

who lived there. For thousands of years before the arrival of Europeans, Native Americans used fire and land clearing to shape the forest to meet their needs. Accounts of early European settlers indicate that Native Americans burned large portions of the landscape each year (Pyne, 1982). Dry fuels in the late spring before ‘greenup’ and again after leaf fall during ‘Indian summer’ provided favourable conditions for burning. Fires often eliminated forest

Chapter 1

ELTPs on dry ice-contact and sand hills

ELTPs on outwash plains

1

10

11

12

20

21

ELTPs on mesic ice-contact and sand hills

34

35

24

25

WATER TABLE

WATER TABLE

32

22

ELTPs on herb-poor moraines

37

40

42

43

WATER WATER

Fig. 1.8.  Ecological landtype phases (ELTPs) for the upland forests of the Huron–Manistee National Forests in the lower peninsula of Michigan (Province 211: Mixed Deciduous–Coniferous Forests Province) (adapted from Cleland et al., 1993). Site productivity generally increases with increasing ELTP value. ELTP 1: northern pin oak–white oak/Deschampsia type; ELTP 10: black oak–white oak/Vaccinium type; ELTP 11: black oak–white oak/Vaccinium type with loamy sand to sandy loam bands in substrata; ELTP 12: black oak–white oak/Vaccinium type with perched water table at 6–15 ft; ELTP 20: mixed oak–red maple/Trientalis type; ELTP 21: mixed oak–red maple/Trientalis type with loamy sand to sandy loam bands in substrata; ELTP 22: mixed oak–red maple/Trientalis type with perched water table at 6–15 ft; ELTP 24: mixed oak–red maple/Trientalis type with perched water table at 3.5–6 ft; ELTP 25: mixed oak–red maple/Trientalis type with coarse loamy substrata; ELTP 32: northern red oak–red maple/Viburnum type with perched water table at 6–15 ft; ELTP 34: northern red oak–red maple/Viburnum type with perched water table at 3.5–6 ft; ELTP 35: northern red oak–red maple/Viburnum type with fine loamy substrata; ELTP 37: northern red oak– red maple/Desmodium type with sandy loam over fine loamy substrata; ELTP 40: sugar maple–beech–/Maianthemum type; ELTP 42: sugar maple/Maianthemum type with perched water table at 6–15 ft; ELTP 43: sugar maple–northern red oak/Maianthemum type with fined texture substrata.

Oak-dominated Ecosystems

21

understorey layers, which in turn encouraged the growth of edible berries and increased forage that attracted edible wildlife. A combination of fire and tree cutting or girdling also was used by Native Americans to create and maintain openings for cultivated crops (Pyne, 1982). Maintaining an open understorey condition by burning also helped defend Indian villages from surprise enemy attacks. Repeated burning helped maintain large areas of oak savannahs and barrens. Topography greatly influenced the spatial distribution of fires, and the frequency and intensity of burning were lower on wet or mesic sites and at higher elevations. This produced a landscape mosaic of diverse species composition. The land clearing and burning practices used in New England were carried westward by settlers who immigrated to the Lake States. Oaks were known as indicators of fair to good conditions for agriculture. Oak forests therefore were often girdled, felled and burned in preparation for agriculture. Over time, enormous areas in the Northern Hardwood Region were cleared for agriculture. Ultimately, however, it was logging that had the greatest impact on the region’s forests. Logging gradually accelerated with the influx of Europeans to New England in the 17th century. Demands for forest products were initially modest in the developing agrarian society. Local timber harvesting supplied wood for homes, barns, fences, heating and cooking, and making potash and tannin. Forests were considered more an impediment to agriculture than a valued resource. However, this changed with industrialization during the 19th century (Williams, 1989). The industrialization of America provided the capacity and economic incentive to exponentially increase lumber production from less than 1 to nearly 45 billion board ft between 1800 and 1900. That lumber, which was principally white pine and other softwoods, came primarily from the Northern Hardwood Region. In 1839, 30% of the nation’s lumber (on a value basis) came from New York. Combined, New York, Pennsylvania, New Jersey and New England produced two-thirds of the nation’s lumber. As supplies of white pine dwindled in the eastern part of the region, timber production moved westward. Although New York reached peak production in 1849, the north-eastern states together had by then dropped to half of the national lumber output. The shift in lumber production from the Northeast to the Lake States occurred between

22

1840 and 1860. Lake States harvest reached a peak of 10 billion board ft annually in 1889. Relatively level terrain, easy access and high demand facilitated the rapid rise in timber harvesting across the Lake States. By 1940, Lake States production dropped below a billion board ft as the timber industry moved south (Williams, 1989). Although white pine was the preferred species, oaks and other hardwoods were utilized locally where they were abundant. Oaks were in less demand by the logging industry. Because of their high density, oak logs could not be floated down rivers as easily as white pine and required overland transportation to avoid losses (Williams, 1989). Over time, repeated timber harvesting removed the trees of greatest economic value leaving behind stands of inferior quality and composition. Farmers emigrating westward subsequently completed the land clearing. As a preferred fuelwood, the oak’s utilization for that purpose significantly affected the region’s forests during the early agricultural period. A colonial family used 20–60 cords of wood annually for heating and cooking. Although the per capita volume of wood used for fuel decreased over time, the total volume increased because of a growing population. In 1880, more than half of America’s energy needs were still met with fuelwood (Whitney, 1994). Iron furnaces in the region were fired with hardwood charcoal. Less than 1% of the fuelwood burned from 1800 to 1930 was used to produce charcoal for iron smelting, but large areas of forest surrounding iron furnaces were greatly affected (Whitney, 1994). A typical 18th-century smelting operation consumed 100 acres of forest annually to produce charcoal. Because forest regrowth could be repeatedly harvested for this purpose every 25 years, about 2500 acres of forest were required to sustain production of the ironworks. By the late 19th century, charcoal production for large ironworks required annual harvests of 1000–2000 acres, and some large companies owned 100,000 acres of forest adjacent to their smelters for that purpose (Whitney, 1994). The large clearcuts surrounding the ironworks radically changed the age structure of the forests and also influenced their species composition. Harvesting hardwoods for fuel favoured the development of hardwood sprouts and increased the relative proportion of hardwood trees in areas that were not converted to agricultural land (Whitney, 1994). During the last half of the 19th

Chapter 1

century, many cleared acres marginally suited to agriculture were abandoned and subsequently reverted to forest. During the latter half of the 20th century, the combination of abandoned agricultural lands and natural regeneration of cutover lands resulted in large increases in timber volumes throughout the region. In New England, forest volumes increased by 16% between 1970 and 1982 (cubic foot basis) (Seymour, 1995). The increase was predominantly in hardwoods that as a group increased in volume by 24%. The increase in oak volume (16%) was low relative to other hardwoods. In 2007, net annual hardwood growth in the Northern Hardwood Region remained at twice the annual harvest (Smith et al., 2009).

(A)

(B)

Oaks as components of the region’s forests The forests of the Northern Hardwood Region today are dominated by more than half a dozen recognized northern hardwood forest types comprising various combinations of sugar maple, red maple, beech, paper birch, yellow birch and eastern hemlock. Although northern red oak typically occurs as a minor component within these types, it sometimes forms pure or nearly pure stands (Fig. 1.9A). Wherever it occurs, it is a valuable and desirable species for timber, acorn production and species diversity. White, black, northern red and chestnut oaks also occur in the southern portions of the region. Oaks are thus a relatively small component of northern hardwood forests. They are most abundant and attain their best development in the southern parts of the region including New York, Massachusetts, northern Pennsylvania, and central and southern Minnesota, Wisconsin and Michigan. There the oak and mixed-hardwood forests grade into the oak–hickory forests of the Central Hardwood Region. In both New England and the Lake States, oaks comprise about 10% of the growing stock. Throughout the Northern Hardwood Region, sugar maple, red maple and aspen are the most abundant hardwoods. Conifer forests (red and eastern white pines, spruce and balsam fir) also exceed oak in acreage and volume (Smith et al., 2009; Miles, 2016). Oaks often reach their greatest density on sites that have been repeatedly disturbed by fire, timber harvesting and other events. After burning or timber harvesting, oaks originating from vigorous seedling sprouts and stump sprouts often dominate stands. However, in the absence of disturbance the

Oak-dominated Ecosystems

Fig. 1.9.  (A) A 130-year-old stand of northern red oak in the Northern Hardwood Region of northern Wisconsin (Province 211: Mixed Deciduous–Coniferous Forests Province; Southern Superior Uplands Section). The absence of oak reproduction and a sparse subcanopy of shade-tolerant red and sugar maples are indicators of what is likely to eventually replace the oaks in the absence of disturbance. (B) Xeric northern pin oak–white oak/Deschampsia type (see Fig. 1.8) on deep outwash sand in the northern lower peninsula of Michigan (Province 211: Mixed Deciduous–Coniferous Forests Province; Northern Great Lakes Section). This oak stand is mixed with jack pine; oak site index is ≤ 50 ft. (Photographs courtesy of USDA Forest Service, North Central Research Station.)

oak forests of the Northern Hardwood Region are usually successional to other hardwoods on all but the poorest sites. On the poorer sites, oaks are often relatively permanent members of the forest. There, oaks frequently invade and successionally replace established pine stands (Seymour, 1995). The loss of the American chestnut to chestnut blight fungus in New England oak forests began in the early 1900s (Fig. 1.10). This increased the relative importance of oaks because oaks often captured the growing space vacated by dying American chestnuts. Today, the single-tree selection method of silviculture is often applied to northern hardwood forests dominated by shade-tolerant species such as

23

Fig. 1.10.  A standing dead American chestnut (minus bark). Chestnut was a common associate and dominant member of eastern oak forests throughout the Appalachians from Maine to Alabama and westward to Missouri. The chestnut blight, which decimated the species throughout its range, permanently altered the ecology of eastern oak forests. The blight was first identified in New York in 1904. Fifty years later it spanned the entire natural range of chestnut. Oaks and associated hardwoods quickly captured the growing space vacated by dead and dying chestnuts. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

sugar maple. This practice focuses on maintaining stands of high quality trees while largely relying on the natural regeneration of shade-tolerant species to sustain the silvicultural system. Although this system favours the development of high quality oaks in stands where oaks are already present, regenerating oaks beneath the relatively closed canopies of selection forests is usually difficult in this region. On the poorer sites, oaks may develop beneath a pine overstorey and eventually displace the less shade-tolerant pine through natural succession or the exposure of oak reproduction in the understorey to full light after timber harvest. On deep sandy soils of the upper Lake States, stands of northern pin, black and white oaks, often mixed

24

with jack pine, form relatively stable forest types of low productivity (Fig. 1.9B). The Central Hardwood Region Geographic extent The Central Hardwood Region includes the predominantly deciduous broadleaved forests of Central USA. The region lies entirely within the Humid Temperate Domain. The region includes the two Hot Continental Divisions (Divisions 220 and M220), and intergrades with the eastern part of the Forest–Steppes and Prairies Province (251) of the Prairie Division (250) (Plate 1; Fig. 1.6; Table 1.2). The Hot Continental Division is subdivided into

Chapter 1

two provinces: (i) Broadleaved Forests, Oceanic (221a); and (ii) Broadleaved Forests, Continental (221b). The Hot Continental Mountains Division (M220) also is divided into two provinces: (i) Deciduous or Mixed Forest–Coniferous Forest–Meadow (M221); and (ii) Broadleaf Forest–Meadow (M222). The Northern Hardwood Region, the Southern Pine–Hardwood Region, the Forest–Prairie Transition and the western edge of the Appalachian Mountains bound the Central Hardwood Region. The Central Hardwood Region extends 1200 miles from southwest to north-east and covers approximately 220 million acres; about half the region is forested. Approximately three-quarters of the forest area in the Central Hardwood Region is in non-industrial private ownership. Within that ownership, most holdings are 50 acres or smaller (Birch, 1996; USDA Forest Service, 2018). There are seven national forests in the region comprising about 4 million acres distributed across the southern half of the region in Arkansas, Missouri, Illinois, Indiana, Ohio, Kentucky and Tennessee. Climate, physiography and soil The climate in the Central Hardwood Region is hot continental with warm summers and cold winters. Mean annual temperature ranges from 40 to 65°F (4–18°C), with the warmer temperatures in the south. Annual precipitation ranges from 20 inches in the north-west to 65 inches in the south-east and reaches as much as 80 inches on some Appalachian peaks (Fig. 1.7). Precipitation occurs throughout the year, but tends to be somewhat greater in spring and summer. Droughts may occur during the summer when evapotranspiration is high. Frost-free periods range from 100 days in the northern Appalachians to 220 days in the southern part of the region (Bailey, 1995). Topography is diverse in this region. Elevations in the Appalachian Highlands (Province M221) range from 300 to 6000 ft with as much as 3000 ft of local relief. Further west (Province 221a), the hills and low mountains of the dissected and uplifted Appalachian Plateau (including the Allegheny and Cumberland Plateau) range from 1000 to 3000 ft in elevation. In the western half of the Central Hardwood region (Province 221b), most of the land is rolling but varies from extensive, nearly level areas to areas like the Ozark Highlands where relief reaches 1000 ft. Most of the northern portions of this province were glaciated with the

Oak-dominated Ecosystems

exception of the driftless area of south-western Wisconsin and adjacent states. Major soils are Alfisols, Inceptisols, Mollisols and Ultisols (Bailey, 1995). Forest history The utilization and exploitation of forests in the Central Hardwood Region has passed through various historical phases (Hicks, 1997). Even before the arrival of Europeans, humans influenced the nature and extent of the region’s forests (Whitney, 1994). The use of fire to control vegetation by Native Americans significantly influenced the extent and character of presettlement forests (Pyne, 1982; DeVivo, 1991; Olson, 1996). These human-caused alterations of the landscape continued for thousands of years before the arrival of Europeans (Hicks, 1997). After settlement by Europeans, human impacts on the forest expanded and intensified. Burning, grazing, exploitative timber harvesting and clearing of forests for agriculture occurred on an unprecedented scale. These practices occurred about 200 years earlier in the eastern part of the Central Hardwood Region than in the western part. Historically, different human disturbances were further confounded by intrinsic ecological differences among oak forests within the various ecoregion provinces. Each subregion of the Central Hardwood Forest has its own unique combination of disturbance history, climate, physiography, soils, species associations and successional possibilities. This complicates generalizing the application of silvicultural methods to oak forests across the region. As in the Northern Hardwood Region, the loss of American chestnut to chestnut blight increased the relative proportion and importance of oaks throughout the region. Shortly after 1900, the disease became epidemic and within 40 years it had invaded the entire natural range of the chestnut (Kuhlman, 1978). The loss represents one of the greatest recorded changes in a natural population of plants caused by an introduced organism (Liebhold et al., 1995). The chestnut comprised 25% of the eastern hardwood forest that covered 200 million acres. In the Appalachians, it was the most ecologically and economically important tree species (Kuhlman, 1978). There and in other regions, it grew faster and taller than associated oaks. Before the blight, chestnut was especially important in moist upland forests where it sprouted vigorously and often increased in dominance after logging.

25

In  1900, half the standing timber in Connecticut was chestnut, which was largely comprised of young stands of stump sprout (coppice) origin (Smith, 2000). Although American chestnut provided only about 1% of the nation’s hardwood lumber even at the peak of its importance, its loss (beginning in the early 1900s) had a significant impact on local economies in the Appalachians. There, its nuts and bark (for tannin) provided scarce cash income, and its wood was valued for a variety of uses (Youngs, 2000). The practice of silviculture in the Central Hardwood Region dates back to the genesis of North American forestry in the late 19th century (Fernow, 1911; Pinchot, 1987). From then until the 1960s, the major emphasis was on uneven-aged silviculture (Roach, 1968). During the 1960s, the emphasis shifted to even-aged silviculture, especially clearcutting, and this emphasis persisted for about 20 years (Roach and Gingrich, 1968; Johnson, 1993a). Where applied, hardwood silviculture in the region usually follows the ‘ecological model’, which relies on the existing forest vegetation and its natural regeneration capacity. Silvicultural prescriptions are usually focused on controlling stand structure and species composition using cutting methods such as those recommended by Roach and Gingrich (1968). This approach contrasts with the more intensive ‘agronomic model’ of silviculture based on artificial regeneration, the introduction of improved genotypes, use of herbicides and fertilizer, prescribed burning, and other intensive cultural methods like those commonly used in the silviculture of pine monotypes in the south and elsewhere. Where applied, silviculture in the Central Hardwood Region usually focuses on growing high quality sawtimber with secondary consideration of other amenities and ecosystem services. During the course of stand management (but before final harvest of even-aged stands), this requires ‘leaving the best’ and ‘cutting the worst’ at each harvest. In evenaged silviculture, each timber harvest concentrates on removing small, subcanopy trees and poor quality trees in the main canopy, with concomitant attention to species composition. Similarly, in uneven-aged silviculture, timber removals are concentrated on poor quality trees, but cutting occurs across a wide range of diameter classes in order to create and maintain the uneven-aged stand structure. In both systems the objective is the improvement of the quality and the economic value of the residual stand. Today, only a small fraction of the forests of the Central Hardwood Region receive systematic

26

s­ilvicultural treatment. This is largely due to the pattern of forest ownership, which is characterized by numerous small tracts owned by private individuals. Many forest owners are uninterested in silviculture or lack information on its benefits (Bliss et al., 1994, 1997; English et al., 1997). Consequently, the systematic application of silviculture has largely been limited to industrial forests and public lands. The predominant methods of timber harvesting on private lands are probably commercial clearcutting and other forms of high-grading. Not to be confused with silviculturally prescribed methods, these methods consist of harvesting all trees with commercial value without regard to regeneration needs and future stand condition. Such malpractice typically leaves stands of highly variable residual stocking comprised of trees of poor vigour, low quality and undesirable species composition. These practices persist and continue to impact negatively on the quality of the region’s forests. Nevertheless, annual forest growth for the region exceeds annual harvest, and total standing volume of timber has increased steadily since the 1950s (Powell et al., 1994; Smith et al., 2009). Oaks as components of the region’s forests The predominant oaks are black, white, scarlet, chestnut, post, northern red, southern red and bur oak (Fig. 1.11). These species typically occur in various combinations with hickories, sassafras, flowering dogwood, blackgum, black cherry, red maple and other upland oaks and deciduous tree species. The Ozark Highlands Section of the region, which covers southern Missouri, and parts of north-­ eastern Oklahoma, northern Arkansas and southwestern Illinois, comprises one of the largest contiguous areas dominated by the oak–hickory association in the Central Hardwood Region. Many oak–hickory forests of today may have originated from extensive fire-maintained oak savannahs of the presettlement period; these formed closed canopy forests when fires were suppressed (Johnson, 1993a; Olson, 1996). The oak cover types of the Central Hardwood Region include various combinations of oaks, hickories and other tree species that vary geographically (see Appendix 2, this volume). Although hickories are common and persistent members of this forest type, they seldom represent more than a small proportion of trees in the main canopy of a mature forest (Braun, 1972). Oak–hickory forests develop on relatively dry sites where oaks persist as dominant

Chapter 1

Fig. 1.11.  A mature black–northern red–white oak stand on a good site in the Central Hardwood Region of southeastern Ohio (Province 221a: Broadleaved Forests, Oceanic). (Photograph courtesy of USDA Forest Service, North Central Research Station.)

members of the forest through successive disturbance events. This persistence is facilitated by the oaks’ drought tolerance and by light intensities in dry ecosystems that are sufficient for the regeneration of the relatively shade-intolerant oaks (Bourdeau, 1954; Carvell and Tryon, 1961; Abrams, 1990). Oaks and hickories are found together on the drier sites throughout the region and comprise a commonly occurring species association. These forests dominate the landscape in the western part of the region. Elsewhere, oaks and hickories as a group commonly occur on dry ridges and south-facing slopes. On the more mesic sites, oaks are often interspersed with other hardwoods. Slope position and aspect strongly influence the spatial distribution of these forests and are thus useful in defining ELTPs in much of the region (Figs 1.12 and 1.13). From southern Illinois eastwards and in northern Arkansas, the more mesophytic forests of the Central Hardwood Region generally include more complex

Oak-dominated Ecosystems

species mixtures than found in drier oak forests (Fig. 1.14). Although oaks commonly share dominance with non-oaks on these sites, in the absence of recurrent fire and grazing the oaks are often successionally displaced by more moisture-demanding and more shade-tolerant non-oaks (Jokela and Sawtelle, 1985; Lorimer, 1985, 1989; Nowacki et al., 1990; Abrams, 1992; Nowacki and Abrams, 2008). Understoreys of these stands are typically lacking in oak reproduction, especially large oak seedling sprouts. Over time, the dominance of oaks decreases while the proportion of non-oaks increases. The latter include various combinations of maples, American beech, black cherry, white ash, American basswood and yellow-poplar. Timber harvesting may accelerate the successional replacement of the oaks (Abrams and Nowacki, 1992). Diverse mixtures of hardwoods are common throughout the Ohio Valley, the Cumberland Plateau and Highland Rim areas of Tennessee and Kentucky,

27

2.1, 2.2

1.3 1.1, 1.2 sinkholes

3.1 Exposed, rocky RO/UG Ultic backslope (some 3.2 Alfic)

1.1 Ultic rocky, 1.2 Silty 2.1 Ultic, rocky RO Summits RO/UG Shoulder (some 2.2) 4.1 Protected RO/UG Ultic backslope (some 4.2 Alfic)

7.1 to 7.3 Cherty, non-cherty & glade, variable depth to dolomite 2.4 UG Alfic crypt reef bench

3.1, 3.2

Roubidoux (RO)

8.1 Cherty protected, variable depth to dolomite 2.3 UG Ultic crypt reef bench 4.1,4.2

Upper Gasconade (UG) 2.3 Cryptozoan Reef

Upper Gasconade (UG) 10 Footslope Exposed aspects 135–315°

10 Footslope Protected aspects 315–135°

Fig. 1.12.  Ecological landtype phases (ELTPs) for the Ozark Highlands of Missouri (Province 221b: Broadleaved Forests, Continental Province; Ozark Highlands Section) (adapted from Nigh et al., 2000). Aspect, landform and bedrock geology are factors in the classification system. ELTP 1.1, 1.2, 1.3, 2.1, 2.3 and 3.1: pine–oak/Vaccinium dry ultic (chert) woodland; ELTP 2.2 and 3.2: mixed oak–pine/Desmodium, Vaccinium dry–mesic alfic (chert) woodland; ELTP 4.1: mixed oak–hickory/dogwood/Desmodium dry–mesic ultic (chert) forest; ELTP 4.2: mixed oak (white, red) dogwood dry–mesic alfic (chert) forest; ELTP 7.1: post oak (blackjack oak, pine) bluestem xeric chert woodland; ELTP 7.2: redcedar–hardwood/redbud dry dolomite woodland; ELTP 7.3: bluestem, Missouri coneflower dolomite glade; ELTP 8.1: mixed oak–sugar maple/redbud dry–mesic dolomite forest; ELTP 10: mixed oak (white)/dogwood dry–mesic alfic (chert) footslope forest.

the Appalachian and Allegheny Plateau of western West Virginia and western Pennsylvania, the southern Lake States, and other parts of the region. Specific combinations of canopy dominants often form distinct geographic species groupings. Examples include the beech–maple forests of central Indiana and eastern Ohio, the maple–basswood–northern red oak forests of the driftless area of south-western Wisconsin, and the black cherry–ash–yellow-poplar forests of the Allegheny Plateau of Pennsylvania. Towards the eastern end of the region, eastern white pine and eastern hemlock may be locally important members of mixed hardwood forests. Mixtures of oak and mesophytic species also occur in northern Arkansas in the Broadleaf Forest–Meadow Province (M222) (Plate 1; Table 1.2) (Braun, 1972). These are the most mesophytic forests in the western end of the region. However, unlike the mixed mesophytic forests further to the east, yellow-poplar is absent. Some species combinations are formally recognized as cover types (Eyre, 1980); others form mixtures that are only locally distinguished silviculturally.

28

It is within these mesic, mixed hardwood stands that northern red oak, one of the most commercially valuable tree species of the region, reaches its best development. It is also within these forests that the oaks are also the most difficult to regenerate silviculturally (Carvell and Tryon, 1961; Arend and Scholz, 1969; Trimble, 1973; Loftis, 1988; Johnson, 1993b, 1994a, b). Mesophytic mixed hardwood forests generally occur where oak site index (see Chapter 4, this volume) is 65 ft at an index age of 50 years. Oak–pine mixtures occur most frequently in the southern and eastern parts of the region and are closely correlated with fire and succession in old fields, heavily disturbed hardwood stands and pine plantations. Oak–pine mixtures represent an early- to mid-stage in the succession towards oak–hickory or mixed hardwood forests. In the absence of fire or other disturbances, oak–pine forests may change successionally from predominantly short-leaf pine, pitch pine or Virginia pine to hardwoods as the more shade-tolerant hardwoods replace the intolerant

Chapter 1

Brown County Hills Subsection South

North Chestnut oak White oak Painted sedge Blueberry

Chestnut oak White oak Painted sedge Blueberry

White oak Black oak Pignut hickory Virginia creeper

Tulip tree Sugar maple Chinkapin oak Jack-in-the-pulpit

Sugar maple Beech Jack-in-the-pulpit

White oak Black oak Pignut hickory Virginia creeper

Sugar maple Beech Jack-in-the-pulpit

Sugar maple Beech Jack-in-the-pulpit

Sugar maple Beech Sycamore Wild ginger Fragile fern Green violet

ELTP 60: Mesic headwater bottoms

ELTP 40: Broad, mesic ridgetops ELTP 21: Moderately dry slopes

ELTP 11: Moderately dry ridgetops

Silver maple Boxelder Green ash Sycamore Wood nettle False nettle Gray’s sedge

Limestone

ELTP 10: Dry ridgetops ELTP 20: Dry south-west slopes

Sugar maple Black maple Chinkapin oak Jack-in-the-pulpit American gromwell Twinleaf

ELTP 41: Calcareous mesic, broad ridgetops

ELTP 50: Mesic slopes

ELTP 52: Moist-mesic calcareous slopes ELTP 62: ELTP 63: Floodplains of large streams

ELTP 61: Moist-mesic bottoms

Crawford Upland and Escarpment Subsections South

North Chestnut oak White oak Blueberry Chestnut oak White oak Blueberry Red maple Mountain laurel Lady fern Hydrangea

Sugar maple Beech Jack-in-the-pulpit

ELTP 10: Dry ridgetops

ELTP 30: Sandstone cliffs

Post oak Blackjack oak Little bluestem Blazing star

White oak Black oak Pignut hickory Virginia creeper

White oak Black oak Pignut hickory Virginia creeper

ELTP 60: Mesic headwater bottoms

Sugar maple Beech Wild ginger Fragile fern

Sugar maple Sugar maple Chinkapin oak Beech Jack-in-the-pulpit Jack-in-the-pulpit Twinleaf

Sugar maple Beech Sycamore Wild ginger Fragile fern

ELTP 11: Moderately dry ridgetops

ELTP 20: Dry south-west slopes

Sugar maple Beech Jack-in-the-pulpit

ELTP 40: Broad, mesic ridgetops ELTP 50: Mesic slopes

ELTP 22: ELTP 23: Dry clay oak barrens

ELTP 61: Moist-mesic bottoms

ELTP 21: Moderately dry slopes

Silver maple Boxelder Green ash Sycamore Wood nettle False nettle Gray’s sedge

ELTP 51: Moist-mesic north-east lower slopes

ELTP 52: Mesic calcareous slopes ELTP 62: Floodplains of large streams

Fig. 1.13.  Ecological landtype phases (ELTPs) for the forests of the Brown County Hills, Crawford Upland and Escarpment subsections of southern Indiana (Province 221b: Broadleaved Forests, Continental Province; Interior Low Plateau, Shawnee Hills Section). Oaks and pines typically dominate the exposed (hotter aspects) whereas sugar maple, American beech, yellow-poplar and other shade-tolerant hardwoods dominate the protected (cooler aspects). (Adapted from Van Kley et al., undated.) Oak-dominated Ecosystems

29

Fig. 1.14.  A large white oak (47 inches diameter at breast height (dbh)) in Dysart Woods in south-eastern Ohio (Province 221a: Broadleaved Forests, Oceanic Province). This 55 acre old-growth oak forest is dominated by white and northern red oaks and is the largest known remnant of the original mixed mesophytic forest of the Central Hardwood Region in south-eastern Ohio. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

pines (Cunningham and Hauser, 1989; Sheffield et al., 1989; Smith et al., 1989; Orwig and Abrams, 1994). The oak–pine mixtures are important for maintaining biodiversity as well as economic timber production (Phillips and Abercrombie, 1987; Cooper,

30

1989; Kerpez and Stauffer, 1989; Leopold et al., 1989). Consequently, there is increasing interest in methods to create and maintain oak–pine forests (Waldrop, 1989). Specific combinations of oaks and pine vary with subregion and site quality. Because the pines

Chapter 1

tend to be associated with the driest (xeric) sites, the associated oaks often include species such as post oak and blackjack oak. On somewhat less xeric sites, pines are commonly associated with black, white, scarlet, southern red or chestnut oaks. In the extreme north-western part of the region in Minnesota and Wisconsin, jack pine and northern pin oak commonly occur together. Stands of eastern redcedar are closely affiliated with the oak–pine mixtures. Eastern redcedar is a common invader of old fields and glades (Lawson, 1990). It may eventually form dense pure stands if succession is allowed to progress unimpeded by disturbance. However, such stands are short lived. As the redcedar matures and forms canopy gaps conducive to hardwood or pine regeneration, stands may succeed to oak–pine and oak–hickory mixtures. The Southern Pine–Hardwood Region Geographic extent The Southern Pine–Hardwood Region includes broadleaved forests, conifer forests and various hardwood–pine mixtures. The region includes the two subtropical divisions (230 and M230) of the Humid Temperate Domain (Plate 1; Fig. 1.6; Table 1.2). The region covers approximately 270 million acres of which 60% are forested. The extent of the Southern Pine–Hardwood Region is best illustrated by the joint ranges of the oak–pine and oak–gum–cypress forest types (Fig. 1.5B and C). The region extends 1300 miles from eastern Texas to Virginia and occurs in a band extending 200–400 miles inland from the coast. At its northern boundary, the Southern Pine–Hardwood Region meets the Central Hardwood Region. The Southern Pine–Hardwood Region’s oak–hickory forests typically attain their best development along this northern boundary. About 85% of the forest area in this region is privately owned. Four million non-industrial private forest owners control about 60% of all timberland. About 45% of this ownership is comprised of tracts smaller than 100 acres (Birch, 1996; USDA Forest Service, 2018). The 25 national forests in the region comprise 11 million acres of forest land (Smith et al., 2009). Climate, physiography and soil Annual precipitation in the region ranges from about 40 to 60 inches and is well distributed throughout the year. Mean annual temperature ranges from

Oak-dominated Ecosystems

60 to 70°F (16–21°C) and the growing season from 200 to 300 days (Bailey, 1995) (Fig. 1.7). The Southern Pine–Hardwood Region includes four major physiographic regions: (i) the Piedmont (Province 232); (ii) the Coastal Plain (Province 231); (iii) the Interior Highlands (Province M231); and (iv) the lower Mississippi Valley (Riverine Intrazonal Province (R)) (Plate 1). Gentle slopes characterize 50–80% of the area. Elevations range from sea level to 600 ft in the Coastal Plain, 300–1000 ft in the Piedmont, and up to 2600 ft in the Ouachita Mountains of the Interior Highlands. Numerous low-gradient streams, lakes, swamps and marshes characterize the flat Coastal Plain. The wet habitats along the Coastal Plain, the Mississippi Valley and other major rivers support bottomland forest types that are largely absent from the Piedmont and Interior Highlands. The principal soil groups are Ultisols, Spodosols, Vertisols and Entisols, all of which tend to be low in fertility. Exceptions are the Inceptisols, which occur in the alluvial bottoms of the Mississippi River (Bailey, 1995). Forest history Here, as in other regions, fire greatly impacted the early forests. Fire was an essential tool for maintaining agricultural openings, eliminating brush and hardwood reproduction from pine forests, and increasing forage for grazing. Native Americans regularly burned the forests where they lived. Increased burning associated with European settlement increased the proportion of pine in the region relative to earlier periods (Pyne, 1982; Skeen et al., 1993). Fire was combined with land clearing to open the hardwood forests of the south for agriculture. Even today, forest burning is a prominent practice throughout the region. In the Piedmont and alluvial river bottoms, vast areas were cleared for agriculture before industrial logging peaked in the region (Sargent, 1884; Hodges, 1995). Logging and the production of naval stores began on a small scale in the 1600s. But by 1880, forests accessible by water and close to population centres were heavily cut over. Charles Mohr noted the rapidity at which the cypress swamps were being logged in some localities and the apparent lack of forest regeneration. He observed that: the large number of logs harvested shows clearly with what activity the destruction of these treasures of the forest is being pushed; and the reports, as of heavy thunder, caused by the fall of the mighty trees,

31

resounding at short intervals from near and far, speak of its rapid progress. (Sargent, 1884)

However, Mohr also noted that immense areas of pine forest remained unaffected by logging and that many former hardwood forests that were earlier cleared for agriculture had reverted to pine after their abandonment. The South did not become the centre of the US logging industry until shortly after logging peaked in the Lake States in 1890. By 1900, lumber production in the South exceeded that of the Lake States and by 1910 the South produced half of all US lumber. The movement of large lumber companies to the Southern Pine–Hardwood Region coincided with technological advances that increased the speed with which logs could be removed from the woods and transported to the mills (Williams, 1989). Steam-powered stationary skidders and loaders were mounted on boats and railcars. As rail lines were extended into the southern forest, logging trains followed and systematically removed virtually all timber within the long reach of a cable skidder mounted on a rail car. The joint enterprise of rail construction and logging greatly accelerated the harvest of southern pines (Williams, 1989). By 1925 southern lumber production began to decline and western production increased. Following the Great Depression, timber production in the South never returned to the levels of 1910–1930, and the bulk of US timber production moved west. The subsequent establishment of southern paper mills coupled with successful fire prevention and a reduction in open-range grazing accelerated the reforestation of one million cutover acres. This gave rise to the South’s ‘third forest’ which today again produces a greater volume of wood than any other region of the USA. Oaks as a component of the region’s forests The oak forests in this region can be divided into upland and lowland types. Were it not for the complex spatial intermingling of upland and lowland forests, they could be treated as two ecologically distinct regions. The upland and bottomland oak forests of the region differ substantially in species composition, ecology and the application of silvicultural practices. The Southern Pine–Hardwood Region today includes about 170 million acres of forest land and 23% of standing volume is oak (Smith et al., 2009;

32

Miles, 2016). Of the broadly defined forest types recognized in national inventories, the oak–hickory, oak–gum–cypress and oak–pine occur on about 45% of the area. Loblolly-shortleaf pine and longleaf-slash pine make up most of the remaining forest acreage. A more detailed cover type classification (Eyre, 1980) recognizes 63 cover types that occur within the region (Walker, 1995) – 15 of those include oaks as primary species, and several others include oaks as important associated species (see Appendix 2, this volume). Southern silviculture has largely focused on pine, especially on industrial forest lands and on former industrial lands owned by timber investment management organizations and real-estate investment trusts. There, intensive silviculture is commonly practised to maximize timber and wood fibre yields through site preparation, planting genetically improved seedlings, frequent thinning, prescribed burning and the use of fertilizers, herbicides and pesticides. However, annual softwood removals are nearly equal to annual growth and may soon exceed annual growth (Walker, 1995). The importance of pine in the Piedmont is related to the region’s history – the historical sequence of lumbering, land clearing and farming deforested large areas that were abandoned before 1930 and burned frequently. This disturbance favoured the establishment of pine forests, which greatly increased in acreage relative to other species. Oaks and other hardwoods occur in most natural southern pine stands, and on these sites they increase in importance through succession. Fire suppression, silvicultural thinnings and partial harvests often accelerate this trend (Skeen et al., 1993). The large oak–hickory acreage in the region, the increasing hardwood volumes in pine–hardwood mixtures, and the nearly full utilization of the annual pine growth in the region has recently shifted the utilization of the region’s forests towards the hardwoods. Much of this change has resulted from the utilization of hardwood chips for paper production and composite products. Chips can be made from low quality, small diameter (> 4 inches) hardwoods. This technology created new markets for the abundant low-quality trees that previously had been considered a silvicultural liability. However, this utilization capability has also raised concerns about the potential for over-utilization of hardwoods, especially well-formed, small hardwood trees that comprise the future hardwood growing stock for solid hardwood products.

Chapter 1

Closely related to the oak–hickory forests are mixtures of oak and pine (Fig. 1.15). These mixed forests are increasingly recognized for their importance in maintaining forest biodiversity and their historical importance in the region. The oak–pine type, which occurs on 16% of the timberland of the region, rates high in aesthetic appeal and species

richness compared with even-aged pine stands. However, relatively little is known about the longterm management and productivity of oak–pine mixtures for lumber, fibre or other values. In the absence of disturbance, the oaks tend to successionally displace the pines and harvesting the pine often accelerates the process.

Fig. 1.15.  A black oak–white oak–shortleaf pine stand in the Ozark Highlands of Missouri (Province 221b: Broadleaved Forests, Continental Province; Ozark Highlands Section). (Photograph courtesy of USDA Forest Service, North Central Research Station.)

Oak-dominated Ecosystems

33

Southern bottomland hardwoods commonly include 11 species of oaks (cherrybark, Delta post, laurel, Nuttall, overcup, pin, Shumard, swamp chestnut, water, white and willow oaks) (Hodges, 1995). These oaks occur in a mixture with other bottomland species along the major rivers of the Coastal Plain as well as the lower reaches of the Mississippi, Arkansas, Missouri, Ohio and Wabash Rivers (Fig. 1.16). In total, southern bottomland hardwoods cover more than 27 million acres (16%

of the region’s timberland) and are physiographically and ecologically distinct from surrounding upland oak–hickory, oak–pine and pine forests. Although bottomland forests are relatively flat, elevational differences of only a few feet alter soil formation processes, soil moisture regimes and species composition. Thus, changes in species composition are often associated with relatively minor differences in physiography (Fig. 1.17). Moreover, floodplain physiography can quickly and frequently change as a

Fig. 1.16.  A cherrybark oak–sweetgum bottomland stand near the Tombigbee River in Alabama (Province 232: Coniferous–Broadleaved Semi-evergreen Forests Province). (Photograph courtesy of USDA Forest Service, Southern Research Station.)

34

Chapter 1

BA W R illo w FR ON Co tt T elmonw o pe , syc od ca am n, su ore ga FL Nu rbe AT t t a rry SL su ll o OU ga ak rbe , g GH rry ree W illo ,e n o w lm ash v RI , re DG ercu dm po E a ap Sw k le wi eet , wa llo gu ter w m h oa , w ick FL k, AT gre ater ory Ov en oak erc as h up oa k, wa ter SW hic AM ko ry P Cy pre ss ,w ate r tu S we pe RI DG re etg lo E d o um wi ak , h ng , s ick FL AT Sw ed e wam ory SL lm p e OU c , g et GH O ree gum blac hest n k t nu ve as , w u t a wa rcup h, N ter pelo oak ter oa utt oak g hic k, al o , w um ko ak illo ry w oa k

River base level

BA

Be

VE

R

River base level

LE

Ri

ve

rb

irc

Minor bottom

h ec E s h, y yc a el oa mo low-p re, ks o sw pla FL ee r AT Sw tgu e m, bla etgu SL sp m c OU ruc kg , o um ak GH e, s pin ,w ,h Cy e i i n c pre ge ko d e rie ss ,s s lm wa FL mp AT O tup ve elo Nu rcu tta p o l o ak ak , w illo TE w RR W oa hi AC k E hic te oa ko k, ye ries red llo , o lob w-p swe ak lol op etg ly lar um pin e

Major bottom

Fig. 1.17.  The topographic distribution of southern bottomland oaks and associated species in major and minor stream valleys of the Southern Pine–Hardwood Region (Province 232: Coniferous–Broadleaved Semi-evergreen Forests Province). (From Hodges and Switzer, 1979.)

result of scouring and deposition of sediments. These factors, coupled with the high tree species diversity of bottomland forests, complicate classifying forest types and developing silvicultural prescriptions appropriate to each. Up to 70 tree species occur in southern bottomland forests (Putnam et al., 1960), and species mixtures often change over short distances within stands. Consequently, species associations are difficult to classify meaningfully into more than a few broad types. Although Eyre (1980) listed 14 bottomland cover types (six named for oaks), Hodges (1995) reduced the number to three types (cottonwood–­ willow, bald cypress–tupelo, mixed bottomland hardwoods), and the USDA Forest

Oak-dominated Ecosystems

Service usually reports only two types (oak–gum– cypress, elm–ash–­cottonwood) in regional summaries. The Forest–Prairie Transition Region Geographic extent Within the USA the Forest–Prairie Transition Region extends from southern Texas northwards to Minnesota and North Dakota (Fig. 1.6). The region coincides with two ecoregion provinces: Forest–Steppes and Prairies (251) and Prairies and Savannahs (252) within the Prairie Division (250) (Plate 1; Table 1.2). The region includes Braun’s (1972) Grassland or

35

Prairie Region, Forest–Prairie Transition and Prairie Peninsula Sections, which fall within her Oak– Hickory Forest Region. As its name implies, the Forest–Prairie Transition Region is transitional between the eastern forests and the prairies and dry woodlands of the Dry Domain to the west. On its eastern border, the region adjoins the Northern Hardwood Region, the Central Hardwood Region and the Southern Pine–Hardwood Region. The Forest–Prairie Transition Region spans 1400 miles in latitude and varies in width from as little as 100 miles along the Canadian border to 600 miles between eastern Nebraska and western Indiana. The region includes approximately 191 million acres, about 7% of which are forested. Between Canada and Oklahoma, forests cover 5% of the landscape with most of the remainder devoted to tilled cropland or pasture. The forest cover increases to 13% in parts of Oklahoma and Texas. Most of the forest land in the Forest–Prairie Transition Region is privately owned. Although there are three national grasslands within the region, only 15,000 acres of national forest land (in central Missouri) are included. Climate, physiography and soil Precipitation within this vast region varies from less than 20 inches/year in the north to 55 inches along the gulf coast of Texas. One-half to two-thirds of the precipitation typically falls during the growing season and snowfall is common north of Texas. From north to south, mean annual temperature ranges from 36° to 70°F (2–21°C) with corresponding growing seasons ranging from 111 to 320 days (McNab and Avers, 1994) (Fig. 1.7). Throughout the region precipitation is largely offset by evapotranspiration, creating soil moisture conditions in many localities that are marginal for tree growth. Most of the region is comprised of gently rolling plains, although high rounded hills occur and steep bluffs border some river valleys. Elevations range from sea level to 2000 ft. Local relief is less than 165 ft throughout most of the region, but it reaches 500 ft in the Flint Hills of Kansas (McNab and Avers, 1994). Soils are predominantly Mollisols although Vertisols occur on the prairies, and Alfisols occur on savannahs and within the Mississippi Valley (Bailey, 1995). Forest history Native Americans who were largely nomadic inhabited the region for at least 10,000 years.

36

Crops were cultivated as early as 1000 years ago. A  few large Native American communities developed in the major river valleys. One of these was Cahokia (near present-day St Louis), which flourished between ad 1000 and 1400 with an estimated population of 25,000. The forests in the region were an important resource for both nomadic people and larger permanent communities. The demise of Cahokia may have been caused by the exhaustion of the surrounding forests that were used for fuel and for the construction and maintenance of a 2-mile-long perimeter wall around the city (Lord, 1999). Frequent fires were essential to the maintenance of prairie and savannah vegetation in many parts of the Forest–Prairie Transition Region, and Native Americans burned the grasslands and woodlands where they lived. Grazing and trampling by herds of bison and other ungulates also were important in maintaining prairies and preventing the encroachment of forests and other woody vegetation. By the mid-19th century, European settlers began farming the prairies and draining prairie wetlands. The latter produced some of the nation’s most productive agricultural lands. Trees were largely confined to riparian corridors, slopes and scattered savannahs. With the exclusion of fire and the elimination of free-ranging ungulates, forests frequently encroach upon abandoned fields and pastures. In 1884, Sargent (1884) stated that, ‘Dakota, with the exception of its riverlands and the small territory between the north and south forks of the Cheyenne River, is practically destitute of timber. The bottoms of the principal streams contain extensive groves of hardwood.’ In Iowa he observed that ‘since the first settlement of the state the forest area has increased by the natural spread of trees over ground protected by fire, and by considerable plantations of cottonwood, maples, and other trees of rapid growth made by farmers to supply fuel and shelter’. Further south, in Texas, Charles Mohr noted, ‘The timber growth immediately west of the Brazos is stunted and scanty; large areas of grass land intervene between the scrubby woods until all at once ligneous growth disappears and the seemingly boundless prairie, in gently undulating swells expands before the view on all sides’ (Sargent, 1884). Since that time, farms have been established on virtually all the lands suitable for row crops or forage production (McNab and Avers, 1994). Depending on the farm economy, the forested acreage has decreased or increased as forests and woodlands

Chapter 1

were cleared to create more farmland, or as marginal farmland reverted to forest through tree planting or abandonment. Oaks as components of the region’s forests The best forest development in this region occurs on its eastern border where it abuts the Northern Hardwood Region, the Central Hardwood Region and the Southern Pine–Hardwood Region. Of the 7% of the Forest–Prairie Transition Region that is forested, three-quarters is classified as oak–hickory or oak–pine. Few of the savannahs that formerly occupied the transition zone between forest and prairie exist today. The prairie fires that historically restricted the extent of the region’s forests have been replaced by agricultural practices that now limit most forests to riparian areas or to slopes unsuitable for forage or other crops (Fig. 1.18). Before the mid-19th century, fire was the primary regulator of the distribution of tree species in the region. Narrow bands of forest along streams and ravines, sometimes called gallery forests, provided refuges for trees from the frequent fires that burned across the prairie. Oaks dominated many of these forests. With the advent of farming in this region, the frequency of wildfires was greatly reduced. This allowed the gallery forests to expand into untilled areas that were formerly covered by native grasses (Abrams and Gibson, 1991). However, the invading woody species were generally species such as American elm, hackberry and eastern redcedar rather than oaks. The reduction in wildfires also allowed those species to increase in abundance within existing forests that were formerly dominated by oaks, especially on the more mesic sites. In much of this region, frequent fires are required to prevent the displacement of the oaks by other species (Penfound, 1968; Abrams, 1988; Abrams and Gibson, 1991). Oak–hickory forests extend from the Central Hard­ wood Region westwards across eastern Oklahoma and into northern Texas (Fig. 1.5A). From east to west the forests become increasingly scrubby and open. An exception is the relatively dense oak forest of the Cross Timbers Region. In Texas, the Cross Timbers comprise two bands of scrubby oak woodland extending 175 miles southwards from the Oklahoma border. These bands are 20–50 miles wide and separated by the Fort Worth Prairie. Forest cover occurs along outcrops of sandy soils of greater porosity than adjacent prairie soils (Braun, 1972).

Oak-dominated Ecosystems

The Cross Timbers were prominent landmarks for westward travellers who otherwise traversed relatively open landscapes (Dyksterhuis, 1948). Although the heavier forest cover in the Cross Timbers area of Texas is somewhat evident from Fig. 1.6, the two distinct strips of woodland are not distinguishable at the resolution shown. Post oak and blackjack oak are the dominant tree species in the Cross Timbers and account for 60% and 20% of the trees, respectively. Except in floodplains, these oaks seldom exceed 12 inches in diameter and 30–45 ft in height. At one time, the herbaceous vegetation in the Cross Timbers was probably similar to that of the surrounding prairie, but grazing during the last century has greatly altered the species composition of the herbaceous layer (Dyksterhuis, 1948). The Cross Timbers vegetation extends northwards through Oklahoma and eventually disappears in southern Kansas. Except for the Cross Timbers Region, the upland woodlands of eastern Oklahoma were formerly post–blackjack oak savannahs maintained by frequent fires. Grazing and a reduction in burning have since reduced grass cover and facilitated the establishment of dense tree reproduction in many areas; post and blackjack oaks dominate most stands. Although the average basal area of these forests historically has been relatively low, in the absence of burning it has increased from 49 ft2/acre in 1957 (Rice and Penfound, 1959) to 80 ft2/acre in 1993 (Rosson, 1994). From Kansas northwards there are few forests, but where they do occur, oaks often dominate (Plate 1; Fig. 1.6). Many of the oak forest types and conditions occurring in the Central Hardwood Region extend westwards through the central portion of the Forest–Prairie Region. The central part of the region is capable of supporting forest vegetation and is successional to forest in areas protected from cultivation. However, because agriculture is the dominant land use, forests are usually restricted to riparian corridors, wet areas, steep slopes and highly erodible lands, or other sites unsuited to agriculture. Nevertheless, oaks and other hardwoods often develop into commercially valuable stands in those parts of the region lying within Illinois, Iowa, northern Missouri and eastern Kansas. Bur oak is the dominant oak species in the northern reaches of the Forest–Prairie Transition Region. It is the only major oak species with a natural range that extends across western Minnesota and into the Dakotas. Bur oak is well adapted to this region because its deep taproot makes it resistant to

37

(A)

(B)

Fig. 1.18.  (A) Aerial view of the distribution of forests in the Forest–Prairie Transition Region (Province 251: Forest– Steppes and Prairies Province) of north-western Missouri. Throughout much of this ecoregion, forests are largely restricted to narrow belts occupying steep slopes along rivers and drainages interspersed with agricultural lands. (B) Forested bluffs dominated by oaks (background) along the Missouri River in central Missouri fronted by cultivated bottomland fields. Before settlement by Europeans, these bottomlands were covered by lowland forests dominated by American elm, silver maple, green ash, eastern cottonwood, bur oak and pin oak. (Photographs courtesy of USDA Forest Service, North Central Research Station.)

38

Chapter 1

drought and able to invade prairie grasslands (Johnson, 1990). Its thick bark makes it highly resistant to fires that eliminate most other woody species. Bur oak also thrives on moist alluvial bottoms that support dense hardwood forests in the northern portion of the Forest–Prairie Transition Region. Here, the bur-oak type covers approximately 2% of the land area and is the principal forest type. Cottonwood, quaking aspen and American elm are other abundant hardwoods in the northern part of the Forest–Prairie Transition Region.

Western Oak Forests The Southwestern Desert–Steppe Region Geographic extent The Southwestern Desert–Steppe Region includes the scattered oak forests of Arizona, New Mexico, southern Utah, west Texas and south-west Oklahoma (Plate 1; Fig. 1.6; Table 1.3). Although the range of Gambel oak extends northwards as far as southern Wyoming, the oaks there are a small component of the vegetation. Forests and woodlands cover about 20% of the area, but only 7% of this is considered productive forest. Soil moisture deficiencies limit the distribution of oaks and other plant life throughout the region. Oaks occur as scattered trees and in open woodlands. Their distribution within the region is often limited to discontinuous elevational zones that provide the required regime of precipitation and temperature. The region lies entirely within the Dry Domain and comprises parts of three divisions: (i) Tropical/ Subtropical Steppe (310); (ii) Tropical/Subtropical Steppe Mountains (M310); and (iii) Tropical/ Subtropical Desert (320). Included are six ecoregion provinces: (i) Coniferous Open Woodland and Semideserts (311); (ii) Steppes and Shrubs (313); (iii) Shortgrass Steppes (314); (iv) Steppe or Semidesert– Mixed Forest–Alpine Meadow or Steppe (M311); (v) Semideserts (321); and (vi) Deserts on Sand (323) (Plate 1; Table 1.3). The Southwestern Desert–Steppe Region extends 1200 miles from north-western Arizona to the Gulf of Mexico in southern Texas. It varies from 300 to 700 miles in width, and encompasses roughly 250 million acres including the Mojave Desert, the Sonoran Desert, the Painted Desert, the Colorado Plateau, the southern Rocky Mountains, Texas High Plains and the Edwards Plateau. Within the region, oak forests are widely scattered and cover

Oak-dominated Ecosystems

only a small fraction of the landscape (Plate 1). The federal government owns more than half of the forests and woodlands in Arizona and New Mexico, but in Texas and Oklahoma more than 90% are privately owned (Smith et al., 2009). Climate, physiography and soil A defining characteristic of this region is a rate of surface evaporation that exceeds precipitation. The climate varies from dry to desert. Annual precipitation ranges from less than 10 inches to 30 inches (Bailey, 1995). Even in areas with greater precipitation, high rates of evaporation limit moisture availability. Average annual temperature ranges from 40 to 70°F (4–21°C). Although temperatures decrease with increasing elevation, mean monthly temperatures generally exceed 32°F (0°C) (Fig. 1.19). Elevation ranges from sea level along the southern Texas Gulf Coast to 7000 ft in the Colorado Plateau; some mountain peaks are substantially higher. Soils are variable throughout the region and include Mollisols, Aridisols and dry Entisols (Bailey, 1995). Forest history As in other regions of the USA, Native Americans customarily burned the forests and woodlands where they lived. Lightning was also a common cause of combustion. These fires maintained an open understorey in the extensive ponderosa pine forests of higher elevations. In 1880 alone, about 75,000 acres burned – which accounted for 0.1–1% of the woodland within the settled area (Sargent, 1884). Beginning in the mid-19th century, European settlers were drawn to the region by opportunities for mining and livestock production. Lands were not suitable for agriculture, and the great land clearing that decimated the eastern oak forests did not occur here. However, logging, grazing and changes in fire regimes changed the species composition of forests and woodlands. In recent decades, the suppression of fires has increased the amount of tree reproduction, especially conifers, and decreased grasses and forbs growing beneath forest canopies (Long, 1995). Throughout the region, oaks have historically had little commercial value. Sargent (1884) described the forests in and around New Mexico: ‘The deciduous trees of this entire southwestern region, often of considerable size, are generally hollow, especially the oaks; they are of little value for any mechanical purpose, although affording abundant and excellent

39

310 Tropical/Subtropical Steppe 12

200 180 160 140 120 100 80 60 40

8 6 4 2

Degrees F

10

0

120

4

100

3

80 60 40

J FMAMJ J ASOND Month Pasadena, CA 62°F 19 in. 260 Mediterranean

J FMAM J J A SOND Month Brawley, CA 72°F 2 in. 320 Trop./Subtrop. Desert

60

1

40 J FMAMJ J ASOND Months

3

80

2

60

0

J FMAM J J A SOND Month

Tahoe, CA 42°F 31 in. 100

2

60

1

40

0 J FMAMJ J ASOND Month

Degrees F

3

Inches

4

80

0

Colorado Springs, CO 48°F 14 in.

M260 Mediterranean 100

1

40

Inches

2

100

330 Temperate Steppe

80

3 2

60

1

Inches

80

Degrees F

3 Inches

Degrees F

1

4

100

Degrees F

2

Inches

Abilene, TX 65°F 25 in.

240 Marine

Inches

Degrees F

Astoria, OR 51°F 76 in.

40 0 20

J FMAM J J A SOND Month

Fig. 1.19.  Representative climates for selected ecoregion divisions where oaks occur in the western USA. Mean monthly precipitation is shown by the solid lines (right axis) and temperature by dashed lines (left axis). Mean annual values are given above each graph. Periods of drought are indicated where the precipitation line falls below the temperature line (e.g. as in Division 260). Division boundaries are shown in Plate 1 and Fig. 1.5. (Ecoregion and climatic data from Bailey, 1995.)

fuel.’ Then, as now, ponderosa pine was the principal timber species. Oaks as components of the region’s forests Forests and woodlands cover only a small portion of the total area in the region, and the oaks comprise only a small percentage of that. In Arizona and

40

New Mexico, 23% of the land base is forested. Only 5% of the land of those two states can produce more than 20 ft3 of timber/acre/year, and virtually none of that is oak forest (Smith et al., 2009; Miles, 2016). Principal commercial forests include ponderosa pine, Douglas-fir, spruce–fir and aspen. The only recognized oak cover type here is western live oak (see Appendix 3, this volume) (Eyre, 1980).

Chapter 1

It occurs at elevations from 4000 to 6000 ft in the foothills and lower mountain slopes of Arizona and New Mexico. At higher elevations, the western live oak cover type gives way to ponderosa pine and pinyon–juniper, with oak–conifer mixtures occurring in the transition. At lower elevations the western live oak type yields to an open growth of shrubby evergreen oaks. Mesquite and desert vegetation typically occur below that. Characteristic species of the western live oak type include Emory, Arizona white, Mexican blue and silverleaf oaks (Eyre, 1980) (Fig. 1.20). Ajo oak, Dunn oak, grey oak and Havard oak also occur in Arizona and New Mexico. At the eastern end of the Southwestern Desert– Steppe Region towards the High Plains and Edwards Plateau of west-central Texas, precipitation increases and oaks become more prominent. The Mohr (shin) oak forest type covers more than 8 million acres in Texas where it develops best under 20–25 inches of precipitation annually (Eyre, 1980). However, that amount of precipitation represents the upper end of the range for the region (e.g. see Fig. 1.19, Division 310). Other oaks that occur in west-central Texas include Arizona white, blackjack, bur (marginally), chinkapin, Durand, Emory, Havard, Lacey, live, sandpaper, Nuttall and Texas live oaks.

The Pacific Mediterranean–Marine Region Geographic extent The Pacific Mediterranean–Marine Region includes the oak forests and woodlands of California, Oregon and Washington (Fig. 1.6). The region lies within the western portion of the Humid Temperate Domain and includes the Mixed Forest–Coniferous Forest–Alpine Meadow Province (M261) and the Mediterranean Woodland or Shrub–Mixed or Coniferous Forest–Steppe or Meadow Province (M262) within the Mediterranean Mountains Division (M260) (Table 1.3; Fig. 1.6). Oaks also occur within the Coast Ranges of California, which includes the Mediterranean Hardleaved Evergreen Forests, Open Woodlands and Shrubs Province (262) and the Redwood Province (263). At its northern extent, the Pacific Mediterranean–Marine Region also reaches the Mixed Forests Province (241) of the Marine Division (240) in Oregon and Washington. The Pacific Mediterranean–Marine Region also includes California’s Central Valley (Province 261) and the mountainous zones of Washington and northern Oregon (M261) where oaks are not abundant. The Pacific Mediterranean–Marine Region extends nearly 900 miles from Washington to southern California but less than 200 miles from

Fig. 1.20.  Emory oak woodland in the Peloncillo Mountains of south-western New Mexico, Coronado National Forest (Province 321: Semideserts Province). (Photograph courtesy of USDA Forest Service, Rocky Mountain Research Station.)

Oak-dominated Ecosystems

41

the Pacific Ocean to the eastern slopes of the Sierra Nevada Mountains. Although the region covers about 75 million acres, the oaks are limited to relatively narrow elevational zones. In California, Oregon and Washington, 40% of the timberland is publicly owned. However, about three-quarters of the region’s oak forest and woodland acreage are in non-industrial private ownership (Thomas, 1997; Smith et al., 2009). Climate, physiography and soil Climate is strongly influenced by the Pacific Ocean and by the Coast and Sierra Nevada Ranges, which dominate the physiography of the region. Elevations range from sea level to more than 14,000 ft. In the mountain ranges, increasing elevation is associated with decreasing temperatures and variation in precipitation. For a given elevation and latitude, precipitation is generally greater on western slopes than on eastern slopes. Latitude also influences climate so that a given climatic zone occurs, from north to south, at progressively higher elevations. However, mountainous topography creates climatic irregularities and discontinuities, and the distribution of oaks and associated tree species varies accordingly. Most of the precipitation occurs during the autumn, winter and spring. Annual precipitation generally ranges from 10 to more than 60 inches in the ecological provinces where oaks occur. Temperature extremes and moisture stress are reduced near the coast where fog supplements precipitation and the ocean reduces fluctuations in temperature. Elsewhere the region’s Mediterranean climate is characterized by 2–4 months of drought during the summer (Fig. 1.19). Low precipitation generally occurs at lower elevations and on the east faces of mountain ranges. Soils include Ultisols, Alfisols, Mollisols, Entisols and Inceptisols (Bailey, 1995). Forest history The historical importance of oaks is recorded in ancient bedrock mortars that were used by Native Americans to grind acorns into flour. Acorns were a staple food of Native Americans in this region, and Biswell (1989) suggests that oaks were so important to their diet that they burned oak woodlands to both encourage oak reproduction and to facilitate acorn gathering. Although human-caused fires have been historically associated with the oaks of the region for thousands of years, there is

42

­ncertainty about what proportion of the landu scape was regularly affected by humans. During the post-settlement period of 1850–1950, the mean interval between fires in the oak–pine forests of the foothills of central California was 8 years (Stephens, 1997). Commercial logging in the region has largely focused on the conifers. Sargent (1884) stated: The forests of California, unlike those of the Atlantic States, contain no great store of hardwoods. The oaks of the Pacific forests, of little value for general mechanical purposes, are unfit for cooperage stock. No hickory, gum, elm, or ash of large size is found in these forests, California produces no tree from which a good wine cask or wagon wheel can be made. The cooperage business of the state, rapidly increasing with the development of grape culture, is entirely dependent upon the forests of the Atlantic region for its supply of oak.

Sargent further noted that large quantities of chestnut oak (sic tanoak), once common in the northern Coast Range of California, are ‘now becoming scarce and in danger of speedy extermination’ due to utilization by the tanning industry. Sargent’s reference to the oaks of Oregon and Washington is slightly less disparaging. In the Willamette Valley, he noted that Oregon white oak woodlands were becoming re-established after reductions in fire frequency. Along the Yakima River in Washington, he noted that Oregon white oaks were limited to 15 ft in height and 6 inches in diameter. The logging industry on the Pacific Coast was established in the 18th century under Hispanic influence. Through the middle of the 19th century the relatively small industry served markets in South America, Australia and the Pacific Rim (Williams, 1989). The gold rush of 1849 and the completion of the transcontinental railroad opened additional markets, but the increase in lumber production in this region occurred gradually, beginning about 1900 when the large timber companies and railroads moved west after exhausting the ready supply of timber in the Lake States. Increases in timber production in the region continued into the Great Depression, but output eventually dropped by 75%. By 1950, however, annual timber production in the West exceeded 16 billion board ft annually, which was greater than that produced in other regions of the USA. Today lumber production in this region lags significantly behind that of the south. Harvest of hardwood growing stock has remained

Chapter 1

nearly constant since 1976, but the volume of hardwoods harvested annually is only about 5% of the region’s total. Historically, oak forests were little affected by commercial logging, but locally they were widely utilized for firewood and fence posts. Ranchers and farmers had the greatest influence on the oak woodlands of the foothills and lower slopes as a consequence of clearing them for agriculture and grazing. Sargent (1884) noted: The permanence of the mountain forests of California is severely endangered, moreover, by the immense herd of sheep, cattle, and horses driven to the mountains every year, at the commencement of the dry season, to graze. From the foothills to the highest alpine meadows, every blade of herbage and every seedling shrub and tree is devoured.

In California, oak woodlands were reduced from an estimated 10–12 million acres to about 7 million acres today (Thomas, 1997). The oak woodlands are predominantly owned by farmers and ranchers, and between 1945 and 1970 the primary loss of woodland acreage resulted from conversion to rangeland. Invasion of non-native grasses and the suppression of fire have created problems in maintaining oak woodlands and savannahs (see Chapter 12, this volume, for details of savannah restoration and management). More recently, the greatest losses of oak woodland have resulted from suburban residential development (Bolsinger, 1988). This has given rise to concern for property damage from the wildfires historically associated with the oak woodlands. Oaks as components of the region’s forests Most of the region’s oak forests and woodlands occur in California where they account for approximately one-quarter of the wooded acres (Miles, 2016). Oaks surround California’s Central Valley in the foothills of the Sierra Nevada, Cascade and Klamath Ranges (Figs 1.5 and 1.6). Although oaks were formerly abundant within parts of California’s Central Valley (Province 261), their distribution has been greatly reduced there (Griffin, 1977). Oaks also occur on the western slopes of the Coast Ranges in central and southern California. The range of Oregon white oak extends northwards into central Oregon and Washington in the Willamette Valley and the Puget lowlands between the Cascade and Coast Ranges. Included are 18 species of oak trees and shrubs plus additional hybrids (Bolsinger,

Oak-dominated Ecosystems

1988; Thomas, 1997). Eight oak species that reach tree size are abundant: California black, blue, interior live, coast live, canyon live, valley, Oregon white and Engelmann oaks (Plumb and McDonald, 1981). Western oak forests are often categorized as either timberland (forests suitable for commercial wood production and capable of producing at least 20 ft3/ acre/year of merchantable volume), or woodlands (sites of lower productivity primarily utilized for forage and firewood). In California, only about 1 in 4 acres of hardwood forest qualifies as timberland. Oak woodlands are sparsely covered with trees compared with oak timberlands. The statewide volume of oaks in woodlands and in timberlands is nevertheless nearly equal because the acreage of woodlands is approximately three times that of timberlands (Table 1.4). Three-quarters of the oak woodlands are grazed and these account for about one-third of California’s total forage (Thomas, 1997). In California, annual removals of oaks and other hardwoods are less than 2% of annual growth (Miles, 2016). The combined effects of temperature and precipitation (which latitude, elevation, slope and aspect affect) regulate the distribution of oaks. In the Pacific Mediterranean–Marine Region, many oak forests and woodlands are restricted to elevational zones in the transition between grassland and chaparral at lower elevations and coniferous forest at higher elevations. Mean temperatures within the region increase with decreasing latitude, and the oaks occur at higher elevations at lower latitudes. Due to the interaction of climate and mountainous topography, the distribution of oaks in this region is more geographically restricted than in the eastern USA. Several classification schemes have been proposed for the complex vegetation relationships that occur in the Pacific Mediterranean–Marine Region (e.g. Griffin, 1977; Paysen et al., 1980, 1982; Barbour, 1988; Allen, 1990) (Fig. 1.21). Eyre (1980) recognized five oak cover types and two additional types where oaks commonly occur in mixtures with other species (see Appendix 3, this volume). The Oregon white oak type is found in the northern portion of the Pacific Mediterranean–Marine Region from northern California to Vancouver Island. This type occurs at lower elevations (0–3900 ft) and primarily in inland valleys or lower slopes between the Coast Ranges and the Cascade or Sierra Nevada Ranges (Eyre, 1980). The type makes its best ­development in the vicinity of the Willamette Valley

43

Table 1.4.  Standing oak volumes in California timberlands and woodlands. Although oaks make up 63% of the total volume of California’s hardwoods, oak timberlands (commercial forest lands) comprise only 8% of the 50 billion ft3 total volume on those lands (softwoods plus hardwoods). (Adapted from Bolsinger, 1980, 1988; Shelly, 1997.)

Species California black oak Canyon live oak Blue oak Coast live oak Oregon white oak Interior live oak California white (valley) oak Engelmann oak Total oak Total hardwoods (all species)

Volume in timberlands (million ft3)

Volume in woodlands (million ft3)

Total (million ft3)

Species total as a proportion of all oaks (%)

2,254 1,302 1 126 211 45 34 0 3,973 7,661

277 731 1,112 755 389 508 164 10 3,946 4,855

2,531 2,033 1,113 881 600 553 198 10 7,919 12,516

32 26 14 11 8 7 3 0 100 –

where closed-canopy Oregon white oak stands developed from former oak savannahs when periodic ground fires were excluded (Thilenius, 1968). The species also occurs in mixtures with other hardwoods and conifers including California black oak, canyon live oak, ponderosa pine and Douglasfir (Appendix 3). California black oak attains a greater volume (Table 1.4) and is distributed across a greater area than the other California oaks (Plumb and McDonald, 1981). The California black oak type occurs from central Oregon to the Mexican border across elevations ranging from 200 to 8000 ft with corresponding annual precipitation of 25–85 inches. Best development of the forest type occurs in the northern half of California in the Klamath and Cascade Mountains and the Coast and Sierra Nevada Ranges. There the forest type is found at elevations between 1500 and 3000 ft with corresponding annual precipitation between 30 and 50 inches (Eyre, 1980). After disturbance, this species maintains itself through sprouting to form evenaged stands. On suboptimal sites it is successional to other forest types. Associated species include other oaks, ponderosa pine, Douglas-fir and Pacific madrone (Appendix 3). Canyon live oak occurs from the Willamette Valley to the Baja Peninsula and east into Arizona at elevations from near sea level in the north to 9000 ft in the south (Eyre, 1980). It comprises about one-quarter of California’s oak volume and is second only to California black oak in this regard (Table 1.4). Canyon live oak forms pure stands on very steep slopes and dry canyon bottoms.

44

Elsewhere it occurs in mixture with Douglas-fir, ponderosa pine and other conifers. The species is shade tolerant when young and often maintains itself in relatively stable communities (Eyre, 1980). The blue oak–digger pine forest type surrounds California’s Central Valley at elevations between 500 and 5000 ft, although blue oak occasionally extends to the valley floor (Fig. 1.22). This forest type occurs between the valley grasslands and the montane forests above, where it can endure a meagre 10 inches of annual precipitation (Eyre, 1980). Forest cover ranges from 30 to 80% with canopy heights between 15 and 50 ft. Associated species include California live oak, interior live oak, valley oak and California black oak (Barbour, 1988). At low elevations blue oak and valley oak mixtures develop savannah communities. Valley oak savannahs extend into the Central Valley where they make their best development on alluvial soils (Griffin, 1977). The California coast live oak forest type (sometimes referred to as southern oak woodland) occurs on the west side of the Coast Range in the southern two-thirds of California. It extends inland on north-facing slopes of narrow valleys and other cool sites. This type occurs at elevations of up to 3000 ft in the northern part of its range and to 5000 ft in the southern portion. Although it can form pure, closed canopy stands, it is considered a woodland type and commonly occurs in savannahs comprised of scattered oaks or in mixture with conifers (Appendix 3). California coast live oak is long lived, moderately shade tolerant and forms relatively permanent woodlands. When trees reach

Chapter 1

14,500 12,000 Apine

Meadow

Subalpine forest

Lodgepole pine forest

8,000

Jeffrey pine forest

Elevation (ft)

Red fir forest

Juniper woodland

10,000

White fir forest 6,000

2,000

Upland live oak woodland

Mixed evergreen forest and black oak woodland

Mi ch xed ap arr al

Lo oa wlan kw d oo live dla nd

4,000

Ponderosa pine forest

Chamise chaparral

Blue oak woodland

Mesic

Xeric Topographic moisture gradient

Fig. 1.21.  Relation of oak forests to elevation, moisture gradients and other forest types found in the Pacific Mediterranean–Marine Region of California (ecoregion Provinces 261, 262, 263 and M261). Oak forests and woodlands are usually found above the chaparral zone and below the ponderosa pine zone. (Redrawn from Vankat, 1982.)

about 8 inches diameter at breast height (dbh) they are also highly resistant to fire (Eyre, 1980). The ecological importance of California’s oak woodlands and timberlands is receiving increased attention (Pillsbury et al., 1997). Although their value for commercial products is low, their importance to wildlife, water quality, aesthetics, soil protection, recreation and fuelwood is widely acknowledged (Helms and Tappeiner, 1996). A principal silvicultural problem

Oak-dominated Ecosystems

related to the oak woodlands of the Pacific Mediterranean–Marine Region is ensuring that the regeneration of oaks is sufficient for replacing trees periodically lost to natural mortality and timber harvesting.

The Influence of Climate Change There is widespread scientific consensus that the global climate is changing as a result of human-caused

45

Fig. 1.22.  Blue oak woodland in the Sierra Nevada Range (Province M261: Dry Steppe Province). (Photograph courtesy of USDA Forest Service, North Central Research Station.)

atmospheric emissions of so-called greenhouse gases including carbon dioxide and methane. With current rates of emissions, the concentration of atmospheric greenhouse gases is expected to continue to increase and result in warmer mean temperatures, new patterns of precipitation, and greater extremes in weather events (IPCC, 2014, 2016; USDA Forest Service, 2016). Climate change information and related effects on trees and forests is updated frequently with the latest information found online. Movement of oaks and other species across the landscape in response to climate change is complicated (Prasad et al., 2013), and outcomes are affected by current species distributions, patterns of future climate change, barriers to species migration (e.g. fragmented forests, mountain ranges), genetic plasticity of existing oak populations, acorn dispersal ranges, and forest management actions including assisted species migration. Silviculturists and ecologists should understand that climate change is a gradual but persistent forest disturbance that complicates forest dynamics, management decisions and silvicultural prescriptions. Chapter 14 in this volume discusses in detail many of the anticipated effects of climate change on oak trees and forests. 46

Climate change adds variation and disorder to the ecoregions described earlier in this chapter. Ecoregions are directly and indirectly defined by differences in climate and by vegetation response to climate. In time, climate change may influence either the geographic boundaries that define ecoregions or the species of oaks and other vegetation considered characteristic of a given ecoregion.

Notes 1

 Subgenus Erythrobalanus in earlier classifications.  Subgenera Lepidobalanus and Leucobalanus in earlier classifications. 3  Subgenus Protobalanus in earlier classifications. 4   Timberland is forest land that is producing, or is capable of producing, more than 20 ft3/acre/year of industrial wood crops under natural conditions and that is not withdrawn from timber use, and that is not associated with urban or rural development. Currently inaccessible and inoperable areas are included. 2

References Abrams, M.D. (1988) Effects of prescribed fire on woody vegetation in a gallery forest understory in northeastern

Chapter 1

Kansas. Transactions of the Kansas Academy of Science 91, 63–70. https://doi.org/10.2307/3628339 Abrams, M.D. (1990) Adaptations and responses to drought in Quercus species of North America. Tree Physiology7,227–238.https://doi.org/10.1093/treephys/ 7.1-2-3-4.227 Abrams, M.D. (1992) Fire and the development of oak forests. BioScience 42, 346–353. https://doi.org/ 10.2307/1311781 Abrams, M.D. and Gibson, D.J. (1991) Effects of fire exclusion on tallgrass prairie and gallery forest communities in eastern Kansas. USDA Forest Service General Technical Report SE-69. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 3–10. Available at: https://www.srs.fs.usda.gov/pubs/gtr/gtr_se069/gtr_ se069-section02.pdf (accessed 30 August 2018). Abrams, M.D. and Nowacki, G.J. (1992) Historical variation in fire, oak recruitment, and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119, 19–28. https://doi. org/10.2307/2996916 Aizen, M.A. and Patterson, W.A. (1990) Acorn size and geographical range in the North American oaks (Quercus L.). Journal of Biogeography 17, 327–332. https://doi.org/10.2307/2845128 Allaby, M. (ed.) (1994) The Concise Oxford Dictionary of Ecology. Oxford University Press, New York. Allen, B.H. (1990) Classification of oak woodlands. Fremontia 18(3), 22–25. Arend, J.L. and Scholz, H.F. (1969) Oak forests of the Lake States and their management. USDA Forest Service Research Paper NC-31. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10552 (accessed 30 August 2018). Axelrod, D.I. (1983) Biogeography of oaks in the ArctoTertiary Province. Annals of the Missouri Botanical Gardens 70, 629–657. https://doi.org/10.2307/2398982 Bailey, R.G. (1995) Descriptions of the Ecoregions of the United States. USDA Miscellaneous Publication 1391. United States Department of Agriculture (USDA), Washington, DC. Bailey, R.G. (1997) Ecoregions of North America, 1:15,000,000 Scale Map (Rev.). USDA Forest Service, Washington, DC. Bailey, R.G. (1998) Ecoregions. Springer-Verlag, New York. Barbour, M.G. (1988) Californian upland forests and woodlands. In: Barbour, M.G. and Billings, W.D. (eds) North American Terrestrial Vegetation. Cambridge University Press, New York, pp. 131–164. Barnes, B.V., Pregitzer, K.S., Spies, T.A. and Spooner, V.H. (1982) Ecological forest site classification. Journal of Forestry 80, 493–498. https://doi.org/10.1093/ jof/80.8.493 Birch, T.W. (1996) Private forest-land owners of the United States, 1994. USDA Forest Service Resource

Oak-dominated Ecosystems

Bulletin NE-134. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.5962/bhl.title.70696 Biswell, H.H. (1989) Prescribed Burning in California Wildlands Vegetation Management. University of California Press, Berkeley, California. Bliss, J.C., Nepal, S.K., Brooks, R.T., Jr and Larsen, M.D. (1994) Forestry community or granfalloon. Journal of Forestry 92(9), 6–10. https://doi.org/10.1093/jof/92.9.6 Bliss, J.C., Nepal, S.K., Brooks, R.T., Jr and Larsen, M.D. (1997) In the mainstream: environmental attitudes of Mid-South forest owners. Southern Journal of Applied Forestry 21, 37–43. https://doi.org/10.1093/sjaf/21.1.37 Bolsinger, C.L. (1980) California forests: trends, problems, and opportunities. USDA Forest Service Resource Bulletin PNW-89. USDA Forest Service, Pacific Northwest Research Station, Portland, Oregon. https://doi.org/10.2737/PNW-RB-89 Bolsinger, C.L. (1988) The hardwoods of California’s timberlands, woodlands, and savannas. USDA Forest Service Resource Bulletin PSW-148. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PNW-RB-148 Bourdeau, P. (1954) Oak seedling ecology determining segregation of species in Piedmont oak–hickory forests. Ecological Monographs 24, 297–320. https:// doi.org/10.2307/1948467 Braun, E.L. (1972) Deciduous Forests of Eastern North America. Hafner, New York. Burns, R.M. and Barber, J.C. (1989) Silviculture of southern pines. USDA Forest Service General Technical Report WO-55. USDA Forest Service, Washington, DC, pp. 31–38. Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Volumes 1 and 2. USDA Forest Service, Washington, DC. Available at: https:// www.srs.fs.usda.gov/pubs/misc/ag_654/table_of_ contents.htm (accessed 30 August 2018). Carvell, K.L. and Tryon, E.H. (1961) The effect of environmental factors on the abundance of oak regeneration beneath mature oak stands. Forest Science 7, 98–105. https://doi.org/10.1093/forestscience/7.2.98 Cavender-Bares, J. (2016) Diversity, distribution and ecosystem services of the North American oaks. International Oaks 27, 37–48. Chase, A. (1995) In a Dark Wood: the Fight Over Forests and the Rising Tyranny of Ecology. Houghton Miflin, Boston,Massachusetts.https://doi.org/10.2307/3985563 Cleland, D.T., Hart, J.B., Host, G.E., Pregitzer, K.S. and Ramm, C.W. (1993) Ecological Classification and Inventory System of the Huron-Manistee National Forests. USDA Forest Service, Washington, DC. Cooper, A.W. (1989) Ecology of the pine–hardwood type. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 3–8.

47

Available at: https://www.fs.usda.gov/treesearch/pubs/ 947 (accessed 1 July 2018). Cunningham, R.J. and Hauser, C. (1989) The decline of the Missouri Ozark forest between 1880 and 1920. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 34–37. Available at: https://www.fs.usda.gov/treesearch/pubs/947 (accessed 1 July 2018). Curtis, J.T. (1959) The Vegetation of Wisconsin. University of Wisconsin Press, Madison, Wisconsin. DeVivo, M.S. (1991) Indian use of fire and land clearance in the southern Appalachians. USDA Forest Service General Technical Report SE-69. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 306–310. Available at: https:// www.srs.fs.usda.gov/pubs/gtr/gtr_se069/gtr_se069section04.pdf (accessed 1 July 2018). Dyksterhuis, E.J. (1948) The vegetation of the western Cross Timbers. Ecological Monographs 18, 325–376. https://doi.org/10.2307/1948576 English, B.C., Bell, C.D., Wells, G.R. and Roberts, R.K. (1997) Stewardship incentives in forestry: participation factors in Tennessee. Journal of Forestry 21(1), 5–10. https://doi.org/10.1093/sjaf/21.1.5 Eyre, F.H. (ed.) (1980) Forest Cover Types of the United States and Canada. Society of American Foresters, Bethesda, Maryland. Fernow, B.E. (1911) A Brief History of Forestry. University Press, Toronto, Canada. https://doi.org/10.5962/bhl. title.34937 Flora of North America Editorial Committee (1997) Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York. Available at: http://floranorthamerica. org/ (accessed 1 July 2018). Godman, R.M. (1985) What are northern hardwoods? In: Hutchinson, J.G. (ed.) Northern Hardwood Notes. USDA Forest Service, North Central Research Station, St Paul, Minnesota, pp. 1.1–1.3. Griffin, J.R. (1977) Oak woodland. In: Barbour, M.G. and Major, J. (eds) Terrestrial Vegetation of California. Wiley, New York, pp. 383–415. Hardin, J.W., Leopold, D.J. and White, F.M. (2001) Harlow and Harrar’s Textbook of Dendrology, 9th edn. McGraw Hill, New York. Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Helms, J.A. and Tappeiner, J.C. (1996) Silviculture in the Sierra. In: Status of the Sierra. Vol. II. Assessments and Scientific Basis for Management Options. Sierra Nevada Ecosystem Project final report to Congress. University of California, Davis, California, pp. 439–476. Hicks, R.R., Jr (1997) A resource at the crossroads: a history of the central hardwoods. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 1–22. Available at:

48

https://www.fs.usda.gov/treesearch/pubs/15603 (accessed 1 July 2018). Hipp, A.L., Manos, P.S., González-Rodríguez, A., Hahn, M., Kaproth, M., McVay, J.D., Avalos, S.V. and CavenderBares, J. (2018) Sympatric parallel diversification of major oak clades in the Americas and the origins of Mexican species diversity. New Phytologist 217, 439–452. https://doi.org/10.1111/nph.14773 Hodges, J.D. (1995) The southern bottomland hardwood region and the brown loam bluffs subregion. In: Barrett, J.W. (ed.) Regional Silviculture of the United States. Wiley, New York, pp. 227–270. Hodges, J.D. and Switzer, G.L. (1979) Some aspects of the ecology of southern bottomland hardwoods. In: Gateway to Opportunity: Proceedings of the Society of American Foresters Convention. Society of American Foresters, Bethesda, Maryland, pp. 360–365. Intergovernmental Panel on Climate Change (IPCC) (2014) Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 1–32. https://doi.org/10.1017/ CBO9781107415416.005 Intergovernmental Panel on Climate Change (IPCC) (2016) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Available at: http://www.ipcc.ch/report/ ar5/wg2/ (accessed 7 September 2016). https://doi. org/10.1080/01944363.2014.954464 Jensen, R.J. (1997) Quercus Linnaeus sect. LOBATAE Loudon, red or black oaks. In: Flora of North America North of Mexico (Flora of North America Editorial Committee). Oxford University Press, New York, pp. 447–468. Available at: http://floranorthamerica.org/ (accessed 1 July 2018). Johnson, P.S. (1990) Quercus macrocarpa Michx. Bur oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 686–692. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Johnson, P.S. (1993a) Perspectives on the ecology and silviculture of oak-dominated forests in the central and eastern states. USDA Forest Service General Technical Report NC-153. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs. usda.gov/treesearch/pubs/10217 (accessed 1 July 2018). Johnson, P.S. (1993b) Sources of oak reproduction. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 112–131. Available at: https://www.fs.usda.gov/treesearch/pubs/939 (accessed 1 July 2018).

Chapter 1

Johnson, P.S. (1994a) The silviculture of northern red oak. USDA Forest Service General Technical Report NC-173. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 33–68. Available at: https://www.fs.usda.gov/treesearch/pubs/ 10237 (accessed 1 July 2018). Johnson, P.S. (1994b) La sylviculture du chene rouge aux USA. In: Timbal, A., Kremer, A., LeGoff, N. and Nepveu, G. (eds) Le Chene Rouge d’Amerique. Institut National de la Recherche Agronomique, Paris, pp. 272–283. Jokela, J.J. and Sawtelle, R.A. (1985) Origin of oak stands on the Springfield Plain: a lesson on oak regeneration. In: Proceedings of the Central Hardwood Forest Conference V. University of Illinois, Urbana-Champaign, Illinois, pp. 181–188. Kerpez, T.A. and Stauffer, D.F. (1989) Avian communities of pine–hardwood forests in the Southeast: characteristics, management, and modeling. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 156–169. Available at: https:// www.fs.usda.gov/treesearch/pubs/947 (accessed 1 July 2018). Kroeber, A.L. (1925) Handbook of the Indians of California. Bureau of American Ethnology of the Smithsonian Institution Bulletin 78. Smithsonian Institution, Washington, DC. Kuhlman, E.G. (1978) The devastation of American chestnut by blight. In: MacDonald, W.L., Cech, F.C., Luchok, J. and Smith, H.C. (eds) Proceedings of the American Chestnut Symposium. West Virginia Books, Morgantown, West Virginia, pp. 1–3. Lawson, E.R. (1990) Juniperus virginiana L. Eastern redcedar. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 1. USDA Forest Service, Washington, DC, pp. 131–140. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Leopold, B.D., Weaver, G.H., Cutler, J.D. and Warren, R.C. (1989) Pine–hardwood forests in north-central Mississippi: an ecological perspective. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 211–222. Available at: https:// www.fs.usda.gov/treesearch/pubs/947 (accessed 1 July 2018). Liebhold, A.M., MacDonald, W.L., Bergdahl, D. and Mastro, V.C. (1995) Invasion by exotic forest pests: a threat to forest ecosystems. Forest Science Monograph 30. Society of American Foresters, Bethesda, Maryland. https://doi.org/10.1093/forestscience/41.s1.a0001 Little, E.L., Jr (1971) Atlas of United States Trees. Volume 1, Conifers and Important Hardwoods. USDA Forest Service Miscellaneous Publication 1146. USDA Forest Service, Washington, DC. Available at: https://archive. org/details/CAT87209954 (accessed 1 July 2018).

Oak-dominated Ecosystems

Little, E.L., Jr (1977) Atlas of United States Trees. Volume 4, Minor Eastern Hardwoods. USDA Forest Service Miscellaneous Publication 1342. USDA Forest Service, Washington, DC. Available at: https:// archive.org/details/atlasofunitedsta1342litt (accessed 1 July 2018). Little, E.L., Jr (1979) Checklist of United States Trees (Native and Naturalized). USDA Forest Service Agriculture Handbook 541. USDA Forest Service, Washington, DC. Available at: https://archive.org/ stream/checklistofunite00litt/checklistofunite00litt_ djvu.txt (accessed 1 July 2018). Loftis, D.L. (1988) Regenerating red oak in the southern Appalachians: predictive models and practical applications. PhD dissertation, North Carolina State University, Raleigh, North Carolina. Long, J.N. (1995) The middle and southern Rocky Mountain region. In: Barrett, J.W. (ed.) Regional Silviculture of the United States. Wiley, New York, pp. 3335–3386. Lord, L. (1999) The Americas. US News and World Report (16–23 August), 84–87. Lorimer, C.G. (1985) The role of fire in the perpetuation of oak forests. In: Proceedings of Challenges in Oak Management and Utilization. University of Wisconsin, Madison, Wisconsin, pp. 8–25. Lorimer, C.G. (1989) The oak regeneration problem: new evidence on causes and possible solutions. University of Wisconsin Forestry Research Analyses 8. University of Wisconsin, Department of Forestry, Madison, Wisconsin. Manos, P.S. (1997) Quercus Linnaeus sect. PROTOBALANUS (Trelease) A. Camus, Intermediate oaks. In: Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York, pp. 468–471. McMinn, H.E. (1964) An Illustrated Manual of California Shrubs. University of California Press, Berkeley, California. McNab, W.H. and Avers, P.E. (1994) Ecological subregions of the United States: section descriptions. USDA Forest Service Administrative Publication WO-WSA-5. USDA Forest Service, Washington, DC. Miles, P.D. (2016) Forest Inventory evalidator web-­ application Version 1.6.0.03. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. Available at: http://apps.fs.fed.us/ Evalidator/evalidator.jsp (accessed 18 October 2016). Miller, H.A. and Lamb, S.H. (1985) Oaks of North America. Naturegraph Publishers, Happy Camp, California. Nigh, T.A., Grabner, J.K., Becker, C. and Kabrick, J.M. (2000) Ecological Classification of the Current River Hills Subsection: Draft Manual. Missouri Department of Conservation, Jefferson City, Missouri. Nixon, K.C. (1997a) Lithocarpus Blume. In: Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York, pp. 442–443. Available at: http:// floranorthamerica.org/ (accessed 1 July 2018).

49

Nixon, K.C. (1997b) Quercus Linnaeus, oak. In: Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York, pp. 445–447. Available at: http://floranorthamerica.org/ (accessed 1 July 2018). Nixon, K.C. and Muller, C.H. (1997) Quercus Linnaeus sect. QUERCUS, White oaks. In: Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York, pp. 471–506. Available at: http://­ floranorthamerica.org/ (accessed 1 July 2018). Nowacki, G.J. and Abrams, M.D. (2008) The demise of fire and ‘mesophication’ of forests in the eastern United States. BioScience 58, 123–138. https://doi. org/10.1641/B580207 Nowacki, G.J., Abrams, M.D. and Lorimer, C.G. (1990) Composition, structure, and historical development of northern red oak stands along an edaphic gradient in north–central Wisconsin. Forest Science 36, 276–292. https://doi.org/10.1093/forestscience/36.2.276 Olson, S.D. (1996) The historical occurrence of fire in the Central Hardwoods, with emphasis on southcentral Indiana. Natural Areas Journal 16, 248–256. Orwig, D.A. and Abrams, M.D. (1994) Land-use history (1720–1992), composition, and dynamics of oak–pine forests within the Piedmont and Coastal Plain of northern Virginia. Canadian Journal of Forest Research 24, 1216–1225. https://doi.org/10.1139/x94-160 Oswalt, S.N., Smith, W.B., Miles, P.D. and Pugh, S.A (2014) Forest Resources of the United States, 2012: a technical document supporting the Forest Service update of the 2010 RPA Assessment. USDA Forest Service General Technical Report WO-91. USDA Forest Service, Washington Office, Washington, DC. https://doi.org/10.2737/WO-GTR-91 Pavlik, B.M., Muick, P.C., Johnson, S. and Popper, M. (1991) Oaks of California. Cachuma Press, Los Olivos, California. Paysen, T.E., Derby, J.A., Black, H., Jr, Bleich, V.C. and Mincks, J.W. (1980) A vegetation classification system applied to southern California. USDA Forest Service General Technical Report PSW-45. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-45 Paysen, T.E., Derby, J.A. and Conrad, C.E. (1982) A vegetation classification system for use in California. USDA Forest Service General Technical Report PSW-63. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-63 Penfound, W.T. (1968) Influence of a wildfire in the Wichita Mountains Wildlife Refuge, Oklahoma. Ecology 49, 1003–1006. https://doi.org/10.2307/1936555 Petrides, G.A. (1988) A Field Guide to Eastern Trees. Houghton Mifflin, Boston, Massachusetts. Petrides, G.A. and Petrides, O. (1992) A Field Guide to Western Trees. Houghton Mifflin, Boston, Massachusetts.

50

Phillips, D.R. and Abercrombie, J.A., Jr (1987) Pine– hardwood mixtures – a new concept in regeneration. Southern Journal of Applied Forestry 11, 192–197. https://doi.org/10.1093/sjaf/11.4.192 Pillsbury, N.H., Verner, J. and Tietje, W.D. (1997) Proceedings of symposium on oak woodlands: ecology, management, and urban interface issues. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-160 Pinchot, G. (1987) Breaking New Ground. Island Press, Washington, DC. Plumb, T.R. and McDonald, P.M. (1981) Oak management in California. USDA Forest Service General Technical Report PSW-54. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-54 Powell, D.S., Faulkner, J.L., Darr, D.R., Shu, Z. and MacCleery, D.W. (1994) Forest resources of the United States, 1992. USDA Forest Service General Technical Report RM-234 (revised). USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado. https://doi.org/10.2737/ RM-GTR-234 Prasad, A.M., Gardiner, J.D., Iverson, L.R., Matthews, S.N. and Peters, M. (2013) Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Global Change Biology 19, 2196–2208. https://doi.org/10.1111/gcb.12204 Putnam, J.A., Furnival, G.M. and McKnight, J.S. (1960) Management and Inventory of Southern Hardwoods. USDA Forest Service Agriculture Handbook 181. USDA Forest Service, Washington, DC. Available at: https://www.srs.fs.usda.gov/pubs/28908 (accessed 1 July 2018). Pyne, S.J. (1982) Fire in America. Princeton University Press, Princeton, New Jersey. Rice, E.L. and Penfound, W.T. (1959) The upland forests of Oklahoma. Ecology 40, 593–608. https://doi.org/ 10.2307/1929813 Roach, B.A. (1968) Is clear cutting good or bad? Keep Tennessee Green Journal 8, 4–5, 12–14. Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA Forest Service Agriculture Handbook 355. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Rosson, J.F., Jr (1994) Quercus stellata growth and stand characteristics in the Quercus stellata–Quercus marilandica forest type in the Cross Timbers region of central Oklahoma. In: Proceedings of the North American Conference on Barrens and Savannas. US Environmental Protection Agency, Great Lakes National Program Office, Chicago, Illinois, pp. 329–333.

Chapter 1

Sargent, C.S. (1884) Report on the Forests of North America (exclusive of Mexico). Government Printing Office, Washington, DC. https://doi.org/10.5962/bhl. title.25902 Seymour, R.S. (1995) The northeastern region. In: Barrett, J.W. (ed.) Regional Silviculture of the United States. Wiley, New York, pp. 31–80. Sheffield, R.M., Birch, T.W., Leatherberry, E.C. and McWilliams, W.H. (1989) The pine–hardwood resource in the Eastern United States. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina, pp. 9–19. Available at: https://www.fs.usda. gov/treesearch/pubs/947 (accessed 1 July 2018). Shelly, J.R. (1997) An examination of the oak woodland as a potential resource for higher-valued wood products. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 445–455. Available at: https://www.fs. usda.gov/treesearch/pubs/28206 (accessed 1 July 2018). Skeen, J.N., Doerr, P.D. and Van Lear, D.H. (1993) Oak– hickory–pine forests. In: Martin, W.H., Boyce, S.G. and Echternacht, A.C. (eds) Biodiversity of the Southeastern United States. Wiley, New York, pp. 1–34. Smith, D.M. (2000) American chestnut: ill-fated monarch of the eastern hardwood forest. Journal of Forestry 98(2), 12–15. https://doi.org/10.1093/jof/98.2.12 Smith, H.C., Lamson, N.I. and Miller, G.W. (1989) An esthetic alternative to clearcutting? Journal of Forestry 87(3), 14–18. https://doi.org/10.1093/jof/87.3.14 Smith, W.B., Miles, P.D., Perry, C.H. and Pugh, S.A. (2009) Forest Resources of the United States, 2007. USDA Forest Service General Technical Report WO-78. USDA Forest Service, Washington, DC. Data tables. Available at: http://fia.fs.fed.us/programfeatures/rpa/default.asp (accessed 7 May 2008). https://doi.org/10.2737/WO-GTR-78 Starrs, P.F. (2002) Perspectives on cultural values of California oaks. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 21–30. Available at: https://www.fs.usda.gov/treesearch/pubs/26106 (accessed 1 July 2018). Stegner, W. (1995) Where the bluebird sings to the lemonade springs, living and writing in the West. Wings Books, New York. Stein, J., Binion, D. and Acciavatti, R. (2003) Field Guide to Native Oak Species of Eastern North America. USDA Forest Service, Morgantown, West Virginia. Document number FHTET-2003-01, 161 pp. Available at: https://www.fs.fed.us/foresthealth/technology/pdfs/ fieldguide.pdf (accessed 14 October 2008). Stephens, S.L. (1997) Fire history of a mixed oak–pine forest in the foothills of the Sierra Nevada, El Dorado County, California. USDA Forest Service General

Oak-dominated Ecosystems

Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 191–198. Available at: https://www.fs.usda.gov/treesearch/pubs/28174 (accessed 1 July 2018). Thilenius, J.F. (1968) The Quercus garryana forests of the Willamette Valley, Oregon. Ecology 49, 1124–1133. https://doi.org/10.2307/1934496 Thirgood, J.V. (1971) The historical significance of oak. In: Oak Symposium Proceedings. USDA Forest Service Northeastern Forest Experiment Station, Upper Darby, Pennsylvania, pp. 1–18. Available at: https://www.nrs. fs.fed.us/pubs/4465 (accessed 1 July 2018). Thomas, J.W. (1997) California’s oak woodlands: where we have been, where we are, where we need to go. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 3–9. Available at: https:// www.fs.usda.gov/treesearch/pubs/28078 (accessed 1 July 2018). Trimble, G.R., Jr (1973) The regeneration of Central Appalachian hardwoods with emphasis on the effects of site quality and harvesting practice. USDA Forest Service Research Paper NE-282. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15387 (accessed 1 July 2018). Tucker, J.M. (1980) Taxonomy of California oaks. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 19–29. Available at: https://www.fs.fed.us/psw/ publications/documents/psw_gtr044/index.shtml (accessed 1 July 2018). USDA Forest Service (1993) Forest Type Groups of the United States (map). USDA Forest Service, Washington, DC. USDA Forest Service (2008) Fire Effects Information System (FEIS). Available at: https://www.feis-crs.org/ feis/ (accessed 30 August 2018). USDA Forest Service (2016) Climate Change Resource Center. Available at: http://www.fs.usda.gov/ccrc/ home (accessed 7 September 2016). USDA Forest Service (2018) National Woodland Owner Survey. Available at: https://www.fia.fs.fed.us/nwos/ results/index.php (accessed 1 July 2018). USDA Natural Resources Conservation Service (2008) Plants Database. Available at: http://plants.usda.gov/ (accessed 7 May 2008). Vankat, J.L. (1982) A gradient perspective on the vegetation of Sequoia National Park, California. Madronˇo 29, 200–214. Available at: https://www.jstor.org/stable/ 41424371 (accessed 1 July 2018). Van Kley, J.E., Parker, G.R., Franzmeier, D.P. and Randolph, J.C. (undated) Field Guide: Ecological

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Classification of the Hoosier National Forest and Surrounding Areas of Indiana. USDA Forest Service, Hoosier National Forest, Bedford, Indiana. Waldrop, T.A. (ed.) (1989) Proceedings pine–hardwood mixtures: a symposium on management and ecology of the type. USDA Forest Service General Technical Report SE-58. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, Carolina. Available at: https://www.fs.usda.gov/treesearch/pubs/947 (accessed 1 July 2018).

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Walker, L.C. (1995) The southern pine region. In: Barrett, J.W. (ed.) Regional Silviculture of the United States. Wiley, New York, pp. 271–334. Whitney, G.G. (1994) From Coastal Wilderness to Fruited Plain. Cambridge University Press, Cambridge. Williams, T. (1989) Incineration of Yellowstone. Audubon 1989(1), 38–89. Youngs, R.L. (2000) A right smart little jolt: loss of the chestnut and a way of life. Journal of Forestry 98(2), 17–21. https://doi.org/10.1093/jof/98.2.17

Chapter 1

2



Regeneration Ecology I: Flowering, Fruiting and Reproduction Characteristics Introduction

Ecologically, the terms ‘regeneration’ and ‘reproduction’ are closely associated. In the narrow biological sense, regeneration refers to the regrowth of lost or destroyed parts of organs. However, regeneration also can be used in a population context to refer to ‘rebirth’, for example the rebirth of a forest after its destruction by natural or human causes. The latter meaning is useful in ecology and silviculture because it connotes population process, and has been so used in the literature of those fields, albeit inconsistently (cf. Harper, 1977; Keeley, 1981; Bartolome et  al, 1987; Muick and Bartolome, 1987; Helms, 1998). We herein use regeneration to refer to the ecological processes involving the establishment, growth and population changes of juvenile trees, cooccurring plants, and their propagules rather than to a physiological process (e.g. see Grubb, 1977). In this context, juvenile trees are those not yet capable of flowering and producing seed, which for most of the oaks takes 15–25 years.1 Similarly, the term ‘reproduction’ has a narrow biological definition referring to sexual or asexual mechanisms by which organisms generate others of their own kind.2 Like ‘regeneration’, ‘reproduction’ also can more generally imply the process of reproducing something. We have chosen to use the term reproduction to refer to individual or populations of juvenile trees (or other plants) already reproduced. Unlike regeneration, reproduction in this context does not connote process. Although the two terms have been treated as synonymous in the silvicultural literature, with reproduction relegated to obsolescence by Helms (1998), we believe it is conceptually important to distinguish between objects (young trees) and process (forest renewal). The regeneration of oak forests accordingly can be defined as a multifaceted ecological process. It includes the flowering, fruiting and seed dispersal of mature trees, as well as the germination, seedling

establishment, growth and population changes of oak reproduction and associated plants. Forest regeneration thus involves time frames related to stages of stand development. In even-aged silviculture (Chapter 8, this volume), the regeneration period for oaks (at the stand scale) may span the last two and the first two decades of a rotation,3 during which reproduction becomes established and develops into the new stand. In contrast, uneven-­aged silviculture (Chapter 9, this volume) and old-growth forests (Chapter 13, this volume) are characterized by a regeneration period that is essentially continuous or frequently periodic. Within any time frame, each step of the regeneration process is beset with uncertainties and unknowns that have contributed to our inability, in some cases, to successfully manage and sustain oak forests. Yet, oaks have thrived throughout North America for millennia without modern silviculture. Much of this apparent enigma can be untangled by considering what we know about the regeneration process, which begins with the oak flower.

Flowering Oaks of the USA fall into one of three species groups. Species in the white oak group require one growing season to complete their reproductive cycle, while most of the species in the red oak group require two growing seasons. The five species in the intermediate group, which are found only in western North America, also require two growing seasons (Tucker, 1980). Oaks are monoecious, that is they produce male and female flowers on the same tree. All trees that have attained flowering age and size usually bear both kinds of flowers in abundance each year. In many species, flowering does not occur until trees are 15–25 years old. The annual regularity of flower production in oak contrasts with its irregular acorn

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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production, which may range from bumper acorn crops in some years to poor or no crops in others. In a sexually mature oak, buds of several types occur along the outer branches comprising current-, 1-yearold and 2-year-old shoots. Some buds are strictly vegetative, that is they produce only leaves and other vegetative structures. Other buds are the progenitors of male and female flowers. Some produce only male flowers (catkins) whereas others produce both male flowers and vegetative structures. Female flowers arise from tissues located in leaf axils (Cecich and Larsen, 1997). In white oak, strictly vegetative buds account for about half of all buds and the remaining half represent approximately equal numbers of male and female buds (Cecich and Larsen, 1997). The distribution of buds also varies with position along the branch. About 72% of all buds occur on currentyear shoots, 26% on 1-year-old shoots and 2% on 2-year-old shoots. The latter are all vegetative buds occurring in leaf axils. Among the buds that produce flowers, female flower buds occur only on current-year shoots whereas male flowers are approximately equally represented on 1-year-old and current shoots. However, in the red oak group, female flowers in different states of development may be present on both current-year and 1-year-old shoots at the same time. In both the white oak and the red oak groups, female flowers occur throughout the crown but are most abundant in the upper crown. In contrast, male flowers occur mainly in the top and middle portions of the crown (Cecich and Larsen, 1997). Of the species native to the USA, flowering is most completely described for white oak (Turkel et  al., 1955; Sharp and Chisman, 1961; Stairs, 1964; Mogensen, 1965, 1975; Sharp and Sprague, 1967; Merkle et  al., 1980; Feret et  al., 1982), although our knowledge of flowering in other oaks is growing (Cecich, 1997). Accordingly, the following account of the ontogeny of oak flowering focuses on white oak. Discussion of other oaks is limited to illustrating differences among species and species groups. Male flowers The male (staminate) flowers of oaks develop in the axils of scale leaves of the current vegetative buds, or separate male buds bear the staminate flowers (Fig. 2.1A). Species in the white oak group initiate catkin primordia in the buds of shoots produced

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the year before acorn maturation. Species in the red oak group initiate catkin primordia in the buds of shoots formed 2 years before acorn maturation. In white oak, catkin initiation occurred in late May in Virginia (Merkle et al., 1980), and about a month later in Pennsylvania (Sharp and Chisman, 1961). In Virginia trees, the catkin primordia were macroscopically distinguishable by early August. Individual staminate flowers formed within the primordial catkins in late June or early July and were structurally complete before the onset of dormancy in October. These structures resumed development in mid-March and the catkins emerged from the bud in early April; further north in Pennsylvania they emerged in late April. In eastern forests, swelling and opening of male flower buds usually is later in the white oak group than in the red oak group. When catkins in the white oak group are emerging or slightly drooping (semipendent), pollen dispersal for the red oak group is already complete. Emergence of white oak catkins occurs when daily temperature minima are 50°F (10°C) or higher for 10 days (Sharp and Chisman, 1961). They emerge from the base of the inner scales of buds clustered at the ends of the woody twigs of the previous year’s growth. Numbers of catkins range from three to ten or more per twig tip and are erect shortly after emerging. Within a few days they become semipendent and are usually fully pendent and in full bloom, or anthesis, within 5–12 days (Fig. 2.2). The pollen matures while catkins are elongating. The catkins appear first in the topmost branches and emergence progresses downwards. Meiosis, the process that reduces chromosome numbers by one-half, begins in the male flowers of white oak when catkins have elongated slightly beyond the bud scales. In the same geographic area, scarlet oak and bear oak (both in the red oak group) begin similar activity about 2 weeks earlier at the time of early bud swell (Stairs, 1964). The diploid (2n) chromosome number (i.e. the number before reduction division in cells takes place) is 24 for all oak species investigated. In white oak, the individual male flowers (Fig. 2.1B) within the catkins reach anthesis 11–16 days after emergence from the bud (Sharp and Chisman, 1961). The basal portions of the catkins mature first. The time from catkin emergence to the completion of pollen shedding (Fig. 2.1C) ranges from about 11 to 19 days. The topmost branches shed pollen first in forest-grown trees, but the reverse may occur in open-grown trees. Pollen shedding occurs when

Chapter 2

(A)

Bud scales

(B)

Bract Catkin Inflorescence Staminate flowers

Perianth

Filament

Pollen sacs

(C)

Pollen sac

Filament

Pollen grains

Fig. 2.1.  The male (staminate) oak flower. (A) Catkins bearing numerous flowers originate from the axils of bud scales. (B) A single male flower with pollen sacs. (C) Pollen sacs split open to release pollen grains when environmental conditions are favourable. (From Cecich, 1994.)

catkins are 3–6 inches long. Individual trees shed most of their pollen within a 48 h period if weather is favourable. In New York, pollen shedding in white oak lags that of scarlet oak and bear oak by 2 weeks (Stairs, 1964). About the time white oak catkins are semipendent, new unfolded leaves appear. However, there is a lag in further leaf expansion if catkins contain pollen. At the time of pollen shedding, usually during the last 2 weeks of May in central

Regeneration Ecology I

Pennsylvania, leaf length averages about 2 inches in white oak and 3 inches in chestnut oak (Sharp and Chisman, 1961). The day after pollen dispersal, leaf area increases by about 50%. It is thus possible to identify trees that have shed pollen by the state of leaf development. The period of arrested leaf expansion may favour pollen dispersal by minimizing canopy interference. Dispersal of oak pollen is by wind and usually occurs before there is significant insect activity.

55

Fig. 2.2.  Northern red oak catkins in full bloom in ­mid-May (northern Wisconsin). Leaves are not yet fully expanded.

This contrasts with many associated hardwoods, which flower later and are insect pollinated. Pollen dispersal in white oak occurs only on days when relative humidity is less than 45% for several hours (Sharp and Chisman, 1961). To complete pollen shedding, both white oak and chestnut oak require 2–3 such days with air temperatures of 63–69°F (17–21°C). In contrast, dwarf chinkapin oak (a shrubby member of the white oak group) only needs a few hours of such weather to disperse pollen. A growth chamber study confirmed the adverse effects of high humidity on bear oak (a shrubby member of the red oak group), which produced no immature (first-year) acorns when humidity during the flowering period was 61–70% (Wolgast, 1972). When

56

humidity was reduced to 38–50%, a significantly larger proportion of the flowers (10.5% of 467) produced immature acorns. A positive association between high relative humidity and acorn production was reported for white oak in Missouri when relative humidity was averaged over the 1-week pollination period (Cecich and Sullivan, 1999). This apparent discrepancy with other studies points out the importance of the observed time interval used to express the effect of relative humidity (or other weather factors). If a few hours of low humidity occurring over a few days during the pollination period are sufficient for effective pollen shedding (Sharp and Chisman, 1961), measurements of relative humidity should then represent this time-dependent sensitivity if the intent is to directly relate relative humidity to pollination and acorn production. Otherwise relative humidity measurements and their apparent effects become potentially confounded with other factors. Such confounding arises from correlated but indirect relations between humidity and other weather variables such as air temperature. A light wind of 5–8 miles/h is further conducive to pollen shedding. However, in closed-canopy forests, the canopy may act as a windbreak that minimizes the influence of light winds on the ripening catkins. The canopy nevertheless offers little protection against strong winds during dry rainless periods. In open fields, oaks shed pollen on the windward side several hours before shedding occurs on the leeward size when weather conditions are otherwise favourable. High winds on humid, cool days do not induce pollen shedding and prolonged cool, wet weather can cause overripened catkins to drop intact with anthers filled with pollen. Although male flowers can tolerate light frosts, temperatures below 25°F (–4°C) are lethal (Sharp and Chisman, 1961). In northern Utah, a freeze on 5 May averaging 27°F (–3°C) across 37 Gambel oak sites killed semipendent male flowers (Neilson and Wullstein, 1980). Subsequent acorn production was less than in nearby areas unaffected by the freeze. In bear oak in Massachusetts, the number of male flowers per branch at or near the time of anthesis decreased from the top to the bottom of a slope representing a 66 ft (20 m) vertical gradient (Aizen and Kenigsten, 1990). Within a given topographic position, the number of male flowers also decreased with decreasing tree height (i.e. taller trees produced more male flowers). A higher incidence

Chapter 2

of low temperatures and frost occurred at lower points along the combined vertical topographic and tree-height gradient. Thus frosts, prolonged desiccating winds, and prolonged cool humid weather can be detrimental to male flowering and pollen dispersal. Any of these events may result in reduced or failed acorn production. Moreover, forest fragmentation and isolation of trees can reduce pollen availability and thus acorn production (Knapp et al., 2000). Female flowers In white oak in Virginia, the inflorescences bearing female (pistillate) flowers are first identifiable, but only microscopically, in early August of the year before acorn maturation (Merkle et  al., 1980). These primordial inflorescences usually arise from the axils of the top three or four leaves within the developing bud. There they remain invisible to the unaided eye and continue to develop until late September or early October. Resumption of development occurs the following spring when dramatic changes occur during the last week of March and the first week of April. Each inflorescence then elongates rapidly, usually producing two to three functional flowers, but sometimes up to five. At this stage, flowers remain within the swollen buds. Like male flowers, the time of appearance and development of female flowers varies with the weather and thus year. In central Pennsylvania, the leaves of white oak usually emerge from the buds during the second week of April (Sharp and Sprague, 1967). As the buds expand they carry with them the female inflorescence. A single elongating stalk then pushes the one to five pistillate structures upwards. The individual female flowers become visible within 5–10 days after emergence of the male catkins (Merkle et al., 1980). At this time, the pendent catkins are about 50% of their final length, new vegetative shoots are about 2–3 inches long, and new leaves are 1–2 inches long (Sharp and Sprague, 1967). At this stage of development, the flowers resemble miniature acorns that envelop all the requisite, preformed floral structures including the cupule (which develops into the ‘cup’ of the mature acorn). The stalks of pistillate white oak flowers continue to elongate until the flowers are mature. Maturation occurs when the three stalk-like styles bearing the pollen-receptive organs, the stigmas, extend beyond the surrounding floral structures

Regeneration Ecology I

(the perianth) (Fig. 2.3A). Pollen grains released into the air by the male flowers land on the stigmas, germinate and produce pollen tubes (Fig. 2.3B). A series of events involving callose plugs then occur, which isolate the contents of each pollen tube from other pollen grains (Fig. 2.3C–D). Pollen tubes then stop growing in 2–3 weeks (Fig. 2.3E). There is then a pause in pollen tube growth, which is resumed in 2 weeks in the white oak group and after about 13 months in the red oak group. Pollen tubes then enter the locules (Fig. 2.3F), and the egg of one ovule is fertilized; the other five eggs and ovules soon die. Approximately 1 month after pollination, or about 1 July in Pennsylvania, meiosis and fertilization have taken place (Turkel et  al., 1955; Sharp and Sprague, 1967). Fertilization of Gambel oak (a white oak) in Arizona also occurs then (Brown and Mogensen, 1972). In central Missouri, fertilization of white oak flowers occurs during mid-June (Cecich, 1997). Fertilization of black and northern red oak flowers, which require two growing seasons for ovule maturation, occurs during mid-June of the second year for black oak and 2 weeks later for northern red oak (Fig. 2.3G). Although not all eggs of the six ovules in each ovary may become fertilized, fertilization occurs at nearly the same time on those that do (Mogensen, 1965). Of 30 ovaries studied in three species (Gambel, white and black oak), nearly half the 180 ovules observed aborted because they were not fertilized (Mogensen, 1975). Thus, on average, 2.8 ovules per ovary were fertilized. The remaining aborted ovules were approximately equally divided between those with zygote or embryo failure (28%) and those without an embryo sac (26%). However, all but one fertilized embryo normally aborts very soon after fertilization to produce a one-seeded fruit. Although the reason for the abortion of fertilized ovules is not well understood, the functional ovule may be fertilized before the others. This, in turn, may suppress the growth of the other ovules in a process analogous to apical dominance in stems (Mogensen, 1975). Occasionally, more than one ovule develops to seed maturity within the same ovary to produce a multiple-seeded acorn. Some trees are especially prone to producing these (Coker, 1904; Buchholz, 1941). In four of five species observed in Illinois, from 1 to 2% of acorns were multi-seeded, which suggests that multi-seeds may be common in most oaks (Hosner, 1959). By mid-summer of the year of acorn maturation, acorn enlargement and embryo development begin

57

(A) Flower

(B) Pollen grains

(D) Third plug

(E) Pollen tubes stop growing

(G) Fertilization

(J) Mid-July

(H) Embryo enlarging

(K) Late July

(C) First callose plug

(F) Locules are entered

(I) Early July

(L) Mature acorn

Fig. 2.3.  The development of a pollinated pistillate flower to a mature acorn. (A) The flower contains six ovules (two shown) in each of three locules (chambers) of the ovary. Three styles with stigmatic tips contain the transmitting tissue through which the pollen tubes reach the locules. (B) Pollen grains, released into the air by male (staminate) Continued 58

Chapter 2

Factors Affecting Acorn Production

Fig. 2.4.  A cluster of nearly mature northern red oak acorns (left) in late August of their second year of development. Acorns in their first year of development are visible in the leaf axils of the current-year’s shoot (right); flower styles are still intact and visible.

in most species. The generalized sequence of acorn maturation events in central Missouri is illustrated in Fig. 2.3(H–L). However, rates of acorn maturation may vary among species, climates, weather conditions and other factors. In white oak and chestnut oak in Pennsylvania, the acorn and cupule begin rapid growth in late July. By the first week in August the acorn begins to emerge from the cup. By midAugust, the acorns are about one-third filled out. By the last week in August they are full size (Fig. 2.4). By mid-September mature acorns are usually dropping from the cups. In years of heavy acorn production, all acorns on a stalk may mature, but often only one develops and the remainder die (Sharp and Sprague, 1967).

Most oak species produce good acorn crops 1 year in 3 or 4 (Olson, 1974). Although consistent annual production of male and female flowers is an inherent characteristic of oaks, the large annual variation in acorn production is at least partially controlled by environmental factors (Sharp and Sprague, 1967; Sork and Bramble, 1993). Weather-related factors directly influence the early flowering process, as discussed in the previous section. Those and other factors also may impact the later stages of acorn development. The total number of female flowers produced and their percentage survival together explain about 90% of the variation in acorn production in black, northern red and white oaks in Missouri (Sork and Bramble, 1993). In contrast, factors related to site productivity such as soil nutrients, topographic position and site index (Chapter 4, this volume) appear to have little or no influence on acorn production (Tryon and Carvell, 1962; Wolgast, 1972). Yet few studies have rigorously examined site productivity as a factor in acorn production in most species. Some trees appear to be under complete genetic control. Some never produce acorns even when they grow in favourable environments and occupy superior crown positions (Wood, 1934; Downs and McQuilkin, 1944; Sharp, 1958; Sharp and Sprague, 1967). Thus environment, genetics and the interactive effects of those factors are all potential determinants of acorn production.

Fig. 2.3.  Continued. flowers, land on the stigmas, germinate and produce pollen tubes. (C) A callose plug isolates the contents of each pollen tube from other pollen grains, which soon fall from the stigma. (D) Successively formed plugs isolate the growing tips of pollen tubes from earlier-formed remnants, which maintains turgor pressure in the tip. The third plug appears in the pollen tube after about 1 week. (E) Pollen tubes stop growing in 2–3 weeks. After a 2-week pause, the pollen tubes of species in the white oak subgenus resume growth towards the locules. In the red oaks, the pause lasts about 13 months. Thus, the red oaks require two growing seasons for pollinated flowers to mature into acorns whereas white oaks require only one growing season. (F) Pollen tubes enter the locules at an open space called the paracarpous region. This space allows pollen tubes from any style to randomly enter any of the six ovules. (G) Fertilization of the egg in one ovule, which occurs several days after the pollen tube enters the locule, produces an embryo. The other five eggs and ovules soon die. (H) The embryo enlarges and other parts of the flower differentiate into the cup and shell of the acorn. (I) By early July, the flower is recognizable as a young acorn. The bright green shell begins to protrude from the cup in response to the growing embryo. (J) By mid-July, the embryo is heart shaped. The two lobes of the ‘heart’ represent the early stages of cotyledon development. (K) By late July, the embryonic axis (root tip, shoot tip and hypocotyl) appear between the two cotyledons. (L) Mature acorns begin to drop from their branches in September. The acorns are filled with starch, lipids and proteins. (Drawings and text contributed by Dr Robert A. Cecich (Retired), USDA Forest Service, Northern Research Station, Columbia, Missouri.)

Regeneration Ecology I

59

60

Number of acorns/acre

Good white oak acorn crops in Pennsylvania occurred when temperatures in late April were above normal but followed by a sudden drop in temperature in early May (Sharp and Sprague, 1967). A 10-day period in late April with warm nights followed by a 2-week period with cool nights produced good acorn set. Poor acorn production occurred when there was an even progression in mean daily April and May temperatures. In a Missouri study, warm spring temperatures during the year of acorn maturation were positively correlated with the size of acorn crops in white, black and northern red oaks (Sork and Bramble, 1993). In contrast, summer drought and late spring frost the previous year were negatively associated with acorn production. In another Missouri study, high daily maximum temperatures were negatively associated with white oak acorn production and flower survival and black oak flower survival (Cecich and Sullivan, 1999). Growth chamber studies of immature bear oak acorns revealed that the acorns did not develop under conditions of high humidity (Wolgast, 1972). This effect may have been directly related to a failure in pollen shedding (Sharp and Chisman, 1961). Other investigators have reported little or no evidence that relative humidity, precipitation, drought, site quality and wind velocity affect acorn yield (Downs and McQuilkin, 1944; Sharp and Sprague, 1967; Feret et al., 1982). These disparate findings may be related to inherent differences among species. But even for a given species, discrepancies in findings may arise from differences in how the duration, intensity and frequency of potential predictors of acorn production are measured and quantitatively expressed. Foresters and physiologists have long speculated about frost as a cause of failure in acorn production. However, the frequency of killing frosts in central Pennsylvania indicates that during any given 50-year period, frost accounts for only 4 years of complete acorn crop failures and 4 years of severely reduced crops (Sharp and Sprague, 1967). In white oaks in Missouri, a late spring freeze reduced the number of white oak flowers to about 20% of nonfrost years and emergence of surviving flowers was about 2 weeks later than normal (Cecich and Sullivan, 1999). In immature bear oak acorns in New Jersey, spring temperatures of 29–32°F (–2°C–0°C) for 3 consecutive days in late May reduced acorn yields (Wolgast and Trout, 1979). Acorn yield increased

250

30 Number

Pounds

25

200

20

150

15

100

10 5

50

Pounds of acorns/acre (lb)

300

Weather

0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Proportion of crown area undamaged Fig. 2.5.  Bear oak acorn production (fresh weight) in New Jersey in relation to crown frost damage. (From Wolgast and Trout, 1979.)

as the proportion of the crown not damaged by frost increased (Fig. 2.5). Premature abscission Most female flowers of oaks succumb to premature separation (abscission) from their stalks (peduncles) before they can develop into acorns. Although premature abscission occurs primarily during the pollination–fertilization period, it continues through the acorn maturation period (Turkel et  al., 1955; Williamson, 1966) (Fig. 2.6). About 52% of acorns of several species caught in acorn traps from midsummer onwards in Missouri were immature (Christisen and Kearby, 1984). These acorns fell continuously from early July through to August. Observed losses from premature abscission in white oak range from 68 to 99% of female flowers observed early in the growing season (Williamson, 1966; Feret et al., 1982). Yet the lower abscission rate represented a ‘good’ acorn crop. Substantial premature abscission thus can occur even in years of high acorn production. Premature abscission is common not only to oaks but to many other hardwoods including willow, poplar, basswood, black locust and elm (Kramer and Kozlowski, 1979; Kozlowski and Pallardy, 1997). Fruit set can be diminished by premature flower abscission, limited pollination or fertilization, inadequate nutrition, embryo abortion, premature abscission of young fruits and other factors (Greulach, 1973). Premature abscission of fruits also may involve competition for photosynthates, which can occur among fruits or

Chapter 2

(A) 140

Number of live flowers

120 100 80 60 40 20 0 May

July

Sep May Month

July

Sep

(B) 90

Number of live flowers

80 70 60 50 40 30 20 10 0 Apr

May

June July Month

Aug

Sep

Fig. 2.6.  Survival of female black oak and white oak flowers in central Missouri trees. (A) Black oak flowers. The line connected by circles is the average of five flower-bearing branches in the upper crowns of eight trees in 1991/92; unconnected circles show the range. The line connected by triangles is the average of 11 trees in 1992/93; the unconnected triangles show the range. (B) White oak flowers in nine trees in 1992; the mean and range are shown as in (A). (Data courtesy of Robert A. Cecich, USDA Forest Service, Northern Research Station.)

between fruits and vegetative growth (Abbott, 1960; Luckwill, 1977). In the oaks, treehoppers and other insects have been implicated in premature abscission (Cecich et  al., 1991; Cecich, 1994). Hail storms also may damage pistillate oak flowers (Cecich, 1997; Cecich and Sullivan, 1999). The long slender peduncles (stems) of pistillate white oak flowers may be especially

Regeneration Ecology I

susceptible to hail damage. However, the long styles of black oak flowers may be more susceptible to hail damage than the sessile styles of white oak flowers. If the styles of oak flowers are broken off before pollen tubes cease growing, flowers will not be fertilized and will abort. Hail also can dislodge fragile catkins and thus eliminate the pollen source if hail occurs before pollen is shed. Failed or limited pollination in any species can result from inadequate production of pollen caused by unfavourable weather conditions or a limited period of stigma receptivity to pollen. Also, fertilization failure may result from pollen sterility, failure of ovary development, slow pollen tube growth and failure of meiosis. If pollen tube growth is slow, the tube may not reach the embryo sac or sperm viability may be lost before the tube reaches the sac. A white oak clonal seed orchard in Tennessee demonstrated the strong genetic control over premature abscission (Farmer, 1981). Among 111 trees representing 31 clones, the percentage of fertilized flowers averaged over all clones ranged from 19 to 38% 7–11 years after orchard establishment. But among individual clones, that percentage ranged from 0 to 75%. Among fertilized flowers, only 26–29% (5–11% of all flowers) developed into acorns during the 4 study years. However, among individual clones, that percentage ranged from 0 to 42%. Statistical analysis showed that clones explained a significant proportion of variation in flowering, fertilization and fruiting. But acorn yield was most strongly correlated with clonal differences in percentage of flowers fertilized and only weakly correlated with flower abundance. Though there was significant variation among clones in flower abundance, the greater limitation to acorn production was low fertilization rate. Variation in acorn production The most prominent and consistent attributes of acorn production are large year-to-year and tree-to-tree variation (e.g. see Koenig et al., 1994; Nakashizuka et  al., 1997). This variation occurs within species and locales. In white oak and northern red oak in West Virginia, tree-to-tree variation was significantly greater than year-to-year variation in trees (Tryon and Carvell, 1962). In bear oak, tree-to-tree variation in acorn production was under strong genetic control (Wolgast, 1972). Forest-grown trees of three acorn-producing ranks (low, medium and high) transplanted to a homogeneous nursery environment

61

conformed to their original rankings. In contrast, soil factors related to the forest environment did not explain a significant proportion of the observed variation in acorn production before transplanting. White oak similarly demonstrated strong genetic control over flowering and acorn production in a clonal seed orchard (Farmer, 1981). The large variation in time of acorn fall among individual willow oaks and water oaks during the same year suggests that factor also may be largely under genetic control (Cypert and Webster, 1948). Although there are a few reported studies on the allocation of net annual biomass production to acorns, on average the proportion apparently is relatively small but highly variable among trees. During a 5-year study of 29 open-grown sawtooth oaks in Japan, acorns accounted for an estimated 2.8% of net annual biomass production when averaged across years (Nakashizuka et  al., 1997). However, among trees this proportion varied from 0 to 20%. The proportion of production allocated to acorns was independent of tree size. The absolute allocation to acorn production increased in proportion to whole-tree production but only after whole-tree production reached 0.76 kg (1.7 lb)/ year. Above that threshold, acorn production was closely proportionate to leaf production. Other factors being equal, large trees generally produce more acorns than small trees (Downs and McQuilkin, 1944; Goodrum et  al., 1971), and open-grown trees produce more acorns than trees growing in a closed-canopy forest (Sharp, 1958; Sharp and Sprague, 1967). Because both tree size and stand density can be measured and controlled silviculturally, these relations have practical application in managing oak forests for acorn production. There also are large differences among species in acorn-producing potential. Methods for assessing and predicting acorn crops based on these and other relations together with silvicultural guidelines for sustaining acorn production are presented in Chapter 13, this volume. Periodicity Year-to-year variation in acorn production within an individual tree or population of trees can result from the single or joint effects of variation in inherited factors that control the timing and frequency of flowering and fruiting, and environmental factors. Environmental factors that can reduce acorn production below a tree’s inherent potential often

62

occur as essentially random events, which may obscure the expression of any inherently regular pattern, or periodicity, in acorn production. Obscuring factors include weather events unfavourable to flowering and acorn production (Sharp and Sprague, 1967; Sork and Bramble, 1993; Koenig et  al., 1996; Cecich, 1997), and insect damage to leaves (Crawley and Akhteruzzaman, 1988), flowers (Cecich et al., 1991) and acorns (Myers, 1978; Christisen and Kearby, 1984). Because of confounding environmental factors, determining the existence of periodicity in natural environments usually requires the use of statistical methods that can reveal inherent cycles, or alternatively, experimental studies in controlled environments designed to eliminate or separate the confounding factors. Periodicity in acorn production thus refers to an inherent potential of a tree to recurrently produce acorn crops at a fixed time interval. Periodicity may characterize an individual tree or a population of trees of the same species. Even though an individual tree may be inherently able to produce acorns at a regular interval, the population as a whole may not if it includes trees with no discernible pattern of acorn production, trees that produce at regular intervals but of different lengths, or trees that produce at the same interval length but which are out of phase with other trees in the population. Periodicity in the occurrence of large or above average acorn crops, or ‘masting’, is also a possibility. The occurrence of periodicity and various patterns of periodicity is usually determined by observing trees known to be consistent, albeit not necessarily prolific, acorn producers (e.g. see Koenig et  al., 1994). The occurrence of periodicity and periodicity characteristics in acorn production vary among oak species. In some species in the white oak group, acorn production is noticeably periodic. In the absence of adverse weather, acorn crops tend to occur every other year and good crops about once every 4 years (Downs and McQuilkin, 1944; Goodrum et  al., 1971; Beck, 1977; Myers, 1978; Christisen and Kearby, 1984; Koenig et  al., 1994). This pattern suggests that white oaks require at least 1 year to recover after a large investment in acorn production. Periodicity in the white oak group also is apparently synchronized, with all fruitful trees within a species, locale and year producing at their inherent individual, but often environmentally constrained, capacity (Sharp, 1958; Myers, 1978). For four California oak species, selected weather factors (temperature and precipitation) explained

Chapter 2

38–78% of the variation in mean annual acorn production (Koenig et al., 1996). In valley and blue oaks, which mature in one growing season, mean annual April temperature (which includes the fertilization period) was positively associated with acorn production. In canyon live and coast live oaks, which mature in two growing seasons, total precipitation from September through to the next acorn fall was positively associated with acorn production. The influence of weather factors thus may obscure any tendency in periodicity as confirmed by the predominance of reported irregularities in acorn production (Sharp, 1958; Grisez, 1975; Godman and Mattson, 1976; Sork and Bramble, 1993). Periodicity tends to be less consistently expressed in the red oaks than in the white oaks (Sharp, 1958; Tryon and Carvell, 1962; Beck, 1977). For example, in north-eastern Wisconsin northern red oak bumper crops (defined as 91% or more of maximum potential) occurred at 7-year intervals (Godman and Mattson, 1976). Yet, such crops occurred as few as 2 years apart over the 21-year study period. Good or better crops (60% or more of potential) occurred at 3-year intervals. Other species in the red oak group behave similarly (Sharp, 1958; Christisen and Kearby, 1984). In the red oak group, the combined effects of apparently asynchronous acorn production and unpredictable weather often obscure any tendency towards periodicity. This suggests that the apparent asynchrony of acorn production among individual red oaks may result in lower year-to-year variation in acorn production at the population level than in the white oaks (Tryon and Carvell, 1962). Acorn production among species is generally asynchronous (Sork and Bramble, 1993; Koenig et  al., 1994). However, among five species of California oaks observed for 12 years, acorn production was relatively synchronous within species despite large variation among trees in numbers of acorns produced (Koenig et  al., 1994). The same study found no evidence of periodicity in the occurrence of large crops (‘masting cycles’) at the population level but found that such cycles did occur in individual trees. Individual trees of some species also produced large crops in successive years, but within a population such occurrences occurred no more often than expected by chance. There was no evidence of masting cycles at the population level in any of the five species studied, which included two species in the white oak group, two in the red oak group (including one evergreen oak), and one evergreen oak in the intermediate group. The investigators

Regeneration Ecology I

noted that regular masting cycles have yet to be demonstrated for any oak species. One method of detecting inherent periodicity in acorn production in individual trees is based on the correlation between the acorn production of a tree in one year with its production in other years. The correlation coefficients for various time intervals or ‘lag times’ (e.g. 1, 2, 3 and 4 years before and after a specified time) thus can be used to statistically identify time intervals associated with acorn production (positive correlations), time intervals associated with the absence of production (negative correlation), or the absence of correlation (0 correlation). For example, the individual-tree correlation coefficients for a 1-year time lag during a 10-year study period can be derived from the nine pairs representing year 1 versus year 2, year 2 versus year 3, . . . year 9 versus year 10. Similarly, the correlations for 2-, 3- and 4-year or longer time lags can be calculated. The method can be used to derive, for each tree, a series of correlation coefficients each representing a given time lag. If this were done for several trees, the correlation coefficients for each tree then could be evaluated. A general consistency among individuals in the signs of the correlation coefficients (positive or negative) and their statistical significance (i.e. the likelihood that they differ from 0) provides a basis for accepting or rejecting the hypothesis of inherent periodicity in acorn production. For a given species, the average correlation coefficient of several trees for a given time lag can be used as the basis for testing the hypothesis. Accordingly, if prior acorn production does not affect current acorn production, the mean correlation coefficient should not differ significantly from zero for any given time lag. A positive correlation would indicate a significant positive relation between a given time lag and acorn production, and a significant negative correlation would indicate a negative relation (i.e. inhibition of acorn production). This method of assessing periodicity also has the advantage of removing differences related to possible asynchrony in acorn production among individual trees among years because correlations are derived for each tree by lag interval rather than the year by itself. Such statistical tests provided empirical evidence of an inherent 3-year cycle in white oak acorn production, that is years of high production tended to be followed by 2 successive years of little or no production (Sork and Bramble, 1993). Also there is evidence of inherent 2- and 4-year cycles for black oak and northern red oak, respectively (Fig. 2.7).

63

Number of years prior to current year

***

4

* ***

3

*** 2

1

***

*

*

**

–0.8 –0.6 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 Correlation of current with prior years’ acorn production Black oak

Northern red oak

White oak

Fig. 2.7.  Correlation of current acorn production (mature acorns per tree) with prior years’ production for three species in Missouri. (Redrawn from Sork et al., 1993.) The significant positive correlations (right side of graph) suggest 2-, 3- and 4-year acorn production cycles (time lags) for black, white and northern red oaks, respectively, based on 13, 15 and 12 trees (n), respectively, observed for 8 years. Each correlation coefficient shown is the average of the individual-tree coefficients within a species/time-lag interval (see text for discussion). Asterisks (*, **, ***) indicate significance at P < 0.05, < 0.01 and < 0.001, respectively, based on t tests with n-1 degrees of freedom.

Significant negative correlations tended to occur in intervening years. Because all three species were observed in the same locale and yet had different cycles, variation in weather does not provide a likely explanation for variation in apparent periodicity. Evidence for periodicity was strongest for white oak because none of the numerous, measured weather variables were correlated with its apparent 3-year periodicity cycle. Among California oaks that have been similarly studied, a 3-year cycle was statistically evident in blue oak (a member of the white oak group) and coast live oak (a member of the red oak group with a 1-year acorn maturation period) (Koenig et  al., 1991) (Fig. 2.8A). Evidence for periodicity was less conclusive in valley oak (a member of the white oak group), canyon oak (intermediate group) and California black oak (red oak group) (Fig. 2.8B). Plausible explanations for inconclusive results include: (i) the ‘real’ absence of periodicity; (ii) real but weakly expressed periodicity; (iii) the overriding influence of one or more environmental factors during a given study period; (iv) errors in measuring acorn crop size; (v) a sample size that is too small (i.e. too few trees observed) to substantiate that a ‘real’ association as expressed by a correlation coefficient of a given value is statistically

64

significant; and (vi) possible genetic variation in periodicity within a species. Statistical relations (Figs 2.7 and 2.8) suggest that periodicity may be of two types: manifest and latent (i.e. readily expressed and seldom expressed). Accordingly, evidence of manifest periodicity would express itself as a consistent temporal pattern of positive and negative correlations such as that observed in blue oak (Fig. 2.8A). In contrast, latent periodicity would occur in species or populations of trees with an inherent periodicity that is seldom or never expressed because of acute sensitivity to adverse environmental events or frequent exposure to such events. Demonstrating the existence of latent periodicity would require experiments that eliminate or control the factors obscuring periodicity. In the absence of such experiments we can only speculate about the nature of possible genetic variation in periodicity. The occurrence of populations that include trees with both high and low lag-time consistencies (e.g. as expressed by the correlation patterns in Figs 2.7 and 2.8) nevertheless would be consistent with a population that possesses high genetic variation in periodicity. An alternative hypothesis is that, among trees that produce acorns, some individuals are inherently periodic whereas others are inherently non-periodic.

Chapter 2

Number of years prior to current year

(A) ** ***

6 5 *** ***

4 3

**

**

2 1

***

*** –0.2

–0.4

0.0

Valley oak

0.2

Blue oak

0.4

Coast live oak

Number of years prior to current year

(B) 6 5

**

4

**

3

***

2 1

** –0.4

–0.3

–0.4

–0.1

0.0

0.1

0.2

0.3

Correlation of current with prior years’ acorn production

Canyon live oak

California black oak

Fig. 2.8.  Correlation of current acorn production with prior years’ production for five species in California. (A) Correlations for three species requiring 1 year for acorns to mature based on 87, 57 and 63 valley, coast live and blue oaks, respectively. For blue and coast live oaks, the patterns of significant correlations suggest a 3-year cycle. For valley oak, the pattern of correlations suggests that it is not strongly cyclic. (B) Correlations for two species requiring 2 years for acorns to mature based on 21 trees of both species. For both species the correlations are inconclusive in relation to cyclic patterns because of the absence of significant positive correlations. For all species, correlations are based on the number of acorns counted in a 30 s sampling period on each tree in each of 10 consecutive years. Asterisks (*, **, ***) indicate significance at P < 0.05, < 0.01 and < 0.001, respectively, based on binomial tests. (Redrawn from Koenig et al., 1991.)

Spatial variation within tree crowns Acorns are usually unevenly distributed throughout the tree crown. However, acorns in open-grown trees were more evenly distributed than acorns in trees in closed stands. In closed stands, most acorns occurred on branches exposed to light (Sharp and Sprague, 1967). Post (1998) found that even in

Regeneration Ecology I

open-grown northern red oaks, production was greatest in the lower section of the south-facing side of crowns. Shading on one or more sides of the crown resulted in a non-discernible pattern of production, although branches exposed to light tended to produce more acorns than those that were not. In a relatively open-grown stand (six oaks/acre) of

65

coast live oak on a south-facing slope in California, acorn numbers were about two times greater on south-facing sides of crowns than north-east- and north-west-facing sides (Lewis, 1992). Although numbers of acorns in lower and mid-crowns were about three times greater than in upper crowns, the opposite trend occurred in trees in another stand that were pruned and irrigated (Lewis, 1989). Such inconsistencies suggest that environmental factors may influence the within-crown distribution of acorns. Acorn destroying insects and other organisms may prefer different parts of the crown and thereby influence the distribution of sound acorns. The infestation of coast live oak acorns by filbert weevils and filbert worms was 1.5–3 times greater on the north-easterly side of crowns than on south and north-westerly sides (Lewis, 1992). Preferences for cooler crown aspects may reflect insect avoidance of overheating. Moreover, the occurrence of acorns that were split open (possibly from bursting from within) occurred on south and north-westerly sides of crowns, making them vulnerable to invasion by ants, wasps, microbial pathogens and other parasites (Lewis, 1992). The numerous confounded biotic and physical factors that apparently can influence the spatial distribution of acorns within crowns (especially sound acorns) thus complicates generalizing about such distributions other than that they vary greatly among trees.

Acorn Predation and Dispersal Many organisms consume acorns including insects, millipedes, fungi, birds and mammals. Consumption by one or more biotic agents is often so complete that in any given year few acorns remain to germinate and become seedlings. Although this consumption contributes to oak regeneration failures (Marquis et al., 1976; Galford et al., 1991c), acorns are valuable food for many birds and mammals because of their high caloric content, nutritiousness and availability during seasons when other food is often scarce. Oak forests thus can be managed specifically for acorn production to benefit wildlife (Chapter 13, this volume). The term dispersal is used to refer to the transport of acorns from their place of origin (i.e. tree crowns or the ground directly beneath them) to some other location irrespective of the distance, mode of transport or subsequent fate of the acorns. Because of their relatively large size and mass, acorns are not dispersed by wind. Although gravity

66

and water may affect localized dispersion, birds and mammals are the most important dispersers of acorns. Dispersal by animals may carry acorns to places that are more favourable or less favourable for germination and seedling establishment than their site of origin. But even under the most favourable circumstances, the oaks pay a price for animalmediated dispersal because at least some of the dispersed acorns are consumed by the dispersers. Whether an oak benefits from dispersal by a given disperser depends on: ●● where the acorns are dispersed; ●● the number dispersed; ●● the pattern of dispersal (scattered or concentrated); and ●● the proportion of acorns consumed by the disperser (including acorns consumed at their place of origin before dispersal has occurred). With respect to the oak, dispersal is ineffective if acorns are largely dispersed to habitats unfavourable for maintaining acorn viability and seedling establishment. If the proportion of total numbers of acorns dispersed is very small, the impact of the dispersing agent (whether positive or negative) on oak regeneration may be negligible. Numbers of dispersed acorns being equal, dispersers that scatter individual acorns or small groups of acorns are likely to benefit oak regeneration more than dispersers that cache large quantities of acorns in one or a few locations. Dispersers that consume virtually all the acorns they disperse clearly represent some loss to potential seedling establishment. Dispersers also may consume significant quantities of acorns at their place of origin. If a disperser consumes proportionately few acorns, dispersal may be advantageous to the oak if acorns are dispersed in significant numbers to habitats favourable for seedling establishment. Insects: destroyers of acorns Insects may begin destroying acorn crops during flowering. However, the focus of this section is on insects as destroyers of already formed acorns (from immature to mature) through the germination period. During these stages of development, there is a predictable sequence of damaging events and agents involving not only insects but other organisms including bacteria, fungi, algae, protozoa, nematodes, mites and other organisms (Winston, 1956). Insects often initiate damage before acorns mature. For example,

Chapter 2

the adult female Curculio weevil can chew through the immature acorn’s shell to deposit her eggs inside the acorn. The developing larvae then consume part or all of the acorn’s interior. Invading insects also can carry pathogenic fungi and bacteria into the acorn, which may kill the embryo even though the insect itself may not. Direct insect damage occurs during the several weeks required for the larvae to develop. The developing larvae usually consume most acorns by mid- to late autumn while they remain on the tree or shortly after they fall. The larvae eventually cut exit holes in the shell of the acorn, making it accessible to other organisms. One of these is the acorn moth, which lays its eggs on or in the exit hole. Their larvae feed on the remainder of the embryo and on the faeces of the previous occupant. After acorns fall, fungi and other decomposers take over the final stages of destruction until the acorn is thoroughly decayed and eventually incorporated into the soil (Winston, 1956). Only the acorns that escape this fate are

available to wildlife and forest regeneration. Insects typically destroy 50% or more of acorns annually (Gibson, 1982; Kearby et al., 1986). In some years, destruction approaches 100%. Although many kinds of insects destroy acorns (Winston, 1956), most of the damage through the germination period is caused by a relatively small number of insect groups (Gibson, 1972, 1982; Kearby et  al., 1986; Bellocq et  al., 2005). These include: ●● acorn weevils (Curculio spp. and Conotrachelus spp.); ●● moths (the filbertworm moth and the acorn moth); ●● acorn gall wasps (cynipids); and ●● nitidulid sap beetles. Acorn insects are sometimes studied using acorn traps and ground emergence traps from which the insects and acorns can be identified and counted (Fig. 2.9). State and regional studies reveal the relative importance of the various acorn insects (Table 2.1).

Fig. 2.9.  Acorn collection traps (on stakes) and insect emergence traps (on ground) used to study acorn production and acorn insects in a Missouri forest. (Photograph courtesy of Dr William H. Kearby, Regional Entomologist (Retired), Wisconsin Department of Natural Resources.)

Regeneration Ecology I

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Table 2.1.  The relative importance of the four major acorn-insect groups expressed as a percentage of insectinfested acorns. (From Gibson, 1972; Kearby et al., 1986.) Species, region and years observed White oak Range-wide Insect group Curculio weevils Conotrachelus weevils Filbertworm and acorn mothe Acorn gall wasps

Ohio 1961a

1962b

1963c

Average of seven species in Missouri 1973–1976d

79.1 15.1 5.7 0.1

59.5 38.0 2.5 < 0.1

57.5 37.8 4.7 0

54.4 5.3 21.0 19.3

a

From acorns collected in three counties; may include immature acorns (from Gibson, 1972). From acorns collected in ten states; may include immature acorns (from Gibson, 1972). c From acorns collected in 13 states and the District of Columbia; may include immature acorns (from Gibson, 1972). d From mature acorns collected in four or five areas (depending on year); species include black, northern red, scarlet, Shumard, blackjack, post and white oaks (from Kearby et al., 1986: Table 9). e Both are moths (Lepidoptera) but the filbertworm is a primary invader and the acorn moth is a secondary invader of acorns. b

Curculio weevils in any given year typically account for 50–80% of infested acorns, and Conotrachelus weevils about 5–40%. The moths and gall wasps each typically account for about 0–20% of infestations. However, these percentages can vary greatly among oak species, stages of acorn development, locations and years (Gibson, 1972, 1982; Kearby et al., 1986; Bellocq et al., 2005). The major acorn insects can be divided into primary and secondary invaders. Primary invaders directly enter the acorn whereas secondary invaders require a crack or other opening in the acorn for entry (Murtfeldt, 1894; Winston, 1956; Kearby et  al., 1986). The most important primary insect invaders are the Curculio acorn weevils, the filbertworm moth and the cynipid gall wasps. Some species of fungi also are primary invaders. Although there are numerous secondary insect invaders of acorns, the most important are the Conotrachelus acorn weevils and the acorn moth. Primary invaders The Curculio weevils comprise the most important group of acorn destroyers. Among the 27 American Curculio species, 23 breed and feed on acorns; 22 species do so exclusively on oaks (Gibson, 1969). They occur throughout the oak range in the USA. Two to three weeks before acorns mature, the adult female uses the tiny mouthparts at the end of her long, slender snout to chew a small hole in the acorn shell (Fig. 2.10A). She then makes a chamber

68

near the inner surface of the shell, turns around to deposit an egg, and pushes it through the hole into the chamber (Brown, 1980; Kearby et  al., 1986). Although only one egg is usually laid in each chamber, the female may lay several eggs in a single acorn. The eggs hatch in 5–14 days and the larvae then feed on the acorn’s contents. After maturing, the larvae exit the acorn through a circular hole they cut in the shell (Fig. 2.10B). Most larvae exit the acorn in the autumn although occasionally they remain there until spring. After leaving the acorn, the larvae burrow as deep as 12 inches into the soil where they form pupal cells. Most larvae overwinter in the soil for 1 or 2 years, whereas others remain there longer (Gibson, 1969). The transformation of pupae into adults occurs in the spring or early summer. After the adults emerge from the soil and mate, the females complete the life cycle by depositing their eggs in the new acorns (Myers, 1978). Curculio weevils are vulnerable to parasitism by other insects, fungi, bacteria and nematodes (Gibson, 1969; Kearby et al., 1986). In a Missouri study, as many as 17% of Curculio weevils were parasitized by other insects (Kearby et  al., 1986). Predators of adult Curculio weevils include more than 80 bird species (Brooks and Cotton, 1929). The short-tailed shrew is an important predator of weevil larvae in the soil (Brooks, 1910). In a 4-year Missouri study, Curculio weevils infested 31% of more than 10,000 mature acorns of seven species collected from seed traps (Kearby et  al., 1986). These weevils occurred in 43% of

Chapter 2

(A)

(B)

(C)

(D)

(E)

(F)

Fig. 2.10.  Acorn-destroying insects. (A) An adult Curculio weevil on a black oak acorn. The adult female uses the mouthparts at the end of her long, slender snout to chew a small hole in the acorn shell. She then makes a chamber near the inner surface of the shell, turns around and deposits an egg, and pushes it through the hole into the chamber. (B) A Curculio larva emerging from an exit hole in a black oak acorn. (C) The adult filbertworm moth (about five times actual size). (D) Larval chambers and larvae of a stone gall wasp. The gall and its occupants completely fill the inside of this black oak acorn. (E) A pip gall protruding from beneath the cup of a black oak acorn. The inset photo shows the exit hole of the adult gall wasp, which is visible only after removing the gall and acorn cup. (F) The adult acorn moth (about five times actual size). This insect is a secondary invader of acorns (i.e. it requires a preestablished crack or other opening in the acorn shell as an entryway). The other insects shown are primary invaders. (Photographs courtesy of Dr William H. Kearby, Regional Entomologist (Retired), Wisconsin Department of Natural Resources.)

white oak acorns and 25% of black oak acorns. By individual years, infestations ranged from 10 to 54% for white oak and 19–40% for black oak. In coast live oak in California, 27% of acorns collected in a single acorn season were infested with

Regeneration Ecology I

the filbert weevil, a species of Curculio (Lewis, 1992). The filbertworm moth is an important primaryinvading moth (and is not closely related to the filbert weevil) (Fig. 2.10C). The larvae feed in

69

acorns and a wide variety of other nuts and fruits (Murtfeldt, 1894; USDA Forest Service, 1985). The adult moth usually oviposits her eggs through the bottom of the acorn cup while the acorn is still forming (Gibson, 1972). Unlike the larvae of the Curculio weevils, which are born inside the acorn, the larvae of the filbertworm moth must bore into the acorn. A bacterial infection of acorns called drippy nut disease is often associated with this boring activity (Hildebrand and Schroth, 1967). The moth larvae commonly exit through the acorn shell and cup after consuming the interior of the acorn and then may enter a nearby acorn (Kearby et al., 1986). The larvae usually require two or three acorns for the completion of their development (Winston, 1956). Pupation normally takes place in the leaves and other debris on the forest floor. Reported moth damage to acorns often combines that of the filbertworm moth with the acorn moth. Combined moth attacks in Missouri ranged from 4 to 64% of all insect attacks on mature acorns, depending on year and location, and often comprised 20–40% of attacks (Kearby et al., 1986). Of those, most were attributed to the filbertworm moth. For immature acorns, moths accounted for 85% or more of all insect attacks in some locations and years, and most of those were attributed to the filbertworm moth. Although moths generally infest less than 10% of acorns (Korstian, 1927; Gibson, 1971, 1972), in some instances infestation rates have exceeded 50% (Sidney, 1948; Gibson, 1964). Moths infested 2.5% of acorns in a range-wide study of white oak (Gibson, 1972). In a similar study of northern red oak, up to 34% of acorns were infested with moth larvae (Gibson, 1982). The filbertworm moth infested 27% of coast live oak acorns in a California study (Lewis, 1992). In a 4-year Missouri study, moths infested 11% of mature acorns averaged across all species. Moth infestation rates on mature acorns were higher in species in the red oak group (14–22%) than in species in the white oak group (5–8%). Infestation rates also were higher in years of low acorn production than in years of high production (Kearby et al., 1986). The acorn gall wasps are members of the Cynipidae family of insects. Over 700 species are found in the USA and Canada, and about threequarters of them induce gall formation on oaks (USDA Forest Service, 1985). The adult wasps deposit their eggs in the tissues of all parts of the oaks, from the roots to the flowers. Gall formation

70

apparently results from the reaction of the cambium and other living tissues to larvae-induced stimuli. The gall wasps have alternating sexual and asexual generations. Among the species that affect acorns, adults of the asexual generation oviposit their eggs in oak buds and flowers in the spring, which causes galls to develop in or on the acorn. The sexual generation oviposits in or on immature acorns in June. The life cycle usually requires 2 years to complete (Weld, 1922). Gall wasps that attack acorns fall into three groups (Myers, 1978; Kearby et al., 1986) based on the location of the gall on the acorn: ●● galls that form on or in the acorn cup but not in the acorn itself; ●● ‘pip’ galls, which form between the acorn and the cup; and ●● ‘stone galls’, which form inside the acorn. Some gall wasps produce growths resembling small acorns on the side of the acorn cup (Gibson, 1972). These galls usually do not affect acorn viability but may cause premature acorn abscission if they are numerous. The galls occurring inside the acorn are usually caused by species of Callirhytis wasps. Some species cause stone galls, which are embedded in, but separate from, the interior material of the acorn (i.e. the cotyledon). They may not affect acorn viability unless they form in the area containing the rudimentary root and foliar structures, which lie near the pointed end of the acorn. Some stone galls are comprised of groups of connected larval chambers occupied by several soft white larvae (Fig. 2.10D). Pip galls, also caused by Callirhytis wasps, cause the acorn’s cotyledons (the large fleshy kernel inside the acorn shell) to shrivel, which destroys their viability and value to wildlife (Kearby et al., 1986) (Fig. 2.10E). In a 4-year Missouri study of four to five oak stands, gall wasps accounted for 19% of all insect attacks on mature acorns (all oak species combined) and ranged from 5 to 56% of attacks among years and locations (Kearby et al., 1986). Infestation rates averaged 11% of acorns, but ranged from 3 to 32% among locations and years. On average, 6% of white oak, 10% of black oak and 45% of scarlet oak acorns were infested by gall wasps. Infestation rates of gall wasps on immature acorns averaged 10% across all oak species observed and ranged from 2 to 30% among locations and years. In a range-wide study of northern red oak, gall

Chapter 2

wasp infestations ranged from 0 to 37% of acorns (Gibson, 1982). The relation between insect damage and acorn crop size is unclear. Although some reports indicate an inverse relation, other reports indicate the opposite, while yet others report no relation (Sidney, 1948; cf. Beal, 1952; Brezner, 1960; Janes and Nichols, 1967; Gibson, 1972; Beck, 1977; McQuilkin and Musbach, 1977). Such inconsistencies may result from interactions between acorn crop size, insect population size and other factors (Myers, 1978; Kearby et  al., 1986). Potentially important interactive factors include competition among insects, the searching ability of insects to locate hosts, predation and parasitism among insects, preference of an insect for one host species over another and weather conditions adverse to insects. The Curculio weevils illustrate one type of competitive effect. An adult Curculio usually does not oviposit in an acorn already attacked by another Curculio. Consequently, as the number of weevils and infestations increase, the longer it may take an individual weevil to find a suitable host (Kearby et al., 1986). Secondary invaders In the USA, there are three species of Conotrachelus weevils that breed in acorns (Gibson, 1964, 1982; Kearby et  al., 1986). However, a single species accounted for 93% of all Conotrachelus infestations in a range-wide study of white oak acorns (Gibson, 1972). Conotrachelus life histories are similar to the acorn-infesting Curculio weevils. The adults lay their eggs in acorns that have been cracked or opened by biotic agents or other factors. The larvae feed on the interior of the acorn, and after maturing cut an exit hole in the shell. Whereas each Curculio larva usually chews its own exit hole, all Conotrachelus larvae within an acorn usually exit through a common hole (Gibson, 1971, 1972). After the larvae exit the acorn, they drop to the ground and overwinter in the soil where pupation occurs. After pupation in spring and early summer is complete, the adults emerge from June to August, depending on region (Brooks, 1910; Gibson, 1964). The adults live longer than Curculio adults (Gibson, 1964). Parasites of the Conotrachelus weevils include tachinid flies and other insects (Pierce, 1908; Gibson, 1964). Conotrachelus infestation rates are usually higher in the acorns of white oaks than red oaks (Gibson, 1964; Kearby et al., 1986), which is consistent with

Regeneration Ecology I

their role as secondary invaders. The shell splitting of white oak acorns during autumn germination allows the weevils to easily gain entry into the acorns’ interiors. Acorn infestation rates are therefore likely to be highest after acorns fall to the ground and germinate. Consequently, Conotrachelus populations are likely to be underestimated based on acorns collected in the autumn from acorn traps (Kearby et  al., 1986). In years of poor white oak acorn production, Conotrachelus weevils may become more prominent in red oak acorns. In the autumn, adult weevils can enter red oak acorns that have been damaged but not completely consumed by wildlife and other insects, or that have otherwise cracked open. Some weevils also can overwinter in the adult stage (Gibson, 1964) and therefore oviposit in germinating or damaged red oak acorns in the spring. Among acorns collected throughout the range of white oak in two different years, Conotrachelus weevils occurred in 38% of all insect-infested acorns in both years, whereas Curculio weevils occurred in 58–60% of such acorns in each year (Gibson, 1972). In a Missouri study, Conotrachelus weevils accounted for 11% of all insect-infested white oak acorns while Curculio weevils accounted for 66% (Kearby et  al., 1986). In a range-wide study of autumn-collected northern red oak acorns, Conotrachelus were present in less than 3% of the acorns (Gibson, 1982). Over a 4-year period in Missouri, Conotrachelus weevils on average occurred in 7% of white oak acorns, but occurred in less than 1% of black oak acorns based on autumn acorn collections (Kearby et  al., 1986). However, data from autumn-collected acorns may grossly underestimate the impact of Conotrachelus weevils not only on acorns but also on newly germinated seedlings. A central Pennsylvania study revealed that nearly all of the northern red oak acorns observed in the spring were infested with Conotrachelus larvae (Galford et  al., 1991c). Moreover, the overwintering adult weevils of some species feed on the emerging roots and shoots of germinating acorns in the spring, which may kill most of the newly developing seedlings that are so attacked (Galford et al., 1988, 1991c; Galford and Weiss-Cottrill, 1991). Nevertheless, many acorns partially damaged by insects manage to germinate. Moreover, there is evidence that seedlings from these acorns grow faster than undamaged acorns, although the reasons are not clear (Branco et  al., 2002; Wada and Kamata, 2003).

71

The acorn moth, known primarily as a secondary invader of acorns, is now also recognized as a primary pest (Galford, 1986) (Fig. 2.10F). It can be a primary pest of germinating acorns in both spring and autumn. However, in the autumn, it is primarily a secondary pest, invading acorns damaged by other insects or animals. The larvae also can enter previously undamaged acorns by chewing through the base (cup end, Fig. 2.3L) of the acorn, but apparently only in immature acorns (Galford et al., 1991c). Because damage is confined to the cup ends (and thus furthest from the rudimentary root and leaf structures), this type of infestation has little effect on acorn viability. Acorn moth larvae are also more attracted to damaged than undamaged acorns, based on experiments comparing acorns cut in half with uncut acorns (Galford and WeissCottrill, 1991). The larvae, which live in the forest litter, apparently are attracted to the cut acorns because of the volatile substances they emit. Damaged acorns thus may become primary targets for both feeding and reproducing insects. The acorn moth overwinters in the larval stage, and in the Central Hardwood Region may begin to feed in February, making it one of the first acorn pests to become active. The acorn moth was ranked fourth in overall importance among insects that feed on germinating acorns and new seedlings in Ohio (after two species of Conotrachelus weevils and a nitidulid sap beetle) (Galford et  al., 1988). During acorn germination, the moth larvae feed on the emerging and developing roots (radicles) and then enter the acorn through the splitting shell to complete their development (Galford and WeissCottrill, 1991). In a Pennsylvania study that protected surface-sown acorns from large mammal predation by wire screens, insects destroyed 87% of the acorns and developing seedlings (Galford et al., 1991b). The acorn moth and Conotrachelus weevils were the primary causes of destruction. Thus, even when mammal predation is low and acorns are abundant, high rates of seedling establishment may not occur because of insect predation (Galford and Weiss-Cottrill, 1991). Two species of nitidulid sap beetles are known to feed on germinating acorns (Galford et al., 1991a, b). Adults of both species consume elongating radicles during germination and then oviposit in the acorns. At least one of the two species also feeds on the tips of developing shoots (Galford, 1987). The combined adult and larval feeding destroys the acorn. They are thus both primary and secondary

72

pests of acorns (Galford et al., 1991a). In Ohio, the insect is one of the most serious pests of germinating red oak acorns in the spring. When the overwintering adults become active during acorn germination, they feed heavily on the radicles. It is the most abundant of the acorn insects in southern Ohio in the spring (Galford et al., 1988). A millipede (Ptyoiulus impressus) also has been observed to feed on the radicles of germinating acorns; these accounted for 17% of the damage to developing acorns of northern red oak in a Pennsylvania study (Galford et al., 1992). Among white, chestnut and northern red oaks, the millipede showed a preference for northern red oak based on experimental feeding tests. In Pennsylvania, millipedes become active a few days before insect activity in late winter or early spring. Under natural conditions, oak seedling establishment is largely limited to acorns that have been buried by rodents (Korstian, 1927; McQuilkin, 1983). Based on field experiments, acorns buried 1 inch deep in the soil have a greater chance of escaping insect predation than acorns on the soil surface. In a Pennsylvania study where northern red oak acorns were protected from mammal predation by screens, 40% of those sown on the soil surface and covered only with the current leaf litter were destroyed by insects; only 25% of acorns placed 1  inch below the soil surface were destroyed (Auchmoody et  al., 1994). Conotrachelus weevils and nitidulid sap beetles accounted for 97% of the destruction. In a similar Pennsylvania study, insects destroyed 92% of surface-sown acorns beneath mammal-excluding screens, but only 16% of those planted 1 inch deep in the soil (Galford et  al., 1991c). The burial of acorns by natural mechanisms thus may account for the origin and subsequent regeneration of many, if not most, oak forests. Most of the acorn-damaging insects, both primary and secondary invaders, live in the forest floor for at least a part of their life cycle. Consequently, the use of fire to control these insects has been proposed (Wright, 1987). Some of the Conotrachelus weevils live in the forest floor in both spring and autumn, which is also when prescribed burning is most feasible in oak forests of the eastern USA. Similarly, the filbertworm moth, acorn moth and nitidulid sap beetles are present in the forest floor in various stages of their life cycles during those periods. A preliminary study of fire to control these insects in Ohio suggests that burning can reduce populations of several acorn insects,

Chapter 2

including Conotrachelus and nitidulid sap beetles (Wright, 1987). However, the investigators emphasized that the necessary ecological changes brought about by fire are difficult to determine based on short-term burning experiments. Fire is nevertheless recognized as a natural regulator of insects (Komarek, 1970; Ahlgren, 1974; Martin and Mitchell, 1981). The significance of acorn dispersal by animals Because insects destroy most acorns in years of poor acorn production, significant surpluses occur only in years of good production. For many oak species, this occurs about 1 in 4 years or less frequently. Years of surplus nevertheless are critical to both acorn consumers and oaks. The most important non-insect consumers of acorns are rodents and certain species of birds. Only in years of surplus can acorn dispersal benefit the oak. But any possible benefit depends on the failure of a disperser to consume all of the acorns it disperses. Thus, there is always some cost to the oak for dispersal: the proportion of acorns consumed by dispersers. For this cost to advantage the oak, it must be more than offset in some way. Possible benefits depend on the following factors: (i) the number of acorns dispersed versus the number consumed; (ii) the quality of acorns dispersed; (iii) dispersal distance; and (iv) the favourableness of the places they are dispersed to with respect to acorn survival, germination and seedling development. It is the last factor, seedling development, that ultimately determines the significance of acorn dispersal to oak regeneration. If dispersal did not result in some regeneration advantage, the process would be exclusively negative with respect to the oak. Although this is not the case, not all acorn dispersers benefit the oak, and even among those that do, not all are equally beneficial. The following section discusses these relations. Rodent predation and dispersal Among the most important predators/dispersers of acorns are the rodents, including squirrels, mice, voles, chipmunks and gophers. Even before acorns fall, about 10–25% of acorns are taken directly from the trees by birds and squirrels (Downs and McQuilkin, 1944; Dalke, 1953; Burns et al., 1954; Gysel, 1957; Beck, 1977; Myers, 1978). After

Regeneration Ecology I

acorns fall, predation from rodents and other animals such as racoons, deer, wild turkey and blue jays often consume most of the acorn crop. As a result, substantial numbers of acorns survive only in years of above average production. Nevertheless, there are potential benefits to the oak from the relation between acorn predation and dispersal. Tree squirrels (Sciurus spp.) including the grey and fox squirrels common in eastern oak forests, rank among the most important acorn-dispersing rodents. Although acorns comprise a large proportion of the diet of squirrels, other foods are necessary to satisfy their protein and phosphorus requirements (Short, 1976; see also Vander Wall, 1990, 2001; Kirkpatrick and Pekins, 2002; and Steele et  al., 2005a for information on the nutritional value of acorns). Acorns of species in the red oak group have a higher tannin content and therefore are less digestible and palatable than those of the white oaks (Short, 1976; Robbins et al., 1987; Kirkpatrick and Pekins, 2002). Tannins are also a deterrent to insect infestation of acorns (Schultz, 1989). Thus, it has long been assumed that when both kinds of acorns are available, squirrels and other rodents would choose the white oaks (Smith, 1962; Short, 1976). To the contrary, recent studies have shown that when both kinds of acorns are available, grey squirrels prefer to cache red oak acorns (Smallwood and Peters, 1986; Steele et  al., 2001). This would seem to negate the tannin avoidance hypothesis. What then compels the squirrel’s caching behaviour? Research suggests that squirrels ‘manage’ their acorn caches based on the perishability of white oak versus red oak acorns and the squirrels’ innate ability to distinguish. The caching of acorns is a multifaceted activity and involves: (i) acorn embryo excision or acorn ‘notching’ by the squirrel; (ii) dispersal and consumption preferences; (iii) dispersal distance; and (iv) mode of caching. These aspects of caching of acorns by squirrels and other rodents are discussed below. acorn embryo excision (‘notching’).  White oak acorns begin germination in the autumn. Their primordial roots or radicles emerge from the acorn first, while the plumule, or primordial shoot, usually does not emerge until spring. Squirrels dislike radicles and avoid eating them (Smith and Follmer, 1972). Moreover, within a few days after germination begins, 30–50% of the nutrient value of the acorn has been translocated to the developing radicle

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(Fox, 1982). Although squirrels may detach the acorn itself from the radicle and then eat the acorn, the radicle and attached plumule sometimes remain intact so that the acorn may further develop into a seedling (Fox, 1982). Autumn germination thus might seem to provide the acorns of the white oaks an ‘escape’ from predation. However, squirrels employ a counter tactic to autumn germination involving embryo excision, or ‘notching’ (Wood, 1938; Fox, 1982; Smallwood et al., 2001). A notch consists of a neat, small hole cut into the tip of the acorn by the squirrel’s incisors before acorn germination occurs. The tip of the acorn contains the embryonic components that develop into the radicle and plumule. Notching thus prevents germination and thereby preserves the nutrient value of the remaining acorn. Notched white oak acorns remain intact in squirrel caches for up to 6 months (Steele et al., 2001). Based on the recovery of metal-tagged acorns in a Pennsylvania study, notching affected 70.4% of cached white oak acorns but only 3.4% of northern red oak acorns; similar rates of embryo excision by Mexican grey squirrels were observed in several species of white oaks and red oaks in Mexico (Steele et  al., 2001). In an Illinois study, only 18% of notched acorns germinated compared with 94% of unnotched acorns (Wood, 1938). When cached red oak acorns germinate in the spring, they also may be notched when squirrels revisit their caches – thus extending their usefulness as food. Acorn notching appears to be unique to tree squirrels (Sciurus spp.) and is not known to be used by other small rodents such as mice and voles (Steele et  al., 2004). These counteracting traits between oaks and squirrels have been hypothesized as coevolved (Barnett, 1977; Fox, 1982). preferential dispersal and consumption. Acorn notching by squirrels is but one way that squirrels manage their acorn stores. They prefer caching red oak to white oak acorns. This contradicts the earlier assumption that squirrels discriminate against red oaks because of a presumed aversion to their tannin content. However, the squirrel’s preference for caching red oak acorns is now well established (Steele et  al., 2004). This preference is apparently based on the squirrel’s innate ability to detect acorn dormancy. In turn, this ability seems to depend on a chemical cue in the acorn shell (pericarp) (Steele et al., 2001). There is conflicting

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evidence as to whether tannins provide the cue (Steele et  al., 2004). But regardless of the specific mechanism, the cue is important in the squirrel’s decision as to which acorns they cache. Because of the squirrel’s ability to detect dormancy state, the dormant spring-germinating red oaks are preferentially cached while the autumn-germinating, nondormant and more perishable white oak acorns are preferentially eaten (Steele et  al., 2005b). The greater nutritional value of red oak acorns provides another benefit to the squirrel’s caching preference. Many other small mammals also show a propensity to selectively disperse and cache red oak acorns and consume white oak acorns (Steele and Smallwood, 2002; Steele et  al., 2005a). Squirrels also prefer sound acorns for caching and can detect insectinfested acorns, which they consume along with the larvae (Steele et al., 1996). This further extends the value of preferential caching and dispersal from an oak regeneration perspective. dispersal distance.  Among the rodents of eastern US oak forests that disperse acorns, the grey squirrel (Fig. 2.11A) perhaps benefits the oak the most because of where they live (primarily closedcanopy oak forests), the relatively long distances they carry acorns, and their mode of caching, as discussed below. Although the acorn caching of fox squirrels is similar to that of grey squirrels, the fox squirrel lives in more open places such as forest–field edges, urban parks and yards, and open woodlands; it avoids shaded forest interiors (Kurta, 1995). Based on the recovery of radio-tagged acorns from experimental feeding studies, grey squirrels carry acorns up to 650 ft or more from their source (Barnett, 1977). However, others have found that distances of 200 ft or less are more common (Steele and Smallwood, 2002). Dispersal distance moreover increases as acorn availability decreases. Grey squirrels also are quicker to return to the acorn source when other acorn consumers are present. The rapid return rate thus occurs at the expense of transporting acorns over longer distances (Hopewell et al., 2008). Such interactions between competing species thus tend to obscure, without qualifying conditions, the meaning of maximum or average dispersal distances. Nevertheless, from the perspective of the oak, dispersal distance is significant because it: (i) removes some acorns from their high-density locations directly beneath parent trees, thereby reducing

Chapter 2

(A)

(B)

(C)

Fig. 2.11.  Three rodents that are important consumers and dispersers of acorns in eastern US oak forests: (A) grey squirrel (a scatter-hoarder); (B) red squirrel; and (C) chipmunk. The last two are primarily larder-hoarders (see text). (Photograph (A) of grey squirrel courtesy of Dr Robert Mosier (Retired), University of Wisconsin-Stevens Point.)

acorn susceptibility to predation by non-dispersers; (ii) reduces intraspecific competition among seedlings by increasing average spacing between seedlings; and (iii) increases genetic spatial diversity by intermixing seedlings of different parentage. Because of the lower rates of caching in the white oaks, we might conclude that populations of white oak seedlings would be more aggregated or

Regeneration Ecology I

‘clumped’ genetically, and that this would be reflected in differences in average dispersal distance. However, studies based on DNA ‘fingerprinting’ methods used to pair seedlings with parents have not substantiated this assumption. To date, there is no evidence that red oak and white oak seedlings differ in average dispersal distance from parent tree based on a study in four states (Indiana,

75

Pennsylvania, Maryland and Virginia) (Smallwood et al., 2003). mode of caching. 

Grey and fox squirrels disperse acorns to numerous scattered, single-nut caches in the litter or soil. This method of caching is called scatter-hoarding (Cahalane, 1942; Brown and Yeager, 1945; Stapanian and Smith, 1978). Scatter-hoarders typically place acorns just beneath the forest floor or soil surface. This minimizes acorn dehydration and freezing, and also provides a favourable environment for initial seedling establishment. Some rodents are larder-hoarders, so-named because of their habit of concentrating acorns and other seeds in large quantities in one or a few places. The red squirrel (Fig. 2.11B) employs a mixed tactic of larder-hoarding and scatter-hoarding, and may concentrate acorns in a few large caches in tree cavities as well as under forest leaf litter (Layne, 1954; Hurly and Lourie, 1997). However, they primarily rely on larder-hoarding (Goheen and Swihart, 2003). Recent migrations of the red squirrel from more northerly regions into the Central Hardwood Region have occurred along with simultaneously declining populations of grey squirrels and increasing forest fragmentation. These events have raised concerns about a possible overall decline in the scatter-hoarding of acorns and its possible negative effect on oak regeneration regionally (Goheen and Swihart, 2003). Chipmunks (Fig. 2.11C) and flying squirrels primarily cache acorns in underground burrows, although both animals sometimes cache acorns under the litter or in shallow holes in the soil (Schwartz and Schwartz, 1959). Except for acorns cached in tree cavities, under rocks or other large objects, and deep burrows in the soil, the cache environment often provides favourable conditions for overwintering, germination and initial seedling establishment. Acorns cached in the forest floor or beneath it in the surface layer of the mineral soil are protected from desiccation, freezing, overheating from solar insolation and fire, and to a more variable extent against predation by birds, insects and mammals. White-footed mice, deer mice and voles also disperse acorns by scatter-hoarding. Caches often comprise one or a few acorns placed in the litter, shallow tunnels in the soil, or sometimes in tree stumps and cavities. Small rodents such as whitefooted mice and deer mice are generally more

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destructive of acorn crops than grey or fox squirrels because they often consume all of the acorns they cache (Shaw, 1968; Marquis et  al., 1976; DeLong and Yahner, 1991; Gribko and Hix, 1993). Populations of these species often rise and fall in response to acorn production (Scarlett, 2004). Although acorn dispersers and their competitors consume many cached acorns, some are not consumed and survive to germinate and develop into seedlings. The proportion of acorns consumed thus represents the ‘cost’ of dispersal to the oak. Scatterhoarding decreases the concentration of acorns under the parent tree and thereby reduces the likelihood that other animals such as wild turkey and deer will eat most acorns. Acorn dispersal also reduces intraspecific competition among oak seedlings, between oak seedlings and parent trees, and produces a more spatially even but genetically hetereogeneous distribution of seedlings. Whether burial is beneficial may partly depend on climate. In Britain, frequently saturated soils associated with an oceanic climate and related fungal attacks on acorns greatly reduced the viability of buried acorns (Shaw, 1968). In the Piedmont of North Carolina, burial of white oak acorns increased survival from predation, but did not increase germination (Barnett, 1977). In contrast, burial was clearly advantageous to acorns in a blue oak savannah and a coast live oak–valley oak woodland in the seasonally dry foothills of California Coastal Range (Griffin, 1971). A covering of leaf litter, by itself, has usually been shown to benefit acorn survival (e.g. Korstian, 1927; Barrett, 1931). However in Iowa, acorns planted 1 inch deep in the autumn on plots with litter removed produced 2.4–3 times more seedlings (per 100 acorns sown) the following spring than acorns planted to the same depth with leaf litter replaced over the acorn (Krajicek, 1960). The advantage of litter removal occurred in plots where rodents were excluded by screens as well as those that were not screened. The contrasting results of these studies point out the difficulty in generalizing about the effects of natural environments on acorn viability and germination. The advantages of acorn burial are nevertheless documented by several studies. In Pennsylvania, rodent predation of northern red oak acorns planted 1 inch deep in the soil surface (and then covered with leaf litter) was 78% whereas losses were 100% for surface-sown acorns (Galford et al., 1991c). Surface-sown acorns were also more

Chapter 2

vulnerable to insect predation; insects destroyed 8% of buried acorns whereas 92% of surface-sown acorns were destroyed when both were protected from rodents. In another Pennsylvania study, predation of northern red oak acorns sown on the soil surface was about three times that of acorns buried 1 inch in the soil (91% compared with 34%) (Auchmoody et al., 1994). Predation was mainly by chipmunks and mice. Predation of blue oak acorns by rodents in an oak woodland in California that were buried 1 inch in the soil was about half that of surface-sown acorns, based on the proportion of acorns that produced seedlings (Fig. 2.12). The primary predators were mice and gophers. The advantages of acorn burial also influenced 3-year seedling survival rate (Fig. 2.13). Predation on established seedlings protected from cattle and deer was largely

attributable to gophers; by then, mice were not a significant factor. In contrast, 3-year seedling survival in a more open blue oak savannah was unrelated to acorn burial (Fig. 2.13). Instead, the extremely high seedling mortality rates, which were related largely to grass competition in the savannah, overrode or masked rodent predation as a factor in seedling survival (Davis et al., 1991). There is thus considerable evidence that acorn burial, under many prevailing conditions, favours acorn survival and oak seedling establishment. Although the advantage of burial is not obtained without cost, it has been proposed that scatterhoarding by rodents, especially squirrels, may be essential to sustaining oak forests (Korstian, 1927). The destruction of unburied acorns by insects can be 100%, even when mammals and birds are excluded as predators (Galford et  al., 1991c).

All acorns (both years) n = 4500 P = 0.38

1985 acorns n = 2280 P = 0.27

Surface acorns n = 1140 P = 0.17

Mice n = 660 P = 0.11

No mice n = 480 P = 0.26

1986 acorns n = 2220 P = 0.50

Buried acorns n = 1140 P = 0.36

Gophers n = 540 P = 0.30

Surface acorns n = 1140 P = 0.36

No gophers Mice n = 660 n = 600 P = 0.42 P = 0.31

No mice n = 480 P = 0.50

Buried acorns n = 1080 P = 0.62

Gophers n = 510 P = 0.50

No gophers n = 570 P = 0.73

Fig. 2.12.  The proportion of blue oak acorns that produced seedlings (P) in relation to acorn position (on the soil surface or buried 1 inch beneath the soil surface) and predation by mice and gophers. (From Borchert et al., 1989.) The chart is based on a direct seeding experiment of 4500 (n) visibly sound acorns sown in two different years in a blue oak woodland on a north-facing slope in the Central Coastal Range of California. Overstorey crown cover was 65% (128 trees/acre); ground cover was grasses and forbs. Various types of exclosures were used to selectively limit access to acorns by mice, gophers, birds, deer and cattle. The chart shows only the factors that significantly reduced P (based on statistical criteria). It is also hierarchical (i.e. the factors considered decrease in importance from top to bottom) based on statistical analysis. The bottom stratum of the chart represents the independent effect of each animal (i.e. mice in the absence of other animals, and gophers in the absence of other animals). For example, the lower right box of the chart shows that of the 1080 acorns experimentally buried in 1986, 73% of those protected from Botta pocket gophers produced seedlings (exclusive of mouse predation). When gophers were not excluded (adjacent box), only 50% of acorns produced seedlings. The higher P values in 1986 were attributed to greater acorn viability and less droughty conditions that year.

Regeneration Ecology I

77

All seedlings (both sites) n = 2842 Ps = 0.24

Oak woodland n = 1610 Ps = 0.37

Gophers n = 543 Ps = 0.22

Surface acorns n = 136 Ps = 0.09

Buried acorns n = 407 Ps = 0.27

Oak savannah n = 1232 Ps = 0.06

No gophers n = 1067 Ps = 0.44

Surface acorns n = 404 Ps = 0.30

Mice n = 669 Ps = 0.03

No mice n = 563 Ps = 0.10

Buried acorns n = 663 Ps = 0.53

Fig. 2.13.  The proportion of blue oak seedlings that survived for 3 years (Ps) in an oak woodland and an oak savannah in the Central Coastal Range of California in relation to acorn position (on the soil surface or buried 1 inch beneath the soil surface) and predation by mice and gophers. (From Davis et al., 1991.) The overstorey crown cover is 65% (128 trees/acre) in the woodland and 15% in the savannah (42 trees/acre). On both sites, the ground cover was grasses and forbs. Sample size (n) is the initial number of seedlings observed in their first year. The low average survival of seedlings in the savannah was attributed to grass competition. The chart is hierarchical and is based on the methodology described in Fig. 2.12.

Interactions involving the joint presence of different animal species also can negatively impact oak regeneration. For example, the presence of deer and wild boar in holm oak stands in Spain reduced by a factor of 14 the proportion of acorns cached and not recovered by rodents (Muňoz and Bonai, 2007). The occurrence of an acorn crop initiates a chain reaction that ramifies through an oak forest in ways that are not limited to factors involved only in oak regeneration. Rodents, deer and many other animal populations rise and fall with acorn production cycles (Van Dersal, 1940; Brown and Yeager, 1945; Goodrum et  al., 1971; Pfannmuller, 1991). High rodent populations are also linked to increased transmission of Lyme disease from ticks to other mammals including humans (Ostfeld et  al., 1996, 2001; Jones et  al., 1998). Forest-dwelling rodents also impact other ecological processes that are not always apparent. For example, white-footed mice and shrews prey on gypsy moth larvae and pupae (Campbell, 1981), and are thought to play a significant role in controlling gypsy moth populations (Liebhold et al., 2000). In years of low to moderate gypsy moth abundance, predation by rodents may

78

help to maintain those levels and thus reduce the rate of build up in the gypsy moth population (Liebhold et  al., 2000). In turn, this increases the  time between major outbreaks and thus the defoliation of oaks and other susceptible plants (see Chapter 11, this volume). When defoliation does occur, it causes mortality or reduced vigour of oaks, and thus reduces acorn production itself (Gottschalk, 1990) – the initiator of the chain reaction. Such chain reactions, however, do not imply that all acorn predators are bound to acorns or without strategies for coping with the great temporal variability in acorn production. Insect predators such as some of the acorn weevils have a 2-year life cycle so that a single acorn crop failure does not destroy the entire insect population. Bird predators can move long distances (band-tailed pigeons and blue jays) and thus avoid acorn scarcity or find alternative foods. Deer can use alternative foods and white-footed mice can eat not only acorns but many different kinds of seeds as well as insects. Populations of acorn predators and dispersers also may remain relatively stable in forests where

Chapter 2

s­everal species of oaks co-occur (Carmen et  al., 1987). Because of the asynchronous acorn production among oak species, the probabilities of occurrence of a complete acorn crop failure decreases as the number of oak species increases. What was once perceived as a simple negative relation between oak regeneration and rodent predation is now recognized as a more complex multidimensional forest process involving many organisms and possible effects. Rodents and other seed dispersers are important not only to the oaks, but also in their larger role as dispersers of many kinds of seeds, which in turn collectively contribute to maintaining forest biodiversity and resilience (Healy, 1988). Bird predation and dispersal Birds rank among the most important dispersers and consumers of acorns. In the USA, acorns are eaten by more than 30 species of birds (Table 2.2). They comprise a large proportion of the diet of wood ducks, wild turkeys, band-tailed pigeons, blue jays, scrub jays, Steller’s jay, red-headed woodpeckers and acorn woodpeckers (Koenig, 1980; Pavlik et  al., 1991; Fuchs et  al., 1997, 2000; Johnson et al., 1997). Although other birds also eat acorns, acorns seldom make up a major portion of their diet. Blue jays are especially effective in dispersing acorns from closed-canopy oak stands to open or semi-open habitats such as old fields and forest edges, which often provide a favourable environment for seedling establishment (Darley-Hill and Johnson, 1981; Harrison and Werner, 1984; Johnson and Webb, 1989; Deen and Hodges, 1991). Acorns may be dispersed up to 2.5 miles from their origin, and individual blue jays may cache in the ground up to 3000 acorns/year (Johnson and Adkisson, 1986) (Fig. 2.14). Although only a small proportion of cached acorns survive to germinate and grow to maturity, the large numbers dispersed, a scattered dispersal pattern and transport to favourable habitats facilitates the maintenance and even the expansion of the distributional range of the oak. Acorn dispersal by blue jays thereby contributes to maintaining biodiversity where forests are highly fragmented – a characteristic of much of the contemporary US landscape. Dispersal by blue jays also has been proposed as an explanation for the rapid migration of oaks as the climate warmed during early postglacial times (Johnson and Webb, 1989).

Regeneration Ecology I

Scrub jays, which are widely distributed across the Southwest and Florida, similarly disperse and ground-cache acorns. They disperse even greater numbers of acorns per bird than blue jays, but over shorter distances (De Gange et al., 1989). A single bird may cache up to 5000 acorns in a single season but will consume only about one-third of those cached (Carmen, 1988; Pavlik et al., 1991). The Steller’s jay of western USA dispersed acorns of Oregon white oak up to 0.4 miles (600 m) and sometimes 0.6 miles (1 km) or further from the parent tree in British Columbia (Fuchs et al., 2000). The Steller’s jay buries acorns in the leaf layer as do blue jays. But the Steller’s jay prefers to disperse them to habitats with sparse herbaceous cover, dense shrub cover and a dense forest overstorey. The limited light availability in such habitats probably severely limits the survival and growth of the resultant oak seedlings. But despite the preference for those habitats, only 50% of acorns were cached exclusively in those locations. In Mediterranean Spain, the Euroasian jay has a distinct preference for caching holm oak acorns under pines (Gómez, 2003). They cached more than 95% of dispersed acorns beneath pines; dispersal distances usually exceeded 800 ft (250 m), and sometimes up to 0.6 miles (1 km). Two patterns of dispersal were noted: (i) short distances within the parent oak stand; and (ii) longer distances to other vegetation units. This dispersal pattern to pine stands may explain the commonly but anecdotally observed abundance of oak reproduction under pine stands in eastern USA. Other birds that ground-cache and consume acorns include the tufted titmouse (Kilham, 1958; Bent, 1964a), Clark’s nutcracker and yellow-billed magpie (Pavlik et al., 1991). Several species of woodpeckers also consume and cache acorns. Some species store acorns in tree crevices, bark and cavities, which largely represent a loss with respect to potential seedling establishment. The acorn woodpecker of the Southwest is a specialist in storing acorns in trees. Acorns are the most important item in their diet, and are consumed directly on seed trees from time of ripening in the autumn until none remain. The acorn woodpecker also stores large quantities of acorns by drilling numerous acorn-size holes in the boles of trees. The holes are seldom deeper than the thickness of the bark (Bent, 1964b; Koenig, 1980). These ‘granary’ trees consequently suffer little or no apparent damage from the activity, even though

79

Table 2.2.  Some avian acorn eaters and their acorn dispersal habits. (From Van Dersal, 1940; Kilham, 1958; Bent, 1964a, b, c; Griffin, 1971; Scott et al., 1977; Koenig, 1980, 1990; Gullion, 1984; Petrides, 1988; Pavlik et al., 1991; Fuchs et al., 1997.) Acorn dispersal habit Dispersal Species American crow Band-tailed pigeon Clark’s nutcracker Common grackle Ducks Mallard Wood duck Hooded merganser Jays Blue Scrub Steller’s Grouse Greater prairie chicken Ruffed Sharptailed Mourning dove Quail California Northern bobwhite Ring-necked pheasant Titmice Plain Tufted White-breasted nuthatch Wild turkey Woodpeckers Acorn Arizona Golden-fronted Hairy Lewis’s Northern flicker Nuttall’s Pileated Red-bellied Red-headed Yellow-billed magpie

No dispersala

To tree crevices and bark

To tree cavities

To ground caches

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Xb X

X Xb X

a

These species consume acorns directly from or near seed trees and generally do not disperse or cache acorns. Acorns are often cached as broken pieces.

b

a single tree may be riddled with thousands of holes (Fig. 2.15). Pines are favoured as granary trees but other species including oaks are also used. During the autumn acorn-gathering period, the woodpeckers carry acorns from nearby seed trees

80

to the granaries where each acorn is placed into an excavation. As the acorns loosen from dehydration during the winter, the birds relocate acorns to holes of the appropriate size. A Californian study showed that acorn transport by acorn woodpeckers ranges

Chapter 2

Fig. 2.14.  A single blue jay can disperse several thousand acorns/year. They can carry up to two white oak acorns or five pin oak acorns in their expandable throat and oesophagus for up to 2.5 miles from their source. After filling its throat to capacity, a jay may place one last nut in its bill before departing (lower photo) (Johnson and Adkisson, 1986). (Photographs courtesy of Dr W. Carter Johnson, South Dakota State University, Brookings.)

up to 1.8 miles for males and 5.9 miles for females (Koenig et  al., 2000), although other studies have reported much shorter distances (e.g. Grivet et al., 2005). The acorn woodpecker’s diet consists almost exclusively of acorns during the winter (MacRoberts and MacRoberts, 1976). The woodpecker consumes virtually all acorns stored in granaries. Granary acorns remaining in spring are consumed into the warmer months until supplies are exhausted. The birds form cooperative breeding units of up to 15 usually related birds that maintain and expand their granaries through successive generations (MacRoberts and MacRoberts, 1976; Hannon et al., 1987). The relatively fixed number of granaries within a locale apparently limits bird population density. Estimates of the proportion of the annual acorn crop consumed by this woodpecker range from less than 10% in years of bumper acorn crops to over 50% in years of low acorn production (Koenig, 1980).

Regeneration Ecology I

Fig. 2.15.  Acorns cached in a ‘granary’ tree (in this case a dead pine without bark) by acorn woodpeckers in California. The woodpeckers make holes in the bole and then insert the acorns (see text). (Reproduced from Koenig, 1990, with permission from Fremontia.)

On average, acorns provide about 25% of the annual caloric requirements of acorn woodpeckers and possibly up to 80% during the winter months. These calories are thus available during the critical winter period when other foods are least available. Acorn woodpecker populations consequently rise and fall with annual fluctuations in acorn production. The maintenance of a cooperative social structure and spring breeding success also depend on winter acorn stores (Hannon et al., 1987). High acorn woodpecker populations in coastal California in the winter are associated with a high overall abundance of oaks and the joint occurrence of at least five oak species within the territories of overwintering groups of birds (Bock and Bock, 1974). This assures a high probability of acorn production from at least one species every year that is adequate for sustaining a high bird population.

81

(C)

(A)

(B)

Fig. 2.16.  Stages in the development of a germinating northern red oak acorn. (A) Emergence of the radicle (immature root) of a ripened acorn after 10 days in a favourable germination environment; (B) radicle development after 15 days; plumule (immature shoot) is beginning to emerge; (C) leaves are unfolding after 20 days. (From Korstian, 1927.)

In the semidesert regions of Arizona, New Mexico and Texas, populations of acorn woodpeckers do not attain densities as high as those observed in the Pacific Coast region. This difference may be related to the relatively few oak species that, within the

82

semidesert regions, are likely to occur jointly at the relatively small spatial scales of bird territories. The red-headed woodpecker of eastern forests also caches acorns in trees. Like the acorn woodpecker, red-headed woodpecker densities are correlated with

Chapter 2

acorn abundance and may be more sensitive than blue jays to annual fluctuations in acorn crops (Smith, 1986). Unlike the acorn woodpecker, the redheaded woodpecker favours caching acorns in tree crevices and deep cavities. They will often seal a cavity entrance with tightly compacted wood particles to conceal the cache from competitors (Kilham, 1958). Other woodpeckers known to cache acorns in trees include the red-bellied woodpecker of eastern forests (Kilham, 1963; Bent, 1964b) and Lewis’s woodpecker of the Southwest (Bock and Bock, 1974). Several other species of woodpeckers consume acorns but are not known to cache them (Bent, 1964b; Scott et al., 1977).

Oak Seedling Establishment Germination and initial establishment The acorns of the two major oak groups, the white oaks and the red oaks, differ in their germination characteristics. The white oaks initiate germination in autumn, whereas the red oaks usually do not germinate until spring. In the white oaks, the radicle (immature root) develops rapidly after autumn germination. Acorns of blue oak and valley oak (both in the white oak group) in California have even been observed to start radicle growth in autumn while still hanging from the tree (Griffin, 1971). However, in all the white oaks, the epicotyl (immature stem) usually does not develop until the following spring. In a small proportion of germinants, shoots may begin to develop in the autumn but overwinter as short succulent stems that require additional chilling to develop further (Farmer, 1977). White oaks thus undergo epicotyl dormancy, which is not broken until the acorn or developing germinant undergoes prolonged exposure to low temperatures such as those normally occurring over winter (Farmer, 1977). Acorns of the red oak group undergo embryo dormancy. Germination does not occur until acorns are chilled for a sufficient time. The requisite chilling period is usually met during overwintering. Under artificial conditions, embryo dormancy can be broken by storing acorns for 30–60 days at 32–41°F (0–5°C) in moist sand or polyethylene bags (Olson, 1974; Bonner and Vozzo, 1987). In northern red oak, emergence of the radicle in the spring typically begins after about 10 days of sufficiently warm temperatures. About 5 days later, the plumule (immature shoot) starts to develop; within 20 days, leaves are unfolding (Fig. 2.16). Acorn germination

Regeneration Ecology I

is usually not delayed beyond the first autumn in the white oaks or the first spring in the red oaks. However, in a California study, 10% of winter-sown blue oak germinants did not produce emergent shoots from the soil until the second spring after acorns were planted (Tecklin and McCreary, 1991). These unusual events occurred during an exceptionally cold and wet year. Acorns none the less do not accumulate in the forest floor as do seeds of some other trees and woody plants such as yellow-poplar, white ash, pin cherry, black cherry and briars. To survive over the winter, acorns require protection from desiccation, low temperatures and predation by insects, rodents and other animals. White oak acorns maintain viability at moisture contents above 40%, whereas red oaks maintain viability down to about 25% (Korstian, 1927). For both species, critical moisture levels can be reached after air-drying acorns at room temperature for 2–5 days. In one study, the radicles of germinated white oak and chestnut oak acorns were killed by freezing temperatures, whereas ungerminated acorns were not damaged by temperatures a few degrees below freezing (Korstian, 1927). Although temperatures of 20°F (–7°C) for 1 week killed ungerminated white oak acorns, black and northern red oak acorns were not damaged (Aikman, 1934). Germinating white oak acorns with exposed radicles also were killed by temperatures of 20°F (–7°C) for 1 week, but did survive a 12 h exposure to that temperature. Because acorns fall in the autumn during or somewhat before leaf fall, they are usually protected from desiccation and freezing by the current year’s leaf fall (Korstian, 1927; Barrett, 1931). In northern latitudes, deep snow cover during the winter also protects acorns from desiccation and freezing. The proportion of sound acorns that can germinate is highly variable and even under laboratory conditions ranges from about 50 to 100% depending on species and other factors (Olson, 1974). Conditions that favour acorn germination and seedling establishment in the field include a moist, friable soil that can be easily penetrated by the developing radicle. A covering of leaf litter sufficient to prevent surface soil drying and acorn desiccation and freezing also creates favourable conditions (Korstian, 1927). However, a covering of leaves sometimes can be detrimental depending on its thickness and other factors (Nichols, 1954; Krajicek, 1955, 1960; Lockhart et al., 1995). A forest floor with a thick layer of matted, undecomposed

83

leaves (F-layer) can hinder entry of the radicle into soil. More than 2–3 inches of litter can create a physical barrier to epicotyl emergence, and cause etiolated (pale and fragile) shoots (Barrett, 1931). In France, northern red oak seedling densities exceeding 100,000/acre were attributed to rapid soil turnover by earthworms and associated acorn burial that created favourable conditions for both survival and germination (Steiner et al., 1993). Even after a bumper acorn crop, numbers of oak seedlings that become established vary greatly depending on predation and overwintering and germination conditions. When conditions are unfavourable, seedling establishment may completely fail. In contrast, hundreds of thousands of new seedlings/acre have been observed in some instances (e.g. Barrett, 1931; R.L. Johnson, 1975). More commonly, numbers range from about 1000 to 10,000/acre under favourable conditions in oak forests of the eastern USA (e.g. Scholz, 1955; Tryon and Carvell, 1958; McGee, 1967; Johnson, 1974). In some regions, oak seedling establishment even in good seed years may average only a few hundred/ acre. For example, after a good black oak acorn crop in the Ozark Highlands of Missouri, 270 seedlings/acre became established (Sander, 1979). In a year when black oak acorn crops were rated fair to good, about 150 seedlings/acre were established (McQuilkin, 1983). Most of those originated from squirrel-buried acorns; 5–10% originated from acorns cached by mice or voles in or just below the forest floor. In oak woodlands in California’s Coastal Range, the establishment of large numbers of new oak seedlings occurs only in a wet spring (Griffin, 1971). In that region, burial of acorns under soil or litter in shady places is required to mitigate dry autumn or winter weather and to improve chances of seedling establishment. Early growth Oak seedlings quickly develop a strong taproot, which usually grows to several inches in length within a few weeks after germination begins in the autumn (white oaks) or spring (red oaks) (Fig. 2.16). By the end of the first growing season, taproots are typically about 20 inches long (Holch, 1931; Carpenter and Guard, 1954). In the spring, root growth increases rapidly as soil temperatures rise to an optimum near 75°F (24°C). Because soils are often cooler than this during the early growing season, rate of soil warming is an important factor

84

in oak seedling establishment and growth (Larson, 1970). The early development of a large taproot and delayed shoot growth is characteristic of all oaks (Fig. 2.16). Early root development is facilitated by the acorn itself, which supplies from its cotyledons (the large fleshy interior of the acorn) nutrients to the developing radicle. By the time these food reserves are exhausted, the seedling has typically developed three or four fully expanded leaves and is fully independent of the acorn. The taproots have developed the lateral roots necessary for absorbing water and nutrients. The taproot functions primarily as a food storage organ, whereas the fine lateral roots function mainly to absorb water and nutrients. The taproot is largely non-functional in absorption because it is covered by thick corky (suberized) tissue that is essentially impermeable to water (Carpenter and Guard, 1954). Studies of several oak species have shown that large acorns are positively correlated with the following attributes: (i) a high percentage of germination; (ii) rapid shoot emergence; (iii) high seedling survival and rapid growth; (iv) large root mass and root:shoot ratio; and (v) rapid recovery from herbivory (defoliation and browse damage) (Korstian, 1927; McComb, 1934; Tripathi and Khan, 1990; Tecklin and McCreary, 1991; Bonfil, 1998). These attributes should theoretically confer survival and growth advantages to the large-seeded individual. There is evidence that acorn predators, including dispersers such as blue jays, prefer small over large acorns. This would seem to confer a further advantage to the large-seeded individual in terms of escape from predation. However, a species may generally benefit as much or more from the advantages of dispersal and thus small seed size. Within a species, some trees also produce larger acorns than others. However, there is great variation in acorn size within a parent tree, and thus much overlap in acorn size among parent trees (Kriebel, 1965; Tecklin and McCreary, 1991). Few studies have monitored acorn size effects for more than one or two growing seasons. However, the advantage of large acorn size based on mean heights of 12-yearold half-sib families and mean family seed weight in European oaks persisted after 12 years (Johnsson, 1952). However, the correlation coefficient between height growth and acorn size decreased from 0.94 after the first growing season to 0.47 in the 14th year. Apparent acorn size effects thus diminished in importance with time, suggesting that other factors

Chapter 2

gradually assume more importance. In greenhouse and nursery studies, there was low correlation between acorn size or mass and early shoot growth presumably due to near optimal fertilization and watering in a low competition environment (e.g. Grossman et al., 2003). The first burst of shoot growth, or ‘flush’, may last only about a week. During this time, the seedling typically grows to a height of about 6 inches. The shoot then enters a ‘resting’ (or lag) phase during which a new terminal bud is formed. It is from this bud that the next flush of growth normally begins. Under favourable growing conditions such as those occurring in an irrigated nursery bed, the resting phase typically lasts from 2 to 4 weeks, but is highly variable among seedlings within the bed. The length of the resting phase under forest conditions is even more variable and usually longer (Johnson, 1979a). Under field conditions where seedlings are subjected to water stress, competition from other plants or low light, first-year seedlings typically remain in the first lag stage during the entire growing season. Shoot elongation then does not resume until the following spring. Under more favourable conditions, oak seedlings may flush several times during a single growing season. There are three discrete stages of shoot development for each flush of growth: (i) a period of rapid shoot elongation (stem linear stage); (ii) rapid leaf expansion (leaf linear stage); and (iii) resting (lag) stage. Because multiple flushes can occur during a single growing season, these stages are successively referred to as 1-, 2- and 3-leaf linear stages, 1-, 2- and 3-lag stages, etc. (Hanson et  al., 1986; Dickson, 1991). The term ‘linear’ refers to the essentially linear increase in stem length or leaf area during each observed growth interval (Fig. 2.17A and B). Shoot growth in oak is thus episodic, occurring in a series of bursts of growth (flushes) each followed by a resting period. Root growth, on the other hand, is potentially continuous (Fig. 2.17A and B). Once initiated, it continues until environmental conditions become unfavourable. Thus, under field conditions, the growth of oak roots may exhibit periodicity (Teskey, 1978; Reich et al., 1980). The proportion of photosynthates that are transported from the leaves of oak seedlings varies with the stage of shoot development. For example, in northern red oak, a seedling in the middle of its second flush (i.e. 2-leaf linear stage) transports about 90% of the photosynthates produced from

Regeneration Ecology I

first-flush leaves to developing leaves of the second flush (Fig. 2.18A). After the leaves of the second flush have fully expanded and the shoot is in the resting (2-lag) stage between flushes, about 95% of the currently produced photosynthate is translocated to the lower stem and roots (Fig. 2.18B). Because oak seedlings spend much of their time in the resting stage (Fig. 2.17A and B), this pattern of carbon allocation favours the development of a large root:shoot ratio. With each successive flush during the growing season, an oak seedling increases its leaf area, which in turn increases root growth (Fig. 2.17C). Thus, seedlings that flush frequently quickly develop large leaf and root surface areas. Although lateral roots usually comprise only a small proportion of a seedling’s total root mass, they comprise most of the root’s absorptive surface area. This results from the greater surface area per unit mass of small-diameter roots than large-diameter roots (Fig. 2.19A). Moreover, as root diameters increase, the greater the proportion of non-absorptive corky tissue that covers the roots (Carpenter and Guard, 1954). The absorptive capacity of a taproot is therefore even proportionately lower than its small surface area would indicate. All plants lose water to the atmosphere through microscopic pores in their leaves (stomata) during the physiological process of transpiration (Kozlowski and Pallardy, 1997). Water lost to transpiration is normally replaced through root absorption. The rate of water lost through transpiration can be expressed as grams of water per square centimetre of leaf area per day (i.e. g/cm2/day). In northern red oak seedlings grown in a greenhouse, transpiration rate increased as the ratio of total root surface area to leaf surface area increased (Fig. 2.19B). Thus, when water is non-limiting (as in frequently watered seedlings), its absorption from the soil and conduction through the plant to the atmosphere increases as root surface area increases relative to leaf area (other factors being equal). Under conditions of unlimited water supply, stomata normally remain open during the day. As the transpirational stream passes through open stomata, carbon dioxide (CO2) is simultaneously taken up from the atmosphere. The CO2 is then transformed through photosynthesis and other physiological processes into the various components of the living plant. Large, actively growing roots thus favour efficient water absorption and sustained transpiration and photosynthesis. Although species differ

85

Cumulative root elongation (dm)

(B) 6 5

25

4

20

3

15

2

10

1

5

Leaf area (dm2)

Cumulative shoot elongation (dm)

(A)

Study day

0 10

20

30

40

Study day 50

60

70

10

50

20

30

40

50

60

70

100 150 200 250 300 350

240 200

ot elonga Cumulative ro

tion (dm)

(C)

160 120 80 40

70 60

0 10 Lea

50 20

f ar ea

30 (dm 2 )

40 40

30

y ud

y da

St

Fig. 2.17.  Shoot, leaf and root growth of northern red oak seedlings transplanted to root observation chambers. (A) This seedling flushed twice during the 70-day study period. Of the 61 days of observable shoot and root growth, the seedling was in the resting (lag) stage 43% of the time. Leaf area culminated at 22 dm2 (341 inches2) by day 58. After a 29-day delay, roots grew at a continuous, nearly linear rate. (B) This seedling flushed only once during the study period. Of the 61 days of observable shoot and root growth, the seedling was in the resting (lag) stage 82% of the time. Leaf area increased to 28 dm2 (435 inches2) by day 20 and thereafter remained constant. Root elongation was initiated on day 12. Roots elongated slowly until day 30 but grew rapidly thereafter. (C) Relation between cumulative root elongation and leaf area. (From Johnson et al., 1984.) The model is based on a population Continued 86

Chapter 2

(A)

(B)

90% 5%

5% 5% 95%

Fig. 2.18.  The transport of photosynthates from the leaves of a northern red oak seedling. (From Dickson, 1991.) Percentages are based on the allocation of 14C 48 h after exposure to 14CO2. (A) A seedling in the middle of its second flush (2-leaf linear stage). In this stage of shoot development, stem elongation is complete but leaf expansion is incomplete; carbon transport is largely from first-flush leaves to developing second-flush leaves. (B) Leaves of the second flush are fully expanded and the shoot is in the resting (2-lag) stage. During this stage, carbon transport is largely from second-flush leaves to the lower stem and roots. This pattern of carbon allocation favours the development of a large root:shoot ratio.

in how they allocate carbon to the various parts of the plant, early investment in building a large root system and high root:shoot ratio appears to be an important physiological characteristic common to all oak seedlings. One of the mechanisms by which these high ratios are maintained is through recurrent shoot dieback (see the following section on seedling sprouts). Although root and leaf areas are important morphological characteristics of oak seedlings, by themselves they tell us little about how oak seedlings function physiologically or how oak species differ from each other physiologically with respect to water use. However, oaks vary in the efficiency at which they can take up CO2 from the atmosphere

while simultaneously losing water through transpiration. One measure of water-use efficiency in plants is provided by the ratio of water-loss resistance to CO2 uptake resistance. This was determined experimentally under non-limiting light and soil moisture laboratory conditions (Wuenscher and Kozlowski, 1971). The higher this ratio, the less water will be lost per unit of CO2 fixed. It thus can be considered as one measure of drought tolerance (Parker and Dey, 2008). The ratio is also sensitive to leaf temperature, an important environmental variable. For any given species, this ratio generally increases with increasing leaf temperature up to a threshold value and then declines. Comparisons among sugar maple and black, bur, white and

Fig. 2.17.  Continued. of 60 seedlings grown in root observation chambers for 70 days from late March to early June. The response surfaces shown are for container-grown seedlings with intact shoots (the upper surface shown on the graph) and for shoots clipped 6 inches above the root collar (the lower, partially hidden surface on the graph). Initial stem size of seedlings is held constant at 11 mm (0.4 inches) in basal diameter measured 2 cm above the root collar, and at 50 cm (20 inches) length. Response surfaces for bare-root seedlings (not shown) lie below those of container-grown seedlings for a given shoot clipping treatment. Time (study day), leaf area, shoot clipping, seedling type and initial shoot size explained 78% of the variation in root elongation based on linear regression.

Regeneration Ecology I

87

(A)

1000

Root surface area (cm2)

800

Lateral roots ≤ 2 mm diameter

600 Lateral roots > 2 mm diameter

400 200 0

Taproots

0

5

10

15

20

25

30

35

Root mass (g) (B) Leaf transpiration (g of water/cm2/day)

0.16 0.12 0.08 0.04 0.00 0.0

0.4

0.8

1.2

1.6

2.0

Root area:leaf area ratio Fig. 2.19.  Root relations in northern red oak seedlings. (A) Relation between root surface area and root mass of seedlings for three root size classes (based on 192 seedlings excavated for measurement one growing season after planting in a clearcut). Lateral root surface areas were estimated from electronic area meter measurements; taproot areas from length and diameter measurements. (Data from USDA Forest Service Northern Research Station, Columbia, Missouri.) (B) Relation between leaf transpiration and the ratio of root surface area to leaf surface area in well-watered seedlings in a greenhouse experiment (1 cm2 = 0.16 inches2). (Part B redrawn from Parker, 1949.)

northern red oak seedlings demonstrated that the oaks were more efficient in their water use than sugar maple up to a threshold leaf temperature of about 95°F (35°C) (Wuenscher and Kozlowski, 1971). Under most conditions, the water-use efficiency of black oak exceeded that of sugar maple by a factor of four or more. Bur oak ranked second

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in efficiency, and was followed by white and northern red oaks, which behaved similarly. The high water-use efficiency of black oak at high leaf temperatures (104°F or 40°C) is consistent with its observed ability to grow on hot dry sites. Conversely, the low water-use efficiency of sugar maple is consistent with its occurrence on only relatively moist sites and its absence on dry sites. Forest conditions complicate comparing the water-use efficiency of oak seedlings with other species because of variation in light, soil moisture and other factors. For example, water-use efficiency of sugar maple seedlings growing under a dense forest canopy was greater than that of northern red oak in an Ontario study (Parker and Dey, 2008). However, after moderate reductions in overstorey and understorey density were made (through shelterwood cuttings, see Chapter 8, this volume), the forest environment physiologically favoured the oak seedlings. This shift in advantage largely resulted from the greater photosynthetic response of the red oak to increased light intensities, its comparative drought tolerance, and perhaps its response to increased soil water availability (and a deep taproot) when stand density was reduced. Although the water-use efficiency of oaks under laboratory conditions provides us with one insight into the functioning of oaks, other factors may become overriding under field conditions. Oaks as a group are generally regarded as drought tolerant even though there is much variation among species in this characteristic. Because plants are most vulnerable during the seedling stage of development, species evolve adaptive traits that confer a competitive advantage within their regeneration niche (Chapter 3, this volume). Oak seedlings have evolved several physiological and morphological characteristics that confer drought tolerance including: (i) large seeds that provide food reserves for a protracted period during and after germination; (ii) the rapid development of a long taproot; (iii) the ability to photosynthesize and conduct water through the xylem under high water stress; (iv) flexibility in maintaining high root:shoot ratios through recurrent shoot dieback; (v) physiological plasticity that facilitates adjustment to varying water stresses; and (vi) large genetic variation in drought tolerance within species (Kriebel et al., 1988; Matsuda et al., 1989; Abrams, 1990; Kubiske and Abrams, 1992; Bragg et al., 1993; Pallardy and Rhoads, 1993; Rice and Struve, 1997; Parker and Dey, 2008).

Chapter 2

Among seven commonly occurring species in the eastern USA, McQuilkin (1983) ranked them from most to least drought tolerant based on the literature as follows: blackjack oak > post oak > scarlet oak > chestnut oak > black oak > white oak (Bourdeau, 1954; Mowbray and Oosting, 1968; Racine, 1971; Wuenscher and Kozlowski, 1971; Seidel, 1972; Fralish et al., 1978; Eyre, 1980; Parker et al., 1982). Oak seedlings are also relatively intolerant of shade, although species differ somewhat. For example, among five common upland oaks in the eastern USA, their shade tolerances have been ranked as follows: white oak > chestnut oak > northern red oak > black oak > scarlet oak (Burns and Honkala, 1990). Rates of photosynthesis in oak seedlings increase with increasing light intensity up to about one-third of full sunlight, but increase little with further increases in light intensity (Kramer and Decker, 1944; Bourdeau, 1954; Loach, 1967; McGee, 1968; Musselman and Gatherum, 1969; Phares, 1971; Shafer, 1971; Wuenscher and Kozlowski, 1971). Light levels under dense forest canopies often fall below 2% of that in the open (Hanson et al., 1986). Under those conditions, oak seedlings cannot live for long (Hanson et al., 1986; Crunkilton et  al., 1992; Vivin et  al., 1993). Low light levels also proportionately reduce the allocation of carbon to roots and increase the proportion allocated to above-ground parts of the seedling (Gottschalk, 1985; Kolb and Steiner, 1990; Dillaway et al., 2007; Brose, 2008; Rebbeck et al., 2011). In turn, this impedes the development of a large root:shoot ratio even if the seedling survives. Shade tolerance in the oaks may not be as fixed as species’ shade tolerance ratings might infer. For example, oaks have adapted to variation in light conditions by adjusting their time of spring budbreak according to their exposure to light the previous year. Seedlings or saplings growing beneath a forest canopy begin flushing about 1 week earlier in the spring than open-grown oaks, and before the overstorey begins to leaf out (McGee, 1976, 1988, 1997). Understorey oaks therefore begin spring growth when both light and soil moisture conditions are favourable. This pattern of spring budbreak was common to all six of the oak species observed (white, black, scarlet, post, chestnut and northern red oaks) (McGee, 1997). Other understorey species including hickories, white ash, red maple and sugar maple also began budbreak and growth earlier under shade than in the open. However, some species began growth sooner in the open (serviceberry)

Regeneration Ecology I

or showed no consistent pattern between budbreak and the presence of a forest canopy (sassafras, yellow-poplar and blackgum) (McGee, 1986). In northern red oak seedlings, time of budbreak is also associated with elevation of acorn source. Across the elevational range of 1400–4600 ft above sea level in western North Carolina, acorns from the lowest elevation source flushed 11 days earlier, on average, than the highest elevation source (McGee, 1974, 1997). This difference in flush date was consistent among the four planting sites, which ranged from 1500 to 5400 ft in elevation. These apparent inherited differences in flushing time thus represent genetic adjustments to the timing of budbreak that coincide with ‘safe’ periods of growth initiation. A later onset of growth with increasing elevation provides a frost avoidance mechanism, whereas the earlier onset of budbreak at lower elevations is consistent with spring frost risks and the competitive advantage of the early onset of growth at lower elevations. Collectively, these relations in flushing pattern in oaks have practical silvicultural implications with respect to potential frost damage associated with early flushing related to seed source/planting site relations, and the timing of overstorey removal (McGee, 1997).

Seedling Sprouts Shoot dieback and root:shoot ratio Seedling sprouts are seedlings whose shoots have died back and resprouted one or more times. They are often the predominant form of oak reproduction growing beneath the forest canopy (Fig. 2.20). Sprouts can originate from dormant buds located anywhere along the stem between the root collar and the terminal bud cluster. Dieback and resprouting appear to be important processes in the life of oak reproduction. Those processes are facilitated by a ‘bud bank’ comprised of a reservoir of visible and (to the naked eye) invisible buds that are continually being formed on new branches. In addition, older buds persist at the root collar and on older stems and branches (Wilson, 1993). Shoot dieback is common in all oaks growing under a forest canopy, but is especially prominent in Mediterranean and semidesert climates and elsewhere in droughty uplands. In those environments, oak stands are dominated by species with morphological and physiological adaptations to surviving repeated burning and water stress (Wuenscher and Kozlowski, 1971; Grimm, 1984; Abrams, 1990).

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(A)

(B)

Fig. 2.20.  Shoot dieback in oak seedling sprouts growing beneath a closed forest canopy in central Missouri. (A) Partial dieback (leafless portion of stem) in a northern red oak seedling sprout. New shoots from dormant buds were initiated 10 inches above ground and near the ground line. (B) Complete dieback in a black oak seedling sprout to ground line (leafless stem on right) with two basal sprouts. Both seedling sprouts are approximately 30 inches tall (to tips of tallest dead stems), and both previously died back several times. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

Oak reproduction growing beneath a forest canopy is subject to stresses that periodically reduce shoot mass and leaf area through the process of recurrent shoot dieback. Surviving seedling sprouts thus tend to develop larger root:shoot ratios as they age. In turn, high root:shoot ratio and large root mass enable oak reproduction to opportunistically respond to forest disturbances. The growth potential of oak reproduction is not expressed until it is released from the growthinhibiting shade of the parent stand. This occurs when the forest canopy is partially or completely destroyed by disturbances resulting from fire, windthrow, insects, disease, timber harvesting or other events. Then if the requisite size of the established oak reproduction has been obtained, it can produce two or more long flushes of shoot growth each growing season after disturbance (Johnson, 1979a; Dickson, 1991).

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Variation in the root:shoot ratio within populations of oak reproduction produces great variation in the shoot growth patterns among trees of different basal diameters – which in turn are correlated with root mass. However, estimating root biomass in oaks is problematic because of difficulties in measuring it directly. Instead, indirect measures of root biomass such as stump diameter or root collar diameter are usually used as a surrogate (Fig. 2.21). But few studies have established a quantitatively predictive relation between the two. In South Carolina, the root biomass of white oak reproduction was highly correlated with root collar diameter. Root collar diameter accounted for 86% of the variation in root mass (Knapp et  al., 2006). The proportion of root to total tree biomass in the oaks nevertheless varies greatly among oak-dominated ecosystems (see Chapter 4, this volume).

Chapter 2

Net cumulative shoot growth (ft)

6 5

a

4

b

3

c d

2

e

1 0

1

3

Apr

5

7 9 11 Growth week

13

May

June

July

15

17

Aug

Month Fig. 2.21.  Patterns of shoot growth in oak sprouts for a range of parent-stem basal diameters (and thus root:shoot ratios). The growth shown occurred the second year after clearcutting in a stand in the Ozark Highlands of Missouri. (a) Black oak sprout originating from a 7 inch diameter stump; (b) black oak sprout originating from an 8 inch diameter stump (the sprout died back from frost during weeks 4 and 5 and subsequently flushed twice); (c) white oak sprout originating from a 1 inch diameter parent stem; (d) black oak sprout originating from a 4 inch stump; (e) white oak sprout originating from a parent stem less than 0.6 inches in diameter. (Adapted from Johnson, 1979b.)

Whether oaks become an important part of the succeeding stand thus depends on the pre-­ disturbance development of significant numbers of seedling sprouts with large roots and high root:shoot ratios. Oak reproduction is otherwise usually at a competitive disadvantage. This is especially true of the reproduction of the drought-tolerant upland species which, even under optimal conditions, grow slowly until roots are large enough to support rapid shoot growth. Recurrent shoot dieback thus appears to be an important aspect of the evolutionary development and adaptive strategy of oaks. Occurrence of shoot dieback Recurrent shoot dieback in oak reproduction differs from the ‘dieback and decline’ of older trees, natural pruning or twig abscission. The latter is a normal, non-pathological abscission process occurring in current twigs of mature oaks (Millington and Chaney, 1973). The phenomenon known as

Regeneration Ecology I

dieback and decline is common to older (nonjuvenile) oaks. It is believed to be initiated by drought stress or insect defoliation, which may set up conditions conducive to disease and further insect attack (Staley, 1965; Nichols, 1968; Ammon et  al., 1989; McCracken et  al., 1991; Wargo and Haack, 1991; Dwyer et  al., 1995; Jenkins and Pallardy, 1995). Dieback and decline of older trees usually result in the complete death of the tree (see Chapter 11, this volume). Shoot dieback also can be directly caused by insect damage to shoots of both juvenile and older oaks. But in juvenile oaks, resprouting regardless of the cause of dieback often follows shoot dieback. Whenever oak reproduction grows under a forest canopy, recurrent shoot dieback is a normal part of its life cycle. Dieback is especially prevalent on dry sites where reproduction survives long enough to accumulate over several acorn crops. There, oak seedlings and seedling sprouts must endure low light intensities together with high water stresses. Under those conditions, oak reproduction is likely to subsist near its compensation point4 and thus on the edge of death (Hanson et al., 1986; Crunkilton et al., 1992; Vivin et al., 1993). This process reinforces the oak’s strategy of translocating to the root system most of the photosynthate produced during its juvenile growth period (Fig. 2.18). Through periodic reduction of shoot mass, the shoot can be minimized as a sink for the modest amount of photosynthate it produces when growing in shade (Hanson et al., 1986; Crunkilton et  al., 1992; Dey and Parker, 1996). Seedling sprouts that survive recurrent shoot dieback thus eventually develop large root systems and large root:shoot ratios. The consequences of recurrent dieback enable oak reproduction to respond opportunistically to natural or silvicultural events that eliminate the shade-imposing overstorey. Shoot dieback in oak reproduction is linked to several factors including growing-season water stress and spring frost (Bourdeau, 1954; Johnson 1979a; McGee, 1988). When soil moisture is ample, such as in irrigated nursery beds, shoot dieback is uncommon. In contrast, recurrent dieback is characteristic of oak reproduction growing beneath a forest canopy (Liming and Johnston, 1944; Merz and Boyce, 1956; Tryon and Carvell, 1958; Sander, 1971; Abrams, 1990; McClaran and Bartolome, 1990; Crow, 1992). Experiments with seedlings of five upland oak species grown in pots showed that shoot dieback occurred in some seedlings

91

when they were deprived of moisture (Bourdeau, 1954). After shoot dieback, new shoots developed from surviving dormant buds located below the point of dieback in some individuals of all but one of the species observed; some seedlings died. Water stress thus can directly result in seedling mortality or in shoot dieback followed by the growth of new shoots from surviving buds located below the dieback (Vivin et  al., 1993). Succulent spring shoot growth is also susceptible to late spring frosts. Frost-induced diebacks in the spring are usually shortly followed by the growth of new shoots from dormant buds below the dieback. Although shoot dieback in oak reproduction commonly occurs during the growing season as a result of spring frosts and summer water stress, it also occurs during the dormant season. Winter dieback and mortality of northern red oak seedlings occurring between November and spring was observed at the onset of spring growth in an Ohio greenhouse experiment of potted seedlings grown from seed (Wright et al., 1989). After overwintering out of doors, both mortality and dieback were greater in seedlings experimentally subjected to high water stress and artificially imposed root injury (roots partially removed by severing) the previous summer than in seedlings that were only moderately stressed or that had uninjured roots. In a Missouri study, shoot dieback of English oak seedlings planted in a clearcut occurred between time of planting in late October and the completion of the first flush the following May (Johnson, 1981). Moreover, the average length of dieback was greater for bare-root seedlings than for container-grown seedlings with intact roots. Similarly, in autumn-planted northern red oak, the frequency of winter dieback of 10 cm or more was greater in bare-root than in container-grown seedlings. Frequency of dieback also increased with increasing shoot length, decreased with increasing basal diameter of stems for both types of seedlings, and for a given seedling size was more frequent in clearcuts than in shelterwoods (Fig. 2.22). Dormant season dieback thus was influenced by seedling characteristics (size and morphology) and the presence or absence of a forest canopy. Collectively, these observations confirm that shoot dieback in oaks does occur during the winter. They further show that at least some of the observed variation in the frequency and amount of it is associated with factors that increase or decrease a seedling’s vulnerability to winter desiccation including:

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●● large numbers of functional small roots at the start of the dormant season (greater in container-grown than in bare-root or root-injured seedlings), which are associated with a low frequency or small amount of dieback; ●● large shoot diameters (positively correlated with root mass and root surface area), which are negatively associated with frequency of dieback; ●● long stems, which are positively associated with frequency of dieback; and ●● the presence of a forest canopy (potentially reducing frequency and/or intensity of freeze– thaw cycles), which is associated with reduced frequency of dieback. The observed occurrence of shoot dieback in oak reproduction is consistent with the principle of plant segmentation. According to this principle, the design of the plant’s water transport system (i.e. its hydraulic architecture) favours the preservation of the lower stem over the more vulnerable and less essential shoot tips (Zimmermann, 1983). By extension, shoot dieback in oak reproduction may be related to seasonal cycles of water stress involving: ●● loss of fine roots to desiccation (root shedding) or injury in late summer (Head, 1973; Joslin and Henderson, 1987; Wright et  al., 1989; Yin et  al., 1989), leading to reduced absorption of water during late summer and the following dormant season; ●● xylem dysfunction (the formation of xylem embolisms, i.e. air blockages and tyloses) occurring during both dormant and growing seasons, leading to excessively reduced hydraulic conductivity especially in the terminal sections of stems (Zimmermann, 1983; Tyree, 1989; Cochard et al., 1992; Sperry and Sullivan, 1992; Tyree and Cochard, 1996); and ●● bud desiccation and mortality and the failure of the stem to initiate cambial growth in the spring between dead buds and the next lower live bud.5 Survival of buds through summer droughts and the dormant season is essential to initiating cambial growth the next spring (Romberger, 1963). This growth is initiated by the downward translocation of growth regulators originating in buds (Wareing, 1951). If buds in the terminal cluster die from winter desiccation (or any other cause), subsequent cambial initiation and thus a new annual ring will fail to develop between the terminal cluster and the next lower bud (Zasada and Zahner, 1969). If a new growth ring fails to develop, there can be little or no

Chapter 2

0.6 Container grown

oot dieba Probability of sh

ck ≥ 10cm

Bare root

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ls ho

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itia

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ot

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)

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Fig. 2.22.  Estimated probabilities of occurrence of winter shoot dieback (10 cm or more) in northern red oaks planted in the autumn under shelterwoods in the Ozark Highlands of Missouri. Estimates are shown in relation to initial seedling size and type of nursery stock. Dieback was observed shortly after the completion of spring flushing of the first field growing season. The response surface shown is for trees planted under shelterwoods thinned to 60% stocking based on Gingrich’s (1967) stocking equation (data from USDA Forest Service Northern Research Station, Columbia, Missouri). The model is based on 2950 planted trees and is given by the logistic regression equation: P = 1 / (1 + exp ( −( −6.1098 + 0.0483 × H − 0.1510 × D − 0.4251 × S − 1.1932 × T ))) where P is the estimated probability of shoot dieback ≥ 10 cm between the time of planting (late October) and spring flushing (mid-May), H is initial tree shoot length (cm), D is initial basal diameter 2 cm above the root collar (mm), S is presence of shelterwood (shelterwood absent = 0, shelterwood present = 1), and T is type of nursery stock (bare root = 0; container grown = 1). For all parameter estimates, P < 0.001 except S (P = 0.01). Based on the Hosmer–Lemeshow goodness-of-fit test, differences between observed and estimated probabilities did not differ significantly (P = 0.0001). Estimates for bare-root stock represent the average of 2+0 seedlings and 1+1 transplants, which did not differ significantly (α = 0.05). Estimated dieback probabilities for trees planted in clearcuts are 0.01–0.11 larger than for trees planted under shelterwoods, depending on initial shoot diameter and length.

upward conduction of water from the roots because older annual rings in oaks are largely non-functional (Zimmerman, 1983). Shoots may then die back to the next lower living bud (Fig. 2.20). The downward progression of shoot dieback in oak reproduction frequently can be observed in the spring when new flushes of shoot growth from terminal buds fail to develop and one or more of the normally suppressed lateral buds produce new shoots. In upland oak forests, there is much variation in the amount and frequency of seasonal shoot dieback (Crow, 1992). In dry upland oak forests, this is reflected in the large variation in differences between root and shoot ages among individuals in the same population (Merz and Boyce, 1956; Powell, 1976). Some of this variation may be associated with differences in root:shoot ratios, absolute

Regeneration Ecology I

root size and absorptive capacity, and thus the ability of roots of oak reproduction to replace water losses in stems and buds before and during the dormant season. Despite its role in reinforcing the apparent rootcentred growth and survival strategy of oak reproduction, recurrent shoot dieback exacts a price. Loss of shoots during the dormant season can negatively affect spring root growth. In northern red oak seedlings, the artificial removal of seedling shoots 6 inches above the root collar in late autumn reduced spring root growth of seedlings transplanted to greenhouse root observation chambers by up to 59% (Fig. 2.17C). Similar results have been observed in other oak species (Lee et al., 1974; Farmer, 1975, 1979). The collective evidence suggests that loss of shoots during the dormant

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season, whether through artificial removal or natural dieback, can reduce regulatory root growth promoters that originate in shoots and buds (Vogt and Cox, 1970; Carlson, 1974; Larson, 1975; Farmer, 1979). In turn, reduced root growth in the spring may predispose reproduction to water stress and thus shoot dieback later in the growing season (Johnson, 1979b). Also, during the shoot regrowth period after shoot dieback, there is a greater allocation of growth to shoots than to roots (Cobb et al., 1985; Kruger and Reich, 1989). This consequently lengthens the time required for roots to reach the requisite size for supporting, under field conditions, multiple flushing and thus rapid height growth. The physiological effects of shoot losses vary among oak species (Lee et al., 1974) and with other factors. In a young clearcut in Wisconsin, rates of transpiration and net photosynthesis (per unit leaf area) in northern red oak seedlings with pruned shoots (cut off 1 inch above the root collar to simulate dieback) were 30% greater than in unpruned seedlings (Kruger and Reich, 1989). However, leaf areas of pruned seedlings were 25% lower than those of unpruned seedlings. The joint effects of increased photosynthetic rate and reduced leaf area thus were largely compensatory. The higher photosynthesis and transpiration rates of the pruned seedlings were attributed to possible (but unmeasured) increases in water availability or other factors associated with increased root:shoot ratio resulting from shoot pruning (Kruger and Reich, 1989). This conclusion is consistent with the behaviour of northern red oak seedlings grown in a greenhouse (Parker, 1949). Transpiration rate (and thus photosynthetic rate) increased as root surface area:leaf area ratio increased (Fig. 2.19B). Shoot pruning of cherrybark oaks growing under forest canopies in Mississippi, combined with midstorey and understorey competition control, significantly increased the height growth of seedlings and seedling sprouts based on 2-year observations (Lockhart et al., 1991). However, pruned seedlings in most cases did not completely regain the height lost to pruning. Survival also was lower in the pruned population of seedlings. The investigators nevertheless concluded, based on physiological and morphological measurements, that pruning-simulated dieback and subsequent resprouting confers a growth advantage to cherrybark oak reproduction. Low to moderate losses of leaves and shoots to defoliating insects and browsing animals can similarly result in growth and survival advantages to

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oak seedlings (Wright et  al., 1989; Welker and Menke, 1990). But defoliation also can cause depletion of starch reserves in oak roots, which is augmented by drought (Parker and Patton, 1975). In blue oak seedlings in a California savannah, rapidly induced water stress combined with severe defoliation caused 100% seedling mortality after 2  years, whereas survival under slower rates of induced water stress combined with complete defoliation was associated with 80% survival (Welker and Menke, 1990). Defoliation of oak seedlings in the autumn also may reduce their growth the following spring (Larson, 1975). Despite some apparent negative effects, the widespread occurrence of shoot dieback in oak reproduction at the population level may be consistent with a growth and survival strategy that sacrifices large numbers of individuals for a high probability of obtaining a few individuals with large roots and attendantly rapid height growth. Whatever the physiological explanation, shoot dieback in oak reproduction occurs within populations that are highly variable physiologically, morphologically and genetically. Moreover, individual seedlings and seedling sprouts occupy relatively small, homogeneous microenvironments within which there is a high probability of occurrence of insufficient soil moisture, nutrients or light. Spatial variability of those resources at small scales thus may explain much of the observed variation in the frequency and amount of oak shoot dieback.

Stump Sprouts and Related Growth Forms Definitions and origins Once a seedling has died back and resprouted, its continued survival can lead to other growth forms. Various names have been applied to these growth forms, depending on how and where they originate on the parent stem or root system. One growth form is the stump sprout, sometimes called ‘root crown sprouts’ especially in the western oak literature (see below). These sprouts originate from dormant buds at or near the base of the stump of a cut tree (Fig. 2.23A and B). However, they also can arise from the bases of trees top-killed by fire (Fig. 2.23C). But they do not form when trees are completely killed by insects, disease or other physiological causes. In the Central Hardwood Region, stump sprouts are often defined as those originating from

Chapter 2

(A)

(B)

(C)

(D)

Fig. 2.23.  Basal sprouting in oaks. (A) A newly initiated black oak stump sprout originating from a basal bud on a recently cut tree. (B) A ten-stem clump of northern red oak stump sprouts 10 years after the parent tree was cut; the dominant stem is 27 ft tall. (C) Basal sprouts of scarlet oak originating after the top of the parent tree was killed by fire. (D) A white oak root-crown sprout from a root many times greater than the mass of its shoots (from a sandy outwash plain in northern Lower Michigan). The sprout has probably developed from a tree top-killed by fire but which persisted through recurrent sprouting in the understorey long after the parent tree disappeared. The excavated root section shown is about 6 ft long; scale is shown by hard hat near the root tip (centre foreground). (Photographs courtesy of USDA Forest Service, Northern Research Station.)

a cut tree 2 inches dbh and larger (Roach and Gingrich, 1967). However, that definition has not been universally adopted. Biologically, the distinction between a stump sprout and a seedling sprout is arbitrary because all oaks, from small seedlings to large standing trees, have some potential to produce basal sprouts when the parent stem is cut.

Regeneration Ecology I

Moreover, when wind, fire or other factors destroy an oak stand, sprouts may develop from the bases of trees that have broken off or from standing trees with dead tops. These sprouts are biologically analogous to the epicormic branches that develop under certain conditions higher up on the tree bole (see Fig. 8.12, this volume).

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Sprouts also can arise from dormant buds on the root crowns of large, mature root systems having no above-ground stumps (Fig. 2.22D). Sprouts arising from these structures are sometimes called ‘rootcrown sprouts’ or simply ‘crown sprouts’ (Plumb and McDonald, 1981; Pavlik et  al., 1991).6 They are common in savannahs, old fields and other disturbance-perpetuated communities in both eastern and western oak forests wherever recurrent fire or decay destroys stumps and where, simultaneously, low overstorey densities maintain light levels sufficiently high for sprout survival over many decades. Such disturbances favour the accumulation of this form of reproduction which, like seedling sprouts, recurrently die back and resprout. The dieback process extends over a longer period than would be possible under the low light conditions of a closed canopy forest. Where favourable light conditions are maintained, oak root systems may attain ages of several hundreds of years, even though they often support multistemmed sprouts of a younger age (Curtis, 1959). Like seedling sprouts, stump sprouts and crown sprouts originate from dormant buds at or near the root collar. These buds, connected to the pith of the tree by elements called bud traces, remain just beneath the bark by annually elongating the width of the annual ring (Liming, 1942). They do not develop further unless their vascular connections to the crown are severed by cutting or are otherwise interrupted. As long as the crown of the parent tree is alive, living buds under the bark usually remain in a dormant state imposed by growth-suppressing regulators translocated from the crown (Vogt and Cox, 1970). When buds fail to elongate each year, they are lost as a potential source of sprouts. Sometimes buds that multiply by branching offset these losses (Kramer and Kozlowski, 1979). The rate of bud branching and bud mortality changes with the age and size of the tree. The balance of these processes partially determines the spatial distribution and number of sprouts per stump that develop after cutting the parent tree. Also, some buds fail to produce shoots after the parent tree is cut because of the physical resistance of the bark to shoot emergence or inhibition by sprouts already emerged (Wilson, 1968). Physical resistance increases with increasing bark thickness and thus tree diameter. As trees become older and larger, their ability to sprout consequently decreases (Johnson, 1977; Weigel and Johnson, 1998). Stool sprouts and root sprouts differ from the other forms of vegetative reproduction because

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they originate from buds formed from callous tissue around wounds or other tissues (Kramer and Kozlowski, 1979). These are called adventitious buds and unlike dormant buds do not have bud traces extending to the pith of the tree. Stool sprouts develop adventitiously from the cut or wounded surfaces of stumps. They are usually weakly attached to the stump and are therefore often short lived. Root sprouts, as the name implies, similarly originate from roots. In many oaks, stool sprouts and root sprouts are unimportant ecologically and silviculturally because they tend to be either short lived or rarely occur. California black oak often produces stool sprouts, but not sprouts from dormant buds, when sprouts originate from high stumps of large trees (McDonald, 1990b). Other oaks reported to produce stool and/or root sprouts include canyon live oak (Thornburgh, 1990), coast live oak (Pillsbury and Joseph, 1991), blue oak (McDonald, 1990a), Oregon white oak (Stein, 1990), water oak (Adams, 1983) and swamp chestnut oak (Edwards, 1990). Some arid-region oaks have evolved special below-ground structures that produce sprouts. In Gambel oak, there are three distinct root-like below-ground structures: (i) lignotubers; (ii) rhizomes; and (iii) true roots (Tiedemann et al., 1987). Lignotubers are burl-like structures with adventitious buds. These buds are the primary source of new shoots when tree crowns are killed. Several lignotubers may be connected by rhizomes, which have fewer buds. Rhizomes facilitate the development of wide-spreading clones (Muller, 1951) that quickly develop from the seedling state (Christensen, 1955) (Fig. 2.24). Later, the physical separation of individual lignotubers resulting from the death or destruction of connected lignotubers within a clone represents a form of plant multiplication (and thus population growth) that is important in the regeneration of rhizomatous oaks. Lignotubers and rhizomes are anatomically similar to stem wood in that both possess a pith, buds and bud traces. In contrast, roots are devoid of those features. Rhizomes in oaks are associated with arid and semi-arid climates (Muller, 1951), where the environment is unfavourable for seedling establishment. In addition to Gambel oak, other rhizomatous oaks in the USA include Havard, sandpaper, live, Vasey, Texas live, Brewer, turbinella, Mohr, Ajo and huckleberry oaks (Muller, 1951). Except for live oak, all are shrubby species confined to the arid Southwest.

Chapter 2

(A)

by deer and cattle that they often contribute little to forest regeneration (Pillsbury et  al., 2002). These characteristics nevertheless represent an extremely flexible sprouting strategy and high survival potential. Sprouting probability

(B)

(C)

Fig. 2.24.  Regeneration by rhizomes in live oak. (A) Aerial shoot originating from a rhizome; (B) the distribution of rhizomes in a single clone (black dots indicate locations of aerial shoots); (C) a clump of trees in a single clone. (From Muller, 1951.)

After scorching by fire, some of the western oaks can sprout from boles and branches. For example, coast live oaks extensively damaged by fire sometimes resprout from dormant buds to form a new crown of epicormic branches (see Fig. 8.12, this volume). The remains of the charred crown, sometimes described as ‘scaffold’ branches, may produce new branches and leaves within a few weeks of burning or may be delayed until the following spring (Plumb and Gomez, 1983). Coast live oak also can sprout from dormant buds at the base of the tree after fire injury, and moreover, after timber harvest can produce sprouts from adventitious buds from wound tissue around the cut surface of the stump (Pillsbury and Joseph, 1991). In California, these stump sprouts are usually so heavily browsed

Regeneration Ecology I

Sprouting probability is herein defined as the probability that a stump will produce at least one sprout that is alive at the end of the first growing season after cutting. This definition is not universal and is sometimes reported as sprouting that occurs any time before the end of the first year even though mortality may occur before then (e.g. Lockhart and Chambers, 2007). Moreover, these probabilities may be reported as observed values or as estimated values derived from predictive models. Factors consistently shown to influence sprouting probability include parent tree diameter and age, and site quality. Effects of these factors vary by species and possibly by geographic region. These factors also are highly interactive and confirming their independent effects usually requires data from a wide range of parent tree ages and diameters, balanced sampling across the range of observations, large sample sizes, and statistical testing of interactions among predictors. Few studies have met these criteria. Among those that have, statistical significance of all three of the above factors has been demonstrated in white, chestnut, black, scarlet and northern red oaks in Indiana (Johnson, 1977; Weigel and Johnson, 1998; Weigel and Peng, 2002), and black and white oaks in Missouri (Johnson, 1977). For all species, sprouting probability decreased with increasing tree diameter and age but increased with increasing site quality (expressed as site index) (Fig. 2.25). In general, sprouting in relation to parent tree diameter and age decreased at a faster rate among species in the white oak group than in the red oak group. Other studies confirming a decrease in sprouting probability with increasing parent tree age and/or diameter include: ●● Both parent tree diameter and age in: – black, white and scarlet oaks in Missouri (Dey and Jensen, 2002); – black and white oaks in northern Lower Michigan (Lynch and Bassett, 1987; Bruggink, 1988); and – black, white and scarlet oaks in western Virginia (Roth and Hepting, 1943).

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Fig. 2.25.  Estimated sprouting probabilities (P) for oak stumps in relation to site index and parent tree diameter and age. P is the probability that a stump will have at least one living sprout at the end of the first growing season after cutting. (A) White oak: P = {1/[1 + exp(−(−7.6179 − 1.5760 × ln(dbh) + 2.7069 × ln(SI) − 0.00667 × ln(dbh) × age))]}. (B) Chestnut oak: P = {1/[1 + exp(−(−4.5719 − 1.5760 × ln(dbh) + 2.7069 × ln(SI) − 0.00667 × ln(dbh) × age))]}. (C) Black oak: P = {1/[1 + exp(−(−1.7718 − 0.0014 × dbh × age + 0.0469 × SI))]}. (D) Scarlet and northern red oaks: P = {1/[1 + exp (−(−1.1012 − 0.0014 × age × dbh + 0.0469 × SI))]}, where dbh is dbh of the parent tree (inches), SI is black oak site index in ft at base age 50 years (from Carmean et al., 1989), and age is age of the parent tree in years. Based on trees in southern Indiana (from Weigel and Johnson, 1998).

●● Parent tree diameter in: – chestnut oak in Tennessee (Mann, 1984); – pin, cherrybark and willow oaks in Missouri (Kabrick and Anderson, 2000);

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– white, black and northern red oaks in southern Lower Michigan (Johnson, 1966); – blackjack oak in Missouri (Clark and Liming, 1953);

Chapter 2

– blue oak in California (McCreary et  al., 1991); and – English oak in Russia (Kharitonovich, 1937). Other factors can also affect stump sprouting in oaks including shading and season of cutting. Oak stumps usually sprout within a year of cutting. However, in blue oak in California, a small proportion of stumps sprouted during the second year (McCreary et  al., 1991, 2002); occasional sprouting has even been reported during the third year in Virginia (Roth and Hepting, 1943). Stumps cut in August or later in the year usually do not sprout until the following spring (Roth and Hepting, 1943). For blackjack oak, stump sprouting was greater for trees cut or killed during the dormant season than during the growing season (Clark and Liming, 1953). Although, no relation between sprouting and season of cutting was found in five species of upland oaks in Virginia, season of cutting did affect sprout survival (Roth and Hepting, 1943; also see the following section on ‘Sprout growth and mortality’). Some of the live oaks of the western USA sprout prolifically regardless of season of cutting (Longhurst, 1956). Bark thickness may also affect sprouting because the thicker the bark, the greater the resistance to bud emergence. But because bark thickness and parent tree diameter (or age) are highly correlated, it would be difficult to demonstrate their independent effects. Nevertheless, we might surmise that there could be an effect independent of bark thickness related to parent tree age. This is because tree age influences the time-dependent dynamics of the bud population (i.e. the ‘bud bank’). This dynamic, in turn, determines the balance of bud losses from mortality to gains from bud multiplication by branching as discussed above. Accordingly, the outcome might vary among trees of the same bark thickness that differ in age. Few studies have evaluated the effect of shading (or light) on the frequency of stump sprouting. However, we might expect stump sprouting, like the sprouting of buds higher up on tree boles (epicormic branching), to be influenced by shading or light intensity (Ward, 1966). The underlying controlling factor in oak sprouting, whether from stumps or tree boles, involves plant growth regulators (Vogt and Cox, 1970). Light influences the production and translocation of growth regulators that, in turn, control the dormancy of basal buds. Stump sprouting thus can be considered a special

Regeneration Ecology I

case of epicormic branching. Stump sprouting differs only in the removal of the tree crown. This removal starts the physiological reaction leading to the emergence of stump sprouts. When an oak is cut (or top-killed by fire), the dormant buds on the stump are suddenly released from the growth inhibitors originating in the crown (Vogt and Cox, 1970; Kozlowski et  al., 1991; Kozlowski and Pallardy, 1997). In 60- to 120-year-old upland forests in the southern Appalachians, the occurrence of stump sprouting in five oak species declined with increasing residual overstorey density (Atwood et  al., 2008). The percentage of stumps that sprouted under shelterwoods (52–61 ft2/acre of basal area) averaged across species and sites was about onethird that of trees in clearcuts. Similarly, sugar maple stumps exposed to full light sprouted more frequently than shaded stumps (Church, 1960). These observations suggest that the amount of light received by a hardwood stump may partially determine whether it will sprout. Effects of shading on stump sprouting nevertheless are not always expressed. For example, in a thinned 28-year-old water oak plantation in Louisiana, all stumps sprouted regardless of thinning intensity (Gardiner and Helmig, 1997). Similarly, the frequency of sprouting in stumps of cherrybark oak in a thinned 30-year-old plantation were unaffected by thinning intensity (Lockhart and Chambers, 2007). This seeming discrepancy between sprouting in young water oak and the upland oaks reported above may not necessarily be related to fundamental differences among species. Rather, they may reflect differences in tree diameter, age, and other factors not considered in comparisons. Although there apparently are inherent differences in stump sprouting capacity among the oaks, such differences would be difficult to ascertain with respect to shading (or light) unless potentially confounding factors were also accounted for. There is nevertheless evidence that the amount of shading after cutting can affect stump sprouting. Moreover, there is clear evidence that shading of oak stump sprouts (associated with different levels of residual stand density and silvicultural method) affects the subsequent survival and growth of stump sprouts (Jensen and Dey, 2008). Decay organisms present in stumps at the time of cutting also can affect sprouting probability. In white oak in Virginia, sprouting was 25% lower in stumps with decay than in stumps with no decay

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(Roth and Hepting, 1943). However, in the same study, the presence of decay in black and scarlet oaks had little effect on sprouting. There was no apparent explanation for species differences. The pathogen responsible for 80% of decay occurrences in stumps was Stereum gausapatum (Roth and Hepting, 1969). Armillaria root rot disease is also common in oak stumps (Baucom, 2005; Bruhn et  al., 2005). The pathogen has the potential to inhibit sprouting by killing the roots of oaks. It can enter the living system through stumps and then into roots, where viable Armillaria inocula can remain for years in the decaying root channels (Bruhn et al., 2005). In the Missouri Ozarks, the pathogen occurs in both black oak and white oak stumps, but is more prevalent in the former. Although the pathogen’s effect on frequency of sprouting is quantitatively unknown, its potential impact is significant. Having said that, Lee et  al. (2016) were unable to detect any negative effect of Armillaria infection in white and red oak stump sprouts on sprout number, survival or growth of the dominant sprout 7 years after clearcutting in the Missouri Ozarks. Sprout growth and mortality Young stump sprouts that arise from pole-size and larger parent trees are, in effect, mature root systems connected to juvenile shoots. Even though stump sprouts start out as small as a new seedling (Fig. 2.23A), their large root systems buffer them from many of the adverse competition and site effects associated with the more limited site resources available to smaller reproduction. Stump sprouts thus have the potential to grow rapidly. During their first decade, open-grown stump sprouts in eastern USA may produce four or more flushes of shoot growth/year totalling 3 ft or more even under droughty conditions (Johnson, 1979a; Reich et  al., 1980; Cobb et  al., 1985) (Fig. 2.21). Even when shaded by an overstorey of 34 ft2/acre of basal area, water oak stump sprouts grew at a rate of 1.7 ft/year for the first 5 years. However, this rate slowed to 0. 75 ft/year by age 7 when overstorey density had increased to 52 ft2/acre (Gardiner and Helmig, 1997). The large root mass of stump sprouts and their large carbohydrate storage and absorptive capacity, coupled with other physiological factors, facilitate multiple flushing in oaks. In contrast most other growth forms do not produce multiple flushes.

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Single-flush trees include mature oaks, shaded seedlings and seedling sprouts, and small seedlings and seedling sprouts under water stress (Cook, 1941; Johnston, 1941; Kienholz, 1941; Longman and Coutts, 1974; Borchert, 1976; Buech, 1976). The number of shoot flushes in oak stump sprouts declines with sprout age. In scarlet oak stump sprouts, the numbers of flushes decreased from an average of about two per growing season the first year to about one by the fourth growing season (Cobb et al., 1985). By the fifth year, the numbers of flushes approached that of the single-flush mature tree. Borchert (1976) hypothesized that the progression from multiple to single flushes as trees grow larger may be attributable to a declining root:shoot ratio that results in increasingly longer periods for roots and shoot to restore ‘functional balance’ following periods of shoot elongation and leaf expansion. The number and spatial distribution of sprouts around the stump also influence sprout growth. Excavation of English oak stump sprouts in Russia showed that the development of numerous sprouts that are well distributed around the stump can maintain the complete parent-tree root system (Kharitonovich, 1937). Such sprouts grew rapidly in comparison with sprouts with stems on only one side of the stump. The latter often withered and died unless the new sprouts developed an independent root system. Even then, sprouts were less vigorous than those connected to the living parent root system. An even distribution of sprouts around the stump increases the likelihood that the vascular connections of sprouts to roots facilitate the translocation of photosynthates to all sides of the parent root system (Kharitonovich, 1937; Wilson, 1968; Kramer and Kozlowski, 1979). Numerous sprouts per stump were correlated with rapid early height growth in five species of oaks in the Ozark Highlands of Missouri (Johnson, 1977). Northern red oak stump sprouts in Wisconsin behaved similarly (P.S. Johnson, 1975). However, the initial advantage of high clump density may be quickly lost as competition between stems within clumps intensifies with age. In northern red oak, 20 or more stems per clump are commonly initiated. The resulting crowding of stems induces a rapid decrease in clump density. Through this self-thinning process, numbers of live stems per clump typically decline to about four by age 15 (Fig. 2.26). Multiple stems may persist for 50 years or longer (Fig. 2.27). Similar rates of self-thinning

Chapter 2

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24 20 16 12 8 4 0

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Parent tree dbh (in.) 10 15 20 Fig. 2.26.  Number of living stems in 382 northern red oak sprout clumps in relation to clump age and parent tree dbh. Each data point represents 1–14 clumps in clearcut stands in south-western Wisconsin. Lines are mean trends based on a regression model that accounts for 33% of the variation in number of stems. (From P.S. Johnson, 1975.)

were observed in water oak stump sprouts (Gardiner and Helmig, 1997). The negative effect of persistent multiple stems on the growth rate of individual stems has been demonstrated by clump thinning studies (Haney, 1962; Wendel, 1975; Lamson, 1983; Johnson and Rogers, 1984; Lowell et  al., 1989; Shipek and Ffolliott, 2005; also see Chapter 8, this volume, ‘Tending oak coppice (stump sprouts)’). The diameter of the parent tree, and correlatively the size of the root system, affects the growth of oak stump sprouts. For five species of oaks in the Ozark Highlands, the correlation between stump diameter and the heights of the dominant stem within young sprout clumps in clearcuts was negative for all species (Johnson, 1977). Whether the relation is positive or negative, however, may depend on the range of stump diameters observed. For example, average 4-year shoot elongation of the tallest stem in black oak and white oak clumps ranging from small seedling sprouts up to large stump sprouts increased as stump diameter increased up to a threshold diameter of 6 inches (Johnson, 1979b). For larger stumps, sprout height growth decreased with increasing stump diameter (Fig. 2.28A). The stump diameter associated with

Regeneration Ecology I

maximum sprout growth becomes more evident as the observed range of stump diameters increases. Although this general pattern of height growth in oak sprouts is common to five species in the Ozark Highlands of Missouri, species vary in the diameter (6–12 inches) associated with maximum growth (Fig. 2.28B). The maximum growth rate of reproduction along the continuum of parent stem diameters thus may represent the diameter where the optimum root:shoot ratio occurs most frequently in a genetically, physiologically and morphologically heterogeneous population of trees in a highly variable environment. Other factors also may be significant sources of variation in the height growth of oak sprouts. These include variation in site quality (Fig. 2.29), stand density and thus competition from surrounding trees, overhead shade, insect, disease and frost damage, animal browsing, season of cutting, and in bottomland sites wetness and flooding. In northern Lower Michigan, a stem canker called Botryodip­ lodia gallae that girdles sprout stems was a major cause of mortality of white oak, black and northern pin oak stump sprouts (Croghan and Robbins, 1986). Canker infections ranged from one or a few of the stems within clumps to complete mortality. Although the incidence of cankers was linked to poor site quality and frost damage, those factors did not explain the entire problem. In southern Missouri bottomlands, increasing site wetness was associated with chlorosis (leaf yellowing) and decreased height growth of oak stump sprouts (Kabrick and Anderson, 2000). However, effects associated with the above factors vary greatly among oak species and regions. In Missouri, both survival and growth of stump sprouts were negatively affected by increasing overhead shade (associated with silvicultural method) (Jensen and Dey, 2008; also see Chapter 8, this volume, ‘Tending oak coppice (stump sprouts)’). Dominance probability Sprouting probability and sprout growth jointly determine the relative importance of stump sprouts in regenerating oak stands. This joint effect was used to specify the probability that a tree will produce at least one sprout that becomes competitively successful within a given number of years after timber harvest (Weigel and Peng, 2002; Weigel et al., 2006). For five southern Indiana oaks, these probabilities were estimated using logistic regression

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Fig. 2.27.  Four stems persisted in this 45-year-old clump of northern red oak stump sprouts in south-eastern Minnesota. The largest stem is 14 inches dbh. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

based on data from clearcut stands (see Chapter 8, this volume). The criterion for success was the attainment of codominant or dominant crown status (see Fig. 5.1, this volume) 15 years after the parent tree was cut. Statistically significant predictors

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included parent tree age and diameter, and site quality. Estimated dominance probabilities thus have practical silvicultural value because they can be used with pre-harvest stand inventories to assess the probable contribution of stump sprouts to

Chapter 2

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Stump diameter (in.) Fig. 2.28.  Height growth of oak stump sprouts (the tallest stem per clump) in relation to stump diameter in the Ozark Highlands of Missouri. (A) Predicted and observed 4-year net shoot growth of 43 black oak and 41 white oak sprouts during the first 4 years after clearcutting. Sprouts from stumps ≥ 2.4 inches in diameter represent stump sprouts originating from the parent stand (overstorey trees). These stumps were measured 12 inches above ground. Sprouts from stumps < 2.4 inches in diameter (measured 4–6 inches above ground) represent ‘advance reproduction’ (i.e. reproduction growing beneath the overstorey at the time of clearcutting). The estimate (curved line) illustrates the continuous non-linear relation between shoot growth and stump diameter across the two arbitrarily defined growth forms (i.e. advance reproduction and stump sprouts). The unexplained variation in shoot growth (R2 = 0.39) may largely reflect the imperfect correlation between stump diameter and root size – the presumed (but unmeasured) primary ‘causal’ factor. Growth of the two species did not differ statistically (α = 0.05) (from Johnson, 1979b). (B) Predicted sprout heights of five species in 5-year-old clearcuts. Heights (H5) are based on the regression model: H5 = b0 [exp[–(b1D + b2/D)]], where D is stump diameter measured 6 inches above ground, and b0, b1 and b2 are parameters estimated by regression analysis. (From Dey, 1991.)

Regeneration Ecology I

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Sprout height (ft)

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Fig. 2.29.  Estimated and observed height of the tallest stem within young clumps of northern red oak stump sprouts in south-western Wisconsin in relation to site quality expressed by the topographic site coefficient (TSC). TSC ranges from 0.1 (poorest sites) to 1.0 (best sites) and is based on depth of soil, aspect and slope position (see Fig. 4.11, this volume). (From P.S. Johnson, 1975.)

stand regeneration potential (see Chapter 3, this volume); they also can be incorporated into more comprehensive regeneration models (Chapter 8). For the five southern Indiana oaks, stump sprout dominance probabilities can be estimated for the 15th-year after clearcutting (Fig. 2.30). Dominance probability is thus defined here as the probability that a stump will produce at least one sprout that attains codominance or dominance (see Chapter 5, this volume, Fig. 5.1) 15 years after the stump is cut. By that age, stands are in the stem exclusion stage of development (see Fig. 5.2, this volume) and trees are stratified into well-defined crown classes. In general form, these probabilities parallel the sprouting probabilities from the same study (Fig. 2.25). For all species, dominance probabilities decrease with increasing parent tree diameter and age. But in contrast to sprouting probabilities (Fig. 2.25), dominance probabilities for white and chestnut oaks decrease with increasing site quality (Fig. 2.30A and B) as measured by site index (see Chapter 4, this volume). This reversal of the site quality effect (from positive to negative) may be related to the more intense competition on better sites, which becomes increasingly expressed with time (Weigel et al., 2006).

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In relation to increasing parent tree age and diameter, dominance probabilities for white and chestnut oaks decrease at a faster rate than for black, northern red and scarlet oaks. Also, site index was not a significant predictor of dominance probability for the latter three species (Fig. 2.30C). Among the five species, probabilities were highest for young, small-diameter chestnut oaks; for 50-year-old parent trees, estimated probabilities ranged from 0.78 to 0.95 (depending on site index and parent-tree diameter) (Fig. 2.30B). Dominance probabilities for northern red and black oaks in the same parent-tree age and diameter classes were comparatively low (0.38–0.52) (Fig. 2.30C). There also are equations for estimating 15th-year dominance probabilities from postharvest measurements of stump sprouts 1, 5 and 10 years after clearcutting (Weigel et al., 2006).

Notes 1   Among the oaks of eastern USA, reported exceptions include several of the shrub oaks of south-eastern USA including Chapman, sand live, Florida, turkey, myrtle oaks (Abrahamson and Layne, 2002), and bear oak (Wolgast, 1972). When of sprout origin, 1- to 3-year-old shoots of those species can produce acorns. In chestnut oak, stump sprouts can produce acorns as early as 3 years (McQuilkin, 1990), and sawtooth oak, an Asian species introduced into the USA, can produce acorns by age 3 on trees that are only 3 ft (1 m) tall (Nakashizuka et al., 1997). In swamp white and pin oaks, special seedling propagation methods combined with selecting progenies with early flowering have been used to obtain acorn production within 1–4 years after field planting (Dey et al., 2004). 2   The American Heritage Dictionary, 3rd edn (1993) Houghton-Mifflin, Boston, Massachusetts. 3   A rotation is the period between the establishment and final harvest of an even-aged stand. 4   The compensation point occurs at the light level where carbohydrate breakdown through respiration balances carbohydrate gain through photosynthesis. 5   Winter bud desiccation and shoot dieback at least partially related to low air temperature has been observed in mature Kashiwa oak in Japan (Masaka and Sato, 2002). 6   Use of the expression ‘root-crown sprout’ or ‘crown sprout’, although appropriately descriptive, risk being confused with certain other sprouting phenomena including: (i) ‘crown sprouting’ from dormant buds, which can occur after boles and upper crowns are scorched by fire in some western oaks (e.g. Plumb and McDonald, 1981; Plumb and Gomez, 1983; Dagit, 2002; Fry, 2002); and (ii) the form or shape of dense clumps of repeatedly browsed stump sprouts of coast live oak (originating from adventitious buds) such as those described as ‘crown sprouts’ by Pillsbury and Joseph (1991).

Chapter 2

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Fig. 2.30.  Fifteenth-year dominance probabilities for five oak species in southern Indiana clearcuts in relation to parent tree dbh, age and site index. A dominance probability is defined here as the probability that a stump will produce at least one sprout that attains codominance or dominance 15 years after the parent tree is cut (see text). Probabilities are estimated by logistic regression and are shown for the approximate range of observations. For white oak (A) and chestnut oak (B), site index was significantly associated with dominance probability, but not for species in the red oak group (C). (Equations are given in Weigel et al., 2006.)

Regeneration Ecology I

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References Abbott, D.L. (1960) The bourse shoot as a factor in the growth of apple fruits. Annals of Applied Biology 48, 434–438. https://doi.org/10.1111/j.1744-7348.1960. tb03546.x Abrahamson, W.G. and Layne, J.N. (2002) Relation of ramet size to acorn production in five oak species of xeric upland habitats in south-central Florida. American Journal of Botany 89, 124–131. https://doi.org/10.3732/ ajb.89.1.124 Abrams, M.D. (1990) Adaptations and responses to drought in Quercus species of North America. Tree Physiology 7, 227–238. https://doi.org/10.1093/ treephys/7.1-2-3-4.227 Adams, J.C. (1983) Water oak regeneration in the South’s upland bottomland. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 177–179. https://doi. org/10.2737/SE-GTR-24 Ahlgren, I.F. (1974) The effect of fire on soil organizms. In: Kozlowski, T.T. and Ahlgren, E.E. (eds) Fire and Ecosystems. Academic Press, New York, pp. 47–72. https://doi.org/10.1016/B978-0-12-4242555.50008-0 Aikman, J.M. (1934) The effect of low temperature on the germination and survival of native oaks. Iowa Academy of Science 41, 89–93. Aizen, M.A. and Kenigsten, A. (1990) Floral sex ratios in scrub oak (Quercus ilicifolia) vary with microtopography and stem height. Canadian Journal of Forest Research 68, 1364–1368. https://doi.org/10.1139/b90-174 Ammon, V., Nebeker, T.E., Filer, T.H., McCracken, F.I., Solomon, J.D. and Kennedy, H.E., Jr (1989) Oak decline. Mississippi Agricultural and Forestry Experiment Station Technical Bulletin 151. Mississippi Agricultural and Forestry Experiment Station, Mississippi State, Mississippi, 15 pp. Atwood, C.J., Fox, T.R. and Loftis, D.L. (2008) Stump sprouting of oak species in three silvicultural treatments in the southern Appalachians. USDA Forest Service General Technical Publication NRS-P-24. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 2–7. Available at: https://www.fs.usda.gov/treesearch/pubs/13907 (accessed 1 July 2018). Auchmoody, L.R., Smith, H.C. and Walters, R.S. (1994) Planting northern red oak acorns: is size and planting depth important? USDA Forest Service Research Paper NE-693. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-693 Barnett, R.J. (1977) The effect of burial by squirrels on germination and survival of oak and hickory nuts. American Midland Naturalist 98, 319–330. https:// doi.org/10.2307/2424983

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Barrett, L.I. (1931) Influence of forest litter on the germination and early survival of chestnut oak, Quercus montana Willd. Ecology 12, 476–484. https://doi. org/10.2307/1928993 Bartolome, J.W., Muick, M.C. and McClaran, P. (1987) Natural regeneration of Californian hardwoods. USDA Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 26–31. Available at: https://www.fs.fed.us/psw/publications/documents/psw_gtr100/ (accessed 1 July 2018). Baucom, D.L. (2005) Armillaria species in the Missouri Ozark forests. MSc. thesis, University of Missouri, Columbia, Missouri. Beal, J.A. (1952) The more important insects of Duke Forest and the Piedmont Plateau. Duke University School of Forestry Bulletin 44, 18–24. Beck, D.E. (1977) Twelve-year acorn yield in southern Appalachian oaks. USDA Forest Service Research Note SE-244. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/pubs/ 9237 (accessed 1 July 2018). Bellocq, M.I., Jones, C., Dey, D.C. and Turgeon, J.J. (2005) Does the shelterwood method to regenerate oak forests affect acorn production and predation? Forest Ecology and Management 205, 311–323. https://doi.org/10.1016/j.foreco.2004.10.013 Bent, A.C. (1964a) Life Histories of North American Jays, Crows and Titmice, Part II. Dover, New York. Bent, A.C. (1964b) Life Histories of North American Woodpeckers. Dover, New York. Bent, A.C. (1964c) Life Histories of North American Nuthatches, Wrens, Thrashers and their Allies. Dover, New York. Bock, C.E. and Bock, J.H. (1974) Geographical ecology of the acorn woodpecker: diversity versus abundance of resources. American Naturalist 108, 694– 698. https://doi.org/10.1086/282945 Bonfil, C. (1998) The effects of seed size, cotyledon reserves, and herbivory on seedling survival and growth in Quercus rugosa and Q. laurina (Fagaceae). American Journal of Botany 85, 79–87. https://doi. org/10.2307/2446557 Bonner, F.T. and Vozzo, J.A. (1987) Seed biology and technology of Quercus. USDA Forest Service General Technical Report SO-66. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/10.2737/SO-GTR-66 Borchert, M.I., Davis, F.W., Michaelsen, J. and Oyler, L.D. (1989) Interactions of factors affecting seedling recruitment of blue oak (Quercus douglasii) in California. Ecology 70, 389–404. https://doi.org/10.2307/1937544 Borchert, R. (1976) Differences in shoot growth patterns between juvenile and adult trees and their interpretation based on systems analysis of trees. Acta Horticulturae 56, 123–130. https://doi.org/10.17660/ActaHortic.1976.56.11

Chapter 2

Bourdeau, P. (1954) Oak seedling ecology determining segregation of species in Piedmont oak–hickory forests. Ecological Monographs 24, 297–320. https:// doi.org/10.2307/1948467 Bragg, W.K., Knapp, A.K. and Briggs, J.M. (1993) Comparative water relations of seedling and adult Quercus species during gallery forest expansion in tallgrass prairie. Forest Ecology and Management 56, 29–41. https://doi.org/10.1016/0378-1127(93)90101-R Branco, M., Branco, C., Merouani, H. and Almedia, M.H. (2002) Germination success, survival and seedling vigour of Quercus suber acorns in relation to insect damage. Forest Ecology and Management 166, 159–164. https://doi.org/10.1016/S0378-1127(01)00669-7 Brezner, J. (1960) Biology, ecology and taxonomy of insects infesting acorns. University of Missouri Agriculture Experiment Station Research Bulletin 726. University of Missouri Agricultural Experiment Station, Columbia, Missouri, 40 pp. Brooks, F.E. (1910) Snout beetles that injure nuts. West Virginia Agricultural Experiment Station Bulletin 128, pp. 143–185. Brooks, F.E. and Cotton, R.T. (1929) The chestnut Curculios, Curculio proboscideus Fab. and C. auriger Casey. USDA Technical Bulletin 130. United States Department of Agriculture (USDA), Washington, DC. Brose, P.H. (2008) Root development of acorn-origin oak seedlings in shelterwood stands on the Appalachian Plateau of northern Pennsylvania: 4-year results. Forest Ecology and Management 255, 3374–3381. https://doi. org/10.1016/j.foreco.2008.02.021 Brown, L.G. and Yeager, L.E. (1945) Fox squirrels and gray squirrels in Illinois. Bulletin of the Illinois Natural History Survey 23, 449–536. Brown, L.R. (1980) Insects feeding on California oak trees. USDA Forest Service General Technical Report PSW44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 184–194. https://doi.org/10.2737/PSW-GTR-44 Brown, R.C. and Mogensen, H.L. (1972) Late ovule and early embryo development in Quercus gambelii. American Journal of Botany 59, 311–316. https://doi. org/10.1002/j.1537-2197.1972.tb10098.x Bruggink, J.L. (1988) Oak stump sprouting potential on outwash ecosystems of northern lower Michigan. MSc. thesis, Michigan State University, East Lansing, Michigan. Bruhn, J.N., Dey, D.C., Kkromroy, K.K., Mihail, J.D., Kabrick, J.M. and Wetteroff, J.J., Jr (2005) Armillaria root disease affects oak coppice regeneration in upland Missouri Ozark forests. In: Mańka, M. and Łakomy, P. (eds) Proceedings of the 11th International Conference on Root and Butt Rots. International Union of Forest Research Organizations Working Party 7.02.01, Poznań, Poland, pp. 279–284. Available at: https:// www.fs.usda.gov/treesearch/pubs/18424 (accessed 1 July 2018).

Regeneration Ecology I

Buchholz, J.T. (1941) Multi-seeded acorns. Transactions of the Illinois Academy of Science 34, 99–101. Buech, R.R. (1976) Tree shoot elongation in northern Wisconsin and relationships with temperature and precipitation. Canadian Journal of Forest Research 6, 487–498. https://doi.org/10.1139/x76-067 Burns, P.Y., Christisen, D.M. and Nichols, J.M. (1954) Acorn production in the Missouri Ozarks. University of Missouri Agriculture Experiment Station Bulletin 611. University of Missouri Agricultural Experiment Station, Columbia, Missouri, 8 pp. Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. Volume 2, Hardwoods. USDA Forest Service Agriculture Handbook 654. USDA Forest Service, Washington, DC. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Cahalane, V.H. (1942) Caching and recovery of food by the western fox squirrel. Journal of Wildlife Management 6, 338–352. https://doi.org/10.2307/3795921 Campbell, R.W. (1981) Population dynamics. In: Doane, C.C. and McManus, M.L. (eds) The Gypsy Moth: Research Toward Integrated Pest Management. USDA Forest Service Technical Bulletin 1584. USDA Forest Service, Washington, DC, pp. 65–214. Carlson, W.C. (1974) Root initiation induced by root pruning in northern red oak. Ohio Agriculture Research and Development Center (OARDC) Research Summary 74, 14–16. Carmean, W.H., Hahn, J.T. and Jacobs, R.D. (1989) Site index curves for forest tree species in the eastern United States. USDA Forest Service General Technical Report NC-128. North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10192 (accessed 1 July 2018). Carmen, W.J. (1988) Behavioral ecology of the California scrub jay (Aphelocoma coerulescens Californica): a non-cooperative breeder with close cooperative relatives. PhD dissertation, University of California Press, Berkeley, California, 334 pp. Carmen, W.J., Koenig, W.D. and Mumme, R.L. (1987) Acorn production by five species of oaks over a seven year period at the Hastings Reservation, Carmel Valley, California. USDA Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 429–434. Carpenter, I.W. and Guard, A.T. (1954) Anatomy and morphology of the seedling roots of four species of the genus Quercus. Journal of Forestry 52, 269–274. https://doi.org/10.1093/jof/52.4.269 Cecich, R.A. (1994) The reproductive biology of Quercus with an emphasis on Q. rubra. USDA Forest Service General Technical Report NC-173. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 11–19.

107

Cecich, R.A. (1997) Influence of weather on pollination and acorn production in two species of Missouri oaks. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 252–261. Available at: https://www.fs.usda.gov/treesearch/pubs/15669 (accessed 1 July 2018). Cecich, R.A. and Larsen, D.R. (1997) Bud demography and bifurcation in Quercus alba L. and Quercus velutina Lam. In: Genetics of Quercus, Diversity and Adaptation in Oak Species. Proceedings of Working Party 2.08.05, 12–17 October, International Union of Forestry Research Organizations, Pennsylvania State University, University Park, Pennsylvania, pp. 68–75. Cecich, R.A. and Sullivan, N.H. (1999) Influence of weather at time of pollination on acorn production of Quercus alba and Q. velutina. Canadian Journal of Forest Research 29, 1817–1823. https://doi.org/10.1139/x99-165 Cecich, R.A., Brown, G.L. and Piotter, B.K. (1991) Pistillate flower abortion in three species of oak (abstract). USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 578. Available at: https://www.fs.usda.gov/treesearch/pubs/ 13532 (accessed 1 July 2018). Christensen, E.M. (1955) Ecological notes on the mountain brush in Utah. Proceedings of Utah Academy of Science, Arts and Letters 32, 107–111. Christisen, D.M. and Kearby, W.H. (1984) Mast measurement and production in Missouri (with special reference to acorns). Missouri Department of Conservation Terrestrial Series 13. Missouri Department of Conservation, Jefferson City, Missouri. Church, T.W., Jr (1960) Factors affecting the development and survival of sugar maple sprouts. In: Proceedings of 1960 Society of American Foresters National Meeting, 13–16 November. Society of American Foresters, Bethesda, Maryland, pp. 32–35. Clark, F.B. and Liming, F.G. (1953) Sprouting of blackjack oak in the Missouri Ozarks. USDA Forest Service Central States Forest Experiment Station Technical Paper 137. USDA Forest Service, Central States Forest Experiment Station, Columbus, Ohio. Available at: https://www.fs.usda.gov/treesearch/pubs/40249 (accessed 1 July 2018). Cobb, S.W., Miller, A.E. and Zahner, R. (1985) Recurrent shoot flushes in scarlet oak stump sprouts. Forest Science 31, 725–730. https://doi.org/10.1093/forest science/31.3.725 Cochard, H., Granier, N.B. and Aussenac, G. (1992) Vulnerability to air embolism of three European oak species (Quercus petraea (Matt.) Liebl., Q. pubescens Willd., Q. robur L.). Annales des Sciences Forestieres 49, 225–233. https://doi.org/10.1051/ forest:19920302 Coker, W.C. (1904) Multi-seeded acorns. Botanical Gazette (Chicago) 37, 61–62.

108

Cook, D.B. (1941) The period of growth in some northeastern trees. Journal of Forestry 39, 956–959. Crawley, M.J. and Akhteruzzaman, M. (1988) Individual variation in the phenology of oak trees and its consequences for herbivorous insects. Functional Ecology 2, 409–415. https://doi.org/10.2307/2389414 Croghan, C.F. and Robbins, K. (1986) Cankers caused by Botryodiplodia gallae associated with oak sprout mortality in Michigan. Plant Disease 70(1), 76–77. https://doi.org/10.1094/PD-70-76 Crow, T.R. (1992) Population dynamics and growth patterns for a cohort of northern red oak (Quercus rubra) seedlings. Oecologia 91, 192–200. https://doi. org/10.1007/BF00317783 Crunkilton, D.D., Pallardy, S.G. and Garrett, H.E. (1992) Water relations and gas exchange of northern red oak seedlings planted in a central Missouri clearcut and shelterwood. Forest Ecology and Management 53, 117– 129. https://doi.org/10.1016/0378-1127(92)90039-C Curtis, J.T. (1959) The Vegetation of Wisconsin. University of Wisconsin Press, Madison, Wisconsin. Cypert, E. and Webster, B.S. (1948) Yield and use by wildlife of acorns of water and willow oaks. Journal of Wildlife Management 12, 227–231. Dagit, R. (2002) Post-fire monitoring of coast live oaks (Quercus agrifolia) burned in the 1993 Old Topanga Fire. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 243–249. Available at: https://www. fs.usda.gov/treesearch/pubs/26137 (accessed 1 July 2018). Dalke, P.D. (1953) Yields of seeds and mast in second growth hardwood forest, south central Missouri. Journal of Wildlife Management 17, 378–381. https:// doi.org/10.2307/3797129 Darley-Hill, S. and Johnson, W.C. (1981) Acorn dispersal by the blue jay (Cyanocitta cristata). Oecologia 50, 231–232. https://doi.org/10.1007/BF00348043 Davis, F.W., Borchert, M., Harvey, L.E. and Michaelsen, J.C. (1991) Factors affecting seedling survivorship of blue oak (Quercus douglasii H. & A.) in central California. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 81–86. Available at: https:// www.fs.usda.gov/treesearch/pubs/28402 (accessed 1 July 2018). Deen, R.T. and Hodges, J.D. (1991) Oak regeneration in abandoned fields: presumed role of the blue jay. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 84–93. https://doi.org/10.2737/SE-GTR-70 De Gange, A.R., Fitzpatrick, J.W. and Layne, J.N. (1989) Acorn harvesting by Florida scrub jays. Ecology 70, 348–356. https://doi.org/10.2307/1937539

Chapter 2

DeLong, C.A. and Yahner, R.H. (1991) Impact of small mammals on regeneration of northern red oak. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, 581 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/13536 (accessed 1 July 2018). Dey, D.C. (1991) A comprehensive Ozark regenerator. PhD dissertation, University of Missouri, Columbia, Missouri. Dey, D.C. and Jensen, R.G. (2002) Stump sprouting potential of oaks in Missouri Ozark forests managed by even- and uneven-aged silviculture. USDA Forest Service General Technical Report NC-227. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 102–113. Available at: https://www.fs.usda.gov/treesearch/pubs/19044 (accessed 1 July 2018). Dey, D.C. and Parker, W.C. (1996) Regeneration of red oak (Quercus rubra L.) using shelterwood systems: ecophysiology, silviculture and management recommendations. Ontario Forest Research Institute Forest Research Information Paper 126. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs.usda.gov/ treesearch/pubs/12423 (accessed 1 July 2018). Dey, D.C., Lovelace, W., Kabrick, J.M. and Gold, M.A. (2004) Production and early field performance of RPM seedlings in Missouri floodplains. USDA Forest Service General Technical Report NC-243. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 59–65. Available at: https://www.fs.usda.gov/treesearch/pubs/14707 (accessed 1 July 2018). Dickson, R.E. (1991) Episodic growth and carbon physiology in northern red oak. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 117–124. Dillaway, D.N., Stringer, J.W. and Rieske, L.K. (2007) Light availability influences root carbohydrates, and potentially vigor, in white oak advance regeneration. Forest Ecology and Management 250, 227–233. https://doi.org/10.1016/j.foreco.2007.05.019 Downs, A.A. and McQuilkin, W.E. (1944) Seed production of southern Appalachian oaks. Journal of Forestry 42, 913–920. https://doi.org/10.1093/jof/42.12.913 Dwyer, J.P., Cutter, B.E. and Wetteroff, J.J. (1995) A dendrochronological study of black and scarlet oak decline in the Missouri Ozarks. Forest Ecology and Management 75, 60–75. https://doi.org/10.1016/0378-1127(95)03537-K Edwards, M.B. (1990) Quercus michauxii Nutt. Swamp chestnut oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 693–696. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/table_ of_contents.htm (accessed 1 July 2018).

Regeneration Ecology I

Eyre, F.H. (ed.) (1980) Forest Cover Types of the United States and Canada. Society of American Foresters, Bethesda, Maryland. Farmer, R.E., Jr (1975) Dormancy and root regeneration of northern red oak. Canadian Journal of Forest Research 5, 176–185. https://doi.org/10.1139/x75-025 Farmer, R.E., Jr (1977) Epicotyl dormancy in white and chestnut oaks. Forest Science 23, 329–332. https:// doi.org/10.1093/forestscience/23.3.329 Farmer, R.E., Jr (1979) Dormancy and root growth capacity of white and sawtooth oaks. Forest Science 25, 491– 494. https://doi.org/10.1093/forestscience/25.3.491 Farmer, R.E., Jr (1981) Early growth of black cherry, oaks and yellow-poplar in southern Appalachian plantings. Tree Planters’ Notes 32(3), 12–14. Available at: https:// rngr.net/publications/tpn/32-3/32_3_12_14.pdf (accessed 1 July 2018). Feret, P.P., Kreh, R.E., Merkle, S.A. and Oderwald, R.G. (1982) Flower abundance, premature acorn abscission and acorn production in Quercus alba L. Botanical Gazette (Chicago) 143, 216–218. https:// doi.org/10.1086/337292 Fox, J.F. (1982) Adaptation of gray squirrel behavior to autumn germination by white oak acorns. Evolution 36, 800–809. https://doi.org/10.1111/j.1558-5646.1982. tb05446.x Fralish, J.S., Jones, S.M., O’Dell, R.K. and Chambers, J.L. (1978) The effect of soil moisture on site productivity and forest composition in the Shawnee Hills of southern Illinois. In: Balmer, W.E. (ed.) Proceedings of the Soil Moisture-Site Productivity Symposium. USDA Forest Service, State and Private Forestry, Southeastern Area, Asheville, North Carolina, pp. 263–283. Fry, D.L. (2002) Effects of a prescribed fire on oak woodland stand structure. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 235–242. Available at: https://www.fs.usda.gov/treesearch/pubs/26125 (accessed 1 July 2018). Fuchs, M.A., Krannitz, P.G., Harestad, A.S. and Bunnell, F.L. (1997) Seeds that fly on feathered wings: acorn dispersal by Steller’s jays. USDA Forest Service General Technical Report PSW-GTR-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 648–650. Available at: https://www.fs.fed.us/psw/publications/ documents/psw_gtr160/psw_gtr160_05_posterpapers.pdf (accessed 1 July 2018). Fuchs, M.A., Krannitz, P.G. and Harestad, A.S. (2000) Dispersal of Garry oak acorns by Steller’s jays. In: Darling, L.M. (ed.) Proceedings of a Conference on the Biology and Management of Species and Habitats at Risk, Kamloops, British Columbia, 15–19 February 1999, Volume 1. British Columbia Ministry of Environment, Lands and Parks, Victoria, British

109

Columbia and University College of the Cariboo, Kamloops, British Columbia, pp. 263–266. Galford, J.R. (1986) Primary infestation of sprouting chestnut, red, and white oak acorns by Valentinia glandulella (Lepidoptera: Blastobasidae). Entomo­ logical News 97, 109–112. Galford, J.R. (1987) Effect of Stelidota octomaculata (Coleoptera: Nitidulidae) on germinating acorns under laboratory conditions. In: Proceedings of the Central Hardwood Forest Conference VI. University of Tennessee, Knoxville, Tennessee, pp. 419–422. Galford, J.R. and Weiss-Cottrill, D. (1991) Response of insects to damaged and undamaged germinating acorns. USDA Forest Service Research Paper NE-656. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-656 Galford, J.R., Peacock, J.W. and Wright, S.L. (1988) Insects and other pests affecting oak regeneration. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 219–225. Galford, J.R., Williams, R.N. and Beacom, M. (1991a) Notes on the biology and host of Stelidota ferruginea (Coleoptera: Nitidulidae). USDA Forest Service Research Paper NE-654. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-654 Galford, J.R., Williams, R.N. and Daugherty, A. (1991b) Life history and notes on the biology of Stelidota octomaculata (Coleoptera: Nitidulidae). USDA Forest Service Research Paper NE-644. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/ 10.2737/NE-RP-644 Galford, J.R., Auchmoody, L.R., Smith, H.C. and Walter, R.S. (1991c) Insects affecting establishment of northern red oak seedlings in central Pennsylvania. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experi­ ment Station, Newtown Square, Pennsylvania, pp. 271–280. Available at: https://www.fs.usda.gov/ treesearch/pubs/13504 (accessed 1 July 2018). Galford, J.R., Auchmoody, L.R., Walters, R.S. and Smith, H.C. (1992) Millipede damage to germinating acorns of northern red oak. USDA Forest Service Research Paper NE-667. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Penn­ sylvania. https://doi.org/10.2737/NE-RP-667 Gardiner, E.S. and Helmig, L.M. (1997) Development of water oak stump sprouts under a partial overstory. New Forests 14, 55–62. https://doi.org/10.1023/A:1006502107495 Gibson, L.P. (1964) Biology and life history of acorn-infesting weevils of the genus Conotrachelus (Coleoptera: Curculionidae). Annals of the Entomological Society of America 57, 521–526. https://doi.org/10.1093/aesa/ 57.5.521

110

Gibson, L.P. (1969) Monograph of the genus Curculio in the New World (Coleoptera: Curculionidae), Part I. United States and Canada. Miscellaneous Publication of the Entomological Society of America 6, 239–285. Gibson, L.P. (1971) Insects of bur oak acorns. Annals of the Entomological Society of America 64, 232–234. https://doi.org/10.1093/aesa/64.1.232 Gibson, L.P. (1972) Insects that damage white oak acorns. USDA Forest Service Research Paper NE-220. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/23678 (accessed 1 July 2018). Gibson, L.P. (1982) Insects that damage northern red oak acorns. USDA Forest Service Research Paper NE-492. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/15046 (accessed 1 July 2018). Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Godman, R.M. and Mattson, G.A. (1976) Seed crops and regeneration problems of 19 species in northeastern Wisconsin. USDA Forest Service Research Paper NC-123. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10644 (accessed 1 July 2018). Goheen, J.R. and Swihart, R.K. (2003) Food-hoarding behavior of gray squirrels and North American red squirrels in the Central Hardwood Region: implications for forest regeneration. Canadian Journal of Zoology 81, 1636–1639. https://doi.org/10.1139/z03-143 Gómez, J.M. (2003) Spatial patterns in long-distance dispersal of Quercus ilex acorns by jays in a heterogeneous landscape. Ecography 26, 573–584. https:// doi.org/10.1034/j.1600-0587.2003.03586.x Goodrum, P.D., Reid, V.H. and Boyd, C.E. (1971) Acorn yields, characteristics and management criteria of oaks for wildlife. Journal of Wildlife Management 35, 520–532. https://doi.org/10.2307/3799707 Gottschalk, K.W. (1985) Effects of shading on growth and development of northern red oak, black oak, black cherry and red maple seedlings. I. Height, diameter and root/shoot ratio. In: Proceedings of the 5th Central Hardwood Forest Conference. Department of Forestry, University of Illinois, Urbana-Champaign, Illinois, pp. 189–195. Available at: https://www.fs.usda.gov/treesearch/pubs/47682 (accessed 1 July 2018). Gottschalk, K.W. (1990) Gypsy moth effects on mast production. In: Proceedings of the Workshop on Southern Appalachian Mast Management. USDA Forest Service and University of Tennessee, Knoxville, Tennessee, pp. 42–50. Available at: https://www.fs.usda.gov/treesearch/pubs/47445 (accessed 1 July 2018).

Chapter 2

Greulach, V.A. (1973) Plant Function and Structure. MacMillan, New York. Gribko, L.S. and Hix, D.M. (1993) The effect of small rodents on northern red oak acorns in north-central West Virginia. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 400– 408. Available at: https://www.fs.usda.gov/treesearch/ pubs/15334 (accessed 1 July 2018). Griffin, J.R. (1971) Oak regeneration in the upper Carmel Valley, California. Ecology 552, 862–868. https://doi. org/10.2307/1936034 Grimm, E.C. (1984) Fire and other factors controlling the Big Woods vegetation of Minnesota in the mid-nineteenth century. Ecological Monographs 54, 291– 311. https://doi.org/10.2307/1942499 Grisez, T.J. (1975) Flowering and seed production in seven hardwood species. USDA Forest Service Research Paper NE-315. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs. usda.gov/treesearch/pubs/15443 (accessed 1 July 2018). Grivet, D., Smouse, P.E. and Sork, V.L. (2005) A novel approach to an old problem: tracking dispersed seeds. Molecular Ecology 11, 3585–3595. https:// doi.org/10.1111/j.1365-294X.2005.02680.x Grossman, B.C., Gold, M.A. and Dey, D.C. (2003) Restoration of hard mast species for wildlife in Missouri using precocious flowering oak in the Missouri River floodplain, USA. Agroforestry Systems 59, 3–10. https://doi.org/10.1023/A:1026124717097 Grubb, P.J. (1977) The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biology Review 52, 107–145. https://doi. org/10.1111/j.1469-185X.1977.tb01347.x Gullion, G. (1984) Grouse of the North Shore. Willow Creek Press, Oshkosh, Wisconsin. Gysel, L.W. (1957) Acorn production on good, medium and poor oak sites in southern Michigan. Journal of Forestry 55, 570–574. https://doi.org/10.1093/jof/55.8.570 Haney, G.P. (1962) Growth of thinned white oak sprouts. Journal of Forestry 60, 644–645. Hannon, S.J., Mumme, R.L., Koenig, W.D., Spon, S. and Pitelka, F.A. (1987) Poor acorn crop, dominance and decline in numbers of acorn woodpeckers. Journal of Animal Ecology 56, 197–207. https://doi. org/10.2307/4809 Hanson, P.J., Dickson, R.E., Isebrands, J.G., Crow, T.R. and Dixon, R.K. (1986) A morphological index of Quercus seedling ontogeny for use in studies of physiology and growth. Tree Physiology 2, 273–281. https://doi.org/10.1093/treephys/2.1-2-3.273 Harper, J.L. (1977) Population Biology of Plants. Academic Press, London. Harrison, J.S. and Werner, P.A. (1984) Colonization by oak seedlings into a heterogeneous successional

Regeneration Ecology I

habitat. Canadian Journal of Botany 62, 559–563. https://doi.org/10.1139/b84-083 Head, G.C. (1973) Shedding of roots. In: Kozlowski, T.T. (ed.) Shedding of Plant Parts. Academic Press, New York, pp. 237–293. https://doi.org/10.1016/B978-0-12424250-0.50012-9 Healy, W.M. (1988) Effects of seed-eating birds and mammals on Appalachian hardwood regeneration. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 104–111. Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Hildebrand, D.C. and Schroth, M.N. (1967) A new species of Erwinia causing the drippy nut disease of live oaks. Phytopathology 57, 250–253. Holch, A.E. (1931) Development of roots of tree seedlings. Ecology 12, 259–298. https://doi.org/10.2307/1931633 Hopewell, L.J., Leaver, L.A. and Lea, S.E.G. (2008) Effects of competition and food availability on travel time in scatter-hoarding gray squirrels (Sciurus carolinensis). Behavioral Ecology 19, 1143–1149. https:// doi.org/10.1093/beheco/arn095 Hosner, J.F. (1959) Multi-seeded acorns. Journal of Forestry 57, 127. Hurly, T.A. and Lourie, S.A. (1997) Scatterhoarding and larderhoarding by red squirrels: size, dispersion and allocation of hoards. Journal of Mammalogy 78, 529– 537. https://doi.org/10.2307/1382904 Janes, D.J. and Nichols, J.M. (1967) Acorn Production of Nine Commercial Species of Oak in Missouri. School of Forestry, University of Missouri, Columbia, 19 pp. Jenkins, M.A. and Pallardy, S.G. (1995) The influence of drought on red oak group species growth and mortality in the Missouri Ozarks. Canadian Journal of Forest Research 25, 1119–1127. https://doi.org/10.1139/ x95-124 Jensen, R.G. and Dey, D.C. (2008) Evaluation of oak stump sprouting in the Missouri Ozarks ten years after harvest. Missouri Department of Conservation Science Notes Vol. 3, No. 7. Missouri Department of Conservation, Jefferson City, Missouri. Available at: https://www.fs.usda.gov/treesearch/pubs/14455 (accessed 1 July 2018). Johnson, P.S. (1966) Vegetational developments after logging in southern Michigan xeric oak forests. PhD dissertation, Michigan State University, East Lansing, Michigan. Johnson, P.S. (1974) Survival and growth of northern red oak seedlings following a prescribed burn. USDA Forest Service Research Note NC-177. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11354 (accessed 1 July 2018). Johnson, P.S. (1975a) Growth and structural development of red oak sprout clumps. Forest Science 21, 413–418. https://doi.org/10.1093/forestscience/21.4.413

111

Johnson, P.S. (1977) Predicting oak stump sprouting and sprout development in the Missouri Ozarks. USDA Forest Service Research Paper NC-149. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10670 (accessed 1 July 2018). Johnson, P.S. (1979a) Shoot elongation of black oak and white oak sprouts. Canadian Journal of Forest Research 9, 489–494. https://doi.org/10.1139/x79-079 Johnson, P.S. (1979b) Growth potential and field performance of planted oaks. In: Proceedings of the 1979 J.S. Wright Forestry Conference. Purdue University, Lafayette, Indiana, pp. 113–119. Johnson, P.S. (1981) Early results of planting English oak in an Ozark clearcut. USDA Forest Service Research Paper NC-204. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/ pubs/10725 (accessed 1 July 2018). Johnson, P.S. and Rogers, R. (1984) Predicting 25thyear diameters of thinned stump sprouts of northern red oak. Journal of Forestry 82, 616–619. https://doi. org/10.1093/jof/82.10.616 Johnson, P.S., Novinger, S.L. and Mares, W.G. (1984) Root, shoot and leaf area growth potentials of northern red oak planting stock. Forest Science 30, 1017–1026. https://doi.org/10.1093/forestscience/30.4.1017 Johnson, R.L. (1975b) Natural regeneration and development of Nuttall oak and associated species. USDA Forest Service Research Paper SO-104. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www. fs.usda.gov/treesearch/pubs/43015 (accessed 1 July 2018). Johnson, W.C. and Adkisson, C.S. (1986) Airlifting the oaks. Natural History 10, 41–43, 48–49. Johnson, W.C. and Webb, T. III (1989) The role of blue jays (Cyanocitta cristata L.) in the postglacial dispersal of fagaceous trees in eastern North America. Journal of Biogeography 16, 561–571. https://doi. org/10.2307/2845211 Johnson, W.C., Adkisson, C.S., Crow, T.R. and Dixon, M.D. (1997) Nut caching by blue jays (Cyanocitta cristata L.): implications for tree demography. American Midland Naturalist 138, 357–370. https:// doi.org/10.2307/2426828 Johnsson, H. (1952) Ungdomsutvecklingen hos stjälkek, druvek, och rödek. Svenska Slogsvardsföreningens Tidskrift 2, 168–193. Johnston, J.P. (1941) Height-growth periods of oak and pine reproduction in the Missouri Ozarks. Journal of Forestry 39, 67–68. Jones, C.G., Ostfeld, R.S., Richard, M.P., Schauber, E.M. and Wolff, J.O. (1998) Chain reactions linking acorns to gypsy moth outbreaks and Lyme disease risk. Science 279, 1023–1026. https://doi.org/10.1126/science.279. 5353.1023

112

Joslin, J.D. and Henderson, G.S. (1987) Organic matter and nutrients associated with fine root turnover in a white oak stand. Forest Science 33, 330–346. https:// doi.org/10.1093/forestscience/33.2.330 Kabrick, J. and Anderson, M. (2000) Oak stump sprouting in mature bottomland forests at Duck Creek Conservation Area. Missouri Department of Conservation Forest Research Report 2. Missouri Department of Conservation, Jefferson City, Missouri. Kearby, W.H., Christisen, D.M. and Myers, S.A. (1986) Insects: their biology and impact on acorn crops in Missouri’s upland forests. Missouri Department of Conservation Terrestrial Series 16. Missouri Department of Conservation, Jefferson City, Missouri. Keeley, J.E. (1981) Reproductive cycles and fire regimes. USDA Forest Service General Technical Report WO-26. USDA Forest Service, Washington, DC, pp. 231–277. Kharitonovich, F.M. (1937) Reforestation by coppice shoots. Sbornik Polezaschitnye Lesnye Polosy (Moscow), pp. 175–239. (in Russian; partial English translation on file at USDA Forest Service Northern Research Station, Columbia, Missouri) Kienholz, R. (1941) Seasonal course of height growth in some hardwoods in Connecticut. Ecology 22, 249– 258. https://doi.org/10.2307/1929612 Kilham, L. (1958) Sealed-in winter stores of red-headed woodpeckers. Wilson Bulletin 70, 107–113. Kilham, L. (1963) Food storing of red-bellied woodpeckers. Wilson Bulletin 75, 227–234. Kirkpatrick, R.L. and Pekins, P.J. (2002) Nutritional value of acorns for wildlife. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems, Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 173–181. Knapp, E., Rice, K. and Goedde, M. (2000) Habitat fragmentation limits pollen availability and acorn production in blue oak. Integrated Hardwood Range Management Program (IHRMP) Fact Sheet No. 96. University of California, Berkeley, California. Knapp, B.O., Wang, G.G., Walker, J.L. and van Lear, D.H. (2006) Can root biomass of white oak advance regeneration be reliably predicted from diameter and height? USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, 579 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/23467 (accessed 1 July 2018). Koenig, W.D. (1980) Acorn storage by acorn woodpeckers in an oak woodland: an energetics analysis. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. Available at: https://www.fs.fed.us/psw/publications/documents/psw_gtr044/index.shtml (accessed 1 July 2018). Koenig, W.D. (1990) Oaks, acorns and the acorn woodpecker. Fremontia 18, 77–79.

Chapter 2

Koenig, W.D., Carmen, W.J., Stanback, M.T. and Mumme, R.L. (1991) Determinants of acorn productivity among five species of oaks in central Coastal California. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 136–142. Available at: https://www.fs.usda.gov/treesearch/pubs/28410 (accessed 1 July 2018). Koenig, W.D., Mumme, R.L., Carmen, W.J. and Stanback, M.T. (1994) Acorn production by oaks in central coastal California: variation within and among years. Ecology 75, 99–109. https://doi.org/10.2307/1939386 Koenig, W.D., Knops, J.M.H., Carmen, W.J., Stanback, M.T. and Mumme, R.L. (1996) Acorn production by oaks in central coastal California: influence of weather at three levels. Canadian Journal of Forest Research 26, 1677–1683. https://doi.org/10.1139/ x26-189 Koenig, W.D., Hooge, P.N., Stanback, M.R. and Haydock, J. (2000) Natal dispersal in the cooperatively breeding acorn woodpecker. The Condor 102, 492–502. https://doi.org/10.2307/1369780 Kolb, T.E. and Steiner, K.C. (1990) Growth and biomass partitioning of northern red oak and yellow-poplar seedlings: effects of shading and grass root competition. Forest Science 36, 34–44. https://doi.org/10.1093/ forestscience/36.1.34 Komarek, E.V. (1970) Insect control – fire for habitat management. In: Proceedings of the Tall Timbers Fire Ecology Conference 2. Tall Timbers Research Station, Tallahassee, Florida, pp. 157–171. Korstian, C.F. (1927) Factors controlling germination and early survival in oaks. Yale University School of Forestry Bulletin 19. Yale University, New Haven, Connecticut. Kozlowski, T.T. and Pallardy, S.G. (1997) Physiology of Woody Plants, 2nd edn. Academic Press, San Diego. Kozlowski, T.T., Kramer, P.J. and Pallardy, S.G. (1991) The Physiological Ecology of Woody Plants. Academic Press, San Diego. Available at: https://www.sciencedirect.com/science/book/9780124241602 (accessed 1 July 2018). Krajicek, J.E. (1955) Rodents influence red oak regeneration. USDA Forest Service Central States Forest Experiment Station Note, Note 91. USDA Forest Service, Central States Forest Experiment Station, Columbus, Ohio. Krajicek, J.E. (1960) Some factors affecting oak and black walnut reproduction. Iowa State Journal of Science 34, 631–634. Kramer, P.J. and Decker, J.P. (1944) Relation between light intensity and rate of photosynthesis of loblolly pine and certain hardwoods. Plant Physiology 19, 350–358. https://doi.org/10.1104/pp.19.2.350 Kramer, P.J. and Kozlowski, T.T. (1979) Physiology of Woody Plants. Academic Press, New York.

Regeneration Ecology I

Kriebel, H.B. (1965) Parental and provenance effects on growth of red oak seedlings. In: Proceedings of the 4th Central States Forest Tree Improvement Conference. Nebraska Agriculture Experiment Station, Lincoln, Nebraska, pp. 19–25. Kriebel, H.B., Merritt, C. and Stadt, T. (1988) Genetics and growth rate in Quercus rubra provenance and family effects by the early third decade in the North Central USA. Sylvae Genetica 37, 193–198. Kruger, E.L. and Reich, P.B. (1989) Comparative growth and physiology of stem-pruned and unpruned northern red oak. USDA Forest Service General Technical Report NC-132. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, 302 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/10196 (accessed 1 July 2018). Kubiske, M.E. and Abrams, M.D. (1992) Photosynthesis, water relations, and leaf morphology of xeric versus mesic Quercus rubra ecotypes in central Pennsylvania in relation to moisture stress. Canadian Journal of Forest Research 22, 1402–1407. https:// doi.org/10.1139/x92-186 Kurta, A. (1995) Mammals of the Great Lakes Region. The University of Michigan Press, Ann Arbor, Michigan. Lamson, N.I. (1983) Precommercial thinning increases diameter growth of Appalachian hardwood stump sprouts. Southern Journal of Applied Forestry 7 (May), 93–97. https://doi.org/10.1093/sjaf/7.2.93 Larson, M.M. (1970) Root regeneration and early growth of red oak seedlings: influence of soil temperature. Forest Science 16, 442–446. https://doi.org/10.1093/ forestscience/16.4.442 Larson, M.M. (1975) Pruning northern red oak nursery seedlings: effects on root regeneration and early growth. Canadian Journal of Forest Research 5, 381–386. https://doi.org/10.1139/x75-052 Layne, J.N. (1954) The biology of the red squirrel, Tamiasciurus hudsonicus loquax (Bangs), in central New York. Ecological Monographs 24, 227–267. https://doi.org/10.2307/1948465 Lee, C.A., Dey, D.C. and Muzika, R.-M. (2016) Oak stump-sprout vigor and Armillaria infection after clearcutting in southeastern Missouri, USA. Forest Ecology and Management 374, 211–219. https://doi. org/10.1016/j.foreco.2016.05.014 Lee, C.I., Moser, B.C. and Hess, C.E. (1974) Root regeneration of transplanted pin and scarlet oak. New Horizons 1974. Horticultural Research Institute, Washington, DC, pp. 10–13. Lewis, V.R. (1989) Host–insect interactions in the California oakworm–coast live oak ecosystem. PhD dissertation, University of California, Berkeley, California. Lewis, V.R. (1992) Within-tree distribution of acorns infested by Curculio occidentis (Coleoptera: Curculionidae) and Cydia latiferreana (Lepidoptera: Tortricidae) on the coast live oak. Environmental Entomology 21, 975–982. https://doi.org/10.1093/ee/21.5.975

113

Liebhold, A., Elkinton, J., Williams, D. and Muzika, R.M. (2000) What causes outbreaks of the gypsy moth in North America? Population Ecology 42, 257–266. https://doi.org/10.1007/PL00012004 Liming, F.G. (1942) Blackjack oak in the Missouri Ozarks. Journal of Forestry 40, 249–252. https://doi. org/10.1093/jof/40.3.249 Liming, F.G. and Johnston, J.P. (1944) Reproduction in oak–hickory forest stands of the Missouri Ozarks. Journal of Forestry 42, 175–180. https://doi. org/10.1093/jof/42.3.175 Loach, K. (1967) Shade tolerance in tree seedlings. 1. Leaf photosynthesis and respiration in plants raised under artificial shade. New Phytologist 66, 607–621. https://doi.org/10.1111/j.1469-8137.1967.tb05432.x Lockhart, B.R. and Chambers, J.L. (2007) Cherrybark oak stump sprout survival and development five years following plantation thinning in the lower Mississippi alluvial valley, USA. New Forests 33, 183–192. https://doi. org/10.1007/s11056-006-9022-7 Lockhart, B.R., Hodges, J.D., Toliver, J.R. and Karr, B.L. (1991) Morphology, gas exchange and carbon-14 allocation patterns in advance cherrybark oak reproduction – preliminary results. USDA Forest Service General Technical Report SE-70, Vol. 2. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 513–523. https://doi. org/10.2737/SE-GTR-70 Lockhart, B.R., Shelton, M.G. and Guo, Y. (1995) Effects of forest floor amount and composition and acorn placement on emergence and early establishment of white oak. USDA Forest Service General Technical Report SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 447–453. Longhurst, W.M. (1956) Stump sprouting of oaks in response to seasonal cutting. Journal of Range Management 9, 194–196. https://doi.org/10.2307/3894394 Longman, K.A. and Coutts, M.P. (1974) Physiology of the oak tree. In: Morris, M.G. and Perring, F.H. (eds) The British Oak, Its History and Natural History. Botanical Society of the British Isles, Classey Ltd, Faringdon, Berkshire, UK, pp. 194–221. Lowell, K.E., Garrett, H.E. and Mitchell, R.J. (1989) Early thinning can improve your stand of coppice-regenerated oak. USDA Forest Service General Technical Report NC-132. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 53–58. Available at: https://www.fs.usda.gov/treesearch/pubs/10196 (accessed 1 July 2018). Luckwill, L.C. (1977) The control of growth and fruitfulness of apple trees. In: Luckwill, L.C. and Cutting, C.V. (eds) Physiology of Tree Crops. Academic Press, New York, pp. 237–254. Lynch, A.M. and Bassett, J.R. (1987) Oak stump sprouting on dry sites in northern Lower Michigan. Northern Journal of Applied Forestry 4, 142–145. https://doi. org/10.1093/njaf/4.3.142

114

MacRoberts, M.H. and MacRoberts, B.R. (1976) Social organization and behavior of the acorn woodpecker in central coastal California. Ornithological Monographs 21. American Ornithologists’ Union, The University of New Mexico, Albuquerque, New Mexico. https://doi. org/10.2307/40166738 Mann, L.K. (1984) First-year regeneration in upland hardwoods after two levels of residue removal. Canadian Journal of Forest Research 14, 336–342. https://doi.org/10.1139/x84-062 Marquis, D.A., Eckert, P.L. and Roach, B.A. (1976) Acorn weevils, rodents and deer all contribute to oak-regeneration difficulties in Pennsylvania. USDA Forest Service Research Paper NE-356. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/14481 (accessed 1 July 2018). Martin, R.E. and Mitchell, R.G. (1981) Possible, potential, probable and proven fire–insect interactions. In: Proceedings of the 1980 Society of American Foresters National Convention, 5–8 October, Spokane, Washington. Society of American Foresters, Bethesda, Maryland, pp. 138–144. Masaka, K. and Sato, H. (2002) Acorn production by Kashiwa oak in a coastal forest under fluctuating weather conditions. Canadian Journal of Forest Research 32, 9–15. https://doi.org/10.1139/x01-159 Matsuda, K., McBride, J.R. and Kimura, M. (1989) Seedling growth form of oaks. Annals of Botany 64, 439–446. https://doi.org/10.1093/oxfordjournals.aob. a087862 McClaran, M.P. and Bartolome, J.W. (1990) Comparison of actual and predicted blue oak age structures. Journal of Range Management 43, 61–63. https:// doi.org/10.2307/3899123 McComb, A.L. (1934) The relation between acorn weight and the development of one year chestnut oak seedlings. Journal of Forestry 32, 479–484. https://doi. org/10.1093/jof/32.4.479 McCracken, F.I., Ammon, V., Solomon, J.D. and Nebeker, T.E. (1991) Oak decline in the lower Mississippi River Valley. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 299–306. https://doi.org/10.2737/ SE-GTR-70 McCreary, D.D., Tietje, W.D., Schmidt, R.H., Gross, R., Weitkamp, W.H., Willoughby, B.L. and Bell, F.L. (1991) Stump sprouting of blue oaks in California. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 64–69. Available at: https://www.fs. fed.us/psw/publications/documents/psw_gtr126/ index.shtml (accessed 1 July 2018). McCreary, D.D., Tietze, W.D. and Frost, W. (2002) Stump sprouting of blue oaks ten years after harvest.

Chapter 2

USDA Forest Service General Technical Report PSW 184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 573–580. Available at: https://www. fs.usda.gov/treesearch/pubs/26154 (accessed 1 July 2018). McDonald, P.M. (1990a) Quercus douglasii Hook. & Arn. Blue oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 631–639. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/table_ of_contents.htm (accessed 1 July 2018). McDonald, P.M. (1990b) Quercus kelloggii Newb. California black oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 661–671. Available at: https://www.fs.usda.gov/treesearch/pubs/ 48564 (accessed 1 July 2018). McGee, C.E. (1967) Regeneration in southern Appalachian oak stands. USDA Forest Service Research Note SE-72. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. McGee, C.E. (1968) Northern red oak seedling growth varies by light intensity and seed source. USDA Forest Service Research Note SE-90. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www. fs.usda.gov/treesearch/pubs/5227 (accessed 1 July 2018). McGee, C.E. (1974) Elevation of seed sources and planting sites affects phenology and development of red oak seedlings. Forest Science 20, 160–164. https:// doi.org/10.1093/forestscience/20.2.160 McGee, C.E. (1976) Differences in budbreak between shade-grown and open-grown oak seedlings. Forest Science 22, 484–486. https://doi.org/10.1093/ forestscience/22.4.484 McGee, C.E. (1986) Budbreak for twenty-three upland hardwoods compared under forest canopies and in recent clearcuts. Forest Science 32, 924–935. https://doi.org/10.1093/forestscience/32.4.924 McGee, C.E. (1988) Spring weather, canopy removal and early budbreak threaten oak seedlings. Journal of the Elisha Mitchell Science Society 104, 108–115. McGee, C.E. (1997) The effect of oak budbreak patterns on shade tolerance and regeneration. In: Genetics of Quercus, Diversity and Adaptation in Oak Species. Proceedings of Working Party 2.08.05, 12–17 October, International Union of Forestry Research Organizations, Pennsylvania State University, University Park, Pennsylvania, pp. 279–287. McQuilkin, R.A. (1983) Silvical factors affecting the regeneration of oaks and associated species in Pennsylvania. In: Proceedings of Regenerating

Regeneration Ecology I

Hardwood Stands. Pennsylvania State University, State College, Pennsylvania, pp. 37–65. McQuilkin, R.A. (1990) Quercus prinus L. chestnut oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 721–726. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). McQuilkin, R.A. and Musbach, R.A. (1977) Pin oak acorn production on Green Tree Reservoirs in southeastern Missouri. Journal of Wildlife Management 41, 218–225. https://doi.org/10.2307/3800598 Merkle, S.A., Feret, P.P., Croxdale, J.G. and Sharik, T.L. (1980) Development of floral primordia in white oak. Forest Science 26, 238–250. https://doi.org/10.1093/ forestscience/26.2.238 Merz, R.W. and Boyce, S.G. (1956) Age of oak ‘seedlings’. Journal of Forestry 54, 774–775. https://doi. org/10.1093/jof/54.11.774 Millington, W.F. and Chaney, W.R. (1973) Shedding of shoots and branches. In: Kozlowski, T.T. (ed.) Shedding of Plant Parts. Academic Press, New York, pp. 149–204. https://doi.org/10.1016/B978-0-12-424250-0.50010-5 Mogensen, H.L. (1965) A contribution to the anatomical development of the acorn in Quercus L. Iowa State Journal of Science 40, 221–255. Mogensen, H.L. (1975) Ovule abortion in Quercus (Fagaceae). American Journal of Botany 62, 160–165. https://doi.org/10.1002/j.1537-2197.1975.tb14047.x Mowbray, T.B. and Oosting, H.J. (1968) Vegetation gradients in relation to environment and phenology in a southern Blue Ridge Gorge. Ecological Monographs 38, 309–344. https://doi.org/10.2307/1948531 Muick, P.C. and Bartolome, J.W. (1987) Factors associated with oak regeneration in California. USDA Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 86–91. Available at: https://www.fs.fed.us/psw/ publications/documents/psw_gtr100/ (accessed 1 July 2018). Muller, C.H. (1951) The significance of vegetative reproduction in Quercus. Madroňo 11, 129–137. Muňoz, A. and Bonai, R. (2007) Rodents change acorn dispersal behaviour in response to ungulate presence.Oikos116,1631–1638.https://doi.org/10.1111/j.00301299.2007.15710.x Murtfeldt, M.E. (1894) Acorn insects, primary and secondary. Insect Life (USDA Division of Entomology) 6, 318–324. Musselman, R.C. and Gatherum, G.E. (1969) Effects of light and moisture on red oak seedlings. Iowa State Journal of Science 43, 273–284. Myers, S.A. (1978) Insect impact on acorn production in Missouri upland forests. PhD dissertation, University of Missouri, Columbia, Missouri.

115

Nakashizuka, T., Takahashi, Y. and Kawaguchi, H. (1997) Production-dependent reproductive allocation of a tall tree species Quercus serrata. Journal of Plant Research 110, 7–13. https://doi.org/10.1007/ BF02506837 Neilson, R.P. and Wullstein, L.H. (1980) Catkin freezing and acorn production in Gambel oak in Utah, 1978. American Journal of Botany 67, 426–428. https://doi. org/10.1002/j.1537-2197.1980.tb07668.x Nichols, J.M. (1954) Direct seeding of oak in Missouri. University of Missouri Agriculture Experiment Station Bulletin 609. University of Missouri, Columbia, Missouri. Nichols, J.O. (1968) Oak mortality in Pennsylvania. Journal of Forestry 66, 681–694. Olson, D.F., Jr (1974) Quercus L. Oak. USDA Forest Service Agriculture Handbook 450. USDA Forest Service, Washington, DC, 88 pp. Ostfeld, R.S., Jones, C.G. and Wolff, J.O. (1996) Of mice and mast. BioScience 46, 323–330. https://doi. org/10.2307/1312946 Ostfeld, R.S., Schauber, E.M., Canham, C.D., Keesing, F., Jones, C.G. and Wolff, J.O. (2001) Effects of acorn production and mouse abundance on abundance and Borrelai burgdorferi infection prevalence of nymphal Ixodes scapularis ticks. Vector Borne Zoonotic Diseases 1, 55–63. https://doi.org/10.1089/ 153036601750137688 Pallardy, S.G. and Rhoads, J.L. (1993) Morphological adaptations to drought in seedlings of deciduous angiosperms. Canadian Journal of Forest Research 23, 1766–1774. https://doi.org/10.1139/x93-223 Parker, J. (1949) Effects of variations in the root–leaf ratio on transpiration rate. Plant Physiology 24, 739– 743. https://doi.org/10.1104/pp.24.4.739 Parker, J. and Patton, R.L. (1975) Effects of drought and defoliation on some metabolites in roots of black oak seedlings. Canadian Journal of Forest Research 5, 457–463. https://doi.org/10.1139/x75-063 Parker, W.C. and Dey, D.C. (2008) Influence of overstory density on ecophysiology of red oak (Quercus rubra) and sugar maple (Acer saccharum) seedlings in central Ontario shelterwoods. Tree Physiology 28, 797– 804. https://doi.org/10.1093/treephys/28.5.797 Parker, W.C., Pallardy, S.G., Hinckley, T.M. and Teskey, R.O. (1982) Seasonal changes in tissue water relations of three woody species of the Quercus–Carya forest type. Ecology 63, 1259–1267. https://doi. org/10.2307/1938853 Pavlik, B.M., Muick, P.C., Johnson, S. and Popper, M. (1991) Oaks of California. Cachuma Press, Los Olivos, California. Petrides, G.A. (1988) A Field Guide to Eastern Trees. Houghton Mifflin, Boston, Massachusetts. Pfannmuller, L.A. (1991) Significance of oaks and oak forest communities for nongame wildlife. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 56–64.

116

Phares, R.E. (1971) Growth of red oak (Quercus rubra L.) seedlings in relation to light and nutrients. Ecology 52, 669–672. https://doi.org/10.2307/1934157 Pierce, W.D. (1908) A list of parasites known to attack American Rhynchophora. Journal of Economic Entomology 1, 380–396. https://doi.org/10.1093/ jee/1.6.380 Pillsbury, N.H. and Joseph, J.P. (1991) Coast live oak thinning study in the central coast of California – fifthyear results. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 320–332. Available at: https://www.fs.usda.gov/treesearch/pubs/28452 (accessed 1 July 2018). Pillsbury, N.H., Bonner, L.W. and Thompson, R.P. (2002) Coast live oak long-term thinning study – 12-year results. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 681–692. Available at: https://www.fs.usda.gov/treesearch/pubs/26164 (accessed 1 July 2018). Plumb, T.R. and McDonald, P.M. (1981) Oak management in California. USDA Forest Service General Technical Report PSW-54. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. Available at: https:// www.fs.usda.gov/treesearch/pubs/24107 (accessed 1 July 2018). Plumb, T.R. and Gomez, A.P. (1983) Five southern California oaks: identification and postfire management. USDA Forest Service General Technical Report PSW-71. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-71 Post, L.S. (1998) Seed management in Tennessee: development of seed zones for Tennessee and distribution and protection of northern red oak (Quercus rubra L.) acorns. MSc. thesis, University of Tennessee, Knoxville, Tennessee. Powell, D.S. (1976) Sprouting ability of advance reproduction of undisturbed forest stands in West Virginia. MSc. thesis, University of West Virginia, Morgantown, West Virginia. Racine, C.H. (1971) Reproduction of three species of oak in relation to vegetational and environmental gradients in the southern Blue Ridge. Bulletin of the Torrey Botanical Club 98, 297–310. https://doi. org/10.2307/2483968 Rebbeck, J., Gottschalk, K. and Scherzer, A. (2011) Do chestnut, northern red, and white oak germinant seedlings respond similarly to light treatments? Growth and biomass. Canadian Journal of Forest Research 41, 2219–2230. https://doi.org/10.1139/x11-124 Reich, R.B., Teskey, R.O., Johnson, P.S. and Hinckley, T.M. (1980) Periodic root and shoot growth in oak.

Chapter 2

Forest Science 26, 590–598. https://doi.org/10.1093/ forestscience/26.4.590 Rice, C. and Struve, D.K. (1997) Seedling growth form and water use of selected oak species. In: Genetics of Quercus, Diversity and Adaptation in Oak Species. Proceedings of Working Party 2.08.05, 12–17 October, International Union of Forestry Research Organizations, Pennsylvania State University, University Park, Pennsylvania, pp. 269–278. Roach, B.A. and Gingrich, S.F. (1967) Upland hardwoods can be grown efficiently. Pulpwood Annual 1967, 85–87. Robbins, C.T., Mole, S., Hagerman, A.E. and Hanley, T.A. (1987) Role of tannins in defending plants against ruminants: reduction in protein availability. Ecology 68, 98–107. https://doi.org/10.2307/1939852 Romberger, J.A. (1963) Meristems, growth and development in woody plants. USDA Forest Service Technical Bulletin 1293. USDA Forest Service, Washington, DC. Roth, E.R. and Hepting, G.H. (1943) Origin and development of oak stump sprouts as affecting their likelihood to decay. Journal of Forestry 41, 27–36. https:// doi.org/10.1093/jof/41.1.27 Roth, E.R. and Hepting, G.H. (1969) Prediction of butt rot in newly regenerated sprout oak stands. Journal of Forestry 67, 756–760. https://doi.org/10.1093/ jof/67.10.756 Sander, I.L. (1971) Height growth of new oak sprouts depends on size of advance reproduction. Journal of Forestry 69, 809–811. https://doi.org/10.1093/jof/69.11.809 Sander, I.L. (1979) Regenerating oaks. In: Proceedings of the National Silviculture Workshop. USDA Forest Service, Washington, DC, pp. 212–221. Scarlett, T.L. (2004) Acorn production and winter reproduction in white-footed mice (Peromyscus leucopus) in a southern Piedmont forest. Southeastern Naturalist 3, 483–494. https://doi.org/10.1656/15287092(2004)003[0483:APAWRI]2.0.CO;2 Scholz, H.F. (1955) Effect of scarification on the initial establishment of northern red oak reproduction. USDA Forest Service Lake States Forest Experiment Station Technical Note 425. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Schultz, J.C. (1989) Tannin–insect interactions. In: Hemingway, R.W. and Karchesey, J.J. (eds) Chemistry and the Significance of Condensed Tannins. Plenum Press, New York, pp. 412–433. https://doi.org/10.1007/ 978-1-4684-7511-1_26 Schwartz, C.W. and Schwartz, E.R. (1959) The Wild Mammals of Missouri. University of Missouri Press, Columbia, Missouri. Scott, V.E., Evans, K.E., Patton, D.R. and Stone, C.P. (1977) Cavity-nesting birds of North American Forests. USDA Forest Service Agriculture Handbook 511. USDA Forest Service, Washington, DC. Available at: https://www.gutenberg.org/files/49172/49172-h/49172h.htm (accessed 30 August 2018).

Regeneration Ecology I

Seidel, K.W. (1972) Drought resistance and internal water balance of oak seedlings. Forest Science 18, 34–40. https://doi.org/10.1093/forestscience/18.1.34 Shafer, J.D. (1971) Shade tolerance of scarlet oak seedlings. MSc. thesis, University of Missouri, Columbia, Missouri. Sharp, W.M. (1958) Evaluating mast yields in the oaks. Pennsylvania State University Agriculture Experiment Station Bulletin 635. Pennsylvania State University, State College, Pennsylvania. Sharp, W.M. and Chisman, H.H. (1961) Flowering and fruiting in the white oaks. I. Staminate flowering through pollen dispersal. Ecology 42, 365–372. https://doi.org/10.2307/1932087 Sharp, W.M. and Sprague, V.G. (1967) Flowering and fruiting in the white oaks. Pistillate flowering, acorn development, weather and yields. Ecology 48, 243– 251. https://doi.org/10.2307/1933106 Shaw, M.W. (1968) Factors affecting the natural regeneration of sessile oak (Quercus petraea) in North Wales. II. Acorn losses and germination under field conditions. Journal of Ecology 56, 647–660. https:// doi.org/10.2307/2258097 Shipek, D.C. and Ffolliott, P.F. (2005) Management of thinned Emory oak coppice for multiple resource benefits. USDA Forest Service Proceedings RMRS-P-36. USDA Forest Service Rocky Mountain Research Station, Ft Collins, Colorado, pp. 545–546. Available at: https://www.fs.usda.gov/treesearch/ pubs/23331 (accessed 1 July 2018). Short, H.L. (1976) Composition and squirrel use of acorns of black and white oak groups. Journal of Wildlife Management 40, 479–483. https://doi. org/10.2307/3799950 Sidney, C. (1948) Acorn weevils of the North Carolina Piedmont: their biology and method of sampling infestation. MSc. thesis, Duke University, Durham, North Carolina. Smallwood, P.D. and Peters, W.D. (1986) Grey squirrel food preferences: the effects of tannin and fat concentration. Ecology 67, 168–174. https://doi. org/10.2307/1938515 Smallwood, P.D., Steele, M.A. and Faeth, S.H. (2001) The ultimate basis of the caching preferences of rodents, and the oak-dispersal syndrome: tannins, insects, and seed germination. American Zoologist 41, 840–851. https://doi.org/10.1093/icb/41.4.840 Smallwood, P.D., Terzaghi, W., McEuen, A., Carlson, J.E., Ribbens, E., Contreras, T. and Steele, M.A. (2003) Searching for effects of tree squirrel caching behaviour on the distribution of oak seedlings. In: Abstract booklet: Third International Tree Squirrel Colloquium, Seventh European Squirrel Workshop, Ford Castle, Northumberland, UK, 26–30 May 2003. Smith, C.C. and Follmer, D. (1972) Food preferences of squirrels. Ecology 53, 82–91. https://doi.org/10.2307/1935712

117

Smith, K.G. (1986) Winter population dynamics of blue jays, red-headed woodpeckers and northern mockingbirds in the Ozarks. American Midland Naturalist 115, 52–62. https://doi.org/10.2307/2425836 Smith, R.L. (1962) Acorn consumption by white-footed mice. West Virginia University Agricultural Experiment Station Bulletin 482T. West Virginia University Agricultural Experiment Station, Morgantown, West Virginia. Sork, V.L. and Bramble, J.E. (1993) Prediction of acorn crops in three species of North American oaks: Quercus alba, Q. rubra and Q. velutina. Annales des Sciences Forestieres 50(suppl. 1), 128s–136s. https://doi.org/10.1051/forest:19930712 Sork, V.L., Bramble, J. and Sexton, O. (1993) Ecology of mast-fruiting in three species of North American deciduous oaks. Ecology 74, 528–541. https://doi. org/10.2307/1939313 Sperry, J.S. and Sullivan, J.E.M. (1992) Xylem embolism in response to freeze–thaw cycles and water stress in ring-porous, diffuse-porous and conifer species. Plant Physiology 100, 605–613. https://doi.org/10.1104/ pp.100.2.605 Stairs, G.R. (1964) Microsporogenesis and embryogenesis in Quercus. Botanical Gazette (Chicago) 125, 115–121. https://doi.org/10.1086/336255 Staley, J.M. (1965) Decline and mortality of red and scarlet oaks. Forest Science 11, 2–17. https://doi. org/10.1093/forestscience/11.1.2 Stapanian, M.A. and Smith, C.C. (1978) A model for seed scatterhoarding: coevolution of fox squirrels and black walnuts. Ecology 59, 884–896. https://doi. org/10.2307/1938541 Steele, M.A. and Smallwood, P.D. (2002) Acorn dispersal by birds and mammals. In: McShea, W. and Healy W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. Johns Hopkins University Press, Baltimore, Maryland, pp. 182–195. Steele, M.A., Hadj-Chikh, L.Z. and Hazeltine, J. (1996) Caching and feeding behavior of gray squirrels: responses to weevil infested acorns. Journal of Mammalogy 77, 305–314. https://doi.org/10.2307/ 1382802 Steele, M.A., Turner, G., Smallwood, P.D., Wolff, J.O. and Radillo, J. (2001) Cache management by small mammals: experimental evidence for the significance of acorn embryo excision. Journal of Mammalogy 82, 35–42. https://doi.org/10.1644/1545-1542(2001)0822.0.CO;2 Steele, M.A., Smallwood, P., Terzaghi, W.B., Carlson, J.E., Contreras, T. and McEuen, A. (2004) Oak dispersal syndromes: do red and white oaks exhibit different dispersal strategies? USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 72–77. Available at: https://www.fs.usda.gov/treesearch/pubs/6499 (accessed 1 July 2018).

118

Steele, M.A., Wauters, L. and Larsen, K. (2005a) Selection, predation, and dispersal of seeds by tree squirrels in temperate and boreal forests: are tree squirrels keystone granivores? In: Forget, P.M., Vander Wall, S., Lambert, J. and Hulme, P. (eds) Seed Fate: Predation, Dispersal and Seedling Establishment. CAB Inter­ national, Wallingford, UK, pp. 205–220. https://doi. org/10.1079/9780851998060.0205 Steele, M.A., McCarthy, B.C. and Keiffer, C.H. (2005b) Seed dispersal, seed predation, and the American chestnut. Journal of the American Chestnut Foundation 19(2), 47–54. Stein, W.I. (1990) Quercus garryana Dougl. ex Hook. Oregon white oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 650–660. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/table_ of_contents.htm (accessed 1 July 2018). Steiner, K.C., Abrams, M.D. and Bowersox, T.W. (1993) Advance reproduction and other stand characteristics in Pennsylvania and French stands of northern red oak. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 473– 483. Available at: https://www.fs.usda.gov/treesearch/pubs/15342 (accessed 1 July 2018). Tecklin, J. and McCreary, D.D. (1991) Acorn size as a factor in early seedling growth of blue oaks. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Research Station, Berkeley, California, pp. 48–53. Available at: https://www.fs.usda.gov/treesearch/pubs/28420 (accessed 1 July 2018). Teskey, R.O. (1978) Influence of temperature and moisture on root growth of white oak. MSc. thesis, University of Missouri, Columbia, Missouri. Thornburgh, D.A. (1990) Quercus chrysolepis Liebm. Canyon live oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 618–624. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/table_ of_contents.htm (accessed 1 July 2018). Tiedemann, A.R., Clary, W.P. and Barbour, R.J. (1987) Underground systems of Gambel oak (Quercus gambelii) in central Utah. American Journal of Botany 74, 1065–1071. https://doi.org/10.1002/j.1537-2197.1987. tb08717.x Tripathi, R.S. and Khan, M.L. (1990) Effects of seed weight and microsite characteristics on germination and seedling fitness in two species of Quercus in a subtropical wet hill forest. Oikos 57, 289–296. https:// doi.org/10.2307/3565956 Tryon, E.H. and Carvell, K.L. (1958) Regeneration under oak stands. West Virginia University Agricultural Experiment Station Bulletin 424T. West Virginia

Chapter 2

University Agricultural Experiment Station, Morgantown, West Virginia. Tryon, E.H. and Carvell, K.L. (1962) Acorn production and damage. West Virginia University Agricultural Experiment Station Bulletin 466T. West Virginia Uni­ versity Agricultural Experiment Station, Morgantown, West Virginia. Tucker, J.M. (1980) Taxonomy of California oaks. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 19–29. Available at: https://www.fs.fed.us/psw/publications/documents/psw_gtr044/index.shtml (accessed 1 July 2018). Turkel, H.S., Rebuck, A.L. and Grove, A.R., Jr (1955) Floral morphology of white oak. Pennsylvania State University Agriculture Experiment Station Bulletin 593. Pennsylvania State University Agricultural Experiment Station, University Park, Pennsylvania. Tyree, M.T. (1989) Cavitation in trees and the hydraulic sufficiency of woody stems. Annales des Sciences Forestieres 46(suppl.), 330s–337s. https://doi.org/ 10.1051/forest:19890574 Tyree, M.T. and Cochard, H. (1996) Summer and winter embolism in oak: impact on water relations. Annales des Sciences Forestieres 53, 173–180. https://doi. org/10.1051/forest:19960201 USDA Forest Service (1985) Insects of Eastern Forests. USDA Miscellaneous Publication 1426. USDA Forest Service, Washington, DC. Van Dersal, W.R. (1940) Utilization of oaks by birds and mammals. Journal of Wildlife Management 4, 404– 428. https://doi.org/10.2307/3796011 Vander Wall, S.B. (1990) Food Hoarding in Animals. The University of Chicago Press, Chicago. Vander Wall, S.B. (2001) The evolutionary ecology of nut dispersal. The Botanical Review 67, 74–117. https:// doi.org/10.1007/BF02857850 Vivin, P., Aussenac, G. and Levy, G. (1993) Differences in drought resistance among 3 deciduous oak species grown in large boxes. Annales des Sciences Forestieres 50, 221–233. https://doi.org/10.1051/ forest:19930301 Vogt, A.R. and Cox, G.S. (1970) Evidence for the hormonal control of stump sprouting by oak. Forest Science 16, 165–171. https://doi.org/10.1093/forestscience/16.2.165 Wada, N. and Kamata, N. (2003) Effects of simulated partial cotyledon herbivory on seedling growth in Quercus crispula acorns. In: Proceedings Forest Insect Population Dynamics and Host Influences. International Union of Forestry Research Organizations (IUFRO) Kanazawa 2003, Kanazawa, Japan, pp. 124–126. Ward, W.W. (1966) Epicormic branching of black and white oaks. Forest Science 12, 290–296. https://doi. org/10.1093/forestscience/12.3.290 Wareing, P.F. (1951) Growth studies in woody species IV. The initiation of cambial activity in ring-porous

Regeneration Ecology I

species. Physiologia Plantarum 4, 546–562. https:// doi.org/10.1111/j.1399-3054.1951.tb07692.x Wargo, P.M. and Haack, R.A. (1991) Understanding the physiology of dieback and decline diseases and its management implications for oak. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 147–158. Weigel, D.R. and Johnson, P.S. (1998) Stump sprouting probabilities for southern Indiana oaks. USDA Forest Service Technical Brief TB-NC-7. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11004 (accessed 1 July 2018). Weigel, D.R. and Peng, C.J. (2002) Predicting stump sprouting and competitive success of five oak species in southern Indiana. Canadian Journal of Forest Research 32, 703–712. https://doi.org/10.1139/x02-042 Weigel, D.R., Dey, D.C. and Peng, C.J. (2006) Stump sprout dominance probabilities of five oak species in southern Indiana 15 years after clearcut harvesting. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, Carolina, pp. 551–558. Available at: https://www.fs.usda.gov/treesearch/pubs/53755 (accessed 1 July 2018). Weld, L.H. (1922) Note on American gallflies of the family Cynipidae producing galls on acorns with description of new species. Proceedings of the US Natural Museum 61, 1–32. https://doi.org/10.5479/si.00963801. 61-2440.1 Welker, J.M. and Menke, J.W. (1990) The influence of simulated browsing on tissue water relations, growth and survival of Quercus douglasii (Hook and Am.) seedlings under slow and rapid rates of soil drought. Functional Ecology 4, 807–817. https://doi.org/ 10.2307/2389447 Wendel, G.W. (1975) Stump sprout growth and quality of several Appalachian hardwood species after clearcutting. USDA Forest Service Research Paper NE-329. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/15457 (accessed 1 July 2018). Williamson, M.J. (1966) Premature abscissions and white oak acorn crops. Forest Science 12, 19–21. https://doi.org/10.1093/forestscience/12.1.19 Wilson, B.F. (1968) Red maple stump sprout development the first year. Harvard Forest Paper 18. Harvard forest, Petersham, Massachusetts. Wilson, B.F. (1993) Compensatory shoot growth in young black birch and red maple trees. Canadian Journal of Forest Research 23, 302–306. https://doi.org/10.1139/ x93-040 Winston, P.W. (1956) The acorn microsere, with special reference to arthropods. Ecology 37, 120–132. https://doi.org/10.2307/1929675

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Wolgast, L.J. (1972) Mast production in scrub oak (Quercus ilicifolia) on the coastal plain in New Jersey. PhD dissertation, Rutgers University, Princeton, New Jersey. Wolgast, L.J. and Trout, J.R. (1979) Late spring frost affects yields of bear oak acorns. Journal of Wildlife Management 43, 239–240. https://doi.org/10.2307/ 3800662 Wood, O.M. (1934) A brief record of seed productivity for chestnut oak in southern New Jersey. Journal of Forestry 32, 1014–1016. Wood, O.M. (1938) Seedling reproduction of oak in southern New Jersey. Ecology 19, 276–293. https:// doi.org/10.2307/1929642 Wright, S.L. (1987) Managing insects affecting oak regeneration by prescribed burning. USDA Forest Service General Technical Report SE-46. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 186–192. Available at: https://www.srs.fs.usda.gov/pubs/gtr/ gtr_se046.pdf (accessed 1 July 2018).

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Wright, S.L., Hall, R.W. and Peacock, J.W. (1989) Effects of simulated insect damage on growth and survival of northern red oak (Quercus rubra L.) seedlings. Environmental Entomology 18, 235–239. https://doi.org/10.1093/ee/18.2.235 Wuenscher, J.E. and Kozlowski, T.T. (1971) Relationship of gas-exchange resistance to tree-seedling ecology. Ecology 52, 1016–1023. https://doi.org/10.2307/ 1933807 Yin, X., Perry, J.A. and Dixon, R.K. (1989) Fine-root dynamics and biomass distribution in a Quercus ecosystem following harvest. Forest Ecology and Management 27, 159–177. https://doi.org/10.1016/ 0378-1127(89)90105-9 Zasada, J.C. and Zahner, R. (1969) Vessel element development in the earlywood of red oak (Quercus rubra). Canadian Journal of Botany 47, 1965–1971. https://doi.org/10.1139/b69-288 Zimmermann, M.H. (1983) Xylem Structure and the Ascent of Sap. Springer-Verlag, New York.

Chapter 2

3



Regeneration Ecology II: Population Dynamics Introduction

This chapter is about the establishment and development of populations of juvenile oaks. ­ Variability is a normal characteristic of tree populations, and it can be described in relation to specific tree attributes. For example, an oak forest can be described by the size or age distributions of its member trees, and how those distributions vary in time and space. Populations of one tree species also interact with other species, each with unique ecological requirements and competitive advantages and disadvantages that lead to variation in patterns of establishment, growth and survival. Population variability is further increased by forest disturbances. Predicting the responses of tree populations to forest disturbances, whether natural or of human origin, is fundamental to the practice of silviculture. The fitness or suitability of adult trees to the physical characteristics of an ecosystem (i.e. the site) are often cited as explanations for the observed distribution of species across environmentally heterogeneous landscapes. However, relatively little emphasis has similarly been accorded to species’ regeneration requirements. The concept of the regeneration niche attempts to fill that void. In contrast to the more general notion of species niche (Chapter 1, this volume), regeneration niche refers specifically to regeneration events and associated ecological conditions. Regeneration niche therefore considers both the time and the place where there is a high probability that a mature tree will be replaced by another tree of the same species (Grubb, 1977). Regeneration niches are ephemeral; their abundance and locations vary over time. The resource requirements of seedlings and seedling sprouts (e.g. light, nutrients, heat and soil moisture) differ from those of mature trees. Therefore, it has been proposed that niche differences among species coexisting in the same ecosystem may be best expressed during the vulnerable early stages of plant

establishment (Grubb, 1977; Latham, 1992; Veblen, 1992). Because all autotrophic plants require essentially the same kinds of resources (but not necessarily the same amounts), niche differentiation among species is likely to be strongly expressed during a life history period when one or more of those resources are limiting. Different species, even among the oaks, adapt to gradients in resource availability and competition in different ways, including how they allocate their growth to stems, roots and leaves (Loach, 1967; Gottschalk, 1985, 1987; Matsuda and McBride, 1986; Matsuda et al., 1989; Kolb and Steiner, 1990; Latham, 1992; Pallardy and Rhoads, 1993; Walters et  al., 1993; Callaway and Mahall, 1996; Rice and Struve, 1997). Seedling populations may stratify by species along resource gradients that occur at very small spatial scales. At a spatial scale that corresponds to the area occupied by an adult tree, the distribution of resources may be highly variable (patchy). But at a smaller spatial scale corresponding to the area occupied by a seedling, the same resources may appear very uniform and either favour or inhibit seedling development (Latham, 1992). This variability in the spatial distribution of regeneration niches has been offered as one explanation for the maintenance of diversity in ecosystems (Grubb, 1977; Latham, 1992) and the coexistence of multiple tree species (Veblen, 1992). For the oaks, the critical regeneration events include flowering, fruiting, seed dispersal and germination, seedling establishment, dieback and sprouting, and the growth of oak reproduction. Moreover, numerous biotic and abiotic factors affect the regeneration niche. An oak’s regeneration niche therefore may not pertain to just one ecological factor, but rather to a suite of factors that change over time and result in a corresponding range of regeneration success probabilities. Over time, it is the coupling of regeneration events with ecological conditions that determines probabilities of regeneration success for oaks.

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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Regeneration Strategy A species’ regeneration niche (i.e. when and where its regeneration requirements are met) is partially expressed by the mechanisms that have evolved to facilitate its regeneration. However, an advantage associated with a particular mechanism in one environment may be a disadvantage in another environment. In a sense, regeneration is one of the problems that a species solves during its evolution. The resulting solution constitutes the evolutionary compromise that defines a species’ regeneration strategy.1 Strategies pertaining to oaks can be considered in relation to reproductive mechanisms, the accumulation and fluctuation of populations of reproduction, and responses to site and forest disturbance. Reproductive mechanisms: seeding and sprouting Seed production and sprouting are two reproductive mechanisms or tactics employed by oaks in their regeneration strategy. Although all oaks rely to some extent on both seeding and sprouting, they differ in their dependence on one mechanism versus the other. Even within a species, regeneration tactics may vary among habitats and disturbance regimes. The large number of oak species, their wide distribution, and the disparate habitats they occupy require that regeneration strategies among the oaks vary substantially. At one extreme, oaks of the arid south-western USA may regenerate almost exclusively by sprouting. Among those species, some reproduce largely from root-like lignotubers and rhizomes (Tiedemann et al., 1987). One such species is Gambel oak, whose seedlings are often only a minor source of reproduction (Harper et al., 1985). Hinckley oak, a shrubby rhizomatous oak restricted to a natural range of a fraction of an acre in western Texas, is known to regenerate only from rhizomes (Muller, 1951). Similarly, the shrub oaks of the fire-prone southern California chaparral depend heavily on their ability to sprout after burning. But even when regeneration is largely dependent on sprouting, some new seedlings eventually must be produced to replace the trees and rootstocks that are inevitably lost to mortality, if a species is to persist. In the Ozark Highlands of southern Missouri, oak regeneration also largely depends on sprouting (Liming and Johnston, 1944; Johnson, 1979; Dey

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et al., 1996a). These forests occur on relatively droughty sites and are often dominated by some combination of black, white, scarlet, post, southern red and blackjack oaks. Under mature closed-­canopy stands, the total density of oak reproduction often varies from 1000 to 3000 seedlings and seedling sprouts/acre. After a good acorn crop, 150–300 new oak seedlings/acre may become established (Sander, 1979a). But the continued dominance of the oaks largely depends on a relatively small number of oak seedling sprouts (200–400/acre) with large root systems capable of supporting rapid shoot growth after disturbances that release growing space formerly occupied by trees in the overstorey (Johnson, 1979; Sander et al., 1984). Except on the most mesic habitats within the Ozark Highlands, oaks are seldom successionally displaced by non-oaks. Throughout much of the eastern USA, northern red oak occupies the mesic middle ground between wet and dry extremes in soil moisture. Northern red oak expresses its relatively flexible regeneration strategy by producing thousands of new seedlings/ acre following bumper acorn crops (Johnson, 1974). In northern red oak forests in France, the number of oak seedlings and seedling sprouts growing beneath the forest canopy may exceed 100,000/acre (Steiner et al., 1993). Northern red oak also has flexible seedbed requirements (Crow, 1992), potentially rapid shoot growth (Farmer, 1975), the ability to regenerate from seedlings established after final harvest (Johnson et al., 1989), moderate shade tolerance (Sander, 1990) and the capacity to sprout from large stumps of overstorey trees (P.S. Johnson, 1975; Wendel, 1975; Weigel and Johnson, 1998). But unlike the more xerophytic oaks of the Missouri Ozarks, the relatively mesophytic red oak frequently fails to regenerate because of its vulnerability to successional displacement by the more shade-­tolerant species with which it typically co-occurs (Johnson, 1976; Crow, 1988; Loftis, 1990a; Nowacki et al., 1990; Lorimer, 1993). In bottomland forests of southern USA, seeding appears to be a more important regeneration tactic than in other oak-dominated ecosystems in North America. For example, water oak seedlings established after final harvest can become important members of the succeeding stand (Golden and Loewenstein, 1991; Loewenstein, 1992; Loewenstein and Golden, 1995). But the bottomland oaks also can sprout prolifically, and this facilitates their development in stands disturbed by fire and grazing (Aust et al., 1985). Being neither obligate seeders or

Chapter 3

forests are often followed by periods of low seedling density because of low seedling survival rates and infrequent acorn crops (R.L. Johnson, 1975). Prolonged periods with little or no oak advance reproduction thus frequently occur. Consequently, domination of bottomland forests by oaks is often limited to one generation. Bottomland oaks nevertheless possess characteristics that, under certain conditions, favour their regeneration over associated species. For example, water oak can persist in floodplain forests because, unlike its non-oak competitors, its acorns often germinate after flooding occurs. Oak seedlings also avert flood-caused uprooting because of their long taproots and ability to resprout after their tops are damaged (Streng et al., 1989). Because of this persistence, oak reproduction is often relatively well represented in the older and larger size classes of the total reproduction complex. In an East Texas floodplain forest, water oak seedlings ranged from 5% of all 1-year-old seedlings to 32% of all 5-year-old seedlings (Fig. 3.1). Like oak reproduction in uplands, the older and larger reproduction in bottomlands has the highest probability of capturing growing space after a canopy disturbance. These anomalies in regeneration strategy among the oaks emphasize the difficulty of generalizing their regeneration ecology across species and habitats. Different oaks have solved their regeneration

sprouters, these oaks have very flexible regeneration strategies. The relative importance of seeding as a species’ regeneration tactic is sometimes revealed by the number of seedlings that become established after a heavy acorn crop. For example, more than 100,000 Nuttall oaks/acre became established after a bumper acorn crop in one bottomland forest (R.L. Johnson, 1975). If only 0.1% of those seedlings (100/acre) were competitively successful and well distributed, they would occupy most of the available growing space within two decades following a disturbance that removed the overstorey. Nuttall oak’s seeding strategy is complemented by its rapid height growth (Johnson, 1981), which is necessary for it to successfully compete with the fast-growing and persistent competitors occurring on bottomlands. A species’ seeding strategy also may be reinforced by the dispersal of acorns by rodents and jays to habitats favourable to seed protection, germination and seedling growth (McQuilkin, 1983; Harrison and Werner, 1984; Sork, 1984; Johnson and Webb, 1989). Nevertheless oaks often fail to regenerate in southern bottomland forests. Competing green ash, sweetgum and other tree reproduction may outgrow and suppress oak reproduction shortly after stand-initiating disturbances occur (Johnson and Krinard, 1983; Aust et al., 1985). Moreover, periods of high oak reproduction density in bottomland 100

2574

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110 Water oak

Seedlings (%)

80

Sweetgum

60

Ironwood

40

Red maple

20

American elm

Other species

0 1

2 3 4 Seedling age (years)

5

Fig. 3.1.  Species composition of reproduction under an East Texas floodplain forest in relation to seedling age. (Adapted from Streng et al., 1989.) The numbers of trees in each age class are shown above the bars. ‘Other species’ include blackgum, deciduous holly, American holly and Sebastian bush.

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problems in different ways. Some species are more flexible than others (Fig. 3.2). In turn, each species’ environment, physiology and genetics, shapes its flexibility in regeneration tactics. Successful regeneration of oaks partly depends on the species that oaks must compete with. The regeneration strategies of competing species may differ greatly from the oaks. As a result, non-oaks may be competitively advantaged or disadvantaged, depending on their adaptations to the particular environment. In addition to basal sprouting and producing seedlings from current seed, non-oaks also may regenerate from root sprouts (e.g. aspens, beech, sassafras and sweetgum) and from seed stored in the forest floor (e.g. black cherry, white and green ashes, and yellow-poplar). The seeds of some species such as pin cherry and briars, unlike the oaks, remain buried in the forest floor and soil where they can accumulate for decades (Marks, 1974; Whitney, 1986). Seeds of other prolific seed producers such as yellow-poplar, black cherry and sassafras are shorter lived but nevertheless may be present in high numbers (Wendel, 1977). When environmental conditions are favourable, such as after clearcutting or burning, stored seeds of non-oaks may germinate in enormous numbers and produce dense populations that often outgrow co-occurring oaks. After heavy thinning or complete overstorey removal, competition from non-oak root sprouts and stump sprouts may suppress and kill established

Relative dependence on seeding

100 Nuttall oak 80 Northern red oak 60 White oak 40

Black oak

20 0

Post oak Gambel oak 0

20 40 60 80 Relative dependence on sprouting

100

Fig. 3.2.  Conceptualized dependence of six oak species on seeding versus sprouting as a regeneration tactic. Diameters of circles are proportionate to each species’ postulated flexibility in selecting the alternative tactic.

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oak reproduction (Beck, 1970; Beck and Hooper, 1986). Grasses, sedges, ferns, vines and shrubs also can seriously interfere with the regeneration of oaks (Jarvis, 1964; Beck, 1970; Hanson and Dixon, 1985; Bowersox and McCormick, 1987; Horsley, 1991; Smith and Vankat, 1991; Johnson, 1992). In the oak savannahs and woodlands of Oregon and California, competition from exotic annual grasses that have replaced native perennial bunchgrasses is a probable cause of oak regeneration failures (Gordon et al., 1989; Welker and Menke, 1990; Barnhart et al., 1991; Danielsen and Halvorson, 1991; Pavlik et al., 1991; Riegel et al., 1992). Whether or not oaks regenerate successfully thus depends in no small part on the competition environment. Accumulation of oak reproduction In many oak-dominated ecosystems, the oak reproduction beneath the parent stand includes seedlings and seedling sprouts that originate from several acorn crops. This accumulation of reproduction results from the combined effects of periodic seed production, the relatively large food reserves in acorns that sustain seedlings through the first year, the high sprouting capacity of seedlings, drought tolerance and the ability of seedlings to persist under at least moderate shade. A small proportion of the seedlings originating from a single acorn crop (cohort), may survive for several decades. Silviculturists sometimes refer to the resulting accumulation of seedlings and seedling sprouts as advance reproduction because, in the management of even-aged forests, it is present in advance of final harvest. Its presence and development largely determines the importance of oak after the occurrence of natural or human events that greatly reduce parent stand density. The capacity of oak reproduction to accumulate over several acorn crops may compensate, in part, for the inability of acorns to remain viable for more than a few months during a single dormant season. The term accumulation, as used here, is not meant to imply a continuous, unending increase in oak reproduction density with time. Rather, it refers to the episodic addition of new seedlings to one or more established cohorts. The total population of seedlings and seedling sprouts beneath an oak stand responds to the prevailing overstorey condition, which itself is continually changing and affecting established oak reproduction and other understorey vegetation. However, the changes in

Chapter 3

oak reproduction occur much faster than in the parent stand. Consequently, relatively short cycles of the birth, growth and death of individual cohorts of oak reproduction are embedded in longer-term changes in overstorey species composition and size structure. An accumulated population of oak reproduction can sometimes capture all or some of the growing space in canopy gaps or larger openings. Its success in doing so usually depends on the presence of seedling sprouts with relatively large roots. True seedlings and small seedling sprouts usually have root systems that are too small to support rapid shoot growth (Sander, 1971). And stump sprouts from overstorey trees, by themselves, may not be numerous enough to capture all of the available growing space (Sander et al., 1984). Nevertheless, it is the number, size and spatial distribution of all three classes of reproduction that express the total oak regeneration potential of a stand (Sander et al., 1984). Because this potential is implicit in the advance reproduction and the parent stand itself, the new stand is essentially a ‘legacy’ (sensu Franklin et al., 1989) of the parent stand. Sustaining oak-dominated forests thus largely depends on perpetuating propagules from one generation to the next. Whether or not the overstorey is eliminated in small gaps or over large areas, the resulting spatial units of reproduction are evenaged.2 Although oaks have not always been regarded as species well adapted to capturing small canopy gaps (Ehrenfeld, 1980; Crow, 1988), they have the potential to do so if advance reproduction of sufficient size is present. For example, 10 ft tall advance reproduction of northern red oak captured canopy gaps as small as 1/25 acre in a mixed sugar maple– oak stand in south-western Wisconsin (Lorimer, 1983). This is the approximate area occupied by the crowns of five dominant or codominant northern red oaks 15 inches dbh in a fully stocked stand. The accumulation of oak advance reproduction is most obvious in xeric uplands where recurrent dieback and resprouting of reproduction produce multiple stems of varying size, some of which are attached to large root systems. Large roots may be several decades older than their living stems. The true age of a seedling sprout thus is recorded in the rings of its taproot, and not its stem, which is set back to age zero with each complete dieback.3 In a south-eastern Ohio oak–hickory stand, the roots of oak reproduction were up to 32 years older than the stems (Merz and Boyce, 1956). In West Virginia,

Regeneration Ecology II

root ages of northern red and white oaks were up to 8–10 years older than their stems (Fig. 3.3). Accordingly, the range of ages of the roots of advance reproduction and the number per acre of older living roots is an indicator of an oak stand’s capacity to accumulate oak reproduction. Variation in the accumulation process In the oak forests of the eastern USA, the accumulation of oak reproduction generally increases with decreasing site quality and overstorey density. For oak forests in West Virginia, the relation is represented by the decreasing average age of root systems of oak reproduction (age measured at the root collar) with increasing overstorey density and site index (Fig. 3.4A). Accumulation of oaks tends to be greatest on the drier sites and where overstorey density is low. Root diameter is positively correlated with root age (when both are measured at the root collar) so the accumulation process is similarly evident in the relation of reproduction basal diameter to site quality and overstorey density (Fig. 3.4B). However, oaks in arid and semi-arid regions behave differently. Because of extreme heat, solar radiation and moisture deficiency, establishment and survival of oak reproduction is often favoured on the less severe sites such as north-facing slopes and under partial shade rather than in canopy gaps (Callaway and D’Antonio, 1991; Callaway et al., 1991; Williams et al., 1991; Callaway, 1992; Keeley, 1992). Wherever the accumulation of oak reproduction occurs, it results from survival rates sufficient for reproduction to build up over several successive acorn crops. Because the size and age of oak advance reproduction are positively correlated, properties of the accumulation process are sometimes implicit in the size distribution of the reproduction. The regeneration dynamics of black oak–white oak/Vaccinium forests in northern Lower Michigan (Host and Pregitzer, 1991) (see Chapter 1, this volume, Fig. 1.8) provide an example. These forests occur on droughty outwash sands where the site index for black oak ranges from about 50 to 60 ft at an index age of 50 years (Cleland et al., 1993). The density of oak reproduction averages about 11,000 seedlings or seedling sprouts/acre (Johnson, 1992). However, the frequency of occurrence of black oak and white oak reproduction in successively larger basal diameter classes decreases at an exponential rate (Fig. 3.5). Such rates may represent intrinsic properties of the ecosystem. They also may reflect rates of recruitment

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Root diameter (B) 24 (in.)

24

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Root age (years)

20

0.4

16 12 8 0.2 4 0

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True seedlings (1:1) 0

4

8

12

16

Root age (years)

(A)

0.8 0.6

16 12 8 4 0

0.4 0.2 0

True seedlings (1:1) 4

8 12 Stem age (years)

16

Fig. 3.3.  Relation of root age to stem age and root diameter for (A) white oak, and (B) northern red oak reproduction in a random sample of 56 stems of each species beneath West Virginia oak stands. (Adapted from Powell, 1976.) Based on the linear regression models: 2

(A) Root age = 1.97 + 0.671 × SA + 10.0 × RD; R = 0.86.

(B) Root age = 0.199 + 0.735 × SA + 8.98 × RD; R 2 = 0.90. In both equations, SA = stem age in years and RD = root diameter in inches. Root diameters were measured just below the root collar. Estimates are shown for the approximate range of observed values of stem age and root diameter.

of oak reproduction into successively larger diameter classes. Accordingly, only a proportion of the reproduction in each size class survives to become a member of the next larger size class. This recruitment or ingrowth of oak reproduction into successively larger size classes involves three population processes that jointly determine the accumulation rate: (i) the periodic establishment of new seedlings (seedling input); (ii) growth rate; and (iii) survival rate. If in a given ecosystem all three processes are in equilibrium so that seedling input balances seedling mortality and seedling growth remains relatively constant, then the associated size and age distribution of survivors should remain constant. However, for various reasons, such constancy is unlikely to persist in an oak forest. Variable weather, fluctuating acorn production, fluctuating populations of acorn consumers, changes in stand composition and structure that affect acorn production, seedling growth and seedling survival are some of the factors that mitigate against constancy. The diameter distribution of oak reproduction at a single point in time nevertheless reflects, in some way, the recent history of seedling input, drain and growth in a given stand. Thus, population

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curves such as those in Fig. 3.5 represent a transient recruitment rate of reproduction into successively larger size classes under recently prevailing conditions. Such rates are useful in understanding the variable nature of the accumulation process. A transient recruitment rate can be estimated by fitting certain mathematical functions to observed field data. The negative exponential function has been widely used to express the constant exponential decrease in number of individuals within a cohort per unit time. For the black oak curve in Fig. 3.5, the slope coefficient of the function (−13.1) defines the rate of change in oak seedling and seedling sprout density in relation to increasing basal diameter. However, a more useful expression of the curve’s slope is given by the constant negative exponential rate, k, where: k = e b [3.1] e is the base of the natural logarithm, and b is the curve’s slope coefficient. Equation 3.1 thus restates b as a negative exponential rate of depletion for increasing basal diameter classes. Accordingly, k corresponds to the survival rate of seedlings or seedling sprouts per unit of increase in diameter (as opposed to change

Chapter 3

(A)

(B)

40

0.8 Site index

30

35 Root diameter (in.)

Mean root age (years)

Site index 35 50 20

65

80

10

0.6 50 0.4 65

80 0.2

0

60

70

80

90

60

70

80

90

Crown cover (%)

Crown cover (%)

Fig. 3.4.  Mean age (A) and diameter (B) of roots of oak reproduction in relation to overstorey crown cover and site index in West Virginia forests. Ages and diameters were measured just below the root collar on a random sample of 46 plots. (Adapted from Matney, 1974.) Estimates are based on linear regression models:  

(A) Root age = 63.8 − 0.391 × CC − 0.205 × SI − 0.241 × SLP; R 2 = 0.74 (B) Root diameter = 1.99 − 0.0172 × CC − 0.0185 × SI + 0.000185 × CC × SI − 0.00285 × SLP; R 2 = 0.80 For both models, CC = percentage crown cover, SI = oak site index (ft at an index age of 50 years), SLP = slope percentage. For this graph slope percentage is held constant at 25, and each data point represents seven to ten trees. Estimates are shown for the approximate range of observed values of crown cover and site index. The estimates represent the average of the sample of scarlet, chestnut, black, white and northern red oak reproduction.

over time). Therefore k represents the probability of a seedling or seedling sprout surviving long enough to attain a basal diameter of 1 inch. For the black oak data, k is approximately 0.000001. However, because 1 inch is beyond the observed data range, it may be more meaningful to derive a survival rate per 0.1 inch of basal diameter increase. If we call this rate k0.1, then: k0.1 = e b /10 [3.2] which yields a rate of 0.27/0.1 inch (i.e. 0.27 per 0.1 inch). If current stand conditions were sustained, we would expect about 27% of the population of black oak reproduction to survive to be recruited into the next larger 0.1 inch basal diameter class, 7% (0.272) into the second larger 0.1 inch class, and 0.5% (0.273) in the third larger class. For larger diameter classes, recruitment drops to 0.1% or less. In contrast to black oak’s constant recruitment rate, white oak’s rate continually changes with

Regeneration Ecology II

increasing basal diameter, and this relationship is better described mathematically by the power function (Fig. 3.5). The probability that a white oak seedling or seedling sprout of a given initial size survives to grow into the next larger size class increases as size increases. Assuming that rates are essentially constant over short intervals of basal diameter, the recruitment rate from the 0.1 to the 0.2 inch diameter class is about 18% whereas the rate from the 0.2–0.3 class is 30% (Table 3.1). The magnitude of increase in recruitment rate with increasing size of reproduction suggests that, for a given number of initially established seedlings, white oak is a more aggressive accumulator of seedlings than is black oak in this ecosystem. This is consistent with white oak’s greater shade tolerance, which is also evident in the differences between the overstorey diameter distributions of the two species (Fig. 3.5 inset). The relatively large numbers of white oaks in the small overstorey diameter classes largely represent suppressed trees. Where a canopy gap

127

Overstorey trees (no./acre)

Seedlings and seedling sprouts/acre

2800 2400 2000 1600 1200

20 16 12 8 4 0 2

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10 14 Dbh (in.)

800

18

22

400 0 0.0

0.1

0.2

0.3

0.4

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Basal diameter (in.) White oak

Black oak

Fig. 3.5.  The density of black oak and white oak seedlings and seedling sprouts in relation to their basal diameter under a relatively undisturbed stand on a droughty outwash sand in northern Lower Michigan (black oak–white oak/Vaccinium type as defined by Host and Pregitzer, 1991). The black oak curve is given by the negative exponential model: N = 6581.96(e−13.107d ) where N is the number of seedlings and seedling sprouts per acre, e is the base of the natural logarithm and d is the diameter of seedlings or seedling sprouts measured in inches at the ground line. The white oak curve is given by the power function model: N = 3.81d −1.886 The inset graph shows the diameter distribution of the overstorey, which is at 96% stocking based on Gingrich’s (1967) stocking equation.

occurs, larger seedling sprouts may be recruited into the overstorey. The lower end of the overstorey diameter distribution therefore may include both recently recruited trees, and trees declining in growth and vigour that are succumbing to intertree competition. The paucity of black oak in the smaller diameter classes reflects its shade intolerance and inability to endure in the subcanopy, and its low probability of recruitment into the overstorey. Where overstorey density remains ­uniformly high, the reproduction of both species remains in a largely suppressed state. To remain in that state, the oak reproduction must be continually renewed in endless short cycles of seedling input and mortality. In conceptualizing the reproduction accumulation process, it is convenient to label ecosystems where oak reproduction has a natural propensity to accumulate over long periods as intrinsic accumulators and those with little such propensity as

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recalcitrant accumulators. Oak reproduction probably accumulates, at least at times, over several acorn crops wherever oaks naturally occur. Accumulation is facilitated in part by the relatively large food reserves of acorns that sustain seedlings, even when they grow under low light, through the critical first year of establishment. During germination, roots emerge before shoots (see Chapter 2, this volume, Fig. 2.16). Rapid taproot growth, deep penetration, recurrent shoot dieback and resprouting favour root development over shoot development and thus reinforce the accumulation process. The accumulation of oak reproduction even in mesic and wet forests therefore may be more pronounced than that of associated non-oaks (e.g. in the bottomland oak forests of Texas) (Fig. 3.1). A more rigorously defined classification of accumulator types incorporates the known or assumed capacity of oak advance reproduction to

Chapter 3

Table 3.1.  The estimated probability (P) that a seedling or seedling sprout of white oak advance reproduction of an initial basal diameter survives to a specified future basal diameter class in black oak–white oak/Vaccinium forests in northern Lower Michigan.a Initial basal diameter (in.)

Estimated probability (P) according to future basal diameter class 0.2 in.

0.3 in.

0.4 in.

0.5 in.

0.1 0.2 0.3 0.4

0.18 – – –

0.16 0.30 – –

0.15 0.27 0.41 –

0.14 0.24 0.37 0.52

a

Based on fitting the observed frequency distribution of reproduction by basal diameter classes at one point in time to the power function equation: N = 3.81D−1.886, where N is the estimated number of seedlings and seedling sprouts per acre, and D is basal diameter in inches at the ground line for a seedling or for the largest stem for rootstocks with multiple stems. P is derived from Equation 3.2. The values of P shown assume that rates of change within 0.1 inch intervals of basal diameter are constant.

replace the parent stand and its relative constancy in doing so. In the eastern USA, a high capacity for replacement of overstorey oaks by oak recruitment occurs most frequently in the drier ecosystems. There, the accumulation of oak reproduction is often intrinsic and relatively independent of forces external to the ecosystem itself. Oaks in such ecosystems tend to be self-replacing, and therefore are not readily displaced successionally by non-oaks. These ecosystems therefore are relatively resilient, that is they tend to quickly return to their pre-disturbance state. One of the largest North American ecosystems of this type is the Ozark Highlands of Missouri, where oak reproduction characteristically accumulates in the understorey for decades, even in the absence of large-scale disturbances. The accumulation of oak reproduction is nevertheless strongly correlated with topographic features such as slope position and aspect (Sander et al., 1984; Dey, 1991; Dey et al., 1996b; Kabrick et al., 2014), which collectively influence light, heat and soil moisture. Oak forests that similarly accumulate oak reproduction occur on the drier sites in the Ohio Valley (Minckler and Woerheide, 1965), the Appalachians (Trimble, 1973; Ross et al., 1986) and the Lake States (Johnson, 1966, 1992; Arend and Scholz, 1969). Ecosystems that are intrinsic accumulators do not necessarily accumulate large numbers of oak reproduction. For example, about 1200 seedlings

Regeneration Ecology II

and seedling sprouts/acre characterize some Ozark oak stands (Sander, 1979b). Only 150–300 new oak seedlings/acre may become established, even after a good acorn crop (Sander, 1979b; McQuilkin, 1983). In contrast, more than 100,000 Nuttall oak seedlings/acre may become established after a single bumper acorn crop in bottomland forests that are recalcitrant accumulators (R.L. Johnson, 1975). Thus, large numbers of oak seedlings by themselves may not identify ecosystems that intrinsically accumulate oak reproduction. These anomalies emphasize the degree to which regeneration processes can vary among different kinds of oak forests. Recalcitrant accumulation of oak reproduction is characteristic of mesic and hydric ecosystems. There, oak reproduction may accumulate only after prolonged or recurrent disturbances spanning successive acorn crops. In the absence of disturbance, these oak forests usually are successionally displaced by non-oaks (Carvell and Tryon, 1961; Trimble, 1973; R.L. Johnson, 1975; Loftis, 1990a; Nowacki et al., 1990; Will-Wolf, 1991; Abrams and Nowacki, 1992). In the absence of disturbance, a dense overstorey with a subcanopy of small trees often develops (Braun, 1972; Loftis, 1990b; Motsinger et al., 2010). Vertical stratification of tree crowns together with high total stand density creates extremely low light intensities on the forest floor. Nevertheless, reported oak reproduction ­densities in North American forests exceed 50,000 seedlings and seedling sprouts/acre in mesic habitats (Tryon and Carvell, 1958) and 100,000/acre in bottomlands (R.L. Johnson, 1975). But such ecosystems also may be frequently depauperate of oak reproduction (Sander, 1983). When oak reproduction does occur, few cohorts are represented because of low survival rates. Most of the seedlings from a single cohort often die before the next good acorn crop occurs because respiration rates of seedlings growing in deep shade often exceed their photosynthetic rates (Hanson et al., 1987; Dey and Parker, 1996; Motsinger et al., 2010). Microsites where oak seedlings become established in large numbers may not be where they ultimately survive (Johnson, 1966; Harrison and Werner, 1984). Whereas the cool, moist microclimate of north-east-facing slopes may favour initial establishment, the more favourable places for long-term survival, root development, and thus accumulation occur on the more southerly or neutral aspects (Carvell and Tryon, 1961; Sander et al., 1984; Walters, 1990). In eastern forests, there is a general

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inverse relation between site quality and regeneration success: the better the site the greater the competition and the more difficult it is to regenerate oaks (Arend and Scholz, 1969; Trimble, 1973; Lorimer, 1989; Loftis, 1990b). The accumulation of oak reproduction in relation to combined site and overstorey factors is indirectly illustrated by the demographics of reproduction density in upland forests of south-eastern Ohio (Walters, 1990). There, forests are comprised of black, white, scarlet, northern red and chestnut oaks mixed with maples, American beech, yellow-poplar and other hardwoods. The region is characterized by heavily dissected low hills with relatively short slopes (200–600 ft long) and slope gradients ranging from less than 10 to over 50%. Site quality is highly variable and depends on soil and topographic factors. Oak reproduction densities tend to be greatest on hot south-westerly-facing slopes and least on cool north-easterly-facing slopes (Fig. 3.6). Maximum and minimum densities of oak reproduction greater than 1 ft tall occur at azimuths of 203° and 23°, respectively. However, azimuth effects change with slope gradient and overstorey density. On a given aspect, oak reproduction density is greatest on gentle slopes and decreases with increasing slope gradient. Moreover, for any given combination of slope and azimuth, reproduction density decreases with increasing overstorey density. In the same region, the combined density of maple, American beech, yellow-poplar and other non-­oak reproduction differs markedly from the oaks. The non-oaks attain highest densities on north-facing slopes and lowest densities on south-facing slopes. Maximum and minimum densities of the non-oaks tend to occur at 1° and 181° azimuth, respectively (Fig. 3.6B). But like the oaks, non-oak densities change with changing slope gradient and overstorey density. For a given aspect, highest densities occur on steep slopes and under low overstorey densities. Collectively, these landscape-level models illustrate important differences between the demographics of oak and non-oak reproduction. The environmental conditions that favour successful oak regeneration, and thus the accumulation of oak reproduction, can be conceptualized as a regeneration window (Fig. 3.7). The window is relatively narrow in mesic sites where the slow initial growth of oak reproduction must be matched by a light intensity sufficient for seedling growth but low enough to discourage the development of competitors. At the expense of the oaks, which are intermediate in

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shade tolerance, a lower light intensity favours tolerant species and a higher light intensity favours intolerant species. As site conditions become drier, the window expands in response to the drought tolerance of the oaks that occur there and the concomitant exclusion of long-lived competitors that are less tolerant of drought. Reduced stand density also tends to create warmer and drier microclimates than those prevailing in the undisturbed forest (Dey and Parker, 1996). ‘Xerifying’ disturbances such as fire, grazing and insect defoliation may consequently widen the oak’s regeneration window. The regeneration window emphasizes the gradational nature of the oak regeneration process with respect to light and moisture. The propensity of ecosystems to accumulate oak reproduction is similarly gradational. Accordingly, it is useful to consider an intermediate category: ambivalent accumulators of oak reproduction. These can be defined as ecosystems with uncertain or pliant tendencies towards accumulating oak reproduction. Such ecosystems would be expected to lie near the centre of the regeneration window (xero-mesic ecosystems). There, even minor disturbances could produce pronounced shifts in the accumulation of oak reproduction. Identifying these forests is important because they are likely to be especially amenable to silvicultural manipulation (Bakken and Cook, 1998). Categorizing ecosystems by their propensity to accumulate oak reproduction can be incorporated into ecological classification systems to characterize oak’s replacement potential within each ecological unit. Population characteristics useful for deriving such ratings include the expected number and size distribution of oak reproduction (basal diameters and/ or heights), and the relative constancy of those characteristics over time within an ecological classification unit. Most classification systems generally describe reproduction characteristics and the successional status of oaks within classification units (e.g. Smalley, 1978, 1984; Hix, 1988; Kotar et al., 1988; Cleland et al., 1993; Kabrick et al., 2008). To date, none specifically consider the replacement potential of oaks based on observed reproduction characteristics and associated successional relations, but oak accumulation types can sometimes be inferred from existing descriptions of the ecological units (Table 3.2). Reaction to disturbance The presence of established oaks in the overstorey of a stand is often attributed to past stand disturbances.

Chapter 3

(A)

routs d seedling sp Seedling an ) re ac (thousands/

1.5 10% slopes

1.0 30% slopes

0.5

store

60

y stoc

50

king (

%)

gr ee

ut h im

Over

90

70

0

Az

80

(d e

180

0.0

s)

360 270

40

(B)

30% slopes

3

10% slopes

2

store

90

60

y stoc

king (

50

40

0

ee

ut

70

Over

h

180 80

(d

0

eg r

270

s)

3 360 1

Az im

edling sprouts Seedling and se ) (thousands/acre

4

%)

Fig. 3.6.  Estimated densities of oak (A) and non-oak (B) reproduction beneath stands in south-eastern Ohio in relation to overstorey stocking, slope aspect (degrees azimuth) and slope gradient (%). (Adapted from Walters, 1990.) Includes reproduction at least 1 ft tall and up to 1 inch dbh. Oaks include black, white, scarlet, northern red and chestnut oaks. Non-oaks include red maple, yellow-poplar, hickories, sugar maple, American beech, white ash, American basswood, yellow buckeye, sycamore, black gum, black walnut, elms and aspen. The models explain 25% and 51% of the variation in oak and non-oak reproduction density, respectively. Stocking was defined by stocking (relative density) equations.

Regeneration Ecology II

131

Open

The oak regeneration window

Light gradient

Closed canopy Moist

Dry Moisture gradient

Fig. 3.7.  The ‘regeneration window’ for oaks in eastern USA forests in relation to light and soil moisture. (Adapted from Hodges and Gardiner, 1993.) The window (open area) defines the region most favourable for successful oak regeneration. This region is relatively narrow on moist sites but widens with increasing dryness. On moist sites, intermediate light intensities offer the best opportunities for oak seedling survival and growth. Lower light intensities are insufficient to meet the oak’s minimum light requirements for photosynthesis but are sufficiently high to inhibit the development of many competitors. As site conditions become drier, the window widens because of the oak’s drought tolerance and the exclusion of competitors that are less drought resistant.

This is especially true in stands that are recalcitrant accumulators of oak reproduction even though there is often little or no evidence of previous disturbances that might explain the origin of the oaks. In the absence of future disturbances of the appropriate frequency and intensity, such oak stands are destined to successional replacement by other species. Past forest disturbances are often recorded in the spatial variability of overstorey density and size structure. In turn, variability in the overstorey usually is associated with variability in oak ­reproduction density. This is the case in xeric oak forests on outwash sands in northern Lower Michigan. There, more than half of the spatial variation in the density of white oak and black oak reproduction is explained by variation in overstorey density and the basal area of large oaks (Johnson, 1992) (Fig. 3.8). In this example, large trees are defined as those at least 12 inches dbh (white oak) or 14 inches dbh (black oak) and are of a size generally considered to be better acorn producers than small trees (Downs, 1944). The reproduction density of both white and black oak increases as the basal area of large trees

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increases. In the Missouri Ozarks a similar finding was reported for white oak reproduction density (Kabrick et al., 2014). Reproduction of these two species also differs in reaction to overstorey density (Fig. 3.8). Whereas white oak reproduction density decreases with increasing overstorey density, black oak r­ eproduction density gradually increases with increasing overstorey basal area before declining at about 85 ft2/acre. Moreover, the two species differ in their apparent sensitivity to overstorey density. The rapid decrease in white oak reproduction density per unit increase in overstorey density contrasts with relatively small changes in black oak reproduction density with change in overstorey density. The two species similarly differ in their sensitivity to the basal area of large diameter trees. White oak reproduction density increases more rapidly per unit basal area of large trees than does black oak. This relation may be associated with white oak’s greater acorn production per unit crown area than black oak’s (Myers, 1978). Consequently, the total maximum density of oak reproduction occurs under conditions that favour white oak reproduction.

Chapter 3

Regeneration Ecology II

Table 3.2.  Inferred oak reproduction accumulation types for upland plant associations on the Huron-Manistee National Forests in Michigan. (Adapted from Cleland et al., 1993.) Plant association

Soil and landform characteristicsa

Characteristic overstorey /understorey compositionb

Northern pin oak–white oak/Deschampsia

Excessively well-drained sands of outwash plains

Black oak–white oak/ Vaccinium

Site index (ft)c

Inferred accumulation typed

Northern pin, black and white oaks; jack pine/oaks, black cherry

Northern pin oak: 48

Intrinsic

Excessively well-drained sands of outwash plains

Black, northern pin and white oaks/red maple, oaks

Black oak: 50–56

Intrinsic

Mixed oak–red maple/ Trientalis

Well to excessively well-drained sands of overwashed moraines and kame terraces

White, black and northern red oaks; red maple/red maple, witch-hazel

Northern red oak: 61–65

Ambivalent

Northern red oak–red maple/Viburnum

Moisture-enriched, well-drained sands of moraines and ice-contact topography

Northern red and white oaks; red maple/red maple, black cherry, flowering dogwood and witch-hazel

Northern red oak: 77

Recalcitrant

Northern red oak–red maple/Desmodium

Moderately well-drained sandy loams over loamy substrata of ground moraines and glacial lake beds

Northern red and white oaks; red maple/red maple, black cherry, flowering dogwood and witch-hazel

Northern red oak: 85

Recalcitrant

Sugar maple–beech/ Maianthemum

Well-drained morainal medium to fine sands

Sugar maple, American beech, northern red oak, red maple/sugar maple, red maple, American beech

Northern red oak: 76–88

Recalcitrant

Sugar maple–white ash/ Osmorhiza

Moisture- and nutrient-enriched morainal sands

Sugar maple, white ash, northern red oak, American basswood, red maple/sugar maple, American beech

Northern red oak: 86

Recalcitrant

Sugar maple–white ash/ Caulophyllum

Well-drained to moderately well-drained Sugar maple, white ash, northern sands over fine loamy material red oak, American basswood, red maple/sugar maple, American beech

Northern red oak: 76–88

Recalcitrant

a

See Chapter 1, Fig. 1.8, this volume, for a schematic illustration of the landforms listed in this table. Characteristic composition of relatively undisturbed natural stands. Understorey includes trees and shrubs 1–3 inches dbh. c At an index age of 50 years. d Based on Host and Pregitzer (1991), Johnson (1992), Cleland et al. (1993) and other sources. b

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SppBA (ft2/acre) 16

12

13 10

8

0

50 10

25 5 5 45 90 135 N and E SE and NW S

180 SW

Aspect scale and aspect Moderate disturbance Light disturbance

70

80

90

100

Stand basal area (ft2/acre) White oak

Black oak

Fig. 3.8  Estimated densities of black oak and white oak reproduction in relation to total stand basal area and the basal area of large overstorey trees of the same species (SppBA) in xeric oak forests in northern Lower Michigan. (Adapted from Johnson, 1992.) The trees comprising SppBA are the presumed primary acorn producers. For white oak, SppBA includes trees ≥ 12 inches dbh, and for black oak includes trees ≥ 14 inches dbh. Based on linear regression models that account for 55% and 63% of the observed variation in reproduction density of white oak and black oak, respectively.

134

25

20

0 NE

4 28 16 4 60

50

30

0

7

4

40

Sunlight (%)

Seedlings and seedling sprouts (thousands/acre)

16

disturbance) are statistically significant. For a given disturbance intensity, the density of oak reproduction increases with increasing heat associated with slope aspect progressing from north-east (coolest) to south-west (hottest), and increasing light. Historically, fire was the pre-eminent disturbance factor that shaped and maintained environments favouring oaks (see Chapter 7, this volume). The extensive oak savannahs the early European settlers encountered in Midwestern USA (see Chapter 12, this volume) were products of a centuries-long fire history on a spatial scale unlikely to be repeated. Although the ecological disturbances that created and sustained those savannahs have long disappeared, their effects are still evident in some regions. Within the Forest–Prairie Transition Region of the

Oak reproduction density (thousands/acre)

Despite its regeneration disadvantages, black oak typically maintains a position of dominance in these forests. This persistence may be related to its height and diameter growth, which are greater than that of white oak during the first 100 years of stand development (Trimble, 1960; Carmean, 1971). However, other factors, including stand history and site quality also influence oak reproduction density (Carvell and Tryon, 1961; Arend and Scholz, 1969; Ross et al., 1986; Nowacki et al., 1990; Walters, 1990). Although relations between overstorey density and oak advance reproduction density imply disturbance effects (e.g. Figs 3.7 and 3.8), disturbance effects are directly considered in a predictive model for West Virginia forests (Fig. 3.9). The model expresses disturbance as a subjectively derived index ranging from 0 (none) to 16 (heavy). It considers intensity of disturbance and time since disturbance events that include burning, grazing and logging (Carvell and Tryon, 1961). Although the proportion of variation in reproduction density is unspecified, the factors included in the model (light, aspect and

Fig. 3.9.  The estimated density of oak seedlings and seedling sprouts beneath mixed oak stands in West Virginia in relation to slope aspect, sunlight and disturbance. Includes white, black, northern red, scarlet and chestnut oak reproduction from 1 ft tall to 0.6 inches dbh. Aspect scale is displayed in degrees departure from north-east (45° azimuth). Light is expressed as the amount measured by a photoelectric cell beneath the overstorey as a percentage of that measured in the open. Disturbance is expressed as a qualitative index based on an arbitrary scale from 0 (none) to 16 (heavy) that considers intensity of and time since grazing, fire and logging; estimates for values of 2 (light disturbance) and 6 (moderate disturbance) are shown. (Reproduced and adapted from Carvell and Tryon, 1961, with permission from the Society of American Foresters, Bethesda, Maryland. Not for further reproduction.)

Chapter 3

Midwest, many of today’s closed-canopy oak forests probably originated from the savannahs present before settlement by Europeans (Beilmann and Brenner, 1951; Grimm, 1984; Guyette and Cutter, 1991; Abrams, 1992). The historical record indicates that throughout much of North America during the presettlement and early settlement eras, fire created and sustained the conditions necessary for perpetuating oaks where they would not have otherwise occurred (Day, 1953; McClaran and Bartolome, 1989; Johnson, 1993; Lorimer, 1993; Guyette and Dey, 1995). When fires were frequent and intense, open-grown oak savannahs were created. Those events produced communities comprised of a few large, thick-barked, fire-resistant overstorey trees per acre, a unique and diverse grass and forb flora, and abundant oak reproduction largely comprised of seedling sprouts (Curtis, 1959; Haney and Apfelbaum, 1990). Oak savannahs, which once dominated the landscape throughout much of the central and eastern states, are today rare

plant communities (Haney and Apfelbaum, 1990). Following the cessation of burning and grazing, some savannahs developed into closed-canopy forests; many others were converted to pasture and cultivated fields (Beilmann and Brenner, 1951; Curtis, 1959; Thilenius, 1968; Whitney, 1994). Recurrent fire ­promotes the accumulation of oak reproduction in ­various ways. Fire can destroy seed stored in the forest floor and soil and thereby eliminate or reduce post-fire competition from some shrub, herb and other tree species (Fig. 3.10). However, in some ecosystems, that effect may be offset by the presence of species with fire-resistant seeds and fire-stimulated germination in the post-fire environment. The latter include several chaparral species associated with oaks of south-western USA (Keeley, 1991) and pin cherry in the eastern forests (Wendel, 1990). In other species, seeds may be stored in the humus and soil where they are relatively unaffected by fire. Burning also can reduce overstorey and understorey density (DeSelm et al., 1991), which thereby increases light

Fig. 3.10.  Fire eliminates many of the oak’s competitors by destroying or reducing seed stored in the forest floor and also by killing established fire-sensitive competitors. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

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135

on the forest floor and reduces competition for other site resources. Within a species, small-diameter trees are the most vulnerable to fire because their thinner bark offers less insulation against potentially lethal heat (Plumb and Gomez, 1983; Harmon, 1984; Hengst and Dawson, 1993). Although species vary widely in bark thickness for a given diameter (Fig. 3.11), other physical properties of bark also vary among species. The effectiveness of bark in insulating a tree from heat depends on three physical properties: (i) thermal conductivity (ability to transfer heat); (ii) specific heat (ability to absorb heat); and (iii) thermal diffusivity (ratio of thermal conductivity to the product of specific heat and bark density) (Martin, 1962). Consequently, the survival of trees of a given diameter subjected to a given duration and intensity of heat varies among species (Chapter 7, this volume). Such differences were observed in pine–oak stands on dry sites in the southern Appalachians where low-intensity fires primarily consumed leaf litter. There, the bark thickness required for 50% survival of a population of trees ranged from less than 0.1 inch to over 0.2 inch at dbh, depending on species (Fig. 3.11A). Although chestnut oak is generally considered resistant to fire damage, the amount of bark required for the survival of chestnut oak is greater than that required for some nonoaks such as red maple (generally regarded as fire sensitive). But because the chestnut oak can grow faster in diameter than red maple (at least on dry sites), the former can more quickly attain a ‘safe’ diameter (i.e. an effective fire-insulating bark thickness) (Harmon, 1984). The western oaks, which occur in extremely fireprone environments, also vary greatly in bark thickness. For example interior live oak has relatively thin bark and complete top-kill is common even after light ground fires (Plumb and Gomez, 1983). In contrast, the thick bark of coast live oak (along with the bur oak of eastern USA) ranks it among the most fire resistant of the US oaks (Fig. 3.11B and C; see also Chapter 2, this volume, ‘Stump Sprouts and Related Growth Forms’). In the Ozark Highlands of south-eastern Missouri, oak species differed in their response to fire frequency over a 63-year study period (Knapp et al., 2017). During that time, experimental plots were burned annually, periodically (every 4 years), or not burned. For plots burned annually or periodically, average survival of post oak (a white oak) was greater than

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that of red oaks (black, scarlet and southern red oaks combined) after statistically accounting for differences in initial (pre-burn) diameter between the two species groups. Among all treatments, tree survival increased with increasing initial dbh up to 7 or 8 inches regardless of species and treatment (Huddle and Pallardy, 1996). For post oak, fire frequency had no significant effect on the relation between survival and initial dbh. In contrast, the relation between survival of red oaks and initial dbh differed significantly among the three treatments. Differences in survival generally increased with increasing dbh. Among trees less than 7 inches dbh, lowest survival occurred within periodically burned plots and highest survival occurred within unburned plots. Hickories also responded negatively to burning (Fig. 3.12). Differences among species’ growth characteristics thus may interact with tree size, bark, and other species’ characteristics to determine survival of burned trees. Moreover, fire frequency and pre-burn conditions may further affect these differences. The long-term survival of trees subjected to lowintensity surface fires in the Great Smoky Mountains National Park in eastern Tennessee is largely dependent on tree growth rate and bark thickness. Fire intervals of less than 14 years favour the relatively fast-growing species (pines, blackgum, sourwood and chestnut oak) over slow-growing species (red maple and hickories) and some fast-growing but relatively thin-barked species such as scarlet oak (Harmon, 1984). Longer intervals favoured maple, hickories and the thin-barked scarlet oak by allowing them to attain a sufficient size and bark thickness to resist fire damage. Moreover, once the more fire-sensitive species reach as safe size, similar future fires are less likely to effectively alter overstorey composition. In mixed white oak–yellow-poplar stands in the North Carolina Piedmont (red oak site index 75–80), results of high-intensity fires illustrate the superior resistance of oaks to top-kill (Maslen, 1989). The burns, described as strip head fires, produced flames 3–10 ft high that consumed most of the litter and caused wounding of the residual stand. Seven years after a single burn, top-kill from burning was observed as well as natural dieback (unrelated to burning) on paired burned and unburned areas. Although all reproduction of all species was top-killed by burning, oak saplings and small poles (trees > 12 ft tall but < 6 inches dbh)

Chapter 3

Bark thickness (in.)

(A)

1.0

Chestnut oak

0.8 Red maple

0.6 0.4

Virginia pine

Sourwood

0.2 0.0

(B)

Blackgum

0

4

8

12

16

20

2.0

Bur oak

Bark thickness (in.)

1.6

White oak

1.2

Northern red oak

0.8 Sugar maple 0.4 0.0

Yellow-poplar 0

10

20

30

40

(C) 2.0 Coast live oak

Bark thickness (in.)

1.6 1.2

California black oak

0.8

Canyon live oak

0.4 0.0

California scrub oak Interior live oak 0

4

8 12 Dbh (in.)

16

20

were more resistant to top-kill by fire than nonoaks of the same size (Fig. 3.13A). In unburned areas, the probability of top-kill among non-oaks was consistently lower than that of oaks across all size classes observed except the largest (not shown). Although the relationship shown in Fig. 3.13A tells us something about top-kill in relation to species and tree size, the amount and distribution of heat

Regeneration Ecology II

Fig. 3.11.  Relation between estimated bark thickness and dbh of oaks and associated species. (A) Five species in dry pine–oak forests in the southern Appalachians. Shaded circles identify the bark thickness and the correlated dbh needed to assure that 50% of trees are not top-killed by a ‘low-intensity’ fire. The proportion of the variation in bark thickness accounted for by dbh ranged from 69% (Virginia pine) to 96% (blackgum). (From Harmon, 1984.) (B) Five species in Illinois. The proportion of the variation in bark thickness accounted for by dbh ranged from 56% (northern red oak) to 93% (bur oak). (From Hengst and Dawson, 1993.) (C) Five California oaks. (From Plumb and Gomez, 1983.)

on a tree bole of a given size might further explain variation in top-kill. In blue oaks in California, topkill was not only related to tree size but also, as an indirect measure of heat, the amount of bole charring and charring severity (Fig. 3.13B). The lower incidence of top-kill in larger trees largely reflects increasing bark thickness (Fig. 3.11) and the increasing effectiveness of bark as a heat shield.

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1.0

Post oak Red oaks (control) Red oaks (annual burn) Red oaks (periodic burn)

Survival probability

0.8 0.6

Hickories (control)

0.4 0.2 0.0

Hickories (all burns) 0

2

4 Initial dbh (in.)

6

8

Fig. 3.12.  Estimated survival probabilities for oaks and hickories in relation to initial (pre-treatment) dbh and frequency of burning during a 33–35 year period in south-eastern Missouri. (From Huddle and Pallardy, 1996.) Periodic burns occurred every 4 years. Species in the red oak group include black, scarlet and southern red oaks; hickories include shagbark, mockernut and black hickories. Survival of post oaks in burned plots did not differ significantly (P < 0.05) from trees in control plots. Probability estimates were derived by logistic regression.

This insulating effect is also reflected in the tree mortality trends occurring over several decades of repeated burns (Fig. 3.12). However, in the case of the blue oak single-burned stands, almost 90% of the top-killed oaks produced basal sprouts (e.g. see Chapter 2, this volume, Fig. 2.23C). Accordingly, thinning dense blue oak stands has been proposed as one way to diversify age structure and increase recruitment of oak reproduction in stands with little or no other oak reproduction (McCreary et al., 2002; see also ‘The shelterwood method’ in Chapter 8 and ‘Uneven-aged Silvicultural Methods’ in Chapter 9, both this volume). Oak regeneration is a widespread problem among western oaks, especially in blue, valley and Engelmann oak woodlands and savannahs (Pavlik et al., 1991). Because virtually all the hardwood species associated with oaks can sprout, top-kill from one or a few single low-intensity burns may not effectively reduce or eliminate these competitors (Johnson, 1974; Nyland et al., 1983; Walters, 1990). However, through repeated low-intensity burns (Langdon, 1981) and sometimes through single high-intensity burns (Maslen, 1989), differences among species in fire-caused mortality of tops, root systems, dormant buds near the root collar, and decay resistance

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confer a competitive advantage to the oaks (Fig. 3.14). Oaks therefore are ‘fire persistent’ compared with many associated species, which may be equally prolific sprouters. This persistence may be partly related to the concentration of dormant buds near the root collar, which is often located an inch or more below the soil surface where it may be protected from lethally high fire temperatures (Korstian, 1927). Such protection may be facilitated by burial of acorns by rodents (Galford et al., 1991). Barring soil erosion or deposition, the position of the root collar, and thus many dormant buds capable of sprouting, remains fixed at the original acorn burial depth throughout the life of the tree. Increased light on the forest floor resulting from fire increases rates of photosynthesis and growth of the oak reproduction that survives burning. More light also allocates proportionately more photosynthate to oak roots than shoots (Kolb and Steiner, 1990). Physiological investment in root systems may be further reinforced by fire-induced top-kill. This, in turn maintains a high root:shoot ratio and small shoot mass that might otherwise comprise a greater sink for the relatively modest amount of photosynthate produced by oaks growing under an overstorey. The effectiveness of fire in reducing the height of surviving reproduction is illustrated in a Tennessee study (Fig. 3.15). Burning also increases the number of stems produced by basal sprouting (Thor and Nichols, 1973), which may favour high leaf area:shoot mass ratios that promote root development. In a Wisconsin study, the density of northern pin oak reproduction in canopy gaps was unaffected by a single spring fire, whereas red maple and black cherry reproduction were significantly reduced (Reich et al., 1990). Moreover, rates of photosynthesis after burning were greater for the oak than for associated species. The oak’s higher photosynthetic rates were attributed primarily to increased nitrogen concentrations in leaves, and rates were maintained throughout the growing season after burning. That was not the case for maple and cherry. Photosynthetic rates of oak reproduction that sprout after artificial shoot removal (and by extension, top-dieback from burning) also may be greater than in reproduction with intact shoots (Kruger and Reich, 1989). Reduction of leaf litter by burning also facilitates direct contact between acorn and mineral soil, which in some cases may ease the penetration of

Chapter 3

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red b

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the radicle into the soil and increase the rate of seedling establishment (Krajicek, 1960; Brose et al., 2017); see also Chapters 2 and 7, this volume). The forest floor also harbours insects that consume

Regeneration Ecology II

3 2

4

5

10

.)

6

8

9

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Fig. 3.13.  The probability that a tree is not top-killed by burning. (A) Observed probabilities for trees subjected to a ‘high-intensity’ prescribed burn in immature mixed white oak–yellowpoplar stands on mesic sites (red oak site index 75–80 ft) in the North Carolina Piedmont. (Adapted from Maslen, 1989.) Size classes are expressed as diameters (inches dbh) for trees > 12 ft tall. All trees ≤ 12 ft tall were killed. Based on 794 trees observed before and 7 years after burning in four woodlands; includes trees that later produced basal sprouts. (B) Estimated probabilities for blue oaks in relation to dbh and bole charring extent and severity. (Based on a logistic regression model from Horney et al., 2002.) Data are from four stands in California burned the summer of 1999 and remeasured in August 2000. Light charring: blackened bark surfaces but not on inner crevices; heavy charring: completely blackened bark with some bark reduction. All predictors are statistically significant (P 12 ft – 2–6 ft 12 ft – 2–6 7–12 ft ft ft 1 acre). It  usually involves a disturbance originating from forces outside (exogenous to) the stand such as fire, tornadoes, hurricanes and timber harvest. These events abruptly increase light, moisture and nutrients, which are also accompanied by significant changes in microclimate. This mode of regeneration often facilitates the temporary coexistence of trees of all shade tolerances (Roach and Gingrich, 1968; Dunn et al., 1983; Smith et al., 1997). Coexistence is facilitated by shade-tolerant species originating from advance reproduction together with less tolerant species originating from new or buried seed and sprouts. In some mesic forests, this may produce stands of high tree diversity (Loftis, 1983; McGee, 1987; Smith and Miller, 1987). Even-aged methods of silviculture such as clearcutting, shelterwood, seed tree and their variants simulate the catastrophic mode of regeneration through the removal of all or most of the overstorey in one or a few steps (Marquis and Johnson, 1989). Oaks are well adapted to the catastrophic mode of regeneration, especially in xeric ecosystems where oak reproduction intrinsically accumulates. However, in mesic and hydric forests, successful regeneration of oaks may depend on minor disturbances from fire, grazing or flooding that precede a later catastrophic event that significantly reduces overstorey density. The intensity, frequency and recency of minor disturbances combined with later catastrophic disturbances collectively direct ecological succession (Glenn-Lewin and van der Maarel, 1992). The continuous regeneration mode pertains to species that can attain maturity in the absence of a canopy opening. In the oak forests of the Eastern Deciduous Region of the USA, these include shadetolerant species such as flowering dogwood, redbud, American hornbeam, serviceberry and sourwood. These species usually are relegated to the subcanopy because their maximum attainable heights seldom exceed 35 ft (Burns and Honkala, 1990). However, they do not require canopy gaps for seed production, germination or early growth. Terborgh (1985) theorized that the tops of the relatively flat, spreading crowns of flowering dogwood and redbud occur at a predictable distance below the main canopy. That distance occurs where the beams of solar radiation penetrating different canopy gaps most frequently intersect along the daily solar path during the growing season. Terborgh reasoned that the distinct

Chapter 3

Fig. 3.23.  A northern red oak sapling growing in a canopy gap in a xero-mesic oak–mixed hardwood stand in northern Wisconsin. This sapling’s potential to eventually dominate the gap depends on its height growth and the rate of crown closure of surrounding trees. Adjacent trees in the main canopy include northern red oak, bigtooth aspen, paper birch, red maple and white ash. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

layers of vegetation in a forest may be adaptations to relatively uniform light fields that occur at fixed distances below the main tree canopy. These, in turn, are related to the shape, size and distribution of tree crowns and gaps, together with angles of direct-beam solar radiation. Accordingly, vegetation would occur as distinct vertical layers, as commonly observed, rather than as a vertical continuum of tree crowns (Fig. 3.24). Although Veblen (1992: p. 166) offers that ‘the continuous mode of regeneration appears to be rare,’ some shade-tolerant species such as sugar maple and American beech are capable of growing directly into the overstorey if canopy density is not extremely high (Spurr and Barnes, 1980; Canham, 1989; Godman et al., 1990). In oak forests, high overstorey densities reduce light reaching the forest floor to levels that are usually insufficient for ­sustaining the growth of oak reproduction into the overstorey. The regeneration of oaks thus depends on forest disturbance, the number, size and spatial distribution of oak advance reproduction present at the time of disturbance, and other factors associated with site quality and competition (Sander, 1971; Sander and Clark, 1971; Trimble, 1973; Sander et al., 1984;

Regeneration Ecology II

Kabrick et al., 2008). When overstorey density is reduced, the oak advance reproduction may capture the vacated growing space. The resulting recruitment of oaks into the overstorey is slow in small canopy gaps that admit little light to the forest floor and more rapid in larger openings – provided that adequate advance reproduction is present (Fig. 3.25). Other factors being equal, the growth rate of oak advance reproduction after overstorey removal depends on its pre-disturbance basal diameter (Sander, 1971; Johnson, 1979), which is correlated with total root mass (Canadell and Rodà, 1991) (see Chapter 2, this volume, Fig. 2.28). In oak stands that accumulate oak advance reproduction, stand regeneration potential and succession largely depend on the composition and structure of that reproduction. This type of succession follows the ‘initial floristics’ model (Egler, 1954). Succession is driven by an initial floristics when the condition of a plant community at the time of disturbance largely controls its future development. Similar ideas are implicit in the ‘legacy’ concept of Franklin et al. (1989) wherein the future state of an ecosystem is perceived as partly, if not largely, inherited from propagules carried over from the previous state.

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Fig. 3.24.  Flowering dogwood (seen here in full spring bloom) forms a distinct and permanent subcanopy in this oak–hickory forest. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

(A)

(B)

(C)

Fig. 3.25.  Recruitment of oak reproduction into the overstorey. (A) No recruitment; advance reproduction (trees below the thin horizontal line) is suppressed by high overstorey density. (B) Some recruitment occurs in the centre of small canopy gaps. (C) Recruitment accelerates with increasing gap size. Maximum height growth and recruitment occur in centres of gaps where light is maximal and competition from border trees is minimal.

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The dependence of the future condition of a stand on its initial state is an important concept in the development and application of predictive regeneration models (e.g. Sander et al., 1984; Loftis, 1990a; Marquis et al., 1992; Johnson and Deen, 1993) (Fig. 3.26). When overstorey density is reduced by fire or timber harvesting, the overstorey itself is often an important component of the initial state of an oak stand because a significant proportion of the reproduction may originate from dormant buds at the bases of top-killed trees or stump sprouts (P.S. Johnson, 1975, 1977; Wendel, 1975; Lamson, 1988; Weigel and Johnson, 1998). The importance of this source of reproduction to the future stand depends on the proportion of trees that produce basal or stump sprouts. In general, older and larger trees are less likely to produce stump sprouts than smaller and younger trees (Roth and Sleeth, 1939; P.S. Johnson, 1975, 1977; Ross et al., 1986) (Chapter 2, this volume, Fig.  2.25). But even in older stands, stump sprouts may contribute significantly to the future occupancy of growing space (stocking) because, despite their small numbers, growth and

Chapter 3

ASPECT

0.37

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SW (hot)

Initial size of advance reproduction (height, basal diameter) 4 ft, 0.5 in. P

2 ft, 0.25 in. P

P = probability of attaining intermediate or larger crown class 21 years after overstorey removal. Fig. 3.26.  Estimated probabilities that oak advance reproduction will attain intermediate or larger crown class 21 years after complete overstorey removal in the Ozark Highlands of Missouri. (Adapted from Dey et al., 1996b.) Probabilities are shown in relation to aspect and slope position for two pre-harvest size classes (4 ft tall, 0.5 inches in basal diameter, and 2 ft tall, 0.25 inches in basal diameter). The probabilities illustrate the predictive value of the size of oak advance reproduction (i.e. the ‘initial state’) as predictors of a forest’s future state. Probabilities apply to black, white, scarlet, northern red and post oaks, and are based on the predictive regeneration model acorn.

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survival rates of stump sprouts are u ­ sually high (Johnson and Rogers, 1984; Lamson, 1988). The ecological significance of the pre-disturbance state of an oak forest is not realized until the inhibiting effect of the overstorey is released. The effect accordingly has been referred to as the ‘inhibition’ model of succession (Connell and Slatyer, 1977). Accordingly ‘replacement occurs only when resources are released by the damage or death of the previous occupant [of the site]’ (Connell and Slatyer, 1977: p. 1138). An oak forest’s regeneration potential therefore is often encoded in the composition and structure of its advance reproduction and the overstorey. But this potential is not expressed until it is freed from the inhibitory effect of the overstorey. In a mature oak stand, site resources are partitioned among the various vegetative layers. Large trees utilize resources (e.g. soil moisture and nutrients) from the relatively large and heterogeneous spaces they occupy, whereas smaller trees and other low vegetation are limited to relatively small and homogeneous microsites. The early post-disturbance years after overstorey removal are characterized by the direct competition of oak reproduction with a chaotic mixture of herbaceous plants, shrubs and other tree reproduction, which collectively occupy relatively narrow above- and below-ground strata. Under these conditions, herbaceous and shrub species interfere directly with the growth and survival of tree reproduction. This period is characterized by rapid competitive sorting among and within species. During this period, the composition and structure of the future forest is often neither obvious nor apparently predictable. The oaks may win or lose in the competitive sorting process, depending on antecedent states and events (Fig. 3.27). Nevertheless, a more orderly partitioning of resources gradually re-emerges but only becomes visually conspicuous after tree crowns again vertically stratify. Putting the necessary information on oak regeneration potential into a practical and useful framework for predicting the future composition and structure of oak stands requires quantitative regeneration models. Modelling theory and objectives In their broadest sense, models represent ‘in some way the form and/or the function of real-world entities and processes’ (Kimmins, 1987: p. 460). Models therefore may range from unrevealed thoughts to more externally represented ideas

154

expressed by words, pictures, graphs, charts, mathematical equations and computer simulations (Kimmins, 1987). The latter have largely made modelling the valuable tool that it has become for predicting and understanding the behaviour of ecosystems generally and forests specifically. Many predictive models have been developed including the successional models of ecologists (e.g. Shugart, 1984; Urban and Shugart, 1992; Botkin, 1993) and the growth and yield models of silviculturists (e.g. Belcher et al., 1982; Hilt, 1985; also see Chapter 15, this volume). It has been suggested that complete knowledge of the state of an ecosystem (i.e. its multi-dimensional structure or ‘spacestate’) at any given instant cannot be fully specified because of the large number of factors, living and non-living, that define ecosystems (Margalef, 1963; Jørgensen, 1990). Even if such a model could be constructed, its complexity would probably render it useless. This produces the conundrum that an ecosystem model can only approach reality through increasing complexity, which in turn increasingly reduces its utility. Nevertheless, relatively simple ecosystem models can have surprisingly predictive power (Urban and Shugart, 1992), and such models have become widely used to predict forest behaviour. It is none the less important to recognize the limitations of models. The suitability of a model should be evaluated by how well it meets the objectives of its intended application (Buchman and Shifley, 1983; Blake et al., 1990; Bruce, 1990). Forests represent hierarchies of biotic organization ranging from the cellular level upwards to the individual organism level and extending further upwards to species’ populations, associations of species and beyond. Depending on its purpose, a model should be designed to consider specific levels of detail within hierarchies (Allen and Starr, 1982). Models developed for silviculturists usually focus on predicting population phenomena such as timedependent changes in the number and size of trees in a stand. Providing a silvicultural explanation of phenomena at the population level typically requires consideration of detail at least one level lower (i.e. for the individual trees in the population) (Botkin, 1993). If the ‘explanatory’ level of a model is set too far below the ‘predictive’ level, the detail required by the model may exceed the limits of available knowledge or the ability to meaningfully build such details into a model. On the other hand, a more hierarchically detailed model may

Chapter 3

Forest type

Non-oak Accumulation of oak reproduction: RECALCITRANT

Mixed oak Oak

Accumulation mediated by disturbance Not mediated

Oaks win Mixed outcome Oaks lose or no oaks present

Accumulation of oak reproduction: INTRINSIC

Advance tree reproduction and seed bank inhibited by overstorey

Competitive sorting Disturbance-mediated release from overstorey inhibition

Fig. 3.27.  Disturbance-mediated successional pathways in relation to intrinsic and recalcitrant patterns of accumulation of oak reproduction. In eastern USA, accumulation is usually an intrinsic characteristic of xeric forests. In contrast, accumulation is generally recalcitrant in mesic and hydric forests in the absence of disturbances such as recurrent fire. In western USA (in Mediterranean and semidesert climates), accumulation may be favoured in the more moist ecosystems and microhabitats. The outcome of competitive sorting among species during the first two decades after disturbance is largely determined by advance reproduction characteristics and site factors. In the eastern USA, oaks typically win the post-disturbance race to capture growing space in xeric ecosystems. In mesic and hydric ecosystems, oak’s ascendance to dominance is more variable, and highly dependent on the type, frequency and intensity of disturbance, which in turn influences the accumulation process. Oak reproduction also can occur and accumulate beneath non-oak types via acorn dispersal by birds and mammals.

have more explanatory power. The ‘bottom-up’ approach (aggregating model components from fine to coarser scales) is consistent with the scientific method and the building of scientific theories (Lewis, 1990). It is also the most common approach used in modelling forest dynamics. However, a ‘top-down’ approach to modelling also is possible and begins with a relatively coarse scale, but adds details in subordinate levels (Landsberg, 1986). In addition to hierarchical structure, models also possess other properties that determine their usefulness for meeting defined objectives. Generality,

Regeneration Ecology II

realism and accuracy are three such properties (Levins, 1966; Sharpe, 1990; Botkin, 1993). Generality refers to the range of situations that a model can be applied to. Realism refers to the qualitative similarity between model projections and the real world (e.g. as demonstrated by the similarity in the shapes of projected and actual response curves). Accuracy4 refers to the quantitative closeness of model projections to the real world. The goal or objective of the model determines the relative importance of each of these properties. However, there are limitations to each of the various approaches to

155

modelling because each tends to rely on one of three essentially mutually exclusive approaches that are either: (i) statistical; (ii) mathematical; or (iii) science-­ based. As a consequence, one of the three properties is likely to be sacrificed in any one model (Levins, 1966; Sharpe, 1990). In their construction, statistically derived models require relatively large amounts of data. Relations among variables are typically derived by fitting curves to data using regression analysis. The resulting models tend to lack generality because their efficacy in application is limited to the conditions represented by the data on which they are based. Nevertheless, within that limitation, such models are potentially (but not necessarily) accurate and realistic. Most of the models developed for silvicultural applications, including regeneration and growth and yield models, fall into this category. In contrast, mathematical models are derived from abstract relations assumed to describe systems within narrowly defined conditions. They may require no data and are often designed to investigate generalized theories of ecosystem behaviour such as stability and response characteristics (e.g. Gatto and Rinaldi, 1987). Such models may possess accuracy and generality, but often sacrifice realism. Realism is lost because of the recognized unrealistic assumptions about the system that must be made in order to conform the system to the model’s known limitations. Mathematical models are usually used to explore theory and are not generally applied to predict outcomes for real ecosystems (Prentice and Helmisaari, 1991). Sharpe (1990) defined a third class of models that he termed science-based models. They are more common to ecology than silviculture and are derived from available information on species’ characteristics such as growth rates, longevity, reaction to stress factors and other natural history characteristics. In their construction, science-based models require less data than statistical models but more data than mathematical models. The approach has been widely used to develop forest succession models (e.g. Shugart, 1984; Prentice and Helmisaari, 1991; Botkin, 1993) and to a lesser extent regeneration models (e.g. Waldrop et al., 1986). Sciencebased models are potentially realistic and general but sacrifice accuracy. Accuracy is lost because of the generalized database from which they are derived. Each of these three classes of models also are associated with specific methods and problems of validation (Sharpe, 1990).

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The users of models in forestry are largely forest managers, who are often concerned with the consequences of silvicultural actions. Accurately predicting the outcome of silvicultural actions is important to managers. However, accurate prediction does not infer that ecological or physiological processes are well understood or even explicitly considered in model development. On the contrary, models with high accuracy often are not based on the mechanisms that directly explain the predicted phenomenon. High accuracy and low explanatory power characterize most of the current forest growth and yield models (Sharpe, 1990; Prentice and Helmisaari, 1991). Such models are largely empirical in that the variables used to make predictions are only indirectly related to the actual mechanisms that govern stand development. For example, a model predicting growth of individual trees may employ predictors such as the initial dbh, the crown class, the ratio of the living crown to its total live height (live crown ratio), and some measure of site quality (e.g. site index). Of course, the underlying causes of tree growth (and death) are rates of photosynthesis, respiration, uptake of water and nutrients, and other physiological mechanisms and the stress factors that limit those processes. Models based on causative physiological mechanisms are sometimes referred to as process models (Blake et al., 1990; Bruce, 1990). There is strong interest in the development of ecosystem models that directly consider physiological processes such as photosynthesis and carbon allocation in trees (Dixon et al., 1990). Their potential advantages lie in their greater generality of application and explanatory power over empirical models (Blake et al., 1990; Isebrands et al., 1990; Sharpe, 1990). However, the advantages of generality are likely to be counterbalanced by reduced accuracy and increased complexity of design and efficiency of application (Levins, 1966; Sharpe, 1990). It also can be argued that all models are empirical because causation at some scale (e.g. molecular) is not addressed by the model. Stand-level regeneration models: purpose, problems and limitations From an ecological perspective, regeneration models could be defined as predictors of the outcome of short-term secondary succession associated with planned disturbances. They are designed to function as silvicultural tools for assessing the adequacy of the regeneration potential of stands for meeting

Chapter 3

predefined regeneration goals. Successional models usually do not fulfil that function adequately because they usually do not provide the stand-level accuracy needed by silviculturists (Waldrop et al., 1986). Growth and yield models for established stands generally qualify as possessing accuracy but they usually do not explicitly consider regeneration. For example, changes in tree populations over the first two decades after final harvest of even-aged stands usually are not included in (or are only coarsely simulated by) growth and yield models. This leaves an informational void on stand development for one-fifth to one-fourth of the 80- to 100-year rotations typically used in managing oak forests (Chapter 8, this volume). Like all models, regeneration models are imperfect in mimicking real ecosystems because the models are simplifications of reality. Although regeneration models are largely empirical, they may be more precise and realistic than some other kinds of forest dynamics models. However, they typically possess little generality and explanatory power because they are usually statistically derived and based on correlations rather than on causative mechanisms. Several predictive regeneration models nevertheless have been developed for oak and associated mixed hardwood forests (Sander et al., 1984; Waldrop et al., 1986; Loftis, 1990a; Marquis et al., 1992; Johnson and Deen, 1993; Dey et al., 1996a, b; Fei et al., 2007). While these models differ in their details and complexity, they are all based partly or wholly on the successional concepts of initial floristics (Egler, 1954) and inhibition (Connell and Slatyer, 1977). In other words, they assume that the future state of a forest is encoded in its current or initial state. Moreover, overstorey removal or its reduction in density is required to release the future stand from the inhibiting effects of the present overstorey. Because such models are predictors of short-term secondary succession, they are potentially useful as tools for guiding silvicultural decisions. In application, regeneration models are usually designed for specific geographic regions or ecosystems and require quantitative information on the initial state of a stand including current vegetation and site characteristics. For models to be silviculturally practical, the initial state, or predictor ­variables, must be easily measurable and have predictive power. Those requirements create practical and theoretical problems in the development of regeneration models including: (i) the selection of a

Regeneration Ecology II

relatively small but useful set of easily measured predictors; and (ii) the relatively large variation in tree growth and survival during the post-disturbance regeneration period. These problems are further confounded by the relatively long period of observation (up to two decades) required to obtain data from which to build some types of models. Predictors of the post-disturbance state usually include measurements and/or counts of advance reproduction obtained from sample plots within stands (e.g. Sander et al., 1984; Marquis et al., 1992; Dey et al., 1996b). Information on advance reproduction heights and/or diameters is a requirement for applying many regeneration models. Although those variables are easily measured attributes of reproduction, they may not be the most important determinants of a stand’s future state. For example, the growth potential of an oak seedling may be more directly and accurately related to its root mass and leaf area (Johnson, 1979). But because those measurements are impractical to obtain in silvicultural practice, height and diameter are the measurements of necessity. Other sources of variation not always considered in some models include the density and size of competing shrubs and herbs. The ‘initial floristics’ approach used in prediction also may limit the predictive power of models because it ignores potential contributions of trees originating from seed and stump sprouts. Fluctuations in weather, insect and mammal populations that affect regeneration processes may be impossible or difficult to predict. Models therefore may lack predictive power because of their inability to consider important factors, both known and unknown. Building regeneration models is also complicated by the inherent variation associated with the disturbed states that characterize regenerating forests. The sudden post-disturbance increase in light triggers the germination of seeds of many tree, shrub and herbaceous species. Some seedlings arise from seed buried in the forest floor while some are carried in from outside the disturbed area. Advance tree reproduction also is released from suppression and new sprouts may develop from dormant basal buds of cut trees. This produces a chaotic mix of established plants and new propagules that are s­ uddenly competing for space in the new environment. The result is a relatively unstable ecological state that produces changes in stand composition and structure that are difficult to predict. During this period, stand development

157

INPUT

Stand and site characteristics: overstorey advance reproduction

REGENERATION SIMULATOR

Overstorey harvested

OUTPUT Regenerated stand characteristics: species tree size distributions stocking

Fig. 3.28.  Data input and output elements for a representative regeneration simulation model. Input requirements and output vary among available models (see Chapter 8, this volume).

often is so uncertain that the outcome may best be expressed probabilistically (e.g. Fig. 3.26). Despite these problems, regeneration models attempt to bring order to the chaos of this least predictable stage of stand development. Regeneration models generally fall into one of two categories: (i) decision guides for evaluating the adequacy of a stand’s regeneration potential for meeting defined silvicultural objectives; and (ii) simulation models that predict future stand composition and structure. The former are largely limited to providing a ‘yes’ or ‘no’ answer to the question of the adequacy of regeneration potential. Such models also may be partially or wholly based on experience or ‘expert opinion’. This class of models may

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range from the relatively simple to the complex, and their application often does not require a computer. In contrast, simulation models predict future stand composition and structure. Because of their complexity, application requires computer software (Fig. 3.28). Both types of regeneration models have been developed for oak and mixed forests in several regions (Chapter 8, this volume).

Notes 1

  This and related anthropomorphisms are used for conceptual convenience and should not be interpreted as implying that trees ‘plan’ their evolution or exercise choices in the sense that humans do.

Chapter 3

2

 However, if their age structure were based on age from germination (‘age from birth’), they could be unevenaged, depending on the length of the reproduction accumulation period. 3   Roots of oaks are difficult to age because their annual rings are not easily distinguishable even through a light microscope. Accurate age determination therefore usually requires special techniques such as X-radiography (Renton et al., 1974; Powell, 1976). 4  We use the term accuracy, as Botkin (1993) did, instead of precision as originally used by Levins (1966) and later by Sharpe (1990) to discuss these model properties. In the context of models, accuracy is more consistent with its parallel meaning in statistics and the sciences in g ­ eneral. Also see Sokal and Rohlf (1969).

References Abrams, M.D. (1992) Fire and the development of oak forests. BioScience 42, 346–353. https://doi.org/10.2307/ 1311781 Abrams, M.D. and Nowacki, G.J. (1992) Historical variation in fire, oak recruitment and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119, 19–28. https://doi.org/ 10.2307/2996916 Abrams, M.D. and Scott, M.L. (1989) Disturbancemediated accelerated succession in two Michigan forest types. Forest Science 35, 42–49. https://doi. org/10.1093/forestscience/35.1.42 Allen, T.F.H. and Starr, T.B. (1982) Hierarchy: Perspectives for Ecological Complexity. University of Chicago Press, Chicago, Illinois. Arend, J.L. and Scholz, H.F. (1969) Oak forests of the Lake States and their management. USDA Forest Service Research Paper NC-31. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/­treesearch/pubs/10552 (accessed 1 July 2018). Auchmoody, L.R. and Smith, H.C. (1993) Survival of northern red oak acorns after fall burning. USDA Forest Service Research Paper NE-678. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/ NE-RP-678 Aust, W.M., Hodges, J.D. and Johnson, R.L. (1985) The origin, growth and development of natural, pure, even-aged stands of bottomland oak. USDA Forest Service General Technical Report SO-54. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 163–170. Available at: https://www.srs.fs.usda.gov/pubs/gtr/gtr_so054.pdf (accessed 1 July 2018). Bakken, P.N. and Cook, J.E. (1998) Regeneration potential of six habitat types common to north-central Wisconsin. Northern Journal of Applied Forestry 15, 116–123. https://doi.org/10.1093/njaf/15.3.116

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Barnhart, S.J., McBride, J.R. and Warner, P. (1991) Oak seedling establishment in relation to environmental factors at Annadel State Park. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 25–30. Available at: https://www.fs.usda.gov/treesearch/ pubs/28394 (accessed 1 July 2018). Beck, D.E. (1970) Effect of competition on survival and height growth of red oak seedlings. USDA Forest Service Research Paper SE-56. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Beck, D.E. and Hooper, R.M. (1986) Development of a southern Appalachian hardwood stand after clearcutting. Southern Journal of Applied Forestry 10, 168– 172. https://doi.org/10.1093/sjaf/10.3.168 Beilmann, A.P. and Brenner, L.G. (1951) The recent intrusion of forests in the Ozarks. Annals of the Missouri Botanical Garden 38, 261–282. https://doi.org/10.2307/ 2394637 Belcher, D.M., Holdaway, M.R. and Brand, G.J. (1982) A description of STEMS, the stand and tree evaluation and modeling system. USDA Forest Service General Technical Report NC-79. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10143 (accessed 1 July 2018). Blake, J.I., Somers, G.L. and Ruark, G.A. (1990) Perspectives on process modeling of forest growth responses to environmental stress. In: Dixon, R.K., Meldahl, R.S., Ruark, G.A. and Warren, W.G. (eds) Process Modeling of Forest Growth Responses to Environmental Stress. Timber Press, Portland, Oregon, pp. 9–17. Borchert, M.I., Davis, F.W., Michaelsen, J. and Oyler, L.D. (1989) Interactions of factors affecting seedling recruitment of blue oak (Quercus douglasii) in California. Ecology 70, 389–404. https://doi.org/ 10.2307/1937544 Bormann, F.H. and Likens, G.E. (1979) Pattern and Process in a Forested Ecosystem. SpringerVerlag, New York. https://doi.org/10.1007/978-1-46126232-9 Botkin, D.B. (1993) Forest Dynamics, an Ecological Model. Oxford University Press, New York. Bowersox, T.W. and McCormick, L.H. (1987) Herbaceous communities reduce the juvenile growth of northern red oak, white ash, yellow poplar, but not white pine. In: Proceedings of the Central Hardwood Forest Conference VI. University of Tennessee, Knoxville, Tennessee, pp. 39–42. Braun, E.L. (1972) Deciduous Forests of Eastern North America. Hafner, New York. Brose, P.H., Dey, D.C., Phillips, R.J. and Waldrop, T.A. (2017) A meta-analysis of the fire-oak hypothesis: does prescribed burning promote oak reproduction in

159

eastern North America? Forest Science 59, 322–334. https://doi.org/10.5849/forsci.12-039 Bruce, D. (1990) Development of empirical forest growth models. In: Dixon, R.K., Meldahl, R.S., Ruark, G.E. and Warren, W.G. (eds) Process Modeling of Forest Growth Responses to Environmental Stress. Timber Press, Portland, Oregon, pp. 191–199. Buchman, R.G. and Shifley, S.R. (1983) Guide to evaluating forest growth projection systems. Journal of Forestry 81, 232–234, 254. https://doi.org/10.1093/jof/81.4.232 Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Volumes 1 and 2. USDA Forest Service, Washington, DC. Available at: https:// www.srs.fs.usda.gov/pubs/misc/ag_654/table_of_ contents.htm (accessed 1 July 2018). Callaway, R.M. (1992) Effect of shrubs on recruitment of Quercus douglasii and Quercus lobata in California. Ecology 72, 2118–2128. https://doi.org/10.2307/ 1941460 Callaway, R.M. and D’Antonio, C.M. (1991) Shrub facilitation of coast live oak establishment in central California. Madroño 38, 158–169. Available at: https://www.jstor. org/stable/41424857 (accessed 1 July 2018). Callaway, R.M. and Mahall, B.E. (1996) Variation in leaf structure and function in Quercus douglasii trees differing in root architecture and drought history. ­ International Journal of Plant Sciences 157, 129–135. https://doi.org/10.1086/297329 Callaway, R.M., Nadkarni, N.M. and Mahall, B.E. (1991) Facilitation and interference of Quercus douglasii on understory productivity in central California. Ecology 72, 1484–1499. https://doi.org/10.2307/1941122 Canadell, J. and Rodà, F. (1991) Root biomass of Quercus ilex in a montane Mediterranean forest. Canadian Journal of Forest Research 21, 1771–1778. https://doi.org/10.1139/x91-245 Canham, C.D. (1989) Different responses to gaps among shade-tolerant tree species. Ecology 69, 548–550. https://doi.org/10.2307/1940200 Carmean, W.H. (1971) Site index curves for black, white, scarlet and chestnut oaks in the Central States. USDA Forest Service Research Paper NC-62. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10583 (accessed 1 July 2018). Carvell, K.L. and Tryon, E.H. (1961) The effect of environmental factors on the abundance of oak regeneration beneath mature oak stands. Forest Science 7, 98–105. https://doi.org/10.1093/forestscience/7.2.98 Cecich, R.A. (1991) Seed production in oak. In: Pro­ ceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 125–131. Christisen, D.M. and Kearby, W.H. (1984) Mast measurement and production in Missouri (with special reference to acorns). Missouri Department of Conservation

160

Terrestrial Series 13. Missouri Department of Con­ servation, Jefferson City, Missouri. Cleland, D.T., Hart, J.B., Host, G.E., Pregitzer, K.S. and Ramm, C.W. (1993) Ecological Classification and Inventory System of the Huron-Manistee National Forests. USDA Forest Service, Washington, DC. Connell, J.H. and Slatyer, R.O. (1977) Mechanisms of succession in natural communities and their role in community stability and organization. American ­ Naturalist 111, 1119–1144. https://doi.org/10.1086/ 283241 Crow, T.R. (1988) Reproductive mode and mechanisms for self-replacement of northern red oak (Quercus rubra) – a review. Forest Science 34, 19–40. https:// doi.org/10.1093/forestscience/34.1.19 Crow, T.R. (1992) Population dynamics and growth patterns for a cohort of northern red oak (Quercus rubra) seedlings. Oecologia 91, 192–200. https://doi.org/ 10.1007/BF00317783 Curtis, J.T. (1959) The Vegetation of Wisconsin. University of Wisconsin Press, Madison, Wisconsin. Danielsen, K.C. and Halvorson, W.L. (1991) Valley oak seedling growth associated with selected grass species. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 9–13. Available at: https://www.fs. usda.gov/treesearch/pubs/28391 (accessed 1 July 2018). Davis, F.W., Borchert, M., Harvey, L.E. and Michaelsen, J.C. (1991) Factors affecting seedling survivorship of blue oak (Quercus douglasii H. & A.) in central California. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 81–86. Available at: https:// www.fs.usda.gov/treesearch/pubs/28402 (accessed 1 July 2018). Day, G.M. (1953) The Indian as an ecological factor in the northeastern forest. Ecology 34, 329–346. https://doi. org/10.2307/1930900 DeSelm, H.R., Clebsch, E.E.C. and Bennie, J.C. (1991) Effects of 27 years of prescribed fire on an oak forest and its soils in middle Tennessee. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 409–417. https://doi.org/10.2737/SE-GTR-70 Dey, D.C. (1991) A comprehensive Ozark regenerator. PhD dissertation, University of Missouri, Columbia, Missouri. Dey, D.C. and Parker, W.C. (1996) Regeneration of red oak (Quercus rubra L.) using shelterwood systems: ecophysiology, silviculture and management recommendations. Ontario Forest Research Institute Forestry Information Paper 126. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie,

Chapter 3

Ontario. Available at: https://www.fs.usda.gov/ treesearch/pubs/12423 (accessed 1 July 2018). Dey, D.C., Johnson, P.S. and Garrett, H.E. (1996a) Modeling the regeneration of oak stands in the Missouri Ozark Highlands. Canadian Journal of Forest Research 26, 573–583. https://doi.org/ 10.1139/x26-066 Dey, D.C., Ter-Mikaelian, M., Johnson, P.S. and Shifley, S.R. (1996b) Users guide to acorn: a comprehensive Ozark regeneration simulator. USDA Forest Service General Technical Report NC-180. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10244 (accessed 1 July 2018). Dixon, R.K., Meldahl, R.S., Ruark, G.A. and Warren, W.G. (eds) (1990) Process Modeling of Forest Growth Responses to Environmental Stress. Timber Press, Portland, Oregon. Downs, A.A. (1944) Estimating acorn crops for wildlife in the southern Appalachians. Journal of Wildlife Management 8, 339–340. https://doi.org/10.2307/ 3796028 Dunn, C.P., Guntenspergen, G.R. and Dorney, J.R. (1983) Catastrophic wind disturbance in an oldgrowth hemlock–hardwood forests, Wisconsin. Canadian Journal of Botany 61, 211–217. https://doi. org/10.1139/b83-023 Egler, F.E. (1954) Vegetation science concepts I. Initial floristic composition. A factor in old-field vegetation management. Vegetatio 4, 412–417. https://doi.org/ 10.1007/BF00275587 Ehrenfeld, J.G. (1980) Understory response to canopy gaps of varying size in a mature oak forest. Bulletin of the Torrey Botanical Club 107, 29–41. https://doi.org/ 10.2307/2484848 Farmer, R.E., Jr (1975) Growth and assimilation rate of juvenile northern red oak: effects of light and temperature. Forest Science 21, 373–381. https://doi.org/ 10.1093/forestscience/21.4.373 Fei, S., Steiner, K.C. and Finley, J.C. (2007) Stocking equations for regeneration in mixed oak stands. USDA General Technical Report SRS 101. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 55–59. Available at: https://www.fs.usda. gov/treesearch/pubs/27751 (accessed 1 July 2018). Fischer, B.C. (1979) Managing light in the selection method. In: Proceedings 1979 J.S. Wright Forestry Conference. Purdue University, West Lafayette, Indiana, pp. 43–53. Franklin, J.F., Perry, D.A., Schowalter, T.D., Harmon, M.E., McKee, A. and Spies, T.A. (1989) Importance of ecological diversity in maintaining long-term site productivity. In: Perry, D.A., Meurisse, R., Thomas, B., Miller, R., Boyle, J. and Means, J. (eds) Maintaining the Long-term Productivity of Pacific Northwest Forest Ecosystems. Timber Press, Portland, Oregon, pp. 82–97.

Regeneration Ecology II

Galford, J.R., Peacock, J.W. and Wright, S.L. (1988) Insects and other pests affecting oak regeneration. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 219–225. Galford, J.R., Auchmoody, L.R., Smith, H.C. and Walter, R.S. (1991) Insects affecting establishment of northern red oak seedlings in central Pennsylvania. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 271–280. Available at: https://www.fs.usda.gov/treesearch/ pubs/13504 (accessed 1 July 2018). Gatto, M. and Rinaldi, S. (1987) Some models of catastrophic behaviour in exploited forests. Vegetatio 69, 213–222. https://doi.org/10.1007/BF00038703 Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Glenn-Lewin, D.C. and van der Maarel, E. (1992) Patterns and processes of vegetation dynamics. In: Glenn Lewin, D.C., Peet, R.K. and Veblen, T.T. (eds) Plant Succession, Theory and Prediction. Chapman & Hall, New York, pp. 11–59. Godman, R.M., Yawney, H.W. and Tubbs, C.H. (1990) Acer saccharum Marsh. Sugar maple. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 78–91. Available at: https://www.srs.fs.usda.gov/ pubs/misc/ag_654/volume_2/acer/saccharum.htm (accessed 1 July 2018). Golden, M.S. and Loewenstein, E.F. (1991) Regeneration of tree species 7 years after clearcutting in a river bottom in central Alabama. USDA Forest Service ­ General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 76–83. https://doi.org/ 10.2737/SE-GTR-70 Gordon, D.R., Welker, J.M., Menke, J.W. and Rice, K.J. (1989) Competition for soil water between annual plants and blue oak (Quercus douglasii) seedlings. Oecologia 79, 533–541. https://doi.org/10.1007/ BF00378672 Gottschalk, K.W. (1985) Effects of shading on growth and development of northern red oak, black oak, black cherry and red maple seedlings. I. Height, diameter and root/shoot ratio. In: Proceedings of the Central Hardwood Forest Conference V. University of Illinois, Urbana-Champaign, Illinois, pp. 189–195. Available at: https://www.fs.usda.gov/treesearch/ pubs/47682 (accessed 1 July 2018). Gottschalk, K.W. (1987) Effects of shading on growth and development of northern red oak, black oak, black cherry and red maple seedlings. II. Biomass partitioning and prediction. In: Central Hardwood

161

Forest Conference VI. University of Tennessee, Knoxville, Tennessee, pp. 99–110. Available at: https://www.fs.usda.gov/treesearch/pubs/47683 (accessed 1 July 2018). Gottschalk, K.W. (1990) Gypsy moth effects on mast production. In: Proceedings of Workshop on Southern Mast Management. University of Tennessee, Knoxville, Tennessee, pp. 42–50. Available at: https://www.fs.usda. gov/treesearch/pubs/47445 (accessed 1 July 2018). Grimm, E.C. (1984) Fire and other factors controlling the Big Woods vegetation of Minnesota in the mid-­ nineteenth century. Ecological Monographs 54, 291–311. https://doi.org/10.2307/1942499 Grubb, P.J. (1977) The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biology Revue 52, 107–145. https://doi.org/ 10.1111/j.1469-185X.1977.tb01347.x Guyette, R.P. and Cutter, B.E. (1991) Tree-ring analysis of fire history of a post oak savanna in the Missouri Ozarks. Natural Areas Journal 11(2), 93–99. Guyette, R.P. and Dey, D.C. (1995) A history of fire, disturbance and growth in a red oak stand in the Bancroft District, Ontario. Ontario Forest Research Institute Forest Research Information Paper 119. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs.usda.gov/treesearch/pubs/12352 (accessed 1 July 2018). Haney, A. and Apfelbaum, S.I. (1990) Structure and dynamics of Midwest oak savannas. In: Sweeney, J.M. (ed.) Management of Dynamic Ecosystems. The Wildlife Society, North Central Section, West Lafayette, Indiana, pp. 19–30. Hannah, P.R. (1987) Regeneration methods for oaks. Northern Journal of Applied Forestry 4, 97–101. https://doi.org/10.1093/njaf/4.2.97 Hanson, P.J. and Dixon, R.K. (1985) Allelopathic inhibition of northern red oak by interrupted fern and goldenrod. In: Proceedings of the Central Hardwood Forest Conference V. University of Illinois, UrbanaChampaign, Illinios, pp. 269–274. Hanson, P.J., Isebrands, J.G. and Dickson, R.E. (1987) Carbon budgets of Quercus rubra L. seedlings at selected stages of growth: influence of light. In: Pro­ ceedings of the Central Hardwood Forest Conference VI. University of Tennessee, Knoxville, Tennessee, pp. 269–276. Harmon, M.E. (1984) Survival of trees after low-intensity surface fires in Great Smoky Mountains National Park. Ecology 65, 796–802. https://doi.org/10.2307/ 1938052 Harper, K.T., Wagstaff, F.J. and Kunzler, L.M. (1985) Biology and management of the Gambel oak vegetative type: a literature review. USDA Forest Service General Technical Report INT-179. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah.

162

Harrison, J.S. and Werner, P.A. (1984) Colonization by oak seedlings into a heterogeneous successional habitat. Canadian Journal of Botany 62, 559–563. https://doi.org/10.1139/b84-083 Hengst, G.E. and Dawson, J.O. (1993) Bark thermal properties of selected central hardwood species. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 55–75. Available at: https://www.fs.usda.gov/treesearch/ pubs/15305 (accessed 1 July 2018). Hilt, D.E. (1985) OAKSIM: an individual-tree growth and yield simulator for managed, even-aged, upland oak stands. USDA Forest Service Research Paper NE-562. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/21734 (accessed 1 July 2018). Hix, D.M. (1988) Multifactor classification and analysis of upland hardwood forest ecosystems of the Kickapoo River watershed, southwestern Wisconsin. Canadian Journal of Forest Research 18, 1405–1415. https:// doi.org/10.1139/x88-219 Hodges, J.D. and Gardiner, E.S. (1993) Ecology and physiology of oak regeneration. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 54–65. https://doi. org/10.2737/SE-GTR-84 Horney, M., Standiford, R.B., McCreary, D., Tecklin, J. and Richards, R. (2002) Effects of wildfire on blue oak in the northern Sacramento Valley. USDA Forest Service General Technical Report PSW-GTR-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 261–267. Available at: https://www.fs.usda.gov/treesearch/pubs/26128 (accessed 1 July 2018). Horsley, S.B. (1991) Using Roundup and Oust to control interfering understories in Allegheny hardwood stands. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 281–290. Available at: https://www.fs.usda.gov/ treesearch/pubs/13505 (accessed 1 July 2018). Host, G.E. and Pregitzer, K.S. (1991) Ecological species groups for upland forest ecosystems of northwestern Lower Michigan. Forest Ecology and Management 43, 87–102. https://doi.org/10.1016/0378-1127(91)90078-A Huddle, J.A. and Pallardy, S.G. (1996) Tree survivorship in an oak–hickory forest in southeast Missouri, USA under a long-term regime of annual and periodic controlled burning. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 141–151. Available at: https://www.fs.usda.gov/treesearch/pubs/all/12747 (accessed 1 July 2018).

Chapter 3

Isebrands, J.G., Rauscher, H.M., Crow, T.R. and Dickmann, D.I. (1990) Whole-tree growth process models based on structural–functional relationships. In: Dixon, R.K., Meldahl, R.S., Ruark, G.A. and Warren, W.G. (eds) Process Modeling of Forest Growth Responses to Environmental Stress. Timber Press, Portland, Oregon, pp. 96–112. Jarvis, P.G. (1964) Interference by Deschampsia fluexuosa (L.) Trin. Oikos 15, 56–78. https://doi.org/ 10.2307/3564748 Johnson, P.S. (1966) Vegetational developments after logging in southern Michigan xeric oak forests. PhD dissertation, Michigan State University, East Lansing, Michigan. Johnson, P.S. (1974) Survival and growth of northern red oak seedlings following a prescribed burn. USDA Forest Service Research Note NC-177. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11354 (accessed 1 July 2018). Johnson, P.S. (1975a) Growth and structural development of red oak sprout clumps. Forest Science 21, 413–418. Available at: https://academic.oup.com/­forestscience/ article/21/4/413/4675741 (accessed 1 July 2018). Johnson, P.S. (1976) Modal development of regeneration in clearcut red oak stands in the Driftless Area. In: Proceedings of the Central Hardwood Forest Conference I. University of Southern Illinois, Carbondale, Illinois, pp. 455–475. Johnson, P.S. (1977) Predicting oak stump sprouting and sprout development in the Missouri Ozarks. USDA Forest Service Research Paper NC-149. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10670 (accessed 1 July 2018). Johnson, P.S. (1979) Shoot elongation of black oak and white oak sprouts. Canadian Journal of Forest Research 9, 489–494. https://doi.org/10.1139/x79-079 Johnson, P.S. (1992) Oak overstory/reproduction relations in two xeric ecosystems in Michigan. Forest Ecology and Management 48, 233–248. https://doi. org/10.1016/0378-1127(92)90147-2 Johnson, P.S. (1993) Perspectives on the ecology and silviculture of oak-dominated forests in the central and eastern states. USDA Forest Service General Technical Report NC-153. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10217 (accessed 1 July 2018). Johnson, P.S. and Rogers, R. (1984) Predicting 25th-year diameters of thinned stump sprouts of northern red oak. Journal of Forestry 82, 616–619. https://doi. org/10.1093/jof/82.10.616 Johnson, P.S., Jacobs, R.D., Martin, A.J. and Godell, E.D. (1989) Regenerating northern red oak: three successful case histories. Northern Journal of Applied Forestry 6, 174–178. https://doi.org/10.1093/njaf/6.4.174

Regeneration Ecology II

Johnson, R.L. (1975b) Natural regeneration and development of Nuttall oak and associated species. USDA Forest Service Research Paper SO-104. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www.fs.usda. gov/treesearch/pubs/43015 (accessed 1 July 2018). Johnson, R.L. (1981) Oak seeding – it can work. Southern Journal of Applied Forestry 5, 28–33. https://doi.org/10.1093/sjaf/5.1.28 Johnson, R.L. and Deen, R.T. (1993) Prediction of oak regeneration in bottomland forests. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 146–155. https://doi. org/10.2737/SE-GTR-84 Johnson, R.L. and Krinard, R.M. (1983) Regeneration in small and large sawtimber sweetgum–red oak stands following selection and seed tree harvest: 23-year results. Southern Journal of Applied Forestry 7, 176– 184. https://doi.org/10.1093/sjaf/7.4.176 Johnson, W.C. and Webb, T. III (1989) The role of blue jays (Cyanocitta cristata L.) in the postglacial dispersal of fagaceous trees in eastern North America. Journal of Biogeography 16, 561–571. https://doi. org/10.2307/2845211 Jørgensen, S.E. (1990) Ecosystem theory, ecological buffer capacity, uncertainty and complexity. Ecological Modelling 52, 125–133. https://doi.org/10.1016/ 0304-3800(90)90013-7 Kabrick, J.M., Zenner, D.K., Dey, D.C., Gwaze, D. and Jensen, R.G. (2008) Using ecological land types to examine landscape-scale oak regeneration dynamics. Forest Ecology and Management 255, 3051–3062. https://doi.org/10.1016/j.foreco.2007.09.068 Kabrick, J.M., Villwock, J.L., Dey, D.C., Keyser, T.L. and Larsen, D.R. (2014) Modeling and mapping oak advance reproduction density using soil and site variables. Forest Science 60, 1107–1117. https://doi. org/10.5849/forsci.13-006 Keeley, J.E. (1991) Seed germination and life history syndromes in the California chaparral. Botanical Revue 57, 81–116. https://doi.org/10.1007/BF02858766 Keeley, J.E. (1992) Recruitment of seedlings and vegetative sprouts in unburned chaparral. Ecology 73, 1194–1208. https://doi.org/10.2307/1940669 Kimmins, J.P. (1987) Forest Ecology. Macmillan, New York. Knapp, B.O., Hullinger, M.A. and Kabrick, J.M. (2017) Effects of fire frequency on long-term development of an oak–hickory forest in Missouri, U.S.A. Forest Ecology and Management 387, 19–29. https://doi. org/10.1016/j.foreco.2016.07.013 Kolb, T.E. and Steiner, K.C. (1990) Growth and biomass partitioning response of northern red oak genotypes to shading and grass root competition. Forest Science 36, 293–303. https://doi.org/10.1093/forestscience/36.2.293 Korstian, C.F. (1927) Factors controlling germination and early survival in oaks. Yale University School Forestry

163

Bulletin 19. Yale University School of Forestry, New Haven, Connecticut. Kotar, J., Kovach, J.A. and Locey, C.T. (1988) Field Guide to Forest Habitat Types of Northern Wisconsin. University of Wisconsin and Wisconsin Department of Natural Resources, Madison, Wisconsin. Krajicek, J.E. (1960) Some factors affecting oak and black walnut reproduction. Iowa State Journal of Science 34, 631–634. Kruger, E.L. and Reich, P.B. (1989) Comparative growth and physiology of stem-pruned and unpruned northern red oak. USDA Forest Service General Technical Report NC-132, 302. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Availableat:https://www.nrs.fs.fed.us/pubs/gtr/gtr_nc132. pdf (accessed 1 July 2018). Lamson, N.I. (1988) Role of stump sprouts in regenerating Appalachian hardwood stands. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 31–37. Landsberg, J.J. (1986) Physiological Ecology of Forest Production. Academic Press, New York. Langdon, O.G. (1981) Some effects of prescribed fire on understory vegetation in loblolly pine stands. In: Symposium Proceedings: Prescribed Fire and Wildlife in Southern Forests. Clemson University, Charleston, South Carolina, pp. 143–153. Latham, R.E. (1992) Co-occurring tree species change rank in seedling performance with resources varied experimentally. Ecology 73, 2129–2144. https://doi. org/10.2307/1941461 Levins, R. (1966) The strategy of model building in population biology. American Scientist 54, 421–431. Lewis, R.W. (1990) Theories, speculation and the structure of knowledge. Speculations in Science and Technology 13, 13–17. Liming, F.G. and Johnston, J.P. (1944) Reproduction in oak–hickory forest stands of the Missouri Ozarks. Journal of Forestry 42, 175–180. https://doi.org/ 10.1093/jof/42.3.175 Loach, K. (1967) Shade tolerance in tree seedlings. 1. Leaf photosynthesis and respiration in plants raised under artificial shade. New Phytologist 66, 607–621. https://doi.org/10.1111/j.1469-8137.1967. tb05432.x Loewenstein, E.F. (1992) Relationships between stem origin and growth characteristics of three bottomland hardwood species on a seven-year-old clearcut area in the Coastal Plain of Alabama. MSc. thesis, Auburn University, Auburn, Alabama. Loewenstein, E.F. and Golden, M.S. (1995) Establishment of water oak is not dependent on advance reproduction. USDA Forest Service General Technical Report SRS1. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 443–446. Available at: https://www.fs.usda.gov/treesearch/pubs/355 (accessed 1 July 2018).

164

Loftis, D.L. (1983) Regenerating southern Appalachian mixed hardwood stands with the shelterwood method. Southern Journal of Applied Forestry 7, 212–217. https://doi.org/10.1093/sjaf/7.4.212 Loftis, D.L. (1988a) Regenerating oaks on high-quality sites, an update. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 199–209. Loftis, D.L. (1988b) Regenerating red oak in the southern Appalachians: predictive models and practical applications. PhD dissertation, North Carolina State University, Raleigh, North Carolina. Loftis, D.L. (1990a) Predicting post-harvest performance of advance red oak reproduction in the southern Appalachians. Forest Science 36, 908–916. Available at: https://www.fs.usda.gov/treesearch/pubs/1148 (accessed 1 July 2018). Loftis, D.L. (1990b) A shelterwood method for regenerating red oak in the southern Appalachians. Forest Science 36, 917–929. Available at: https://www.fs.usda.gov/ treesearch/pubs/1146 (accessed 1 July 2018). Loomis, R.M. (1973) Estimating fire-caused mortality and injury in oak–hickory forests. USDA Forest Service Research Paper NC-94. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs. usda.gov/treesearch/pubs/10615 (accessed 1 July 2018). Loomis, R.M. (1974) Predicting the losses in sawtimber volume and quality from fires in oak–hickory forests. USDA Forest Service Research Paper NC-104. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10625 (accessed 1 July 2018). Lorimer, C.G. (1981) Survival and growth of understory trees in oak forests of the Hudson Highlands, New York. Canadian Journal of Forest Research 11, 689–695. https://doi.org/10.1139/x81-095 Lorimer, C.G. (1983) Eighty-year development of northern red oak after partial cutting in a mixed-species Wisconsin forest. Forest Science 29, 371–383. https://doi.org/10.1093/forestscience/29.2.371 Lorimer, C.G. (1989) The oak regeneration problem: new evidence on causes and possible solutions. University of Wisconsin Forest Resource Analyses No. 8. University of Wisconsin, Madison, Wisconsin. Lorimer, C.G. (1993) Causes of the oak regeneration problem. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 14–39. https://doi.org/10.2737/SE-GTR-84 Lorimer, C.G., Chapman, J.W. and Lambert, W.D. (1994) Tall understorey vegetation as a factor in the poor development of oak seedlings beneath mature stands. Journal of Ecology 82, 227–237. https://doi.org/ 10.2307/2261291

Chapter 3

Margalef, R. (1963) On certain unifying principles in ecology. American Naturalist 97, 357–374. https:// doi.org/10.1086/282286 Marks, P.L. (1974) The role of pin cherry (Prunus serotina L.) in the maintenance of stability in northern hardwood ecosystems. Ecological Monographs 44, 73– 88. https://doi.org/10.2307/1942319 Marquis, D.A. and Johnson, R.L. (1989) Silviculture of eastern hardwoods. USDA Forest Service General Technical Report WO-55. USDA Forest Service, Washington, DC, pp. 9–15. Marquis, D.A., Eckert, P.L. and Roach, B.A. (1976) Acorn weevils, rodents and deer all contribute to oakregeneration difficulties in Pennsylvania. USDA Forest Service Research Paper NE-356. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/14481 (accessed 1 July 2018). Marquis, D.A., Ernst, R.L. and Stout, S.L. (1992) Prescribing silvicultural treatments in hardwood stands of the Alleghenies (revised). USDA Forest Service General Technical Report NE-96. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4132 (accessed 1 July 2018). Martin, R.E. (1962) Thermal properties of bark. Forest Products Journal 13, 419–426. Maslen, P. (1989) Response of immature oaks to prescribed fire in the North Carolina Piedmont. USDA Forest Service General Technical Report SO-74. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 259–266. Available at: https://www.srs.fs.usda.gov/pubs/gtr/ gtr_so074.pdf (accessed 1 July 2018). Matney, T.G. (1974) Oak regeneration in natural upland oak stands of West Virginia. MSc. thesis, West Virginia University, Morgantown, West Virginia. Matsuda, K. and McBride, J.R. (1986) Difference in seedling growth morphology as a factor in the distribution of three oaks in Central California. Madroño 33, 207–216. Available at: https://www.jstor.org/stable/41424594 (accessed 1 July 2018). Matsuda, K., McBride, J.R. and Kimura, M. (1989) Seedling growth form of oaks. Annals of Botany 64, 439–446. https://doi.org/10.1093/oxfordjournals.aob. a087862 McClaran, M.P. and Bartolome, J.W. (1989) Fire-related recruitment in stagnant Quercus douglasii populations. Canadian Journal of Forest Research 19, 580–585. https://doi.org/10.1139/x89-091 McCreary, D.D., Tietze, W.D. and Frost, W. (2002) Stump sprouting of blue oaks ten years after harvest. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California,

Regeneration Ecology II

pp. 573–580. Available at: https://www.fs.usda.gov/ treesearch/pubs/26154 (accessed 1 July 2018). McGee, C.E. (1987) Clearcutting in upland hardwoods: panacea or anathema? In: Proceedings of the Central Hardwood Forest Conference VI. University of Tennessee, Knoxville, Tennessee, pp. 21–29. McGee, C.E. (1988) Spring weather, canopy removal and early budbreak threaten oak seedlings. Journal of the Elisha Mitchell Science Society 104, 108–115. Available at: https://www.jstor.org/stable/24333946 (accessed 1 July 2018). McGill, D.W., Rogers, R., Martin, A.J. and Johnson, P.S. (1999) Measuring stocking in northern red oak stands in Wisconsin. Northern Journal of Applied Forestry 16, 144–150. https://doi.org/10.1093/njaf/16.3.144 McLemore, B.F. (1990) Cornus florida L. Flowering dogwood. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 278–283. Available at: https://www.srs.fs.usda.gov/pubs/misc/ ag_654/volume_2/cornus/florida.htm (accessed 1 July 2018). McQuilkin, R.A. (1983) Silvical factors affecting the regeneration of oaks and associated species in Pennsylvania. In: Proceedings of Regenerating Hardwood Stands. Pennsylvania State University, University Park, Pennsylvania, pp. 37–65. Merz, R.W. and Boyce, S.G. (1956) Age of oak seedlings. Journal of Forestry 54, 774–775. Minckler, L.S. and Woerheide, J.D. (1965) Reproduction of hardwoods 10 years after cutting as affected by site and opening size. Journal of Forestry 63, 103–107. https://doi.org/10.1093/jof/63.2.103 Motsinger, J.R., Kabrick, J.M., Dey, D.C., Henderson, D.E. and Zenner, E.K. (2010) Effect of midstory and understory removal on the establishment and development of natural and artificial pin oak advance reproduction in bottomland forests. New Forests 39, 195—213. https://doi.org/10.1007/s11056-009-9164-5 Muller, C.H. (1951) The significance of vegetative reproduction in Quercus. Madroño 11, 129–137. Available at: https://www.jstor.org/stable/41423474 (accessed 1 July 2018). Myers, S.A. (1978) Insect impact on acorn production in Missouri upland forests. PhD dissertation, University of Missouri, Columbia, Missouri. Nowacki, G.J., Abrams, M.D. and Lorimer, C.G. (1990) Composition, structure and historical development of northern red oak stands along an edaphic gradient in north-central Wisconsin. Forest Science 36, 276–292. https://doi.org/10.1093/forestscience/36.2.276 Nyland, R.D., Abrahamson, L.P. and Adams, K.B. (1983) Use of prescribed fire for regenerating red and white oak in New York. In: Proceedings of the 1982 Society of American Foresters National Convention. Society of American Foresters, Bethesda, Maryland, pp. 163–167.

165

Olson, D.F., Jr (1974) Quercus L. Oak. USDA Forest Service Agriculture Handbook 450. USDA Forest Service, Washington, DC, 88 pp. Overton, R.P. (1990) Oxydendron arboreum (L.) DC Sourwood. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 497–500. Available at: https://www.srs.fs.usda.gov/pubs/misc/ ag_654/volume_2/oxydendrum/arboreum.htm (accessed 1 July 2018). Pallardy, S.G. and Rhoads, J.L. (1993) Morphological adaptations to drought in seedlings of deciduous angiosperms. Canadian Journal of Forest Research 23, 1766–1774. https://doi.org/10.1139/x93-223 Pavlik, B.M., Muick, P.C., Johnson, S. and Popper, M. (1991) Oaks of California. Cachuma Press, Los Olivos, California. Platt, W.J., Glitzenstein, J.S. and Streng, D.R. (1991) Evaluating pygrogenicity and its effects on vegetation in longleaf pine savannas. In: Proceedings of the 17th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 143–161. Plumb, T.R. and Gomez, A.P. (1983) Five southern California oaks: identification and postfire management. USDA Forest Service General Technical Report PSW-71. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-71 Powell, D.S. (1976) Sprouting ability of advance reproduction of undisturbed forest stands in West Virginia. MSc. thesis, University of West Virginia, Morgantown, West Virginia. Prentice, I.C. and Helmisaari, H. (1991) Silvics of north European trees: compilation, comparisons and implications for forest succession modelling. Forest Ecology and Management 42, 79–93. https://doi.org/10.1016/ 0378-1127(91)90066-5 Rebertus, A.J., Williamson, G.B. and Moser, E.B. (1989) Longleaf pine pyrogenicity and turkey oak mortality in Florida xeric sandhills. Ecology 70, 60–70. https://doi. org/10.2307/1938412 Reich, P.B., Abrams, M.D., Ellsworth, D.S., Kruger, E.L. and Tabone, T.J. (1990) Fire affects ecophysiology and community dynamics of central Wisconsin oak forest regeneration. Ecology 71, 2179–2190. https:// doi.org/10.2307/1938631 Renton, J.J., Lanasa, M.J. and Tryon, E.H. (1974) Radiography for observing wood features. Journal of Forestry 72, 292–293. https://doi.org/10.1093/jof/72.5.292 Rice, C. and Struve, D.K. (1997) Seedling growth form and water use of selected oak species. In: Genetics of Quercus, Diversity and Adaptation in Oak Species. Proceedings of Working Party 2.08.05, 12–17 October, International Union of Forestry Research Organizations, Pennsylvania State University, University Park, Pennsylvania, pp. 269–278.

166

Riegel, G.M., Smith, B.G. and Franklin, J.F. (1992) Foothill oak woodlands of the Interior Valleys of southwestern Oregon. Northwest Science 66(2), 66–76. Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA Forest Service Agriculture Handbook 335. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Rogers, R. and Johnson, P.S. (1998) Approaches to modeling natural regeneration in oak-dominated forests. Forest Ecology and Management 106, 45–54. https:// doi.org/10.1016/S0378-1127(97)00238-7 Rogers, R., Johnson, P.S. and Loftis, D.L. (1993) An overview of oak silviculture in the United States: the past, present and future. Annales des Sciences Forestieres 50, 535–542. https://doi.org/10.1051/forest:19930602 Ross, M.S., Sharik, T.L. and Smith, D.W. (1986) Oak regeneration after clear felling in southwest Virginia. Forest Science 32, 157–169. https://doi.org/10.1093/ forestscience/32.1.157 Roth, E.R. and Sleeth, B. (1939) Butt rot in unburned sprout oak stands. USDA Technical Bulletin 684. United States Department of Agriculture (USDA), Washington, DC. Rouse, C. (1986) Fire effects in northeastern forests: oak. USDA Forest Service General Technical Report NC-105. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10169 (accessed 1 July 2018). Sander, I.L. (1971) Height growth of new oak sprouts depends on size of advance reproduction. Journal of Forestry 69, 809–811. https://doi.org/10.1093/ jof/69.11.809 Sander, I.L. (1979a) Regenerating oaks. In: Proceedings of the National Silviculture Workshop. USDA Forest Service, Washington, DC, pp. 212–221. Sander, I.L. (1979b) Regenerating oaks with the shelterwood system. In: Proceedings of 1979 J.S. Wright Forestry Conference. Purdue University, West Lafayette, Indiana, pp. 54–60. Available at: https://www.fs.usda. gov/treesearch/pubs/45320 (accessed 1 July 2018). Sander, I.L. (1983) Oak regeneration in the Central States. In: Proceedings of the 11th Annual Hardwood Symposium of the Hardwood Research Council. Hardwood Research Council, Memphis, Tennessee, pp. 18–31. Sander, I.L. (1990) Quercus rubra L. Northern red oak. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 727–733. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ volume_2/quercus/rubra.htm (accessed 1 July 2018). Sander, I.L. and Clark, F.B. (1971) Reproduction of Upland Hardwood Forests in the Central States.

Chapter 3

USDA Forest Service Agriculture Handbook 405. USDA Forest Service, Washington, DC. Available at: https://www.fs.usda.gov/treesearch/pubs/37460 (accessed 1 July 2018). Sander, I.L., Johnson, P.S. and Rogers, R. (1984) Evaluating oak advance reproduction in the Missouri Ozarks. USDA Forest Service Research Paper NC-251. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10038 (accessed 1 July 2018). Scholz, H.F. (1955) Growth of northern red oak seedlings under variable conditions of ground cover competition. USDA Forest Service Lake States Forest Experiment Station Technical Note 430. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Scholz, H.F. (1959) Further observations on seedbed scarification show benefits to northern red oak were temporary. USDA Forest Service Lake States Forest Experiment Station Technical Note 555. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Sharpe, P.J.H. (1990) Forest modeling approaches: compromises between generality and precision. In: Dixon, R.K., Meldahl, R.S., Ruark, G.A. and Warren, W.G. (eds) Process Modeling of Forest Growth Responses to Environmental Stress. Timber Press, Portland, Oregon, pp. 180–190. Shugart, H.H. (1984) A Theory of Forest Dynamics. Springer-Verlag, New York. Smalley, G.W. (1978) Classification and evaluation of forest sites for the Interior Highlands. In: Proceedings of the Central Hardwood Forest Conference II. Purdue University, West Lafayette, Indiana, 257 pp. Smalley, G.W. (1984) Classification and evaluation of forest sites in the Cumberland Mountains. USDA Forest Service General Technical Report SO-50. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi. org/10.2737/SO-GTR-50 Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, P.M.S. (1997) The Practice of Silviculture: Applied Forest Ecology, 9th edn. Wiley, New York. Smith, H.C. and Miller, G.W. (1987) Managing Appalachian hardwood stands using four regeneration practices – 34-year results. Northern Journal of Applied Forestry 4, 180–185. Available at: https://www.fs.usda.gov/ treesearch/pubs/14276 (accessed 1 July 2018). Smith, L.L. and Vankat, J.L. (1991) Communities and tree seedling distribution in Quercus rubra- and Prunus serotina-dominated forests in southwestern Pennsylvania. American Midland Naturalist 126, 294–307. https://doi. org/10.2307/2426105 Sokal, R.R. and Rohlf, F.J. (1969) Biometry: the Principles and Practices of Statistics in Biological Research. W.H. Freeman, San Francisco.

Regeneration Ecology II

Sork, V.L. (1984) Examination of seed dispersal and survival in red oak, Quercus rubra (Fagaceae), using metal-tagged acorns. Ecology 65, 1020–1022. https:// doi.org/10.2307/1938075 Sork, V.L., Stacey, P. and Averett, J.E. (1983) Utilization of red oak acorns in non-bumper crop year. Oecologia 59, 49–53. https://doi.org/10.1007/BF00388070 Spurr, S.H. and Barnes, B.V. (1980) Forest Ecology, 3rd edn. Ronald Press, New York. Steiner, K.C, Abrams, M.D. and Bowersox, T.W. (1993) Advance reproduction and other stand characteristics in Pennsylvania and French stands of northern red oak. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 473–483. Available at: https://www.fs.usda.gov/treesearch/ pubs/15342 (accessed 1 July 2018). Streng, D.R., Glitzenstein, J.S. and Harcombe, P.A. (1989) Woody seedling dynamics in an east Texas floodplain forest. Ecological Monographs 59, 177–204. https://doi.org/10.2307/2937285 Sullivan, N.H. (2001) An algorithm for a landscape model of mast production. PhD dissertation, University of Missouri, Columbia, Missouri. Terborgh, J. (1985) The vertical component of plant species diversity in temperate tropical forests. American Naturalist126,760–776.https://doi.org/10.1086/284452 Thilenius, J.F. (1968) The Quercus garryana forests of the Willamette Valley, Oregon. Ecology 49, 1124–1133. https://doi.org/10.2307/1934496 Thor, E. and Nichols, G.M. (1973) Some effects of fires on litter, soil and hardwood regeneration. In: Proceedings of the 13th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 317–329. Tiedemann, A.R., Clary, W.P. and Barbour, R.J. (1987) Underground systems of Gambel oak (Quercus gambelii) in central Utah. American Journal of Botany 74, 1065–1071. https://doi.org/10.1002/j.1537-2197.1987. tb08717.x Trimble, G.R., Jr (1960) Relative diameter growth rates of five upland oaks in West Virginia. Journal of Forestry 58, 111–115. https://doi.org/10.1093/jof/58.2.111 Trimble, G.R., Jr (1973) The regeneration of Central Appalachian hardwoods with emphasis on the effects of site quality and harvesting practice. USDA Forest Service Research Paper NE-282. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15387 (accessed 1 July 2018). Trimble, G.R., Jr and Tryon, E.H. (1966) Crown encroachment into openings cut in Appalachian hardwood stands. Journal of Forestry 64, 104–108. https://doi. org/10.1093/jof/64.2.104 Tryon, E.H. and Carvell, K.L. (1958) Regeneration under oak stands. West Virginia University Agricultural Experiment Station Bulletin 424T. West Virginia

167

University Agricultural Experiment Station, Morgan­ town, West Virginia. Urban, D.L. and Shugart, H.H. (1992) Individual-based models of forest succession. In: Glenn-Lewin, D.C., Peet, R.K. and Veblen, T.T. (eds) Plant Succession, Theory and Prediction. Chapman & Hall, New York, pp. 249–292. Van Lear, D.H. and Watt, J.M. (1993) The role of fire in oak regeneration. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 6–78. https://doi.org/10.2737/ SE-GTR-84 Veblen, T.T. (1992) Regeneration dynamics. In: GlennLewin, D.C., Peet, R.K. and Veblen, T.T. (eds) Plant Succession, Theory and Prediction. Chapman & Hall, New York, pp. 152–187. Waldrop, T.A., Buckner, E.R., Shugart, H.H., Jr and McGee, C.E. (1986) FORCAT: a single tree model of stand development following clearcutting on the Cumberland Plateau. Forest Science 32, 297–317. https://doi.org/10.1093/forestscience/32.2.297 Walters, R.S. (1990) Site quality, fire and herbicide effects on establishment, growth and development of regeneration three years after partial cutting of oak stands. PhD dissertation, State University of New York, Syracuse, New York. Walters, M.B., Kruger, E.L. and Reich, P.B. (1993) Growth, biomass distribution and CO2 exchange of northern hardwood seedlings in high and low light: relationships with successional status and shade tolerance. Oecologia 94, 7–16. https://doi.org/10.1007/ BF00317294 Ward, J.S. and Stephens, G.R. (1994) Crown class transition rates of maturing northern red oak (Quercus rubra L.). Forest Science 40, 221–237. https://doi. org/10.1093/forestscience/40.2.221 Watt, A.S. (1947) Pattern and process in the plant community. Journal of Ecology 35, 1–22. https://doi. org/­10.2307/2256497 Weigel, D.R. and Johnson, P.S. (1998) Stump sprouting probabilities for Southern Indiana oaks. USDA Forest Service Technical Brief TB-NC-7. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11004 (accessed 1 July 2018). Welker, J.M. and Menke, J.W. (1990) The influence of simulated browsing on tissue water relations, growth and survival of Quercus douglasii (Hook and Arn.) seedlings under slow and rapid rates of soil drought. Functional Ecology 4, 807–817. https://doi.org/ 10.2307/2389447

168

Wendel, G.W. (1975) Stump sprout growth and quality of several Appalachian hardwood species after clearcutting. USDA Forest Service Research Paper NE-329. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/15457 (accessed 1 July 2018). Wendel, G.W. (1977) Longevity of black cherry, wild grape and sassafras seed in the forest floor. USDA Forest Service Research Paper NE-375. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/14508 (accessed 1 July 2018). Wendel, G.W. (1990) Prunus pensylvanica L. f. Pin cherry. In: Burns, R.M. and Honkala, B.H. (tech. coords) Silvics of North America. USDA Forest Service Agriculture Handbook 654, Vol. 2. USDA Forest Service, Washington, DC, pp. 587–593. Available at: https://www.srs.fs.usda.gov/pubs/misc/ ag_654/volume_2/prunus/pensylvanica.htm (accessed 1 July 2018). Whitney, G.G. (1986) A demographic analysis of Rubus idaeus and Rubus pubescens. Canadian Journal of Botany 64, 2916–2921. https://doi.org/10.1139/ b86-385 Whitney, G.G. (1994) From Coastal Wilderness to Fruited Plain. Cambridge University Press, Cambridge. Williams, K., Davis, S.D., Gartner, B.L. and Karlsson, S. (1991) Factors limiting the establishment of a chaparral oak, Quercus durata Jeps., in grassland. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 70–73. Available at: https://www. fs.usda.gov/treesearch/pubs/28400 (accessed 1 July 2018). Williamson, G.B. and Black, E.M. (1981) High temperature of forest fires under pines as a selective advantage over oaks. Nature 293, 643–644. https://doi. org/10.1038/293643a0 Will-Wolf, S. (1991) Role of fire in maintaining oaks in mesic oak maple forests. In: Proceedings of the Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 27–33. Wright, S.L. (1987) Managing insects affecting oak regeneration by prescribed burning. USDA Forest Service General Technical Report SE-46. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 186–192. Available at: https://www.srs.fs.usda.gov/pubs/gtr/ gtr_se046.pdf (accessed 1 July 2018).

Chapter 3

4



Site Quality and Productivity

Introduction Silviculturists use the term site to refer to an area of forested land that is qualitatively characterized by its climate, soil, vegetation, or quantitatively by its productivity. The latter is usually expressed as potential wood production per unit land area per unit of time (Helms, 1998). Climate, soil, vegetation and productivity characteristics may be used singly or in combination to define site productivity. More generally, site productivity refers to the ability of a defined area to produce objects and their attributes that are required for living organisms or human society to function (Lee, 1989). Lee (1989) used this definition to refer to ‘resource site quality’. Site quality is linked to forest nutrition, which in turn is linked to patterns of matter and energy flow and storage within forest ecosystems, or ‘nutrient cycling’. Biogeochemistry is the name ecologists have given to the process of nutrient cycling. It involves the accumulation, storage, transport and recycling of chemical elements (nutrients) between and within the biota and the physical environment. These processes require energy and thus are inseparable from it. Nutrient cycling in forests is a complex process and is discussed more completely by others (e.g. Kimmins, 2004; Perry et al., 2008). Silvicultural practices can influence nutrient cycling through tree removals and associated nutrient losses. Closely related are potential soil nutrient losses from logging and soil disturbance (see ‘Effects of harvesting on site productivity’, this chapter). The researcher’s view of site quality may differ from the land manager’s, and Gholz (1988) pointed out that there is no universally accepted definition. The two prevalent schools of thought nevertheless consider site productivity as either: (i) a hypothetical, optimal or potential level of productivity; or (ii) an index or relative measure of actual productivity. Although production capability is sometimes called ‘yield’, production and yield are not the same (Smith, 1986). Yield usually refers to material that is

usable. In the case of timber, yield refers to the amount of wood that is actually harvested and removed from the site. In contrast, production usually refers to all material that has resulted from tree growth whether or not it is harvested and removed (Zahner and Myers, 1984). Both production and yield on a given site are affected by forest conditions. For a given species or species mix, conditions that commonly reduce stand yields below their maximum potential include low stocking, insect or disease damage, tree defects and high-grading. Both the actual and the potential production and yield are of interest to silviculturists. Site productivity influences many facets of stand development including growth, yield, regeneration and rates of other ecological processes. For a given age, undisturbed stands on good sites have fewer trees, but higher basal areas and average stem diameters than do stands on poor sites. This results from the faster growth and earlier self-thinning of trees on good sites (Chapter 6, this volume). For example, oaks on poor sites (site index 50) in the Piedmont were almost 4 inches smaller in mean dbh at stand age 50 (6.7 inches dbh) than oaks growing on good sites (site index 90) (10.4 inches dbh). Stands on poor sites also carried lower basal areas (94 ft2/acre versus 113 ft2/acre) and twice the number of trees (900/acre versus 450/acre). The amount of clear lumber that develops in an oak largely depends on its rate of height growth and thus on site quality. Rapid height growth increases clear bole length and minimizes the size of the knotty core – and thus increases tree value (Carmean and Boyce, 1973; see also Chapter 15, this volume). Site quality also affects other wood properties including hardness, shrinkage, strength and yield of cellulose (Zahner, 1970).

Measures of Site Quality and Productivity Site productivity can be measured and expressed in various ways. Silviculturists commonly estimate the periodic increment of wood or timber production

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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expressed as volume or weight increase over a year, a decade or a rotation. Units of measure include various volumetric measures1 and weight measures in pounds and tons. In this context, site productivity can be defined as ‘the productive capacity of a site, usually expressed as volume production of a given species’ (Society of American Foresters, 1995). The term ‘site quality’ is generally used when productivity is expressed as qualitative classes (e.g. poor, medium, good), or on a relative scale. Ecologists view forest productivity somewhat differently. They are often interested in total accumulated biomass (dry weight of organic matter) per unit of land area and rates of increase and decrease of biomass per unit area (see Chapter 13, this volume). They also may separate biomass by plant parts such as bolewood, branches, bark, leaves, buds, roots and reproductive structures (Cannell, 1982; Kozlowski et al., 1991). Changes in biomass or weight of forest trees are used to study growth, nutrient cycling and energy flow in forests. An understanding of ecosystem production dynamics is important to both ecologists and silviculturists and consequently has been the subject of numerous workshops and symposia (e.g. Hennessey et al., 1986; Cole and Gessel, 1988). Components of productivity of interest to ecologists include: ●● Gross primary productivity (GPP). GPP is the increase per unit area in dry weight of organic material produced by photosynthesis that remains in the plant (above and below ground) plus the dry weight of matter lost by plant respiration. ●● Net primary productivity (NPP). NPP is the increase per unit area in the sum of three component measures: (i) the increase in live biomass, including leaves, stems, roots and reproductive structures; (ii) litterfall; and (iii) the amount of biomass consumed by animals and microbial decomposers. Annual above-ground net primary production (ANPP) may be the most robust measure of productivity because it includes virtually all commercially harvested material as a subset (although in some cases portions of root systems, or components of below-ground net primary productivity, also are harvested) (Gholz, 1988). ●● Net ecosystem production (NEP). NEP is GPP minus loss of dry matter due to heterotrophic respiration of microbes and other non-photosynthetic organisms (Kozlowski et al., 1991). These components of productivity vary greatly among different kinds of oak forests (Table 4.1).

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The ratio of below-ground to above-ground biomass of individual oaks, and thus the spatial distribution of net production, varies among ecosystems. In Mediterranean climates, the roots of multiplestemmed oaks of coppice origin may comprise 90% or more of total tree biomass, whereas the roots of single-stemmed (non-coppice) oaks typically make up less than 30% of tree biomass (Whittaker and Woodwell, 1968; Canadell and Rodà, 1991). For single-stemmed holm oaks in Mediterranean Spain, root:shoot ratio was higher on dry sites than on mesic sites based on roots 0.4 inches and larger in diameter (Fig. 4.1). This suggests that site quality, itself, can affect the ratio of below-ground to aboveground biomass in oaks. The collective evidence indicates that the ratio of above-ground to belowground biomass in oak forests depends on several factors including the reproductive origin of oaks and related disturbance history, stand or tree age, site quality and species composition. The NPP of oak-dominated ecosystems in regions with Mediterranean or desert climates is much lower than in more humid regions. In dry regions, oaks and other woody plants are often restricted in form to small trees or shrubs. In a Mexican blue oak–Emory oak desert shrub community in Arizona, estimated above-ground NPP was 0.4 t/ha/year (Whittaker and Niering, 1975). This rate is about 3% of that of the above-ground component of a typical oak forest in eastern USA. However, the roots and associated structures (lignotubers) of oaks growing in such climates typically comprise 65–85% of total plant mass (Rundel, 1980). Much of the production in these ecosystems therefore lies below ground. The accurate measurement of the biomass of roots in trees is difficult, especially in oaks, which have taproots that can grow to great soil depths. Moreover, it is even more difficult to obtain accurate measurements of root NPP because of the rapid turnover of fine roots. Reported results therefore tend to underestimate NPP of roots (Cannell, 1982). Economists often express forest site productivity as the capacity of an area to produce financial return through timber production. Financial return can be measured in several ways including net present value, internal rate of return or soil expectation value. Such economic measures of productivity include assumptions about the initial state of the forest, duration of the economic evaluation period, inflation rate, discount rate, management practices, management expenses and the timing of periodic expenses and income. Consequently, financial return

Chapter 4

Site Quality and Productivity

Table 4.1.  Above-ground biomass and net productivity of trees in selected oak forests. (From summaries in Cannell, 1982.)a Forest type, location Serrata–downy oak, Japan Post–blackjack oak, Oklahoma English–sessile oak with beech, Francef Mixed oak–hickory Tennesseeg Northern pin oak, Minnesota Northern red oak, Great Smoky Mts, USA Sessile–European turkey oak, Hungary Mexican blue–Emory oak, Arizona a

Biomassa [with foliage] (t/ha)b

Net annual productivitya Stand age [with foliage] (t/ha)b (years)

Trees/hac

Tree height (m)d

Basal area (m2/ha)e

272.7 [276.9]

9.0 [13.8]

–

561

15.1

39.8

174.4 [179.2]

7.3 [10.6]

80

2600

–

18.3

Katagiri and Tsutsumi (1975, 1976, 1978) Johnson and Risser (1974)

167.4 [171.0]

7.2 [10.8]

66

958

22

32.1

Kestemont (1971)

133.2 [137.9]

5.4 [10.1]

30–80

–

12–25

25.8

120.8 [124.2]

5.3 [8.8]

45–50

1788

c.15

26.5

Harris et al. (1973), Harris and Henderson (1981) Reiners (1972)

132.2 [135.0]

4.7 [7.5]

(Mature)

2660

14

24.6

Whittaker (1963)

199.5 [202.9]

3.0 [6.7]

65–68

–

17.4

15.1

Jakucs (1981)

9.0 [9.4]

0.4 [1.7]

117

190

5.3

 4.0

Whittaker and Niering (1975)

Includes boles, branches and bark of trees; does not include fruits, woody litterfall and understorey vegetation. Dry weight in metric tons/ha; 1 metric ton/ha = 0.446 US tons/acre. c Minimum diameters (dbh) included in calculations vary among studies; 1 ha = 2.471 acres. d 1 m = 3.281 ft. e 1 m2/ha = 4.356 ft2/acre. f This was a plantation. g Species include chestnut, white, northern red and black oaks. b

References

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Dbh (in.)

Biomass of large roots (kg)

100

2.4

3.9

5.5

7.1

8.7

80

60

40

Xeric Mesic

20

0

6

10

14

18

22

Dbh (cm) Fig. 4.1.  Biomass of large diameter roots (≥ 1 cm (0.4 inch)) of single-stemmed holm oak in northeastern Spain estimated from dbh measured 1.3 m (4.3 ft) above ground. (From Canadell and Rodà, 1991.) Mean root biomass of oaks on the two sites differed significantly (P = 0.022) based on analysis of covariance. Log10 (mesic site biomass) = −1.393 + 2.451 log10 (dbh), R2 = 0.81, n = 20; log10 (xeric site biomass) = −0.448 + 1.734 log10 (dbh), R2 = 0.71, n = 12. For both equations, root biomass is in kilograms and dbh is in centimetres.

can be estimated with great detail and associated complexity. Nevertheless, relatively simple measures such as percentage value increase or percentage volume increase are often useful for comparing site productivity among stands of similar initial condition. Because economic return is derived from forest volume, estimating site productivity in terms of product yields is usually an intermediate step in estimating economic site productivity.

The National Cooperative Soil Survey and Site Productivity The National Cooperative Soil Survey (NCSS) programme provides the most comprehensive soil inventory in the USA. Similar programmes occur in Canada and in other countries. Soil surveys produced and published by the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) include detailed maps of natural soil bodies as well as information about soil properties and topographic features associated with

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them. For soil map units that are suitable for timber production, soil surveys provide information about site quality and productivity including estimates of the site index (discussed in detail later in this chapter) and the annual volume production for commercially important tree species (Fig. 4.2). In addition, soil surveys include interpretive information useful for making silvicultural prescriptions, conducting timber harvests, and identifying forest management concerns related to soil and topographic properties. Consequently, it is essential for the silviculturist to understand how to use soil surveys for management planning. Modern soil survey maps are produced at a scale of 1:24,000, and digital versions are readily available. Soil map units were drawn by surveyors by applying a conceptual soil–landscape model with soil boundaries placed where important changes in one or more of the soil-forming factors occur (Hudson, 1992). Detailed information about the soils and about site quality and productivity is not collected within every soil map unit. Rather, detailed information is collected more widely across a soil survey region and correlated to soils within and among survey regions. This process of extensive rather than intensive mapping of soils facilitated the rapid development of soil surveys for nearly every state or province in the USA and Canada. However, as a consequence, detailed information about specific soil properties or forest productivity levels in a given soil map unit may be imprecise. When more detailed information about site index or production is needed, it should be collected on site where possible. More recent efforts by the NCSS staff have been directed towards the development of Ecological Sites, a framework for linking soils and landscapes to natural plant communities and ultimately natural resource management (Struckhoff et al., 2017). Much like multi-factor ecological classification schemes (e.g. see Chapter 1, this volume), Ecological Sites provide a framework for more explicitly considering vegetation along with soil and topographic information for grouping similar soils by factors that strongly influence site productivity.

Relation of Site Productivity to Ecological Classification Determination of site productivity occurs within the broader context of regional factors that influence productivity. For example, climate, geology and soil limit the upper range of oak site productivity.

Chapter 4

73408–Coulstone–Clarksville complex, 35–50 % slopes, very stony

Component Coulstone

Clarksville

Site index (ft) at age 50 years

Species

Yielda (cu. ft/acre per year)

Black oak

58

43

White oak

55

43

Black oak

61

43

White oak

58

43

a Yield

likely to be produced in a fully-stocked, even-aged, unmanaged stand at the age of culmination of the mean annual increment. 63

61

58 60

61

58

Fig. 4.2.  Soil map units (bounded by white lines and identified with a five-digit number) for a portion of the Sinkin Experimental Forest in Missouri. For soil map units that are suitable for timber production, soil surveys include information about site quality and productivity. Estimates of the site index and the annual volume production for black oaks and white oaks are shown for soil map unit 73408 (inset table). Circled numbers are the black oak site index in feet for selected map units to illustrate landscape variation. The contour interval is 40 ft. (The soil map image was generated using SoilWeb developed by the California Soil Resource Lab at the University of California-Davis and Natural Resources in collaboration with the USDA Natural Resources Conservation Service.)

Likewise, localized topographic features such as slope steepness, slope position and aspect influence productivity. Experienced silviculturists often take for granted that these factors are related to site

Site Quality and Productivity

productivity and mentally account for them when assessing a site qualitatively. An ecological classification system (ECS) provides a hierarchical framework for placing individual sites

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within a regional context. An ECS provides a means for grouping similar ecosystems based on relations between the characteristics of living organisms and physical features of a site. Although similar in approach, ecological classification schemes differ from the soil survey in that vegetation information is more explicitly integrated with environmental factors of climate, geology, soils and topography during the development of ecological units. The final product is a set of defined ecological groupings of plants and environmental factors that are repeated across the landscape at various spatial scales ranging from single stands to large regions (Chapter 1, this volume). However an ecologist’s perspective of a forest site is apt to be different from a silviculturist’s. Ecologists are likely to consider a site as a relatively uniform geographical unit characterized by certain stable combinations of physical and climatic factors, whereas foresters generally view a site as a land unit characterized by a specified productive capacity for timber or other forest products. Despite their differences, these views are complementary (Schonau, 1987). An ecological classification system provides information on the ecological context of a site. This, in turn, helps the silviculturist not only to evaluate site productivity, but to better understand and predict the response of the forest to silvicultural treatments. Likewise, localized quantitative estimates of site productivity (e.g. site index) can guide the ecologist in defining the spatial extent of ecological classification units. Killian (1984) proposed that ecological classification precede the determination of site productivity. He reasoned that the goal of site classification should be to identify the possibilities and risks to forest management and to predict potential yields, thereby assisting both short- and long-term local and regional forest planning as well as general land-use planning. That objective is more user oriented than that usually encompassed by ecological site classification, which some believe should focus on interrelations among ecosystem components (i.e. climate, physiography, geology, soils and their biota, and vegetation) (Barnes, 1984). Regardless of viewpoint, ecological classification and site quality are closely linked. Whereas the term site productivity emphasizes the factors that influence tree growth, ecological classification emphasizes the factors that determine the abundance of species and the occurrence of natural groupings of plant species in relation to environment. Moreover, ecological classification usually considers both woody and herbaceous vegetation in

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defining ecological units. Herbaceous vegetation is particularly important because of the relatively large number of herbaceous species present and the fidelity of some of those species to the factors that distinguish one ecological classification unit from another. The presence and/or abundance of many herbaceous species are often less affected by disturbance factors and associated changes in the successional status of a forest than are trees and other woody species. There are potential advantages of ecological classification over traditional site classification. In their development, the latter focuses narrowly on forest productivity whereas the former produces natural ecological groupings that can be used to identify ecological units within which productivity, succession, tree regeneration and responses to silvicultural and natural disturbances are likely to be similar (Chapter 1, this volume). However, site productivity estimates that are based solely on ecological classification units are likely to be less accurate in estimating potential product outputs than those based on direct determination of site productivity (e.g. by measuring site index) at a specific location within the classification unit.

Productivity and Related Self-sustaining Properties of Oak Forests Silvicultural practices in oak forests of the USA have been, by agricultural standards, extensive rather than intensive. With few exceptions, oak silviculture has followed an ‘ecological’ model based on managing natural vegetation and plant propagules in place. Unlike the ‘agronomic’ model, new genetic material (e.g. genetically improved trees) is seldom introduced; herbicides and fertilizers are used only to a limited extent if at all. Although there are exceptions, the usual objective of oak forest management is to control stand composition, structure, growth and quality largely through timber cutting practices. This approach has been and continues to be largely driven by the economics of oak timber production, which is characterized by relatively low returns on investment (e.g. Dwyer et al., 1993). Even when potential economic returns from more intensive silvicultural practices are deemed acceptable, investment in such practices may be discouraged by long deferrals on returns plus associated risks of damage or loss from insects, disease, drought, fire and other uncertainties, one or more of which have a high likelihood of occurrence over the relatively long 80- to

Chapter 4

100-year periods usually required for oak sawtimber production. Moreover, there is growing social demand to manage publicly owned forests for a wide range of products and values that transcend narrow timber production objectives. The control of stand composition, structure and density by cutting (timber harvesting) methods alone is not unique to oak forests, but characterizes hardwood silviculture in the USA in general. Although we have considerable knowledge of how to apply a more ‘agronomic’ or culturally intensive model to hardwood silviculture, such methods have seldom been applied outside of research studies. The resulting silviculture is therefore heavily dependent on capitalizing on natural ecological processes that are especially significant with respect to forest productivity and its sustainability. Forests, whether influenced by humans or not, are endowed with certain attributes that ensure a high capacity for self-sustaining productive capacity (e.g. as expressed by annual above-ground net primary production). What effect, then, does the removal of trees from the forest have on forest site productivity? Effects of harvesting on site productivity The site factors most vulnerable to irreversible change are those associated with the soil. Careless removal of trees from a site can cause soil erosion and nutrient losses. Erosion is the physical loss of soil particles caused by wind and water action. Loss of nutrients also accompanies the physical loss of soil. Because the upper soil layers, which are richest in soil nutrients, are eroded first and most severely, erosion can have a major impact on soil fertility. However, the loss of nutrients also can occur in the absence of the physical loss of soil particles when nutrient ions become dissolved in surface runoff and are lost in nutrient solutions (leachates) that percolate through and from the soil, or are lost through the removal of vegetation. Erosion is nevertheless a normal geologic process that results in some soil losses even in undisturbed forests. The estimated prehistoric rate of soil erosion in forested parts of southern Michigan, an area comprised of extensive oak forests, was estimated at 0.05 t/acre/year (Davis, 1976). In the Appalachian Mountains of West Virginia, annual soil erosion losses from hardwood forests range from 0.05 to 0.10 t/acre/year for both undisturbed and clearcut forests (Patric, 1976). This compares to ‘acceptable’ rates of soil loss from agricultural

Site Quality and Productivity

lands that range from 1 to 5 t/acre/year (Patric, 1977). Moreover, cropland is usually cultivated and harvested annually, whereas a managed oak forest is usually logged less frequently than 1 year in 10. The view that the absence or reduction of tree cover, by itself, causes soil erosion in forests of the eastern USA is largely unfounded (Patric, 1976, 1978; Mills et al., 1987). However, to understand this issue, it is important to distinguish between effects related purely to the temporary absence of the forest canopy following timber harvesting, and associated effects related to skid trails and logging road construction (Simmons and Anderson, 2016). Soil and site effects associated with the removal of trees, per se, are related to the characteristics of forest soils. Four attributes of forest soils are especially important in moderating soil erosion and soil nutrient losses: (i) the forest floor; (ii) soil structure; (iii) soil infiltration rate; and (iv) the dynamic nature of the biomass of forest soils. In eastern deciduous forests, the surface of the forest floor consists of a layer of undecomposed or partially decomposed leaf litter and other organic debris. This litter layer provides protection against the kinetic energy of rainfall, preventing particle detachment and sealing of pores of the underlying mineral soil. In oak forests, this layer is replenished every year with 1–2 t/acre of leaf fall, plus another 0.5 t of woody litter fall (Rodin and Basilevič, 1968; Rochow, 1974). Forest cover thus maintains soil porosity by continually returning leaf litter and woody debris to the forest floor, which in turn is continually decomposing and being incorporated into the soil by soil organisms. Forest soils consequently develop and maintain a physical structure and macro-pore space associated with high soil porosity. High porosity, in turn, is associated with high infiltration rates and low surface runoff. Infiltration rates of 50 or more inches/h are common in forest soils in the eastern USA, whereas rainfall intensities rarely exceed 2 inches/h (Patric, 1978). When the water storage capacity of the soil is reached after prolonged precipitation, the water moves laterally through the pores of the soil to streams. The removal of trees from a site, by itself, does not destroy soil porosity provided the site is quickly revegetated. Soil porosity nevertheless can be greatly reduced by logging road construction and logging practices that scrape, gouge or compact the surface layers of the soil. The risk of accelerated soil erosion from a managed forest thus

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depends largely on logging practices rather than on the temporary absence or reduction of tree cover. Soil compaction varies in its effect on tree growth and not all species are equally affected. Increasing soil density restricts root growth by limiting water and nutrient uptake which can reduce height and diameter growth of species that are sensitive to soil compaction. For example, a longterm study in the south-eastern Missouri Ozarks showed that soil compaction exceeding 30% or more of normal, reduced the growth of planted oak seedlings. The heights of planted northern red oaks was reduced by 14% and the diameter growth of white oak was reduced by 6% 5 years after overstorey removal (Ponder, 2003). The burst of shrub, herb and sprout growth that occurs after overstorey removal tends to mitigate effects that might otherwise result in soil and site degradation. This flush of growth buffers disturbed forests against nutrient losses and facilitates their recovery. For example, 5–6 years after clearcutting a northern red oak stand in south-western Wisconsin, the estimated biomass of fine roots2 was 70% larger than in an adjacent uncut stand (Yin et al., 1989). Most of this biomass was comprised of shrub and herbaceous species. In this case, not only was root biomass maintained, but temporarily increased after timber harvesting. Such responses ensure protection against both soil and nutrient losses during periods when trees are reestablishing their dominance. Harvesting timber also does not necessarily alter the amount of litter on the floor of oak forests (Carmean, 1959). Net losses of nutrients from a site nevertheless can occur when nearly all vegetation is removed and nutrients move out of the system at rates higher than they are replaced (Bormann and Likens, 1979). Such losses may occur when entire trees (boles, branches and foliage) are removed to a central location within a stand for chipping, and the nutrient-rich branches and foliage are piled along the roadside. Windrowing (i.e. pushing slash into long narrow piles) is a site preparation technique that also can remove nutrients from large fractions of the forest. If slash piles are burned, the resulting localized areas of intense heat may produce significant losses of nitrogen. Pushing slash into piles or windrows before burning often is accompanied by the redistribution of topsoil and minerals, which in turn may decrease site quality over a portion of the stand. In highly porous soils in regions of high rainfall and warm temperatures

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that are already low in organic matter, timber harvesting may accelerate nutrient losses from leaching, especially nitrogen. The important issue, however, may not be the amount of mineral nutrients removed from the site, per se, but how that amount compares with that available in the soil for forest regrowth after timber harvesting. This amount depends on initial soil fertility, the relative amounts of minerals in the ecosystem that occur in the soil, forest floor, belowground and above-ground biomass, plus the rate at which the soil mineral pool is replenished by decomposition of slash, roots, nutrient deposition from the atmosphere, mineral weathering and nitrogen fixation by soil organisms. Logic would seem to indicate that, because a major portion of a forest’s biomass is in trees, timber harvesting would remove a proportionately large amount of nutrients. On the contrary, nutrient losses from timber harvesting are relatively low. For example, in an oak–hickory forest in Tennessee, the proportion of nutrients in trees was relatively small compared with that present in the entire ecosystem even though trees accounted for 67% of the biomass. Trees comprised 6% of the nitrogen, 2% of the phosphorus, 1% of the potassium and 16% of the calcium (Binkley, 1986). If only tree boles are considered, the percentages are even smaller: 1% or less of nitrogen, phosphorus and potassium, and 11% of calcium. Compared with harvesting only bolewood, whole-tree harvesting removes two to three times more nitrogen, phosphorus and potassium, and about one-third more calcium than does harvesting only bolewood. Whole-tree removal is also potentially more serious in infertile soils, where nutrient deficiencies already exist. Nutrient losses from whole-tree harvesting, the time required for nutrient replacement, and the economics of whole-tree harvesting are discussed in more detail elsewhere (e.g. Waring and Schlesinger, 1985; Binkley, 1986; Kabrick et al., 2013). Given that nutrient losses resulting from timber harvesting do occur, do such losses reduce forest site productivity? A study conducted in the Ozark Plateau of Missouri was designed to answer that question. The Ozark Plateau is an ecosystem largely dominated by upland oak forests relatively poor in soil nutrients. In that study, biomass ranging from only tree boles to all surface vegetation and the forest floor was removed from the study sites. After 10 years, these removals had not significantly reduced site productivity based on the

Chapter 4

growth of planted oak seedlings nor leaf and soil chemistry (Ponder, 2007; Ponder et al., 2012). However, these early results require cautious interpretation. They nevertheless represent a component of a more comprehensive and integrated set of studies on the long-term effects of organic matter removals and soil compaction on soil productivity across a wide range of North American forests from which longer-term results will be forthcoming (Fleming et al., 2006; Ponder et al., 2012). Most forest soils contain organic matter as a top layer (the forest floor) as well as within the mineral soil beneath it. But it is the upper layer that is most important in cycling nutrients, water and carbon, all of which are integrally related to forest productivity. The temporal complexity of the process with respect to timber harvesting was revealed by a study in the Northern Hardwood Region of northeastern USA. After complete overstorey removal, there was a 50% decline in forest floor organic matter during the next 20 years. But this was followed by a recovery to pre-harvest conditions by the 60th year. This pattern of loss and recovery is attributed to rapid decomposition rates immediately after overstorey removal combined with subsequent accelerated additions of woody litter to the forest floor. The phenomenon is sometimes referred to as the Covington curve after its first describer (Covington, 1981). The missing carbon was assumed to be liberated into the atmosphere, and if true, would have major implications regarding the short-term and long-term roles of timber harvesting in the dynamics of atmospheric CO2 (see also Chapters 13 and 14, this volume). That study has provided a foundation for subsequent studies of carbon budgets and the formulation of carbon budget and climate models (Yanai et al., 2003). More recent studies in forest floor dynamics have shown that soil organic matter changes little unless there are large decreases in inputs from litter and roots (Bowden et al., 2014; Lajtha et al., 2014). However, even after timber harvesting, it has been shown that organic matter mass changes little over time. This suggests that carbon transfer other than release into the atmosphere may also be occurring, including its incorporation into the mineral soil beneath the forest floor (Yanai et al., 2003). Similarly, studies in a mixed-oak forest in Tennessee demonstrated that leaving logging residues on site had no effect on either vegetation or soil carbon 15 years after timber harvest (Johnson and Todd, 1998; Johnson et al., 2002). An analysis

Site Quality and Productivity

of data from the literature also showed that timber harvesting, on average, has little effect on soil carbon or nitrogen (Johnson and Curtis, 2001). Although these short-term observations illustrate the remarkable dynamics and resilience of the forest floor and soil, there is still much uncertainty about effects of forest management on soil productivity (Grigal, 2000). Grigal (2000) nevertheless concluded, with respect to soil productivity, that: ‘Forest management, if carried out with both wisdom and prudence, is not antithetical to good stewardship.’ One consequence of harvesting trees that affects nutrient cycling and forest productivity, but which has largely escaped attention, involves the stumps that are left behind to decompose (Van Lear et al., 2000). For example, in a mixed oak stand in southwest Virginia, decomposing stumps accounted for 26% and 36% of the total soil nitrogen and carbon, respectively (Sucre and Fox, 2008). Some investigators speculate that decomposing stumps may be especially important in nutrient cycling on poor sites. There, trees and other plants are known to be better adapted at taking advantage of pulses of nutrients and other site resources, which are more unevenly distributed than on better sites (Campbell and Grime, 1989; Sucre and Fox, 2008). Because of the concern about the potentially deleterious effects of harvesting on site productivity, guidelines have been developed that include recommendations for preventing or reducing compaction and erosion caused by harvesting equipment and excessive nutrient losses caused by timber removal. These provide recommendations for the judicious placement of skid trails, log landings and logging roads, and for the retention of logging residues and slash. These guidelines are often grouped with other ‘best management practices’ for protecting stream water during and after timber harvesting and are readily available for land owners and practising foresters in most ecoregions containing oaks. Modifying site productivity through fertilization Although the nutrient capital of an oak forest is sufficient to sustain tree growth at some given rate, few forest soils provide nutrient levels that are optimal for tree growth (Smith, 1986; Kozlowski et al., 1991). Suboptimal levels of nutrients in forest soils can be caused by poor land-use practices that

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preceded the establishment of an existing forest or low natural soil fertility (Kabrick et al., 2011). The nutrients that are below optimal levels under oak stands are usually the same elements found in most commercial fertilizers: nitrogen, phosphorus and potassium. Of these, nitrogen is often the most important. Nitrogen is naturally added to soils by rainfall and can be fixed from the atmosphere by some bacteria and other organisms in the soil and in the root nodules of some forest plants. Although oak forests often have an abundant supply of nitrogen, much of it is unavailable for plant growth at any given time because it is tied up in organic matter. Unfortunately, any excess nitrogen does not accumulate, but is changed by bacteria to nitrate (NO3−) and then leached from the soil by rainwater, or used by denitrifying bacteria as a source of oxygen. Phosphorus is sometimes deficient in southeastern oak forests (Singh et al., 2015), particularly on highly leached soils, wet soils and very sandy soils, and potassium is occasionally deficient in highly leached sandy soils. When fertilizers are applied to oak forests, trees often respond better to the application of other nutrients when they are applied with nitrogen. For example, calcium fertilization of poor sites in Pennsylvania increased stand volume growth of oaks by 10%, whereas nitrogen and calcium together increased growth by more than 40% (Ward and Bowersox, 1970). Application rates for nitrogen and other nutrients for oak forests should be matched to soil nutrient characteristics of the site. Although the height growth response of oaks to fertilization is uncertain, diameter growth can be increased by more than 30% (Ward and Bowersox, 1970; Graney, 1987). Responses to fertilization may last for 6 years, but largely disappear after 10 years (see also Chapter 15, this volume).

express productivity. It is nevertheless useful as an index of productivity because of its correlation with productivity and relative ease of measurement. Most timber yield tables for even-aged oak stands report yields by site index classes (Table 4.2). Similarly, growth and yield models often include site index as one of the variables used to predict yield (see Chapter 15, this volume). Height growth (and thus tree height at a specified age) is a useful indirect measure of site quality because it is relatively independent of stand density. Height growth of dominant and codominant trees is reduced only at the extremes of stand crowding (Carmean, 1975; Lloyd and Jones, 1983; Lanner, 1985; Jones, 1986). Site index can be determined directly from observations of trees growing on the site, but in some cases it can be estimated indirectly from physical site characteristics instead. Direct determination of site index Direct determination of site index requires knowing the heights and ages of dominant and codominant trees. This information then is referenced to a set of hypothetical height growth curves, called site index curves, which are indexed to a common age (Fig. 4.3). For oaks in the USA, the usual index age is 50 years. A site index of 65 thus indicates that the dominant and codominant trees on a site attain an average height of 65 ft at 50 years. A set of site index curves therefore represents patterns of height growth of dominant and codominant trees for different sites. Table 4.2.  Gross yields (ft3/acre)a by site index classes for normalb even-aged oak stands in south-western Wisconsin. (From Gevorkiantz and Scholz, 1948.) Site index (ft at base age 50) Stand age (years)

Methods of Evaluating Site Quality Site index In North America, site index is the most commonly used method of expressing forest site quality. Site index is defined as the average height of dominant or both dominant and codominant trees at a standard, or index, age. Tree height growth of dominant and codominant trees in even-aged, fully stocked forest stands is closely related to volume growth. Site index therefore is an indirect measure of site productivity because, by itself, it does not directly

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20 40 60 80 100 120 140 160

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a

Based on gross volume, excluding bark, of all trees 0.6 inches dbh and larger including tops and limbs suitable for cordwood. Species are predominantly black, white and northern red oaks. b Normal oak stands are relatively undisturbed stands at average maximum density (Chapter 6, this volume).

Chapter 4

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Tree age (years) Fig. 4.3.  Site index curves (index age 50) for black oak in the unglaciated uplands of south-eastern Ohio, eastern Kentucky, southern Indiana and southern Missouri. Dashed-line curves represent values beyond the observed range. (From Carmean, 1971, 1972.)

Their purpose is to relate observed heights of trees of any age to their expected heights at the index age of 50 years. Factors that cause actual tree height growth to differ from the patterns expressed by the site index curves will introduce error into the estimated site index. Consequently, it is important to ensure that the site index curves utilized are appropriate for the site being evaluated. For example, three sets of site index curves applicable to white oak differ somewhat for site index 70. Graphic comparison of these curves illustrates that the closer the observed tree age is to the index age, the less discrepancy in site index there is among the curves (Fig. 4.4). However, when site index estimates are based on trees younger than 30 years or older than 70 years, differences in estimated site index due to choice of site index curves can be substantial (Carmean, 1979). Accurately determining site index requires: (i) the presence of trees that are reliable indicators of site quality; and (ii) the availability of suitable site index curves that accurately characterize the height growth pattern of the trees observed (Carmean et al., 1989). Suitable trees for determining site index include freegrowing, uninjured dominant and codominant trees. Such trees most frequently occur in even-aged, fully stocked stands that have not been high-graded by logging, heavily grazed, or otherwise damaged or disturbed. To determine site index, heights and ages

Site Quality and Productivity

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Tree age (years) Carmean et al. (1989) Graney and Bower (1971) Schnur (1937) Fig. 4.4.  Site index 70 curves for white oak (Graney and Bower, 1971; Carmean et al., 1989), and a composite curve (applicable to white oak) for upland oaks in eastern USA (Schnur, 1937). Although all three curves have an identical site index of 70 ft at index age 50, the height–age curves diverge elsewhere. Differences in estimated site index resulting from application of different curves can be substantial, especially when observed tree age differs from the index age by more than 20 years.

of dominant and codominant trees must be ascertained. Tree ages can be obtained from increment cores that reveal annual rings as well as patterns of diameter growth. Trees that are reliable indicators of site index form annual rings during early life that are wide and even. Trees with narrow rings formed in early life indicate suppression of growth and therefore should not be used to determine site index. Trees with forks and other bole defects may indicate earlier top breakage or dieback and therefore may be unreliable indicators of site index. Trees selected for site index determinations also should be from well-stocked even-aged stands comprised of dominant and codominant trees whose ages do not differ by more than 10 years. Greater differences can produce highly variable patterns of height growth, which in turn may cause large errors in site index estimation. For example, evenaged upland oak stands in the Missouri Ozarks sometimes include dominant and codominant trees

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applied to coppice stands (Zahner et al., 1982). Site index curves thus were developed specifically for coppice-origin stands of that region (Fig. 4.5). Although stand site index is usually expressed as the average site index calculated from qualified sample trees and reported to the nearest foot, greater precision should not be attributed to the estimate than is statistically justified. The precision of site index estimates made from site index equations was determined for white, black and scarlet oaks in Missouri. Estimates for trees 20 years younger to 20 years older than the index age were precise to within 3–4 ft with 95% confidence when based on ten sample trees. Similar results were observed for oaks (chestnut, white, northern red, black and scarlet) in north-western West Virginia (Lamson, 1980). Errors in estimating site indices from site index equations arise from three sources: (i) errors in estimating individual sample tree site indices from site index/height regressions; (ii) variation among sample tree heights within even-aged stands; and (iii) measurement errors (McQuilkin and Rogers, 1978). Methods of collecting data for deriving site index curves have evolved greatly since the first site index curves were published. The earliest curves were based on only a few plots with total height and age measurements taken from a few selected trees. These height and age data were used to calculate an average ‘guiding curve’ representing the average

Tree height (ft)

appreciably younger or older than most of the trees in those crown classes (i.e. the predominant age class). Because young oaks tend to grow faster in height, they will indicate a higher site index than oaks in the predominant age class if a common age is assumed. Conversely, appreciably older oaks grow more slowly in height and will indicate a lower site index than trees in the predominant age class if a common age is assumed (McQuilkin, 1975). Site index determined from individual dominant and codominant trees may vary even on small plots that appear to be homogeneous in soil and microsite. This variation in site index can produce problems in determining the number of site index trees required to obtain reliable site index estimates (Carmean, 1975; Lloyd and Hafley, 1977; Lloyd, 1981). The requisite number of site trees depends on several factors, including the desired accuracy of the site index estimates, size of the stand and variation in site index. Sampling trees for site index determination thus is more complicated in large tracts with highly variable stand and site conditions. Ideally, large tracts should be stratified into smaller areas that are relatively homogeneous topographically, edaphically and ecologically. Site index then should be determined within each stratum. Even then, large errors in estimating site index can occur when site index curves are used outside their intended region of application. The height growth of oak coppice stands may differ markedly from seedling-origin stands. This is especially true during the early years of stand development when oak coppice benefits the most from the relatively large parent-tree root systems they are connected to. Large roots and the concomitant capacity of young coppice shoots to produce multiple long flushes (see Chapter 2, this volume, Fig. 2.21) together with clump density effects (Chapter 2) may mask the early expression of site quality effects. Site effects in coppice stands may not be apparent until they are 20–25 years old. By age 50, the productive capacity of coppice stands in the South Carolina Piedmont was well differentiated. On the best sites (oak site index 90), volumes and above-ground biomass were nearly twice that of stands growing on the poorest sites (oak site index 50) (Zahner and Myers, 1984). The height growth curves for these coppice stands differed substantially from the conventional site index curves developed for upland oaks in the Piedmont region of the south-eastern USA (Olson, 1959). The latter overestimated site index by as much as 18 ft when

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Tree age (years) Fig. 4.5.  Site index curves (index age 50) for young mixed oak stands of sprout origin in the South Carolina Piedmont. (From Zahner et al., 1982.)

Chapter 4

Site index comparisons among species Dominant and codominant trees of different species growing on the same site are likely to have different height growth patterns. For example, a comparison of site index curves for white, scarlet and red oaks at a common site index of 70 shows that white oak height growth lags behind the other species for the first 40 years. But by age 80, white oak height surpasses the other oaks (Fig. 4.6). Consequently, separate site index curves are generally required for each species. This is evident from species comparisons in the Missouri Ozarks. There, the observed site index for white oak on a given site

Site Quality and Productivity

120 100 Tree height (ft)

height growth pattern of a given species. Graphical or proportional methods then were used to produce a set of anamorphic curves; within the set each curve has the same shape. Anamorphic curves assume that the pattern of height growth is similar for all levels of site quality, and for all climates, soils and topographic conditions within the region of intended application (Carmean, 1970). This assumption is inappropriate for oaks because the shape of the height curve often varies with site quality (Carmean, 1970, 1975). More recently developed site index curves are based on the assumption that the shape of tree height growth curves can differ by site classes. Such curves can be derived through stem analysis, which requires that sample trees be felled and cut into sections to determine the progression of height growth over time. Stem analysis combined with non-linear regression analysis produces polymorphic site index curves. This is now the most widely used method for developing site index curves for species like the oaks that express polymorphic patterns of growth. For the oaks, polymorphic site index curves have largely replaced anamorphic curves. Methods for constructing site index curves have been described by others (Burkhart et al., 1981; Clutter et al., 1983; Borders et al., 1984; Biging, 1985; Avery and Burkhart, 2002). A comprehensive compilation of 127 site index curves for species in the eastern USA, including 21 sets of curves for oaks, is presented by Carmean et al. (1989). Each set of curves is based on an expanded form of the Chapman-Richards non-­ linear function (Ek, 1971; Payandeh, 1974a, b; Monserud and Ek, 1976). One variant of the model estimates height from site index and age whereas the other estimates site index from height and age.

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White oak Northern red and black oaks Scarlet oak Fig. 4.6.  Comparison of site index curves for scarlet oak, white oak and red/black oaks in the Central Hardwood Region. The curves shown are for site index 70 ft (index age 50). (From Carmean et al., 1989.)

is about 4 ft less than that of black oak; the scarlet oak site index is about 3 ft greater than that of black oak on the same site (McQuilkin, 1974). The co-occurrence of species across a wide range of sites makes possible the development of equations that can be used to convert the observed site index of one species to the estimated site index of another species. Such site index conversions are useful when the species of interest is absent and a common site index basis for all stands or locations within stands is desired. Such conversions are facilitated by graphs or equations designed for this purpose (Nelson and Beaufait, 1956; Trimble and Weitzman, 1956; Doolittle, 1958; Olson and DellaBianca, 1959; McQuilkin, 1974; Carmean and Hahn, 1983). They are available for the major upland oaks and associated species in the eastern USA (Fig. 4.7; Appendices 4–6, this volume). Site index for a given species is not necessarily uniformly higher or lower than for another species. For example, on good sites the yellow-poplar site index (index age 50) may be as much as 30 ft greater than that of white oak. However, as site quality decreases the difference between yellowpoplar and white oak site indexes also decreases. At site indices below 70 ft, the white oak site index

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Site index (ft)

90 80 70 60 50 40 30 Black oak Scarlet oak Yellow-poplar

White oak Northern red oak

Fig. 4.7.  Chart for converting the site index (index age 50) of one species to another in even-aged upland oak and yellow-poplar stands in the Central States. (Reproduced from Carmean and Hahn, 1983, with permission from the Society of American Foresters, Bethesda, Maryland. Not for further reproduction.) The site index of species absent from a stand can be estimated from species present by using this nomogram. Site indexes for all species are read from the vertical axis. For example, assume that height and age measurements of several dominant and codominant yellow-poplars indicate a site index of 94 for that species. The corresponding site index of another species is read by moving vertically downwards from 94 on the yellow-poplar curve to the curve of the species of interest. At that interception point, the unknown site index is read horizontally across on the vertical axis. On this chart, site indexes for scarlet, black, northern red and white oak corresponding to yellow-poplar site index 94 are approximately 90, 89, 87 and 83, respectively. Conversions also can be derived from equations (see Appendices 4–6, this volume).

may exceed that of yellow-poplar (Olson and Della-Bianca, 1959; Carmean and Hahn, 1983). Indirect estimation of site index from soil and topographic factors Site index curves cannot be applied to sites where suitable trees are absent or where no trees are present. Where this occurs there are other means for obtaining estimates of site index. For example, soil

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surveys produced by the USDA-NRCS and some ecological classification systems provide site index estimates for commercially important and abundant tree species. These site index estimates were derived using appropriate site index curves and age and height information from suitable dominant and codominant trees on a small number of soil map units or ecological units and extrapolating those results to the remaining units. However, because of the small number of soil or ecological units that are sampled and the high level of variability in soil properties and other environmental factors used in the classification and mapping of soil and ecological units, site index estimates from these sources are not always sufficiently accurate for developing silvicultural prescriptions. Moreover, in most soil surveys, the site index is not reported by slope aspect, a locally important determinant of site quality. To resolve these problems, equations have been developed in some regions to estimate oak site index from soil, topographic and other site factors. These equations typically account for 70–85% of observed variation in site index. However, they are often not used in practical application because they require information on one or more soil characteristics such as texture, horizon or soil thickness, and soil chemical properties that are difficult or inconvenient to measure. Also, many of these soil-site equations were originally derived from anamorphic (rather than polymorphic) site index curves that may not accurately describe tree height growth in the region to which the equations were applied. Even though such equations are often adequate for practical applications, they only provide an estimate of site index, which itself is only an indirect measure of forest productivity. Thus, these equations are two steps removed from direct determination of productivity (Leary, 1985). Equations for estimating site index nevertheless can provide insight into the factors that influence the height growth of oaks and therefore oak site productivity. Most of these equations include as predictors one or more topographic factors including slope position, aspect, slope gradient and slope shape. Although these factors, by themselves, have no direct effect on tree growth, they are correlated with more directly causative factors. For example, aspect and slope gradient jointly determine the amount of solar radiation received on a slope (Frank and Lee, 1966; Swift, 1976), and thus account for factors more directly related to tree growth such as evapotranspiration, leaf temperature and fluctuation in

Chapter 4

Site index (ft)

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Aspect (degrees azimuth) Fig. 4.8.  Relation between oak site index (index age 50), aspect and four slope gradients in upland forests in north-western West Virginia. (Adapted from Auchmoody and Smith, 1979.) For comparative purposes, site index has been arbitrarily set at 70 for the most favourable aspect (81° azimuth) for each slope gradient. For each gradient, site index is minimized at 261° azimuth.

Site Quality and Productivity

Slope (%)

microclimate. Aspect and slope gradient are correlated with site quality in most oak forests. The better sites usually occur on north to east aspects and the poorer sites on south to west aspects (Carmean, 1965; Hannah, 1968; Hartung and Lloyd, 1969; Graney, 1978; Auchmoody and Smith, 1979). Slope position and slope shape are also related to soil moisture. Lower slopes, for example, usually have higher site indices due to subsurface water flow from upper slopes. One predictive site index equation (Auchmoody and Smith, 1979) considers the interactive effects of slope gradient and aspect. In this equation, the effect of aspect on site index is modified by slope gradient. Site quality accordingly decreases as aspect departs from the cool and moist microenvironment in the north-east quadrant at 81° azimuth. At this most favourable azimuth, slope gradient has no effect. But as aspect changes in either direction towards the least favourable aspect at 261° in the south-west quadrant (180° from 81°), the effect of increasing slope gradient is to reduce site index due to associated increases in solar radiation (Fig. 4.8). Black oak in southern Ohio responded similarly to slope and aspect (Carmean, 1965, 1967). However, in that region as in most other oak regions, the most favourable and least favourable aspects occurred at 45° and 225°, respectively.

The effects of aspect may be asymmetrical (i.e. the most favourable and least favourable aspects may not be separated by 180°). In an Ohio study, the observed optimum aspect occurred at 45° azimuth whereas south, south-west and west aspects (180° to 270°) were almost equally unfavourable (Carmean, 1965, 1967).3 The occurrence of the most favourable aspect in the north-east quadrant was associated with thicker litter layers, thicker A horizons high in base saturation, less acidic A and upper B horizons, greater available soil nitrogen and thicker colluvial accumulations than soils on south-west aspects. Thus, soil development processes related to tree growth also appear to be correlated with variation in topography and solar radiation. Relations between aspect and observed tree growth may not be entirely consistent with the actual incident radiation received on a slope. Commonly observed relations between topographic factors and tree growth suggest that other factors can modify solar radiation effects. These factors include soil development processes that, although partially dependent on incident solar radiation, do not necessarily change linearly with solar radiation. Like topographic factors, soil factors commonly incorporated into site index prediction equations are often associated with variables thought to be more causally related to tree growth. For example, soil texture and stone content are correlated with available moisture. Likewise, soil pH and percentage base saturation are correlated with the availability of nitrogen and other macronutrients, and soil depth or depth to an impenetrable horizon is correlated with the effective volume of the rooting zone. The effectiveness of a given variable as a predictor of productivity may vary among regions, ecosystems within regions and species. Such variability is reflected in the differences among existing sets of site index curves derived from soil and topographic factors. The development of satisfactory site index estimation equations for hardwood sites on alluvial soils in the south has been shown to be difficult, if not unfeasible (Broadfoot, 1969). The apparent reason is the large number of interacting factors (including fluctuating water tables, aeration and associated soil rooting space and nutrient availability problems) that influence site productivity in bottomlands. To overcome these problems, Baker and Broadfoot (1979) developed a field guide for classifying sites occupied by, or potentially suitable to cherrybark, Nuttall, Shumard, water, willow and swamp chestnut oaks. Their method is based on a

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matrix of soil factors that influence tree growth in southern bottomlands. Application requires evaluating four major soil factors: (i) soil physical condition; (ii) moisture availability during the growing season; (iii) nutrient availability; and (iv) aeration. Each of these factors is further comprised of specific soil-site properties whose qualitative or quantitative properties are subdivided into three relative site quality classes: (i) best; (ii) medium; and (iii) poor. A site quality rating (SQR) is then assigned to each soil-site property and site quality class for each of the six bottomland oak species considered by the method. Site suitability and quality for a given species is then evaluated by summing its SQRs across all soil-site properties considered by the method. Estimating site index from tree height and diameter Intensive forest management requires measures of productivity that are sensitive to site differences while retaining validity over time-dependent changes in the tree crop. Site index attempts to satisfy these requirements by extrapolating measured tree height at a given age to tree height at a reference age. Although site index is the most widely used method for assessing site quality in North America, the method has been criticized (Gevorkiantz and Scholz, 1944; Jones, 1969; Gholz, 1988; Avery and Burkhart, 2002). Potential problems in the application of site index include extrapolation errors, measurement errors and possible insensitivity to production expressed as volume (Stout and Shumway, 1982). It is frequently difficult to obtain accurate tree ages from increment cores and to obtain accurate heights of standing trees. Some trees are difficult to age from an increment core because the pith or growing centre of the tree bole is often missed when it is non-circular (Lamson, 1987). In slow-growing trees, growth rings are often obscure. Variability in ages among trees within the same stand also contributes to uncertain age estimates. Although any one of these factors could introduce large errors into estimating site index, age variability is potentially the most serious limitation of site index as an estimator of volume productivity. Moreover, height alone is used in the site index method, even though tree and stand volume depend on both diameter and height. Foresters have been reluctant to discard site index for measures of productivity based on diameter growth, which although sensitive to site quality, is also strongly influenced by stand density.

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Studies nevertheless have shown that, across a wide range of initial stand densities, yields per unit area for undisturbed stands tend to converge with time towards a site-specific maximum value (Drew and Flewelling, 1977; Harper, 1977). This convergence is caused by physiological adjustments in the height and diameter growth of trees to changes in available growing space. When stand density is low, diameter growth increases and height growth decreases, and conversely when stand density is high, diameter growth decreases and height growth increases (Gevorkiantz and Scholz, 1944). Because the two trends are compensatory, they tend to produce approximately the same volume in the average dominant tree. Theoretically, using both diameter and height to estimate site productivity should compensate for variation in stand density. Based on this concept, tree dbh, height and age can be used to place stands in site index classes based on tree ‘volume index’. A tree’s volume index is the product of its basal area (or squared diameter) and height. The volume index method was originally developed for mixed-oak stands in southwestern Wisconsin (Gevorkiantz and Scholz, 1944). However, the method is potentially applicable to any forest type for which site index and average heights and diameters of dominant trees in fully stocked (‘normal’) stands are known for a range of tree ages. Such information is often provided in conjunction with published site index, stand and yield tables. From that information, site index classes based on volume index can be graphically related to the average age of dominant trees (Fig. 4.9). The volume index of any observed stand then can be referenced to the graph. To reliably estimate a stand’s volume index, at least 25 dominant trees of approximately the same age should be measured. The observed stand’s volume index is calculated by multiplying the average basal area by the average height of the sample trees. The stand’s site index class then can be determined by referencing its volume index to the reference curves. Volume index may be especially useful for estimating site quality in understocked oak stands. However, volume indices may need to be ‘corrected’ by reducing volume index when stocking is below 50% or above 100% of normal (Gevorkiantz and Scholz, 1944). Unfortunately, the volume index method does not eliminate the problem of age variability because it requires an age measurement. There is, however, a method for estimating site quality that, in field

Chapter 4

65 60

120

55

80

50

40 0

Site index class (ft)

Volume index

160

45

20

40 60 80 100 120 140 Average age of dominant oaks (years)

Fig. 4.9.  Site index classes (index age 50) derived from volume index and age of dominant oaks in mixed-oak stands in south-western Wisconsin. (Reproduced from Gevorkiantz and Scholz, 1944, with permission from the Society of American Foresters, Bethesda, Maryland. Not for further reproduction.) Classes are represented by areas between the curves. A stand’s volume index is calculated by multiplying the average basal area of dominant trees (ft2) by their average height (ft). For example, by reference to the chart, 90-year-old trees with a volume index of 80 represent a site index class of 55. In this region, the maximum site index for oak is about 70 ft.

H = 4.5 + S(1 − e − bD ) 

[4.1]

where D is dbh in inches, and S and b are site-­ specific and species-specific coefficients, respectively (e signifies the base of the natural logarithm). Equation 4.1 thus can be used to generate families of species-specific height-diameter curves associated with different site index classes. Site index estimation using this technique requires measuring the heights and diameters of several dominant or codominant trees and referencing the paired measurements to height-diameter site index curves (Fig. 4.10). For example, a red oak 90 ft tall with a dbh of 20 inches lies within site class 80 according to Fig. 4.10.

90

100

80 70

90

60

80

Site index (ft)

110

Tree height (ft)

application, does not require determining tree age (Stout and Shumway, 1982). The method is based on families of height-diameter curves formulated for site classes by species. Such curves have been derived for white, black and northern red oaks. They were generated by substituting coefficients for S and b (from Appendix 7, this volume) into the following equation for tree height in feet (H):

50

70 60 50 40

6

10

14

18 22 Dbh (in.)

26

30

Fig. 4.10.  Site index curves (index age 50) for northern red oak in the central Appalachians based on tree height and dbh. (Reproduced and adapted from Stout and Shumway, 1982, with permission from the Society of American Foresters, Bethesda, Maryland. Not for further reproduction.) Application requires the measurement of several dominant or codominant trees in each stand.

Site evaluation alternatives to site index Methods based on soil and physiography Another approach to evaluating oak sites is to place sites into discrete productivity categories or

Site Quality and Productivity

on a continuous site quality scale other than site index. Like soil-site equations for estimating site index, these methods are usually based on soil and topographic factors.

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A categorical site evaluation method applicable to oak forests in southern Michigan is based on: (i) soil texture; (ii) the presence of moist layers (high water table or fine textured materials) within 4–10 ft of the soil surface; (iii) slope steepness; and (iv) slope position (Gysel and Arend, 1953). High productivity is associated with sites with fine textured subsoils, gentle mid- to lower slopes and moisture retentive layers within the upper 4–10 ft of the soil. The associated site evaluation scheme places sites in one of five productivity classes ranging from very poor to very good, which in turn, are associated with the average volume of dominant and codominant oaks (Table 4.3).

The topographic site coefficient (TSC) represents a continuously scaled relative measure of forest site productivity applicable to the driftless area of southwestern Wisconsin and adjacent south-eastern Minnesota, north-eastern Iowa and north-western Illinois (Johnson, 1975). It can be used to assess site productivity on upland soils ranging in texture from sandy loam to silt loam. TSC integrates into a single value the effects of soil depth, slope position and aspect on soil moisture and therefore on tree growth. The index value is based on soil plus parent material depth to bedrock (from a minimum of 10 inches to a maximum of 50 inches) weighted by an index of average growing season

Table 4.3.  Site classification system for upland oak stands in southern Michigan. (From Gysel and Arend, 1953.) Texture of subsoil Fine (loams, clay loams and clays)

Medium (sandy loams and loamy sands)

Coarse (sands)

Position of moist layers in substrata

Topography

Highc High

Flatd Rollinge

High

Hillyf

Low Low

Flat Rolling

Low

Hilly

High High Low Low

Flat Rolling Flat Rolling

Low

Hilly

High High

Flat Rolling

Position on slope

Site class

Average volume of dominant/ codominant oaksa (ft3 (bd ft)b)

– Upper Middle Lower Bottom Upper Middle Lower – Upper Middle Lower Bottom Upper Middle Lower – Middle – Upper Middle Lower Bottom Upper Middle Lower – Middle

Very good Good Good Good Very good Medium Good Good Medium Medium Medium Good Very good Poor Medium Good Good Good Very poor Very poor Poor Medium Good Very poor Poor Good Good Good

54 (210) 35 (130) 35 (130) 35 (130) 54 (210) 24 (70) 35 (130) 35 (130) 24 (70) 24 (70) 24 (70) 35 (130) 54 (210) 17 (43) 24 (70) 35 (130) 35 (130) 35 (130) 12 (25) 12 (25) 17 (43) 24 (70) 35 (130) 12 (25) 12 (25) 17 (43) 17 (43) 17 (43)

a

Average volume of 80-year-old black oaks and northern red oaks. bd ft, board feet. A board foot is an amount of sawn lumber equivalent to a board that is 12 inches by 12 inches square and 1 inch thick. c For the fine-textured subsoil class, ‘high’ refers to the presence of a fine-textured (clayey) subsoil; for other subsoil classes, ‘high’ refers to the presence of a water table within 4–10 ft of the soil surface. d Slope steepness of 5% or less. e Slopes are of moderate length with relatively broad ridges and valleys. f Slopes are relatively steep with narrow ridges and valleys. b

186

Chapter 4

growth of northern red oak stump sprouts (see Chapter 2, this volume, Fig. 2.29) and planted hardwoods in clearcuts in south-western Wisconsin is related to TSC (Johnson, 1975; Johnson and Rogers, 1980, 1982, 1984, 1985). Although the exact relation between TSC and site index is unknown, TSC values span the approximate northern red oak site index range of 45–70 ft based on the site index curves of Gevorkiantz (1957). In addition to the methods mentioned above, information on oak forest productivity in relation to physiography and soil taxa is contained in many soil survey manuals and ecological classification schemes. Productivity estimates are subject to the same error sources as with site index estimates discussed earlier in this chapter. In soil surveys produced by the USDA-NRCS, cubic volume growth for commercially important and naturally abundant

soil moisture associated with slope position and aspect (azimuth). TSC is scaled from 0.1 (poorest sites) representing sites that occupy south-westfacing upper slopes on thin soils, to 1.0 (best sites) that occupy low north-east-facing slopes on deep soils (Fig. 4.11). TSC thus provides an empirical method for assessing forest site quality where it is impossible to obtain site index because of the absence of trees or the lack of suitable trees for its determination. Potential applications include the assessment of site quality in young clearcuts and other harvested areas. TSC also has been used as a predictor of early stand development after timber harvesting (Johnson, 1976). In addition, the method can be used to assess the suitability of forest or non-forest sites for tree planting and for predicting the growth of natural reproduction. For example, the height Aspect SE NW

Slope position NE N

Soil depth (in.) S SW

≤10

20

30

40

50

Lower

Middle Upper

Level

3.0

2.5

2.0

1.5

1.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Topographic site coefficient

[cos(azimuth – 45)]+2 Poor

Medium

Good

Site quality Fig. 4.11.  Topographic site coefficient (TSC) in relation to aspect, slope position and soil depth. (From Johnson and Rogers, 1982.) To find TSC, locate aspect on the transformed azimuth or compass scale. Then proceed downwards until the appropriate slope position is intersected. Use the ‘Level’ slope position line for level topography and all slopes less than 15%. Next, proceed horizontally to the right until the appropriate soil depth is intersected. Finally, proceed downwards and read the corresponding TSC value.

Site Quality and Productivity

187

tree species is calculated at the age of culmination of the mean annual increment. A site classification system for the south-central part of the Central Hardwood Region was developed largely from such information. The classification system is applicable to the northern Cumberland Plateau, central ridge and valley, and interior low plateau highland rim sections, which lie within portions of the Broadleaved Forests, Oceanic Province (221a; Plate 1) and the Broadleaved Forests, Continental Province (221b; Plate 1) (Smalley, 1979, 1982, 1984, 1986). This system has much in common with the ecological classification systems discussed in Chapter 1 (this volume). The system divides the landscape into ‘land types’ based on commonalities in geology, physiography, soils and vegetation. Land type descriptions are provided and each is rated with respect to forest productivity (site index and mean annual growth of forest stands), plant competition, seedling mortality, equipment limitations, erosion and windthrow hazards, and tree species desirability. Biophysical methods Biophysical methods relate primary environmental variables such as air temperature, precipitation, relative humidity and incident solar radiation to the productive potential of forests. It can be argued that such variables represent the underlying forces that are causally related to variation in forest productivity. Accordingly, productivity estimated from such variables would not be subject to the vagaries of changing stand conditions as are the more common empirical indices like site index. Because site index, by definition, is tree height at a specified age, events that alter the pattern of tree height growth may diminish the accuracy and therefore the usefulness of site index. Although foresters assume site index to be constant for a given forest site, certain silvicultural practices such as thinning and fertilizing can affect height growth and therefore the estimate of site index. Biophysical methods of site evaluation attempt to relate forest productivity to factors more directly related to productivity. The biophysical approach is based on the hypothesis that the capability of the land to produce wood depends on the localized characteristics of the continuum representing the movement of water through the soil and plant, and into the atmosphere. Any one of three continuum elements (soil– plant–atmosphere) can potentially limit growth and

188

thus site productivity. The approach assumes that the quantity of soil water consumed in forest transpiration is directly proportional to wood production (Czarnowski, 1964). For a given species, transpiration rate is dependent on the rate of soil water absorption and the leaf–atmosphere relationships that control water loss. Soil depth, moisture content and texture are factors that determine soil water availability, while variation in soil temperature mediates absorption rates at any given moisture level. However, the driving force for transpiration occurs at the leaf surface and is dependent on vapour pressure differences between leaf and air. For a given ambient vapour pressure, transpiration depends on leaf temperature. Consequently, during periods of intense radiation, even trees growing in moist soils can be severely water stressed if transpiration exceeds absorption. The relation between these two processes thus provides a biophysical basis for explaining differences in site productivity such as those that occur between north- and south-facing slopes. The extent to which these processes are related to (and thus are predictable from) easily measured soil and topographic factors forms the basis for most other methods of forest site evaluation. For the same species, differences in productivity between two sites with similar soils but different topographies can be substantial. For example, basal area growth of northern red oak in the Appalachian Mountains of West Virginia was more than three times greater on north-facing slopes than on south-facing slopes even though the physical and chemical properties of the soils were similar. The difference in productivity therefore was largely ascribed to differences in radiant and thermal energy regimes (Lee and Sypolt, 1974). Unfortunately, data relating site productivity to primary environmental variables are difficult to obtain. One difficulty is the measurement of key environmental variables such as relative humidity over sufficiently long periods (greater than 1 year). Moreover, correlations between ANPP and single or even multiple environmental factors are extremely variable. Because of these problems, it is difficult to estimate the direct effect of environmental variables on carbon allocation and fixation and to model short-term ANPP for developing a site quality measure that has predictive and interpretative power (Gholz, 1988). Examples of such ‘physiological process’ models of ANPP driven by environmental factors are presented by Gholz (1988).

Chapter 4

Notes 1

 Volumetric units commonly used in the USA include board feet, cords and cunits. One board foot is a piece of sawn wood 1 inch (2.54 cm) thick, 12 inches (30.48 cm) wide and 12 inches long. One cord is a stacked pile of wood contained within a space measuring 4 ft (1.2 m) deep, 4 ft high and 8 ft (2.4 m) long, which equals 128 ft3 (3.6 m3) of volume, of which about 79 ft3 (2.2 m3) is solid wood, about 13 ft3 (0.4 m3) is bark and about 36 ft3 (1.0 m3) is air space. One cunit equals 100 ft3 (2.8 m3). 2  In this study, fine roots were defined as those 2–10 mm in diameter. The biomass of these roots, which were largely concentrated within the upper 30 cm (12 inches) of soil, fluctuated with the seasons. 3  Stage (1976) describes methods for mathematically identifying and specifying interactive aspect and slope gradient effects on tree growth.

References Auchmoody, L.R. and Smith, H.C. (1979) Oak soil–site relationships in northwestern West Virginia. USDA Forest Service Research Paper NE-434. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/14968 (accessed 1 July 2018). Avery, T.E. and Burkhart, H.E. (2002) Forest Measurements, 5th edn. McGraw Hill, New York. Baker, J.B. and Broadfoot, W.M. (1979) A practical method of site evaluation for commercially important southern hardwoods. USDA Forest Service General Technical Report SO-26. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www.fs.usda.gov/ treesearch/pubs/865 (accessed 1 July 2018). Barnes, B.V. (1984) The ecological approach to ecosystem classification. In: Grey, D.C., Schonau, A.P.G. and Schutz, C.J. (eds) Proceedings of the IUFRO Symposium on Site and Productivity of Fast Growing Plantations. International Union of Forest Research Organizations (IUFRO), Pretoria and Pietermaritzburg, South Africa, 30 April–11 May, pp. 69–89. Biging, G.S. (1985) Improved estimates of site index curves using a varying-parameter model. Forest Science 31, 248–259. https://doi.org/10.1093/forestscience/ 31.1.248 Binkley, D. (1986) Forest Nutrition Management. Wiley, New York. Borders, B.E., Bailey, R.L. and Ware, K.S. (1984) Slash pine site index from a polymorphic model by joining (splining) nonpolymorphic segments with an algebra difference method. Forest Science 30, 411–423. https://doi.org/10.1093/forestscience/30.2.411 Bormann, F.H. and Likens, G.E. (1979) Pattern and Process in a Forested Ecosystem. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4612-6232-9

Site Quality and Productivity

Bowden, R.D., Deem, L., Plante, A.F., Peltre, C., Nadelhoffer, K. and Lajtha, K. (2014) Litter inputs control soil carbon in a temperate deciduous forest. Soil Science Society of America Journal 78, S66– S75. https://doi.org/10.2136/sssaj2013.09.0413nafsc Broadfoot, W.M. (1969) Problems in relating soil to site index for southern hardwoods. Forest Science 15, 354–364. https://doi.org/10.1093/forestscience/ 15.4.354 Burkhart, H.E., Cao, Q.V. and Ware, K.D. (1981) A comparison of growth and yield prediction models for loblolly pine. Virginia Polytechnic Institute and State University, School of Forestry and Wildlife Resources, Blacksburg, Virginia. Campbell, B.D. and Grime, J.P. (1989) A comparative study of plant responsiveness to the duration of episodes of mineral nutrient enrichment. New Phytologist 112, 261–267. https://doi.org/10.1111/ j.1469-8137.1989.tb02382.x Canadell, J. and Rodà, F. (1991) Root biomass of Quercus ilex in a Mediterranean forest. Canadian Journal of Forest Research 21, 1771–1778. https:// doi.org/10.1139/x91-245 Cannell, M.G.R. (comp.) (1982) World Forest Biomass and Primary Production Data. Academic Press, London. Carmean, W.H. (1959) Litter weight not reduced following clearcutting of poor-site oak stands. Journal of Forestry 57, 208–209. https://doi.org/10.1093/jof/57.3.206 Carmean, W.H. (1965) Black oak site quality in relation to soil and topography in southeastern Ohio. Soil Science Society of America Proceedings 1965, 308–312. https://doi.org/10.2136/sssaj1965.036159 95002900030024x Carmean, W.H. (1967) Soil survey refinements for predicting black oak site quality in southeastern Ohio. Soil Science Society of America Proceedings 1967, 805–810. https://doi.org/10.2136/sssaj1967.036159 95003100060027x Carmean, W.H. (1970) Site quality for eastern hardwoods. USDA Forest Service Research Paper NE-144. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 36–56. Available at: https://www.fs.usda.gov/ treesearch/pubs/23737 (accessed 1 July 2018). Carmean, W.H. (1971) Site index curves for black, white, scarlet and chestnut oaks in the Central States. USDA Forest Service Research Paper NC-62. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https:// www.fs.usda.gov/treesearch/pubs/10583 (accessed 1 July 2018). Carmean, W.H. (1972) Site index curves for upland oaks in the Central States. Forest Science 18, 109–120. https://doi.org/10.1093/forestscience/18.2.109 Carmean, W.H. (1975) Forest site quality evaluation in the United States. Advances in Agronomy 27, 209–269. https://doi.org/10.1016/S0065-2113(08)70011-7

189

Carmean, W.H. (1979) Soil-site factors affecting hardwood regeneration and growth. In: Holt, H.A. and Fischer, B.C. (eds) Regenerating Oaks in Upland Hardwood Forests. Purdue University, West Lafayette, Indiana, pp. 61–74. Carmean, W.H. and Boyce, S.G. (1973) Hardwood log quality in relation to site quality. USDA Forest Service Research Paper NC-103. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10624 (accessed 1 July 2018). Carmean, W.H. and Hahn, J.T. (1983) Site comparisons for upland oaks and yellow-poplar in the Central States. Journal of Forestry 81, 736–739. https://doi. org/10.1093/jof/81.11.736 Carmean, W.H., Hahn, J.T. and Jacobs, R.D. (1989) Site index curves for forest tree species in the eastern United States. USDA Forest Service General Technical Report NC-128. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10192 (accessed 1 July 2018). Clutter, J.L., Fortson, J.C., Pienaar, L.V., Brister, G.H. and Bailey, R.L. (1983) Timber Management: a Quantitative Approach. Wiley, New York. Cole, D.W. and Gessel, S.P. (1988) Forest Site Evaluation and Long-term Productivity. University of Washington Press, Seattle, Washington. Covington, W. (1981) Changes in forest floor organic matter and nutrient content following clear cutting in northern hardwoods. Ecology 62, 41–48. https://doi. org/10.2307/1936666 Czarnowski, M.S. (1964) Productive Capacity of Locality as a Function of Soil and Climate with Particular Reference to Forest Land. Louisiana State University Press, Baton Rouge, Louisiana. Davis, M.B. (1976) Erosion rates and land use history in southern Michigan. Environmental Conservation 3, 139–148. https://doi.org/10.1017/S0376892900018269 Doolittle, W.T. (1958) Site index of scarlet and black oak in relation to southern Appalachian soil and topography. Forest Science 3, 114–124. https://doi.org/ 10.1093/forestscience/3.2.114 Drew, J.T. and Flewelling, J.W. (1977) Some recent Japanese theories of yield–density relationships and their application to Monterey pine plantations. Forest Science 23, 517–534. https://doi.org/10.1093/ forestscience/23.4.517 Dwyer, J.P., Dey, D.C. and Kurtz, W.B. (1993) Profitability of precommercially thinning oak stump sprouts. Northern Journal of Applied Forestry 10, 179–183. https://doi.org/10.1093/njaf/10.4.179 Ek, A.R. (1971) A formula for white spruce site index curves. University of Wisconsin Forestry Research Note 161. University of Wisconsin, Madison, Wisconsin. Fleming, R., Powers, R., Foster, N., Kranabetter, J., Scott, D., Ponder, F., Jr, Berch, S., Chapman, W.,

190

Kabzems, R. and Ludovici, K. (2006) Effects of organic matter removal, soil compaction, and vegetation control on 5-year seedling performance: a regional comparison of long-term soil productivity sites. Canadian Journal of Forest Research 36, 529–550. https://doi.org/10.1139/x05-271 Frank, E.C. and Lee, R. (1966) Potential solar beam irradiation on slopes: tables for 30° to 50° latitude. USDA Forest Service Rocky Mountain Forest and Range Experiment Station Paper RM-18. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado. Gevorkiantz, S.R. (1957) Site index curves for red oak in the Lake States. USDA Forest Service Lake States Forest Experiment Station Technical Note 485. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gevorkiantz, S.R. and Scholz, H.F. (1944) Determining site quality in understocked oak forests. Journal of Forestry 42, 808–811. https://doi.org/10.1093/jof/ 42.11.808 Gevorkiantz, S.R. and Scholz, H.F. (1948) Timber yields and possible returns from the mixed-oak farmwoods of southwestern Wisconsin. USDA Forest Service Lake States Forest Experiment Station Publication 521. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gholz, H.L. (1988) Problems in the biophysical determination of forest site quality. In: Cole, D.W. and Gessel, S.P. (eds) Forest Site Evaluation and Long Term Productivity. University of Washington Press, Seattle, Washington, pp. 12–21. Graney, D.L. (1978) Site quality relationships for the oak–hickory forest type. In: Society of American Foresters Proceedings. Society of American Foresters, Bethesda, Maryland, pp. 339–343. Graney, D.L. (1987) Ten-year growth of red and white oak crop trees following thinning and fertilization in the Boston Mountains of Arkansas. USDA Forest Service General Technical Report SO-42. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 445–449. Graney, D.L. and Bower, D.R. (1971) Site index curves for red and white oaks in the Boston Mountains of Arkansas. USDA Forest Service Research Note SO-121. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Grigal, D. (2000) Effects of extensive forest management on soil productivity. Forest Ecology and Management 138, 167–185. https://doi.org/10.1016/S0378-1127 (00)00395-9 Gysel, L.W. and Arend, J.L. (1953) Oak sites in southern Michigan: their classification and evaluation. Michigan State University Technical Bulletin 236. Michigan State University, East Lansing, Michigan. Hannah, P.R. (1968) Estimating site index for white and black oaks in Indiana from soil and topographical

Chapter 4

factors. Journal of Forestry 66, 412–416. https://doi. org/10.1093/jof/66.5.412 Harper, J.L. (1977) Population Biology of Plants. Academic Press, London. Harris, W.F. and Henderson, G.S. (1981) Walker Branch sites 1–4, Oak Ridge, Tennessee, USA. In: Reichle, D.E. (ed.) Dynamic Properties of Forest Ecosystems. Cambridge University Press, Cambridge, pp. 658–661. Harris, W.F., Goldstein, R.A. and Henderson, G.S. (1973) Analysis of forest biomass pools, annual primary production and turnover of biomass for a mixed deciduous forest watershed. In: International Union of Forest Research Organizations (IUFRO) Biomass Studies. University of Maine, College of Life Sciences and Agriculture, Orono, Maine, pp. 43–64. Hartung, R.E. and Lloyd, J. (1969) Influence of aspect on forests of the Clarksville soil in Dent County, Missouri. Journal of Forestry 67, 178–182. https://doi. org/10.1093/jof/67.3.178 Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Hennessey, T.C., Dougherty, P.M., Kossuth, S.V. and Johnson, J.D. (1986) Stress Physiology and Forest Productivity: Proceedings of the Physiology Working Group of the Society of American Foresters. Kluwer Academic Publishers, Boston, Massachusetts. https://doi.org/10.1007/978-94-009-4424-4 Hudson, B.D. (1992) The soil survey as a paradigmbased science. Soil Science Society of America Journal 56, 836–841. Available at: https://casoilresource.lawr.ucdavis.edu/w/images/f/fe/Paradigm_ Based_Science_Hudson_1992.pdf (accessed 1 July 2018). Jakucs, P. (1981) Sikforkut, Hungary. In: Reichle, D.E. (ed.) Dynamic Properties of Forest Ecosystems. Cambridge University Press, Cambridge, p. 586. Johnson, D. and Curtis, P. (2001) Effects of forest management on soil C and N storage: meta analysis. Forest Ecology and Management 140, 227–238. https://doi.org/10.1016/S0378-1127(00)00282-6 Johnson, D. and Todd, D., Jr (1998) Effects of harvesting intensity on forest productivity and soil carbon storage in mixed oak forest. In: Kimble, J.M., Heath, L.S., Birdsey, R.A. and Lal, R. (eds) Management of Carbon Sequestration in Soil. CRC Press, Boca Raton, Florida, pp. 351–363. Johnson, D., Knoepp, J., Swank, W., Shan, J., Morris, L., Van Lear, D. and Kapeluck, P. (2002) Effects of forest management on soil carbon: results of some longterm resampling studies. Environmental Pollution 116, 201–208. https://doi.org/10.1016/S0269-749 1(01)00252-4 Johnson, F.L. and Risser, P.G. (1974) Biomass, annual net primary production and dynamics of six mineral elements in a post oak–blackjack oak forest. Ecology 55, 1246–1258. https://doi.org/10.2307/1935453

Site Quality and Productivity

Johnson, P.S. (1975) Growth and structural development of red oak sprout clumps. Forest Science 21, 413–418. https://doi.org/10.1093/forestscience/ 21.4.413 Johnson, P.S. (1976) Eight-year performance of interplanted hardwoods in southern Wisconsin oak clearcuts. USDA Forest Service Research Paper NC-126. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10647 (accessed 1 July 2018). Johnson, P.S. and Rogers, R. (1980) Predicting growth of individual stems within northern red oak sprout clumps. In: Proceedings of the Central Hardwood Forest Conference III. University of Missouri, Columbia, Missouri, pp. 420–439. Johnson, P.S. and Rogers, R. (1982) Hardwood interplanting in the upper Mississippi Valley. In: Proceedings of the Hardwood Regeneration Conference. University of Minnesota, St Paul, Minnesota, pp. 90–109. Johnson, P.S. and Rogers, R. (1984) Predicting 25thyear diameters of thinned stump sprouts of northern red oak. Journal of Forestry 82, 616–619. https://doi. org/10.1093/jof/82.10.616 Johnson, P.S. and Rogers, R. (1985) A method for estimating the contribution of planted hardwoods to future stocking. Forest Science 31, 883–891. https:// doi.org/10.1093/forestscience/31.4.883 Jones, E.P., Jr (1986) Slash pine plantation spacing study – age 30. USDA Forest Service General Technical Report SE-43. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 45–49. Jones, J.R. (1969) Review and comparison of site evaluation methods. USDA Forest Service Research Paper RM-51. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado. Kabrick, J.M., Goyne, K.W., Fan, Z. and Meinert, D. (2011) Landscape determinants of exchangeable calcium and magnesium in Ozark Highland forest soils. Soil Science Society of America Journal 75, 164–180. https://doi.org/10.2136/sssaj2009.0382 Kabrick, J.M., Dwyer, J.P., Shifley, S.R. and O’Neil, B.S. (2013) Components and nutrient concentrations of small-diameter woody biomass for energy. Northern Journal of Applied Forestry 30, 137–142. https://doi. org/10.5849/njaf.11-030 Katagiri, S. and Tsutsumi, T. (1975) The relationship between site condition and circulation of nutrients in forest ecosystems. III Aboveground biomass and nutrient contents of stands. Journal of the Japanese Forestry Society 57, 412–419. Katagiri, S. and Tsutsumi, T. (1976) The relationship between site condition and circulation of nutrients in forest ecosystems. IV The amount of mineral nutrient returned to forest floor. Journal of the Japanese Forestry Society 58, 79–85.

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Katagiri, S. and Tsutsumi, T. (1978) The relationship between site condition and circulation of nutrients in forest ecosystems. V The differences in nutrient circulation between stands located in upper part of slope and lower part of slope. Journal of the Japanese Forestry Society 60, 195–202. Kestemont, P. (1971) Productivité primaire des taillis simples et concept de nécromasse. In: Duvigneaud, P. (ed.) Productivity of Forest Ecosystems. United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, pp. 271–279. Killian, W. (1984) Site classification and mapping: principles and trends. In: Grey, D.C., Schonau, A.P.G. and Schutz, C.J. (eds) Proceedings of the IUFRO Symposium on Site and Productivity of Fast Growing Plantations. International Union of Forest Research Organizations (IUFRO), Pretoria and Pietermaritzburg, South Africa, 30 April–11 May, pp. 51–68. Kimmins, J.P. (2004) Forest Ecology: a Foundation for Sustainable Forest Management and Environmental Ethics in Forestry, 3rd edn. Prentice Hall, Upper Saddle River, New Jersey. Kozlowski, T.T., Kramer, P.J. and Pallardy, S.G. (1991) The Physiological Ecology of Woody Plants. Academic Press, San Diego, California. Lajtha, K., Bowden, R.D. and Nadelhoffer, K. (2014) Litter and root manipulations provide insights into soil organic matter dynamics and stability. Soil Science Society of America Journal 78, S261–S269. https:// doi.org/10.2136/sssaj2013.08.0370nafsc Lamson, N. (1980) Site index prediction tables for oak in northwestern West Virginia. USDA Forest Service General Technical Report NE-462. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15011 (accessed 1 July 2018). Lamson, N.I. (1987) Estimating northern red oak siteindex class from total height and diameter of dominant and codominant trees in central Appalachian hardwood stands. USDA Forest Service Research Paper NE-605. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/21778 (accessed 1 July 2018). Lanner, R.M. (1985) On the insensitivity of height growth to spacing. Forest Ecology and Management 13, 143–148. https://doi.org/10.1016/0378-1127(85) 90030-1 Leary, R.A. (1985) Interaction Theory in Forest Ecology and Management. Martinus Nijoff, Dordrecht, Netherlands. https://doi.org/10.1007/978-94-0095151-8 Lee, R.G. (1989) The concept and measurement of multi-resource site quality. USDA Forest Service General Technical Report NC-135. USDA Forest

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Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 32–39. Available at: https:// www.nrs.fs.fed.us/pubs/gtr/gtr_nc135.pdf (accessed 1 July 2018). Lee, R. and Sypolt, C.R. (1974) Toward a biophysical evaluation of forest site potential. Forest Science 20, 145–154. https://doi.org/10.1093/forestscience/ 20.2.145 Lloyd, F.T. (1981) How many tree heights should you measure for natural Atlantic Coastal Plain loblolly site index? Southern Journal of Applied Forestry 5, 180– 183. https://doi.org/10.1093/sjaf/5.4.180 Lloyd, F.T. and Hafley, W.L. (1977) Precision and the probability of misclassification in site index estimation. Forest Science 23, 493–499. https://doi.org/ 10.1093/forestscience/23.4.493 Lloyd, F.T. and Jones, E.P., Jr (1983) Density effects on height growth and its implications for site index prediction and growth projection. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 329–333. Available at: https://www.srs.fs.usda.gov/pubs/gtr/gtr_se024.pdf (accessed 1 July 2018). McQuilkin, R.A. (1974) Site index prediction table for black, scarlet, and white oaks in southeastern Missouri. USDA Forest Service Research Paper NC-108. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10629 (accessed 1 July 2018). McQuilkin, R.A. (1975) Errors in site index determination caused by tree age variation in even-aged oak stands. USDA Forest Service Research Note NC-185. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https:// www.fs.usda.gov/treesearch/pubs/11362 (accessed 1 July 2018). McQuilkin, R.A. and Rogers, R. (1978) A method for determining the precision of site index estimates made from site index prediction functions. Forest Science 24, 289–296. https://doi.org/10.1093/ forestscience/24.2.289 Mills, W.L., Jr, Fischer, B.C. and Reisinger, T.W. (1987) Upland hardwood silviculture: a review of the literature. Purdue University Agricultural Experiment Station Bulletin 527. Purdue University, West Lafayette, Indiana. Monserud, R.A. and Ek, A.R. (1976) Site index curves and equations for several northern hardwood forest species. University of Wisconsin School of Natural Resources Bulletin R2771. University of Wisconsin, Madison, Wisconsin. Nelson, T.C. and Beaufait, W.R. (1956) Studies in site evaluation for southern hardwoods. In: Society of American Foresters Proceedings. Society of American Foresters, Bethesda, Maryland, pp. 67–70.

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Available at: https://www.fs.usda.gov/treesearch/pubs /43064 (accessed 1 July 2018). Olson, D.F., Jr (1959) Site index curves for upland oak in the southeast. USDA Forest Service Southeastern Forest Experiment Station Research Note 125. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/pubs/5130 (accessed 1 July 2018). Olson, D.F., Jr and Della-Bianca, L. (1959) Site index comparisons for several tree species in the VirginiaCarolina Piedmont. USDA Forest Service Station Paper SE-104. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/ pubs/7372 (accessed 1 July 2018). Patric, J.H. (1976) Soil erosion in the eastern forest. Journal of Forestry 74, 671–677. https://doi.org/10.1093/ jof/74.10.671 Patric, J.H. (1977) Soil erosion and its control in eastern woodlands. Northern Logger and Timber Processor 25, 4–5, 22–23. Patric, J.H. (1978) Harvesting effects on soil and water in the eastern hardwood forest. Southern Journal of Applied Forestry 2, 66–73. https://doi.org/10.1093/ sjaf/2.3.66 Payandeh, B. (1974a) Formulated site index curves for major timber species in Ontario. Forest Science 20, 143–144. https://doi.org/10.1093/forestscience/ 20.2.143 Payandeh, B. (1974b) Nonlinear site index equations for several Canadian timber species. Forestry Chronical 47, 194–196. https://doi.org/10.5558/tfc50194-5 Perry, D.A., Oren, R. and Hart, S.C. (2008) Forest Ecosystems, 2nd edn. Johns Hopkins University Press, Baltimore, Maryland. Ponder, F., Jr (2003) Effect of site treatments on soiltemperature and moisture and oak and pine growth and nutrient concentrations. USDA Forest Service General Technical Report NC-234. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 213–222. Available at: https://www.fs.usda.gov/treesearch/pubs/15746 (accessed 1 July 2018). Ponder, F., Jr (2007) Biomass removal and its effect on productivity of an artificially regenerated forest stand in the Missouri Ozarks. USDA Forest Service General Technical Report SRS-101. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 135–143. Available at: https://www.fs. usda.gov/treesearch/pubs/27817 (accessed 1 July 2018). Ponder, F., Jr, Fleming, R.L., Berch, S., Busse, M.D., Elioff, J.D., Hazlett, P.W., Kabzems, R.D., Kranabetter, J.M., Morris, D.M., Page-Dumroese, D. and 13 other authors (2012) Effects of organic matter removal, soil compaction and vegetation control on 10th year biomass

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and foliar nutrition: LTSP continent-wide comparisons. Forest Ecology and Management 278, 35–54. https://doi.org/10.1016/j.foreco.2012.04.014 Reiners, W.A. (1972) Structure and energetics of three Minnesota forests. Ecological Monographs 42, 71–94. https://doi.org/10.2307/1942231 Rochow, J.J. (1974) Litter fall relations in a Missouri forest. Oikos 25, 80–85. https://doi.org/10.2307/3543548 Rodin, L.E. and Basilevič (1968) World distribution of plant biomass. In: Eckardt, F.E. (ed.) Functioning of Terrestrial Ecosystems at the Primary Production Level – Proceedings of the Copenhagen Symposium. United Nations Educational, Scientific and Cultural Organization (UNESCO), Liège, Belgium, pp. 45–52. Rundel, P.W. (1980) Adaptations of Mediterraneanclimate oaks to environmental stress. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 43–54. Available at: https://www.fs.usda.gov/treesearch/ pubs/24103 (accessed 1 July 2018). Schnur, G.L. (1937) Yield, stand and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc. nal.usda.gov/download/CAT86200555/PDF (accessed 1 July 2018). Schonau, A.P.G. (1987) Problems in using vegetation or soil classification in determining forest site quality. South African Forestry Journal 141, 13–18. https:// doi.org/10.1080/00382167.1987.9630255 Simmons, L.A. and Anderson, S.H. (2016) Effects of logging activities on selected soil physical and hydraulic properties for a claypan landscape. Geoderma 269, 145–152. https://doi.org/10.1016/j.geoderma. 2016.02.005 Singh, G., Goyne, K.W. and Kabrick, J.M. (2015) Determinants of total and available phosphorus in forested Alfisols and Ultisols of the Ozark Highlands, USA. Geoderma Regional 5, 117–126. https://doi. org/10.1016/j.geodrs.2015.05.001 Smalley, G.W. (1979) Classification and evaluation of forest sites on the southern Cumberland Plateau. USDA Forest Service General Technical Report SO-23. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www.fs.usda.gov/treesearch/ pubs/2381 (accessed 1 July 2018). Smalley, G.W. (1982) Classification and evaluation of forest sites on the mid-Cumberland Plateau. USDA Forest Service General Technical Report SO-38. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/ 10.2737/SO-GTR-38 Smalley, G.W. (1984) Classification and evaluation of forest sites in the Cumberland Mountains. USDA Forest Service General Technical Report SO-50.

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USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/ 10.2737/SO-GTR-50 Smalley, G.W. (1986) Classification and evaluation of forest sites on the northern Cumberland Plateau. USDA Forest Service General Technical Report SO-60. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/ 10.2737/SO-GTR-60 Smith, D.M. (1986) The Practice of Silviculture, 8th edn. Wiley, New York. Society of American Foresters (1995) Silviculture Terminology. Society of American Foresters, Bethesda, Maryland. Stage, A.R. (1976) An expression for the effect of aspect, slope and habitat type on tree growth. Forest Science 22, 457–460. https://doi.org/10.1093/forestscience/ 22.4.457 Stout, B.B. and Shumway, D.L. (1982) Site quality estimation using height and diameter. Forest Science 28, 639–645. https://doi.org/10.1093/forestscience/ 28.3.639 Struckhoff, A.N., Wallace, D. and Young, F. (2017) Ecological sites: a useful tool for land management. USDA Forest Service General Technical Report NRS-P-167. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 72–76. Available at: https://www.fs.usda.gov/ treesearch/pubs/53758 (accessed 1 July 2018). Sucre, E. and Fox, T. (2008) Contribution of stumps to carbon and nitrogen pools in southern Appalachian hardwood forests. USDA Forest Service General Technical Report NRS-P-24. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 233–239. Available at: https:// www.fs.usda.gov/treesearch/pubs/14018 (accessed 1 July 2018). Swift, L.W., Jr (1976) Algorithm for solar radiation on mountain slopes. Water Resources Research 12, 108–112. https://doi.org/10.1029/WR012i001p00108 Trimble, G.R., Jr and Weitzman, S. (1956) Site index studies of upland oaks in the northern Appalachians. Forest Science 2, 162–173. https://doi.org/10.1093/ forestscience/2.3.162 Van Lear, D.H., Kapeluck, P.R. and Carroll, W.D. (2000) Productivity of loblolly pine as affected by decomposing

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root systems. Forest Ecology and Management 138, 435–443. https://doi.org/10.1016/S0378-1127 (00)00429-1 Ward, W.W. and Bowersox, T.W. (1970) Upland oak response to fertilization with nitrogen, phosphorous and calcium. Forest Science 16, 113–120. https:// doi.org/10.1093/forestscience/16.1.113 Waring, R.H. and Schlesinger, W.H. (1985) Forest Ecosystems: Concepts and Management. Academic Press, Orlando, Florida. Whittaker, R.H. and Niering, W.A. (1975) Vegetation of the Santa Catalina Mountains, Arizona. V Biomass, production and diversity along the elevation gradient. Ecology 56, 771–790. https://doi.org/10.2307 /1936291 Whittaker, R.H. and Woodwell, G.M. (1968) Dimension and production relations of trees and shrubs in the Brookhaven Forest, New York. Journal of Ecology 56, 1–25. https://doi.org/10.2307/2258063 Whittaker, R.W. (1963) Net production of heath balds and forest heaths in the Great Smoky Mountains. Ecology 46, 176–182. https://doi.org/10.2307/ 1933200 Yanai, R.D., Currie, W.S. and Goodale, C.L. (2003) Soil carbon dynamics after forest harvest: an ecosystem paradigm reconsidered. Ecosystems 6, 197–212. https://doi.org/10.1007/s10021-002-0206-5 Yin, X., Perry, J.A. and Dixon, R.K. (1989) Fine-root dynamics and biomass distribution in a Quercus ecosystem following harvest. Forest Ecology and Management 27, 159–177. https://doi.org/10.1016/ 0378-1127(89)90105-9 Zahner, R. (1970) Site quality and wood quality in upland hardwoods: theoretical considerations of wood density. In: Youngberg, C.T. and Davey, C.B. (eds) Tree Growth and Forest Soils, Proceedings of the 3rd North American Forest Soils Conference. Oregon State University Press, Corvallis, Oregon, pp. 477–497. Zahner, R. and Myers, R.K. (1984) Productivity of young Piedmont oak stands of sprout origin. Southern Journal of Applied Forestry 8, 102–108. https://doi. org/10.1093/sjaf/8.2.102 Zahner, R., Myers, R.K. and Churchill, L.A. (1982) Site index curves for young oak stands of sprout origin. Clemson University Forestry Bulletin 35. Clemson University, South Carolina.

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5



Development of Natural Stands

Introduction Stands are the basic units of forest management. Although they can vary widely in area, oak stands typically range from 2 to 40 acres. By definition, a stand covers a relatively homogeneous area with respect to vegetation, soil and site quality. Stand boundaries are usually delineated by a combination of stand characteristics including age structure (even-­ aged or uneven-aged), predominant tree size (seedling, sapling, pole or sawlog) and species composition. Site homogeneity is usually evaluated through the use of site evaluation (see Chapter 4, this volume) and ecological classification methods (see Chapter 1).

Forest Canopy Layers The canopy of a mature oak stand can be divided into two broadly defined layers: (i) overstorey; and (ii) understorey. The overstorey consists of a main canopy and a subcanopy. The main canopy includes the upper layers of tree crowns, which intercept most of the sunlight (Fig. 5.1). Beneath the main canopy there is often a subcanopy or midstorey of sapling-size trees (1–5 inches dbh), and beneath that an understorey layer that includes tree reproduction, shrubs and herbaceous plants. Understorey species are usually shade tolerant because only about 1–5% of the sunlight received by the main canopy reaches the forest floor. The understorey is nevertheless the domain of advance tree reproduction including oak seedlings and seedling sprouts. For silvicultural purposes, it is convenient to define an upper size limit for tree reproduction. In the Central Hardwood Region, for example, 1.5 inches dbh is commonly used to define this limit; larger trees are thus defined as members of the overstorey. However, this limit was probably set more by mensurational convention rather than by biological considerations. Overstorey trees can be further categorized by their relative canopy position, or crown class. Four

crown classes are generally recognized: (i) dominant; (ii) codominant; (iii) intermediate; and (iv) overtopped (or suppressed) (Fig. 5.1). The crowns of dominant trees extend partially above the general level of the main canopy where they receive light both from above and from the sides. These trees are the tallest in a stand, and they usually have the largest crowns. The crowns of codominant trees receive full sunlight directly from above but little from the sides. These are among the taller trees in the stand, and their crowns define the upper level of the main canopy. The crowns of intermediate trees occupy the lower part of the main canopy. They are among the smaller trees in the stand, and receive light only on limited portions of crown tops. The crowns of overtopped trees lie completely below the main canopy in the subcanopy. Little direct sunlight is received by any part of the crowns of overtopped trees, which are often flat-topped and irregular in shape. The way trees are spatially arranged is called stand structure. Stand structure can be described by various qualitative and quantitative attributes of forests. Crown class is an example of a qualitative attribute that describes a tree’s vertical crown position. However, stand structure also includes the horizontal distribution of trees including their ages and sizes, and the distribution of other stand components such as herbaceous vegetation and coarse woody debris (Helms, 1998; Pretzsch, 2009). Stand age and size structure can be quantitatively defined by frequency distributions of tree ages or sizes. The frequency distribution of tree ages is used to distinguish even-aged from uneven-aged stands and to measure other age-specific properties of stands. Stand size structure is often described by the frequency distribution of tree diameters; this is usually referred to simply as the diameter distribution. Because the diameter distribution of a stand changes as the stand develops, it is a useful diagnostic for assessing stand development. Stand structure in an even broader context can include how closely packed the trees are. Various measures of stand density and

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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D

D

C C

I

C I

I

Main canopy

I

Gap

Overstorey O O

O

O

O

Subcanopy or Midstorey Understorey

Fig. 5.1.  Forest canopy layers can be subdivided into overstorey (including the main canopy and the subcanopy or midstorey) and understorey (including tree reproduction, shrubs and herbaceous vegetation). Associated tree crown classes are: D = dominant, C = codominant, I = intermediate, O = overtopped (suppressed). The capacity of oak reproduction (especially seedling sprouts) to capture canopy gaps depends on their root size at the time the canopy gap is created, their inherent growth rate (which varies among oak species), gap size, competition from other vegetation and other factors.

stocking have been developed for quantifying this aspect of stand structure (see Chapter 6, this volume). Stands also vary in species composition (i.e. the relative proportions of species present). Collectively, the various measures of stand structure and species composition provide the silviculturist with essential information about the current state of a stand and, by extension, silvicultural options for its management. Central to an understanding of these options is an understanding of forest disturbance events and the related ecological processes and patterns of stand development.

opportunity for new individuals (or colonies) to become established’ (Sousa, 1984); or as ‘a force that kills at least one canopy tree’ (Runkle, 1985). Forest disturbances redistribute resources: moisture, nutrients, light, heat, biomass, forage and economic value. They also alter forest structure and species composition and usually increase the resources available to surviving trees or new reproduction in the vicinity of the disturbance. The type, size, severity and frequency of disturbances greatly affect how oak stands develop. Disturbances also redistribute food and habitats favourable to wildlife.

Disturbance

Disturbance type

Forests continually change as a result of events originating from inside and outside the forest. These events are ubiquitous and in a broad sense can be termed ‘disturbances’. Many forest disturbances are ecologically and silviculturally important because they affect the current structure and composition of a stand as well as its future development. Disturbances have been variously defined as ‘any relatively discrete event in time that disrupts ecosystem, community, or population structure and changes resources, substrate availability, or the physical environment’ (White and Pickett, 1985); ‘as a discrete, punctuated killing, displacement, or damaging of one or more individuals (or colonies) that directly or indirectly creates an

Forest disturbances can be classified as exogenous or endogenous. Exogenous disturbances originate from forces external to the stand and may damage or kill vigorous as well as unhealthy trees. Examples include disturbances caused by weather, invasive species, fire and human activity including silviculture and climate change (e.g. see Chapters 11 and 14, this volume). Endogenous disturbances originate within the stand, and tree death and treefall are primary causes. It is not always apparent which type of disturbance has occurred. For example, stresses resulting from the competition between trees (an endogenous factor) may predispose a tree to a fatal insect defoliation or windthrow (exogenous factors).

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The ecological impact of a disturbance may depend more on its size and severity than its origin (White and Pickett, 1985). Exogenous and endogenous factors represent endpoints of disturbance size that can range from the creation of extensive forest canopy openings with abrupt boundaries to small changes in stand structure associated with the toppling of a single overstorey tree (White and Pickett, 1985). Although exogenous factors typically produce larger disturbances than endogenous factors, endogenous events nevertheless can have substantial localized impacts. For example, when a large, senescent tree falls it may shear the crowns of surrounding trees, topple or crush other trees in the line of fall, disturb the soil at points of impact, and uplift a mound of soil in its root mass (Bormann and Likens, 1979). Disturbance size and frequency of occurrence Disturbances of one type or another occur frequently during the life of every oak stand, and each one changes the structure and composition of the stand to some degree. Reice (1994) suggested that to be in recovery from their last disturbance is the normal state for most natural plant communities, and that they rarely attain a state of equilibrium. This is certainly true of oak forests, which owe their very existence to disturbance. Most are therefore in a state of recovery and transition related to their disturbance history. Disturbances vary greatly in their area, severity and frequency, and in associated ecological responses (Sousa, 1984). Even seemingly small disturbances such as animal browsing or surface fires may significantly alter a forest if they are widespread and frequent. Disturbances that create large openings in the forest canopy are of particular interest to ecologists and silviculturists because such openings significantly alter the course of stand development and redistribute light, moisture, heat and nutrients to the trees that remain. Most silvicultural treatments are disturbances designed to control future stand composition and structure. Although silviculturists primarily focus on standscale management, some disturbances occur at the landscape scale. For example, forest fires can alter individual stands as well as entire landscapes. At an even larger scale, climate change may affect the distribution of oaks and associated species across ecoregions. Because each tree species differs physiologically, expected shifts in species’ geographic

Development of Natural Stands

ranges are potentially predictable from expected climate changes (see Chapters 1 and 14, this volume). For silvicultural purposes, forest disturbances can be categorized according to their relative size and impact on stand development: (i) gap-scale disturbances; (ii) incomplete stand-scale disturbances; and (iii) stand-initiating disturbances. Gapscale disturbances are the smallest and can be endogenous or exogenous in origin. The other two categories result from exogenous forces. The impact of a disturbance on a stand should be evaluated in the context of stand structure and composition. A disturbance of a given type and size will usually elicit a different response from a young stand than from a mature stand or among stands that differ in species composition. Gap-scale disturbances occur when a single tree or a small group of trees are lost from the main canopy. The resulting canopy gaps increase light and soil moisture available to trees within the gap and to trees adjacent to the gap. When stands are well stocked and pole-size or smaller, canopy gaps created by the death of small trees are quickly filled through crown expansion of the surrounding trees. During later stages of stand development, gaps created by the death or harvest of individual trees often are larger and thus may persist for decades. Silvicultural methods designed to create gap-scale disturbances include some types of thinning (see Chapter 8, this volume), and single-tree and group selection methods (see Chapter 9). During a century of even-aged oak stand development, more than 95% of the trees initially present will die. Most of those deaths are caused by inter-tree competition and consequent self-thinning of stands (see Chapter 6, this volume). As a result, canopy gaps are a common occurrence in oak stands. Although trees of all crown classes die from self-thinning, mortality rates are lower for dominant and codominant trees than for trees in subordinate crown classes. For example, in New England stands, 9% and 33% of northern red oaks in dominant and codominant crown classes, respectively, died between stand ages 25 and 55. In contrast, 67% and 90% of trees in intermediate and suppressed classes, respectively, died over the same period (Ward and Stephens, 1994). When an individual tree dies, it leaves a canopy gap that is roughly proportionate in area to its basal area. The vacated space is subsequently captured by the expanding crowns of surrounding trees and/or by tree reproduction. Larger gap-scale disturbances occur when small clusters of trees die

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at the same time, or when individual trees with large crowns die. As gap size increases, the more abrupt and extensive are the associated stand changes. The size of the gap, rate of crown expansion of surrounding trees, and the growth rate of trees within the gap all influence future stand composition and structure (Hibbs, 1982). Incomplete stand-scale disturbances are larger and more disruptive to the stand than gap-scale disturbances. They are of exogenous origin and caused by events that affect a large proportion of a stand’s area. An incomplete stand-scale disturbance removes enough overstorey trees to initiate the development of a new age class of trees within the stand. The resulting stand age structure is often irregular (i.e. the tree age classes occupy unequal areas), and there may be large variation in the size and number of openings in the canopy. Silvicultural practices that can create incomplete stand-scale disturbances include seed tree, diameter limit, shelterwood and group selection harvests. Fires of sufficient intensity to kill patches of overstorey trees also can result in incomplete stand-scale disturbances. The largest and most severe disturbances are those that initiate a new stand. These stand-initiating disturbances remove most or all of the overstorey. Some residual canopy trees may remain, but the net result is the creation of a new, even-aged stand. High wind, severe fire and other destructive events that are an acre or larger can cause stand-initiating disturbances. Silvicultural treatments such as ting and shelterwood removal harvests (see clearcut­ Chapter 8, this volume) are also stand-initiating disturbances. A disturbance of this type resets stand age to or close to zero. In even-aged stands, stand age is thus a measure of the time elapsed since the previous stand-initiating disturbance. In managed forests, silvicultural treatments are typically implemented at 10- to 30-year intervals. Gap-scale disturbances within a stand generally occur with greater frequency than that and are anticipated in silvicultural prescriptions. However, stand-initiating disturbances or incomplete standscale disturbances are infrequent events that are generally not anticipated in a managed stand except as a prescribed timber harvest. Silvicultural prescriptions nevertheless can be designed to reduce the negative consequences of large-scale disturbances such as severe fire, severe weather, insect damage, invasive species and climate change which inevitably affect some managed stands.

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Response to disturbance When a disturbance creates an opening of any size in the forest, it increases the availability of resources in and around the opening. Woody vegetation responds in three ways. First, the crowns of trees surrounding the opening grow laterally to capture the space. Although the rate of lateral crown expansion depends on many factors, oaks expand at rates from 6 to 12 inches/year in eastern forests (see Chapter 3, this volume, Fig. 3.22; Trimble and Tryon, 1966; Hibbs, 1982). Crowns of northern red oaks often expand more rapidly than crowns of cooccurring species. In general, crowns of mature trees tend to expand more slowly than young, vigorous trees. And mature oaks in the superior crown classes may not expand at all (Sampson, 1983). Large trees in mature stands nevertheless leave large gaps when they die. As stands mature, there are more opportunities for such gaps to form, and those that persist for decades may facilitate the establishment of oak and other tree species in the understorey. A second response to gap formation is an increase in the height growth of pre-established tree reproduction and subcanopy trees within the gap. This response, in turn, can facilitate the successional replacement of canopy dominants by trees that have persisted in intermediate and suppressed crown classes and that have the capacity to opportunistically expand into canopy gaps. Thus, gap formation can potentially accelerate the recruitment of shade-tolerant trees and even some fastgrowing, shade-intolerant trees into the overstorey. Whether or not such a replacement process occurs varies with the species composition and the physiological capacity of trees in subordinate canopy layers to capture and dominate canopy gaps. A third response to canopy gap formation is the establishment of new trees (and other vegetation). If the openings are large enough and persist long enough, trees that become established in a gap after its formation may eventually grow into the overstorey. In a hemlock–mixed hardwood forest, seedlings (including oaks) required a canopy opening with a diameter at least half the height of the surrounding trees to facilitate growth into the overstorey (Hibbs, 1982). In smaller openings the crown expansion of surrounding trees usually closed the gap before new trees reached the main canopy. Not only does a canopy gap close laterally with time (Chapter 3, Fig. 3.22), the minimum area required to sustain a tree within the gap increases as the tree

Chapter 5

itself increases in size. A tree therefore must reach the level of the main canopy before its own minimum growing space requirements exceed the gap’s diminishing size. Thus, recruitment of new trees within a gap depends not only on the species, initial size, vigour and growing space requirements of trees within the gap, but also on the size, crown expansion rate and spatial arrangement of trees surrounding the gap. Disturbances that eliminate oaks in the main canopy without also eliminating shade-tolerant trees in the subcanopy may accelerate succession towards dominance by shade-tolerant species (Abrams and Scott, 1989; Abrams and Downs, 1990; Abrams and Nowacki, 1992). However, moderate overstorey disturbances sometimes favour the oaks. For example, prolonged drought reduced numbers of yellowpoplars, which consequently benefited competing oaks in an Ohio stand (Hilt, 1985). Where oaks and yellow-poplar co-occur, yellow-poplars typically overtop the oaks within 10 years of a stand-­ initiating disturbance (Loftis, 1983, 1990; Beck and Hooper, 1986; Weigel and Johnson, 1999). Consequently, when oaks compete with yellow-poplar, the oaks usually lose. Exceptions occur when a disturbance such as drought or fire selects against yellow-­ poplar and alters the usual course of succession. In the absence of timber harvesting, the average rate of canopy disturbance across a wide range of temperate forest conditions is about 1%/year and ranges from about 0.5 to 2%/year (Runkle, 1985). The average time between successive replacements of trees in the main canopy is therefore about 100 years (range 50–200 years). This is consistent with the observed longevity of oaks. Although individual oaks may live 400 years or longer, attaining such ages is rare. Even in old-growth oak forests, only a small fraction of trees survive more than 200 years. The distribution of disturbances varies in both time and space. When a disturbance occurs, it temporarily reduces the likelihood of a subsequent disturbance of the same type. For example, high winds that remove the most susceptible trees from a stand usually also reduce the likelihood of additional wind damage for several years thereafter. Likewise, periodic fires that eliminate the most firesensitive trees and reduce fuels in a stand usually decrease the likelihood of intense fire damage for several years thereafter. Large or severe disturbances therefore tend to occur at infrequent intervals for any given stand and are followed by years

Development of Natural Stands

with below-average rates of disturbance. In contrast, suppression of disturbances (e.g. through fire suppression) may increase the likelihood of a severe disturbance in the future. Frequent low-intensity disturbances that remove some trees may increase the growth rate and vigour of surviving trees, and also may reduce the number and impact of subsequent disturbances (Waring and Schlesinger, 1985). However, this is not always the case. Certain combinations of disturbances (e.g. windthrow followed by fire) may create conditions (e.g. increased fuels) capable of producing catastrophic events. Just as stand size class and species composition are affected by a disturbance, the reverse is also true. The area, spatial pattern and intensity of a natural disturbance may be modified by the size class and species composition of the vegetation and by the landform where it occurs (Reice, 1994). For example, certain species and certain topographic positions are especially susceptible to wind and fire damage. Temporal and spatial variation in disturbance events also tends to be interrelated (Runkle, 1985). During years when the total disturbed area is relatively large, disturbances are likely to be spatially clustered. This happens either because a few disturbances cover an exceptionally large area or because a large number of smaller disturbances occur within a fixed area, and many disturbed areas are adjacent to one another. As stands change over time, their susceptibility to disturbance also may change (White and Pickett, 1985). Small trees are usually more susceptible to fire damage than large trees. Large trees with welldeveloped crowns are more vulnerable to wind damage than small trees. Changes in species composition that occur during the course of stand development also may alter a stand’s susceptibility to disturbance by agents such as fire, flooding, insects, disease or silvicultural activity.

Development of Even-aged Stands An even-aged stand consists of a cohort of trees comprising a single age class. This usually means that ages of trees in the overstorey differ by no more than 20 years. In silvicultural applications, a more specific definition is sometimes preferred. For example, Smith (1986) defined an even-aged stand as one where the difference in age between the youngest and oldest trees does not exceed 20% of the rotation (see Chapter 8, this volume). Although the biological age of an oak (i.e. its age from germination) is

199

most accurately expressed by the number of annual rings just below its root collar (see Chapter 3), stand age is conventionally defined by the average age of tree boles. In even-aged stands this age usually corresponds to the number of years since the previous stand-initiating disturbance. Even-aged stands progress through a relatively predictable series of developmental stages until the next stand-initiating disturbance or incomplete stand-scale disturbance occurs. Defining these stages is useful in understanding the development of oak forests even though the duration of each stage and the accompanying changes in stand structure, density and species composition may differ from stand to stand. Although various terms have been used to define the stages of stand development (e.g. Bormann and Likens, 1979), we herein follow the terminology of Oliver and Larson (1996) as modified by Oliver

Stand initiation stage

Stem exclusion stage

(1997). Four stages have been defined: (i) the stand initiation stage; (ii) the stem exclusion stage; (iii) the understorey reinitiation stage; and (iv) the complex stage (Fig. 5.2). The complex stage of development also has been called the old-growth stage (Oliver, 1981; Oliver and Larson, 1996). The progression of an even-aged oak stand through the various stages of development is accompanied by changes in stand structure and often by changes in species composition. Changes in stand size structure include the accumulation of biomass (Bormann and Likens, 1979), vertical stratification of tree crowns and correlated changes in diameter distributions. For some regions, these changes are generalized in published stand tables that list by site classes the expected numbers of oaks and other species by tree diameter and age classes in well-stocked stands (e.g. Schnur, 1937;

Understorey reinitiation stage

Complex stage

Fig. 5.2.  Stages of stand development that occur after a major disturbance that destroys all or most of the parent stand. (Adapted from Oliver and Larson, 1996; Oliver, 1997.) Stand initiation stage: immediately after the disturbance, pre-established reproduction grows rapidly and new trees and other plants appear. In oak stands this stage typically lasts 10–20 years. Stem exclusion stage: no new trees appear and many die from crowding. Trees are well stratified into crown classes by the end of this period. This stage usually begins after the stand reaches 10 years of age and concludes before age 70. Understorey reinitiation stage: tree reproduction becomes re-established under the maturing overstorey. Re-establishment of trees in the understorey is facilitated by the death of individual trees in the main canopy. Canopy gaps are of sufficient size and frequency to significantly increase light on the forest floor. This stage typically begins after age 50 and concludes before age 120. Complex stage: natural mortality of large overstorey trees produces irregular canopy gaps and accelerates the recruitment of reproduction and subcanopy trees into the overstorey and main canopy, respectively. This stage marks the transition from an even-aged stand to an uneven-aged stand. Oak forests typically require 100 years or longer to reach the complex stage of development. The stated durations for the four stages of stand development are representative of oak forests of eastern USA and assume that no significant stand-scale disturbances occur. Actual durations of stages of development vary with species composition, site productivity and other factors.

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Chapter 5

Gevorkiantz and Scholz, 1948). Such tables thus represent models of average expected change in stand structure associated with different stages of stand development. Unlike changes in stand size structure, which follow a similar progression as stands age (Fig. 5.2), changes in species composition often vary from stand to stand, even within similar ecological settings. Species composition in even-aged oak stands largely depends on the size and initial species composition of tree reproduction and propagules present at the time of the previous stand-initiating disturbance. The trees and the other vegetation present at that time develop into the new oak stand. Differences among tree species in shade tolerance, longevity and growth potential influence how the species composition of the stand will change over time. Changes in composition are also influenced by disturbances such as extreme weather events, disease, animal browsing and human activity. Often these events are only predictable probabilistically. Consequently, young stands of similar composition and structure may follow different developmental trajectories and thus differ greatly in composition and structure at maturity. Structural and compositional changes in forests are attributes of the broader process of ecological succession. Although ecological succession is generally regarded as the natural change in the composition, structure and function of an ecosystem, successional concepts are also applicable and central to silviculture. In this context, silviculture is about directing ecological succession. Ecological succession is of two types: (i) primary; and (ii) secondary. Primary succession involves long-­ term ecosystem changes, usually beginning with bare ground devoid of vegetation, and often spanning thousands of years. Related changes in vegetation are associated with changes in the parent material, soil and other factors that are interactively modified through time. Secondary succession involves shorterterm changes occurring after a disturbance. Sec­ ondary successions therefore usually begin with vegetation, plant propagules and other organisms already in place. Thus, there is a direct connection between ecological succession and silvicultural practice, which seeks to design and create disturbances that direct secondary succession in specific ways. The study of secondary forest succession has traditionally focused on changes occurring over decades and centuries in the absence of significant exogenous disturbance. However, one objective of

Development of Natural Stands

silviculture is to anticipate and control successional changes in stand structure and composition that result from stand disturbances. In turn, such disturbances result from endogenous events associated with natural stand development (e.g. the death of individual canopy trees), from natural exogenous events (e.g. wildfire or weather) or from human intervention (e.g. silviculture). Silvicultural prescriptions are thus applied at various stages of stand development with the intent of directing the course of change in stand structure and species composition in specific ways. Before prescribing silvicultural treatments, it is therefore important first to understand how even-aged stands progress through the stages of structural development in the absence of major exogenous disturbances. The stand initiation stage The development of an even-aged stand begins with the stand initiation stage. In oak forests, this stage may last up to 20 years. In temperate regions it is characterized by a brushy mass of woody vegetation comprised of thousands of trees and shrubs/ acre often mixed with a luxuriant growth of vines and herbaceous plants (Gingrich, 1971) (Fig. 5.3A). It is also a period of rapid change with intense competition among trees and other plants for growing space. Standing biomass is small relative to later stages of development, but the rate of biomass increase is high. During this stage, the quantity of dead wood (standing or on the ground) is often larger than during other stages of stand development. This is due to the stand initiating disturbance itself, which (except for fire) usually leaves a large residue of tree boles and branches on the forest floor (Bormann and Likens, 1979; Jenkins and Parker, 1997; Spetich et al., 1999). During the stand initiation stage, gaps in the new vegetative cover may persist for a decade or longer as new trees and other vegetation become established. New tree seedlings and herbaceous vegetation initially require little growing space, and numerous small openings in the developing forest vegetation provide ‘safe sites’ for their establishment (Harper, 1977). These are places where seeds find the necessary conditions for germination and growth free from predators, competitors and pathogens. Changes in the number, species and sizes of trees during the stand initiation stage are difficult to predict accurately. This is due to the numerous and essentially random events that influence spatial

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(A)

(B)

(C)

(D)

Fig. 5.3.  The four stages of stand development representative of oak stands in the Ozark Highlands of Missouri: (A) stand initiation stage, (B) stem exclusion stage, (C) understorey reinitiation stage, and (D) complex stage. (Photographs courtesy of USDA Forest Service, North Central Research Station.)

and temporal patterns of seed dispersal, seed germination and seedling survival. Stand development during this period is subject to great natural variation, and predictions of stand development during this stage are often specified qualitatively (Johnson and Deen, 1993) or probabilistically (see Chapter 3,

202

this volume; also see Johnson and Sander, 1987; Loftis, 1990; Dey, 1991; Dey et al., 1996). Although the composition of the future stand is strongly influenced by the species initially present at this stage of development, the relative dominance of the various species usually changes as the

Chapter 5

stand matures. Early theories of forest succession (e.g. Clements, 1916) proposed that each species modifies the site to make it more favourable for the establishment and growth of succeeding species. However, studies of mixed oak–hardwood forests in New England have shown that most of the trees that form the overstorey originated within one or two decades after a major disturbance (Oliver and Stephens, 1977; Oliver, 1978). Oaks that attain membership in the main canopy are usually established before the stand-initiating disturbance occurs (see Chapter 2, this volume). Species that are shade tolerant, long-lived and capable of growing to large size may persist in subordinate canopy positions for decades. They may gradually make their way into the main canopy by opportunistically responding to gap-size disturbances. At stand initiation, the presence of 100 seedlings or seedling sprouts/acre of a late-successional species might seem insignificant. However, if that population persistently captures canopy gaps, over time it may eventually dominate the main canopy. Different species that become established at approximately the same time therefore may exert dominance at different stages of stand development (Egler, 1954; Oliver and Larson, 1996; also see Chapter 3, this volume). The overstorey of a mature, even-aged oak stand is largely comprised of species present at the end of the stand initiation stage. During the two stages of stand development that follow the stand initiation stage, few if any new trees are added to the overstorey. Consequently, the species present in an evenaged oak stand at the end of the stand initiation stage is a good indicator of the diversity of species that will be found in the overstorey of the future stand. However, a species’ relative abundance at this stage is often a poor indicator of its future importance. During the ensuing stem exclusion stage, species composition usually shifts towards the species that are best adapted to the site and able to attain and persist in dominant and codominant crown positions. Dominance by oaks at the end of the stand initiation stage therefore does not, in itself, ensure their continued dominance. The stem exclusion stage In temperate regions, crown closure in oak stands has usually occurred by the beginning of the second decade after a stand-initiating disturbance (e.g. Stoll and Frey, 2016). By that time, trees have stratified

Development of Natural Stands

into well-defined crown classes and natural mortality has changed the initially clumped spatial distribution of trees to a more random distribution (Rogers, 1983). This stage of stand development is termed the stem exclusion stage because few, if any, new stems are added to the population of overstorey trees, and many stems are eliminated via competition (Oliver, 1980; Oliver and Larson, 1996) (Figs 5.2 and 5.3B). Mortality rates are high, especially among trees in intermediate and suppressed crown classes. The combined growth, competition and mortality of trees during this stage produce spatial adjustments in the main canopy that lead to full or nearly full crown closure and a corresponding full utilization of growing space (Gingrich, 1967, 1971). It is usually not until after the stem exclusion stage begins that growth and yield tables or other predictive models are applicable (see Chapter 15, this volume). During this and subsequent stages, patterns of stand development and species differentiation are more predictable than during the stand initiation stage. Dense populations of saplings (trees 1–5 inches dbh) and pole-size trees (5–10 inches dbh) dominate the stem exclusion stage. During this stage, the expanding crowns of dominant and codominant trees quickly fill the space vacated by dead and dying trees of the intermediate and overtopped crown classes. Even if tree reproduction is present in the understorey, it is usually unable to capture the small, transient canopy gaps that occur during this stage. Little light reaches the forest floor and understorey vegetation decreases as the herbaceous and low woody vegetation that flourished during the stand initiation stage die from suppression beneath the rising level of the overstorey. During the stem exclusion stage, oaks can sustain a position of dominance in three ways: (i) through inherently faster growth than competitors; (ii) through an initially superior crown position; or (iii) through persistence. Persistence involves survival and continued growth of a species when the population of competitors fares less well under the same conditions. For example, proportionately more oaks may survive drought than faster-growing and potentially long-lived competitors such as red maple or yellow-poplar (Hilt, 1985). Oaks are relatively persistent following disturbance events such as fire in uplands or flood scouring in bottomlands. In some ecosystems, persistence also may allow oaks to eventually grow taller than subcanopy competitors (e.g. flowering dogwood) that may initially overtop

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oaks. Persistence thus involves negative impacts on two or more populations, but one population emerges the ‘winner’ because it persists and is less negatively affected than the other. However, the persistence advantage of one species over another often changes across environmental gradients such as the moisture gradient. The diameter distribution of an even-aged stand changes continually as the stand develops. For example, in 10-year-old upland oak stands of the eastern USA, diameter distributions form reverse J-shaped curves comprised of thousands of trees/ acre (Fig. 5.4). By the time stands reach a mean diameter of 8 inches dbh and in the absence of disturbance, a bell-shaped diameter distribution comprised of a few hundred trees/acre has formed (Schnur, 1937). On sites of average quality, this occurs at about stand age 85, which is well into the understorey reinitiation stage of development. In the absence of exogenous disturbance, numbers of trees decline with time through the process of selfthinning (see Chapter 6, this volume). The development of a bell-shaped diameter distribution is not a universal characteristic of evenaged oak stands. Because oak stands are usually mixtures of species, forest canopies in later stages 800

10

700 Trees/acre

3000

Trees/acre

600 20

500

2000 1000

400 0

300 30

200 100 0

2

4

1

40 50 60

6

5

9 13 Dbh (in.)

17

80 Stand age (years)

8 10 Dbh (in.)

12

14

16

Fig. 5.4.  Diameter distributions of trees for a time series of even-aged upland oak stands on average sites in eastern USA. Inset: the reverse J-shaped diameter distribution resulting from summing the number of trees in each dbh class across all even-aged diameter distributions (for 10-year age classes from 10 to 80). (Adapted from Schnur, 1937.)

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of development tend to stratify vertically by species. This stratification results from species’ differences in shade tolerance, longevity, growth rate and maximum attainable size (see Chapter 6, this volume). Slower growing, shade-tolerant trees may persist for decades in subordinate crown classes (Oliver, 1980; Hibbs, 1983). Differential rates of establishment and growth among species in even-aged stands may produce reverse J-shaped and other types of diameter distributions often associated with uneven-aged stands. However, the correlation between tree age and diameter is often low – and near zero in truly even-aged stands. Diameter distributions, by themselves, therefore are not reliable indicators of age distributions, especially in stands comprised of several species (Oliver, 1980; Loewenstein, 1996; also see Chapter 9, this volume). The development of an Arkansas sweetgum–red oak stand illustrates the transition from the stand initiation stage to the stem exclusion stage of stand development (Johnson and Krinard, 1983, 1988). This bottomland stand in the Southern Hardwood– Pine Region originated from a timber harvest that left only a few seed trees per acre. Unmerchantable trees were killed and areas influenced by seed trees were excluded from the analysis. Consequently the results essentially describe the development of a stand after clearcutting. Estimated site index for sweetgum was 100 ft at 50 years. Numbers of trees and stand basal area increased for the first 15 years (Fig. 5.5A). By age 18, the declining number of trees and increasing basal area marked the onset of the stem exclusion stage (Fig. 5.5B). The transition from stand initiation stage to stem exclusion stage is also apparent from the simultaneous changes in basal area and numbers of trees (Fig. 5.5C). Between stand ages 3 and 9, numbers of trees rapidly increased as trees filled the growing space vacated by the harvested parent stand. By age 15, the new stand had attained its maximum number of trees. Thereafter, the trajectory reversed direction (i.e. basal area continued to increase but numbers of trees decreased). This change in direction also marked the beginning of the stem exclusion stage and the onset of competition-induced self-thinning (see Chapter 6, this volume). Despite the decreasing numbers of trees, the stand continued to increase in basal area after age 15. Although the basal area of undisturbed stands usually increases until stands are in the complex stage of development, the periodic rate of basal area increment slows with increasing stand age.

Chapter 5

(A) 2500

140 Stand initiation stage

120 Basal area (ft2/acre)

Trees/acre

2000

(B) 160

Stem exclusion stage

Stand initiation stage

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Stem exclusion stage

80 60 40

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(50) (37)

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(29) (23)

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(15)

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(15) Other major American species (18) River hornbeam birch (18) (9) (6)

0 500

(18)

(29)

(6) (3)

(23)

(37)

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0

Sweetgum (37)

Red oaks

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(9)

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60 Basal area (ft2/acre)

160

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40

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Stand age (years) (C)

30

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Trees/acre

Fig. 5.5.  Change in number and basal area of trees ≥ 1 inch dbh in an even-aged sweetgum–red oak stand in south-eastern Arkansas during its first 29 years of development. (A) During the first 15 years, the increasing number of trees ≥ 1 inch dbh is characteristic of the stand initiation stage of development. After age 15, the decreasing number of trees > 1 inch dbh marks the stem exclusion stage of development. (B) Basal area increased through both stages of development. (C) Change over time in basal area and number of trees for all species combined. Plotted lines trace changes from stand age 3 to 50 years (stand ages are shown in parentheses). (D) Change over time in basal area and number of trees for five species groups. All species begin near the origin at stand age 3. The plotted age sequence through 50 years is the same as shown in (C). Red oaks include cherrybark, water and willow oaks. Despite its slow start, by age 50 the red oak group had the largest mean diameter and was increasing in basal area faster than any other species group. (Adapted from Johnson and Krinard, 1988; data for ages 37 and 50 provided by Dr James S. Meadows, USDA Forest Service.)

This slowing is indicated by the smaller annual increments of basal area that occurred after age 18 (Fig. 5.5B and C). It is during the stem exclusion stage that oaks must capture growing space from

Development of Natural Stands

their competitors if they are to dominate the later stages of stand development. When numbers of trees decline, it is usually the smallest trees that die first. The vacated growing space and thus the greatest

205

basal area increment usually accrues to large trees with dominant and codominant crown classes. The time-dependent change in the relation between basal area and numbers of trees also can vary among species in the same stand. For example, sweetgum and river birch followed nearly identical trajectories through stand age 9 (Fig. 5.5D). Because river birch declined in both numbers of trees (after age 9) and in basal area (after age 18), it was nearly excluded from the stand by age 29. Although the trajectory for American hornbeam was similar, it did not decline in numbers of trees until later, which may be related to its greater shade tolerance. Although numbers of red oaks also declined after age 18, their survival rate was greater than that of other species. The combination of rapid growth and low mortality rate thus enabled the oaks to attain a dominant position in the stand even though they were relatively few in number. Nevertheless, after 29 years of stand development, sweetgum and other non-oaks accounted for more basal area and trees per acre than the oaks. The format of panels (C) and (D) in Fig. 5.5 with trees per acre on the horizontal axis and basal area per acre on the vertical axis has proven to be a useful framework for assessing utilization of growing space. In Chapter 6 that graphic format is used with supplementary information to describe stocking charts that are widely used to describe stand composition, structure and relative growing space (see Fig. 6.9, this volume). Although stocking charts are used primarily to evaluate stand relative density at a single point in time, like panels Fig. 5.5(C) and (D) they also can be used to trace stand development through time. Knowing changes in the size distribution of trees is also important for understanding stand development. For example an even-aged sweetgum–red oak stand in a southern bottomland maintained a reverse J-shaped diameter distribution during its first 23 years of development (Fig. 5.6). But over time the shape of the diameter distribution gradually flattened as average tree diameter increased and as numbers of trees in the smaller diameter classes decreased. After the stand entered the stem exclusion stage at approximately 15–18 years in age, the shape of the diameter distribution of the oaks began to depart from the overall diameter distribution for the stand. By stand age 23, the largest oaks in the stand formed a bell-shaped diameter distribution, whereas smaller oaks maintained a reverse J-shaped distribution (Fig. 5.6). Oaks larger than

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6 inches dbh formed a ‘hump’ in the diameter distribution, which in previous years had a reverse J-shape. By age 29, the red oaks thus diverged into two distinct populations: (i) a population of main canopy trees; and (ii) a population of subcanopy trees. The structures of these two subpopulations appear destined to diverge further as the largest oaks continue to increase in dominance and the smaller oaks become increasingly suppressed beneath the rising level of the main canopy. Changes in species composition commonly occur after oak stands enter the stem exclusion stage and trees begin to differentiate into well-defined crown classes. Such changes are reflected in differences in proportions of species across diameter classes. Although after stand age 15 the total number of trees per acre decreases with increasing stand age and mean stand diameter (Fig. 5.5A and C), the number of trees of any given species may increase or decrease as a proportion of all trees as the stand matures. For example, sweetgum and other non-oaks were the only trees in the largest diameter classes (≥ 11 inches dbh) at stand age 18, whereas the largest trees in the red oak group (cherrybark, water and willow oaks) were all less than 7 inches dbh at the same age. However, by stand age 29 red oaks represented half the trees in the largest (14 inches) dbh class. Although total numbers of trees decreased by nearly 50% between stand ages 18 and 29, numbers of oaks decreased by only 13%. Because of the rapid diameter growth of the largest oaks, they were among the largest trees by age 29. Thus, it was not until the stand was more than 10 years into the stem exclusion stage that the oaks emerged as a major component of the main canopy. Although sweetgum still dominated the stand after 29 years, the red oaks had increased in dominance at the expense of other species. Thinning to accelerate volume growth is often most effective when initiated during the stem exclusion stage of stand development (see Chapters 8 and 15, this volume). During the stem exclusion stage the stand is actively self-thinning via competition, and silvicultural thinning accelerates that process. Thinning at this stage of development works with the natural dynamic of the stand to accelerate growth by redirecting light, moisture and nutrients to favoured residual trees. Thinning as early as practical during the stem exclusion stage will usually result in the fastest volume growth. However, for certain mixtures of oaks with other species, the natural stand dynamic during the stem exclusion

Chapter 5

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Fig. 5.6.  Change in diameter distributions in an even-aged sweetgum–red oak stand in south-eastern Arkansas during its first 50 years of development, comparing all trees (left) with only the red oak group (right). Red oaks include cherrybark, water and willow oaks. Through to age 15, the stand was in the stand initiation stage of development and thereafter was in the stem exclusion stage. By age 23, the red oaks formed two distinct populations: (i) large trees (6–10 inches dbh) occupying the main canopy; and (ii) smaller trees occurring in the subcanopy. The number of trees is plotted over the midpoints of 2 inch dbh classes. (Adapted from Johnson and Krinard, 1988; data for ages 37 and 50 provided by Dr James S. Meadows, USDA Forest Service.)

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stage favours oak height growth and survival relative to competitors. In those cases, withholding thinning until late in the stem exclusion stage may be recommended to encourage oak height growth and natural pruning (see Chapter 8). Oak stands on droughty sites or sites affected by recurrent, low-intensity disturbances (e.g. oak savannahs maintained by periodic burning) may not experience a stem exclusion stage. In oak savannahs, periodic burning maintains a relatively open canopy, which in turn maintains relatively high light intensities in the understorey. Although oak and other tree reproduction usually accumulate under these conditions, their recruitment into the overstorey is inhibited by burning unless there is a fire-free period of sufficient duration to permit their recruitment into the overstorey (see Chapter 12, this volume). Under these conditions, recruitment of oaks into the overstorey is limited more by the disturbance regime than by competition or light. The understorey reinitiation stage During the understorey reinitiation stage, tree reproduction becomes re-established beneath the parent stand (Figs 5.2 and 5.3C). In oak forests, this reproduction usually becomes a major component of the new stand that develops after the next stand-initiating disturbance. Many factors influence which species become established in the understorey and consequently which species are likely to dominate after a stand-initiating disturbance. In oak forests, light and soil moisture rank among the most important of these factors (Chapter 3, this volume, Fig. 3.7). Understorey light availability can be quantitatively expressed as a function of overstorey stocking per cent, basal area or density (see Fig. 12.11, this volume). The understorey reinitiation stage of development is when oak stands usually attain economic maturity for sawtimber production. Consequently, it is a critical period from a silvicultural perspective. Compared with the stem exclusion stage, trees in the main canopy are larger and fewer in number during the understorey reinitiation stage. At this stage of stand development, some of the main canopy trees periodically produce large quantities of seed. Large crowns are important determinants of acorn production (Chapter 13, this volume) and thus the establishment of oak seedlings (Chapter 3, this volume, Fig. 3.8). Moreover, when large trees die they create larger canopy openings than those

208

created during earlier stages of stand development. The crowns of large trees expand more slowly than they did during earlier stages of stand development. Consequently, canopy gaps remain open for longer periods, and this often results in light intensities near the forest floor that are sufficient for seedling establishment and growth (see Chapter 3). The consequent spatial heterogeneity of the main canopy creates spatial variation in the amount of light reaching the forest floor. This produces microenvironments favourable for the establishment and growth of oak and other tree reproduction. Established trees in subordinate crown positions also benefit from the increased growing space when large overstorey trees die. In oak forests, the onset of the understorey reinitiation stage typically occurs one or two decades before the end of a silvicultural rotation (i.e. stand age at final harvest), which commonly ranges from 80 to 120 years in the eastern USA. Progression to this stage of development may be accelerated by silvicultural actions or by natural disturbances. The establishment of oak advance reproduction during the understorey reinitiation stage is usually a prerequisite to successful oak regeneration (see Chapter 3). The tree reproduction, shrubs and herbaceous vegetation that develop during this period set the stage for regeneration cuttings. If the stand is not regenerated through timber harvesting or other disturbances, some of the reproduction established during this stage of development may eventually grow into the overstorey as the stand approaches the next stage of development – the complex stage. The successional replacement of oaks by more shade-tolerant species is one of the most pervasive problems associated with oak silviculture in mesic and hydric forests. The understorey reinitiation stage is therefore a critical time to intervene silviculturally if the objective is to maintain or increase the proportion of oak in the future stand (see Chapter 8, this volume). However, not all oak forests are successional to non-oaks. In the Ozark Highlands and similar xeric oak forests in the eastern USA, the successional displacement of oaks is limited by the inability of other hardwoods to persist in the superior crown classes beyond the stand initiation stage. Although non-oaks on these sites may aggressively fill canopy gaps immediately after disturbance, their dominance is usually short lived. Moreover, climate change over the next century is expected to alter (and in many cases increase) the area of forest amenable to regenerating oaks (see Chapter 14).

Chapter 5

15 years after cutting (Fig. 5.7A). By that time, oaks dominate the stands and species are stratified into well-defined crown classes (Fig. 5.7B). This outcome is the result of the collective influence of initial floristics, overstorey inhibition and competitive sorting processes (see Chapter 3, this volume)

In the Ozark Highlands, differences among species in their competitive capacity are reflected in their probabilities of attaining an intermediate-orbetter crown class after clearcutting. For a given initial (pre-harvest) basal diameter, these probabilities are higher for oaks than for other hardwoods (A)

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O = Oaks H = Hickories BG = Blackgum S = Sassafras D = Flowering dogwood Fig. 5.7.  (A) The estimated probability (P) that 15 years after overstorey removal a genet of advance reproduction (i.e. an individual seedling or seedling sprout) will occupy an intermediate-or-greater crown class in upland oak stands in the Ozark Highlands of Missouri. Size of advance reproduction (basal diameter) at the time of clearcutting or other major disturbance is a key determinant of future stand composition. Estimates of P are based on logistic regression equations for average sites. The four species groups shown represent the most frequently occurring hardwood species within this ecological region. Oaks are predominantly black, white, scarlet and post oaks. (B) Vertical stratification of species after 15 years illustrates the probabilities in (A). Oaks are predominant in the upper crown classes while non-oaks are largely relegated to the subcanopy. (Based on author’s unpublished data.)

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209

that control secondary succession in oak-dominated ecosystems such as the Ozark Highlands. Oak forests in other ecoregions may develop differently. An example is provided by a composite analysis of four mixed oak–hardwood stands in Connecticut during the stem exclusion and understorey reinitiation stages (Ward et al., 1999). When first measured, the stands were 25 years old, compositionally similar, and oaks comprised 40% of the basal area. Other major species included red maple, yellow birch, black birch and white ash. The mean site index was 67 ft at age 50. At age 25, the stands were well into the stem exclusion stage of development. During the next 70 years, numbers of trees decreased from 1400 to 600/acre while basal area simultaneously increased from 70 to 120 ft2/ acre (Fig. 5.8A and B). Drought combined with defoliation by gypsy moth and canker worm reduced stand basal area between ages 55 and 65, which in turn accelerated the establishment and ingrowth of reproduction into the overstorey. The total number of overstorey trees increased in the following decade. Defoliation by gypsy moth and elm spanworm occurred episodically after age 65, but those events did not reduce total stand basal area. In this example, both the absolute and the relative basal area of the white oak group decreased markedly between stand ages 55 and 65 after insect defoliation and drought. Although those disturbances were not sufficient to initiate a new stand, they did alter species composition by creating conditions that increased numbers of maples and birches. Decreases in basal area and subsequent increases in numbers of trees between ages 55 and 75 are evident from the trajectory of change for all species combined (Fig. 5.8C). Trajectories for individual species-groups reveal a sharp increase in numbers of birches and maples between ages 65 and 75 following the decrease in oak basal area a decade earlier (Fig. 5.8D). The diameter distribution of all trees combined retained a reverse J-shape for the entire 70-year observation period (Fig. 5.9). Like the bottomland oak stand in Fig. 5.6, the shape of the diameter distribution flattened as the stand aged. The number of stems in the smallest diameter class decreased by 600/acre over 70 years; numbers of trees in the largest diameter classes increased by 10–15/acre over the same period. At stand age 25, the diameter distribution of the oaks was similar to the distribution of all species

210

combined. But between ages 35 and 65, the oak diameter distributions became increasingly bellshaped (Fig. 5.9). During that time, oaks decreased from 320 to 55/acre and remained at approximately 50/acre until age 95. Although the largest oaks increased in size, few smaller oaks grew into the overstorey. The range of oak diameters also increased over time. By age 95, oaks were the largest trees present and occurred in all diameter classes. Numbers of oaks in the smallest diameter classes increased modestly after age 65 (understorey reinitiation stage), but birch, maple and beech were four to 12 times more abundant than the oaks in the small diameter classes. Although numbers of birches and maples per acre continually increased over time as a proportion of the total number of trees, the greatest relative increase in basal area occurred in the red oaks. White oaks declined in both numbers of trees and basal area following the disturbances occurring between ages 55 and 65. The diameter distribution of oaks present at the time of stand establishment thus formed a continually changing population in terms of numbers of trees and basal area. Although the changes in the size distribution of the oak component of the stand are consistent with expectations for unthinned oak forests based on normal stand tables (Fig. 5.4; Schnur, 1937), they differ fundamentally from the overall diameter distribution of the composite stand (Fig. 5.9). This and the preceding example of stand development (Fig. 5.6) illustrate how species composition and stand structure of oak forests change over time and how those changes can differ among oak forests with different species mixtures, site characteristics and disturbance histories. In both examples, oaks were not the predominant species at the start of the stem exclusion stage of stand development. However, by persistently capturing growing space, the largest oaks were able to maintain rapid diameter growth and increase in basal area relative to other species present. Although reverse J-shaped diameter distributions are most often associated with uneven-aged forests, they also occur in certain even-aged forests – including those beyond the stem exclusion stage of development. These stands often originate following the disturbance of stands comprised of both shade-­ tolerant and shade-intolerant species. In subsequent stages of stand development, reverse J-shaped diameter distributions may evolve through the ingrowth

Chapter 5

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Fig. 5.8.  Seventy-year change in number and basal area of trees ≥ 0.5 inches dbh in unthinned oak–mixed hardwood stands in Connecticut. The values shown are averages of four stands of similar composition and structure. (A) Number of trees. Stands were in the stem exclusion stage of development though to age 55. Between ages 65 and 75, the number of trees increased due to recruitment into canopy openings resulting from tree mortality caused by gypsy moth and canker worm defoliation in the previous decade (ages 55–65). (B) Stand basal area. Oaks comprised 37% of stand basal area at stand age 25 and 45% by age 95. The decrease in basal area between ages 55 and 65 was caused by tree mortality. (C) Trajectories over time in the relation between basal area and number of trees for all species combined. Stand age at each point is shown in parentheses. (D) Trajectories for four species groups. Canopy gaps created between ages 55 and 65 accelerated the recruitment of new trees into the overstorey between ages 65 and 75 and marked the beginning of the understorey reinitiation stage of stand development. The number of birches and maples increased after canopy gaps were formed. Basal area of red oaks declined temporarily after age 55, but then increased from age 65 to 95. By age 95, red oaks dominated that stand in terms of basal area, but comprised fewer than 46 trees/acre. White oaks declined in both number of trees and basal area. The red oaks include northern red, black and scarlet oaks; white oaks include white and chestnut oaks; birches include yellow, black and paper birches; maples include red and sugar maples. (Adapted from Ward et al., 1999; additional data courtesy of Jeffrey S. Ward, Connecticut Agricultural Experiment Station.)

Development of Natural Stands

211

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Chapter 5

of shade-tolerant reproduction into the overstorey. Meanwhile, the less-tolerant oaks may develop a bell-shaped diameter distribution embedded within the whole-stand diameter distribution. Although the diameter distribution changes for the entire stand are somewhat predictable and are useful for describing patterns of stand development, they explain little about the interactive relations between oaks and other species. Predicting the outcome of the competitive struggle between oaks and associated species is especially important when the co-occurring species differ greatly in growth rate and silvical characteristics. Designing a silvicultural prescription that assures the desired representation of oaks in the future stand is a common silvicultural problem in tending young, even-aged stands comprised of oaks mixed with other species. An appropriate silvicultural action requires understanding how species interact within shared environments. Large differences in rates of height growth of two desirable, long-lived species growing on the same site can create difficulties in maintaining both species. In young stands, species’ differences in height growth may not always be apparent from the current stand structure because those differences may not yet be expressed. And in older stands, the disturbance history that produced the height differences among species may not be evident. Comparing the site index of one species with another is one way to assess the ecological and silvicultural significance of species’ height growth differences (see Chapter 4, this volume). Differences in site index as small as 5 ft may ultimately result in the suppression of the slower-growing species, assuming both species originate about the same time. Such differences, in turn, may limit silvicultural options. Large growth-rate differences between an oak and a co-occurring species may be silviculturally problematic if the oak is the slower-growing species and both species are to be grown to sawlog size, or if the primary objective is to sustain longterm acorn production. However, site index comparisons, by themselves, do not always predict how co-occurring species will interact. Virtually all oak stands eventually stratify vertically by species. However, even for the same initial species mix, patterns of stratification may differ among sites. The development of various species mixtures provides examples of how vertical stratification can differ among ecosystems. For example, the early pattern of height growth of co-occurring northern red oak, black birch and red maple in

Development of Natural Stands

young even-aged stands in New England was similar. Not until the end of the third decade did northern red oak emerge as the dominant species (Oliver, 1978) (Fig. 5.10A). A different pattern of stratification occurs in the mixed forest type that is transitional between the Allegheny Plateau and the Northern Hardwood Region to the north. There, forests are commonly, but only temporarily, dominated by northern red oak. The oaks and other intolerant and mid-tolerant species retain their bell-shaped distribution as they develop (Stout, 1991). In contrast, co-occurring sugar maple and other tolerant species form reverse J-shaped diameter distributions as their reproduction accumulates in the understorey, which ultimately leads to the successional displacement of the oaks. In mixed cherrybark oak and sweetgum stands in minor river bottoms in Mississippi, it takes about 40 years for the oak to dominate the sweetgum (Clatterbuck and Hodges, 1988). The oak thereafter grows faster when directly competing with sweetgum than when not competing with sweetgum (Fig. 5.10B). This effect may be partially related to differences in the crown shape of the two species. Whereas oak crowns are wider at the top than the bottom (excurrent in shape), sweetgum crowns are the opposite (decurrent in shape). Moreover, physical abrasion during storms may inflict greater damage to terminal buds and small, brittle twigs of sweetgum than to the more supple twigs of cherrybark oak (Lockhart et al., 2006). Because the height growth of the two species continues to diverge with age, there is minimal interference between sweetgum crowns and oak crowns as stands mature. The persistence of the sweetgum also promotes long, clear boles on the oaks. Cherrybark oaks growing in competition with sycamore do not fare as well. The extremely rapid height growth and somewhat excurrent crown of the sycamores quickly suppress the oaks unless 20 ft or more separates the two species (Fig. 5.10C). Similar interspecific relations occur when scarlet and white oaks grow in competition with yellowpoplar (Fig. 5.10D). Although these examples are generally consistent with site index comparisons between species, site index alone does not explain the magnitude of such species-specific interactions. The complex stage In the absence of timber harvesting or other exogenous disturbances that eliminate the overstorey,

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Fig. 5.10.  Height growth and vertical stratification of oaks in mixed-species stands. (A) Even-aged stands of northern red oak, red maple and black birch in New England. Red oaks emerge as dominants 20–30 years after disturbance. (Adapted from Oliver, 1978.) (B) Even-aged stands of cherrybark oak and sweetgum in minor river bottoms in central Mississippi. Close spacing between sweetgum and oak accelerates the height growth of oaks; wide spacing results in slower height growth and shorter clear-bole length of oaks. (Adapted from Clatterbuck and Hodges, 1988.) (C) Plantation-grown cherrybark oak and American sycamore in a minor river bottom in Arkansas. Close spacing between oaks and sycamores results in suppression or reduced height growth of oaks. (Adapted from Oliver et al., 1990.) (D) Representative trees in two natural stands of mixed oak and yellow-poplar of unspecified age structure in the North Carolina Piedmont. In this region, yellow-poplar outgrows oaks where yellow-poplar site index exceeds 65 ft. (Adapted from O’Hara, 1986.)

even-aged stands progress towards the complex stage (Oliver, 1997) (Figs 5.2 and 5.3D). This stage evolves as the consequence of the natural mortality of large overstorey trees that create canopy gaps that occur at irregular times and locations within a stand. Because these gaps are often large, crown expansion of trees adjacent to a gap is often insufficient to fill the gap. This lag in crown closure allows subcanopy trees and established reproduction to fill gaps. As new canopy gaps occur, they are filled by new age classes of trees. As this gap-filling process continues in the absence of a

214

stand-­initiating disturbance, an uneven-aged stand eventually evolves. The complex stage of stand development includes old-growth forests, which was the original term applied to this stage (Oliver and Larson, 1996). However, there is a basis for distinguishing between the two. All old-growth forests are complex, but not all complex forests are old growth. Definitions of old-growth oak forests are usually based on overstorey age, stand disturbance history and structural characteristics such as the presence of old trees, snags and down wood (see Chapter 13, this

Chapter 5

As an even-aged stand advances towards the complex stage of development, it gradually evolves into a multi-aged population as a result of stand maturation and associated gap formation and filling. Canopy gaps become more numerous until the stand forms a mosaic of old trees and gaps filled with younger trees of various ages and species. As trees refill the gaps, stand-wide diameter distributions gradually change. In the absence of a stand-initiating disturbance, the diameter frequency distribution is often irregularly shaped or reverse J-shaped. Regardless of the shape of the diameter distribution, the normal evolution of stand structure from evento uneven-aged eventually produces an uneven-aged collection of dispersed, even-­ aged groups of trees, each occupying a relatively small area. Over time, the various tree age classes become visually indiscernible. The resulting uneven-aged stand structure can be generalized from the diameter distributions of a time series of even-aged stands consisting of 10-year age classes with each class comprising an equal area (Fig. 5.4). If numbers of trees in each age class are summed across all diameter classes, numbers of trees in successively larger diameter classes decrease exponentially (Fig. 5.4 inset). This type of diameter distribution therefore could result from either a collection of even-aged stands or from a single uneven-aged stand. However, in the latter, the evenaged spatial units are so small and intermingled that they are largely indistinguishable. During the complex stage of development, differences in shade tolerances and growth rates among the oaks themselves may produce gradual changes in the proportions of oaks among the crown classes and thus corresponding shifts in species composition. For example, through the stem exclusion stage, the relatively shade-tolerant and slow-growing white oak tends to lag behind co-occurring red oaks in capturing growing space. However, during the understorey reinitiation and complex stages of

Development of Natural Stands

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Development of Uneven-aged Stands

development, the white oaks that persist in the inferior crown classes gradually ascend to canopy dominance as canopy gaps are formed (Shifley et al., 1995; Spetich, 1995). Although old-growth forests comprised of oaks mixed with other species often form reverse J-shaped diameter distributions, the oaks themselves may not conform to that distribution. Structural and compositional changes during the complex stage largely depend on differences in the rates at which cooccurring species of tree reproduction are recruited into the overstorey and how persistent they are there. Where non-oaks fill most of the canopy gaps created during the complex stage, those species will eventually predominate among the smaller dbh classes. Depending on site quality and other factors, they may or may not ascend to dominance, inhibit oak regeneration or successionally displace the oaks. In the absence of disturbances that reinitiate the establishment and development of oak reproduction, a bell-shaped, unimodal diameter distribution of oaks embedded within an overall reverse J-shaped distribution is a harbinger of the impending successional displacement of the oaks (Fig. 5.11).

Trees/acre (all species)

volume; also Meyer, 1986; Parker, 1989; Martin, 1992; Batista and Platt, 1997; Greenberg et al., 1997; Murphy and Nowacki, 1997; Tyrell et al., 1998). The various definitions of old growth all assume that human influences in such forests have been minimal. However, older second-growth forests (managed and unmanaged) may have complex structures without meeting the strict definition of old growth.

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Fig. 5.11.  The diameter distribution of trees in Spitler Woods, an old-growth forest in central Illinois. The bell-shaped (normal) diameter distribution of the oaks (inset) is obscured by the reverse J-shaped distribution of all tree species. This mesic forest is dominated by sugar maple, oaks and American basswood. The oaks (white, northern red, black, chinkapin, bur and shingle oaks) comprise 33% of stand basal area but only 6% of trees. Other important species include elms, hickories, hackberry, Ohio buckeye, black walnut and white ash.

215

In the Ozark Highlands, crown stratification among the oaks persists into the complex stage (Shifley et al., 1995). Large numbers of oaks in the smaller diameter classes, even in old-growth stands, reflect the oak’s

permanence in these forests (Fig. 5.12A and B). In mature, relatively undisturbed second-growth forests, diameter distributions approach a reverse J-shape (Fig. 5.12C and D). For all species combined, the

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20

Oak stocking = 69%

10

18

22

26

200 100 0

2

6 10 14 18 22 26 30 Dbh (in.)

30

(C)

0

Oak stocking = 71%

2

6

10

14

18

22

26

30

(D) 50

50 400

400

30 20

300 200

30

100 0

2

10 0

40

Stocking = 103%

6

10

14

18

22

26

Stocking = 87%

300 200 100 0

20

6 10 14 18 22 26 30 Dbh (in.)

2

6 10 14 18 22 26 30 Dbh (in.)

10

Oak stocking = 64%

2

Trees/acre (all)

Trees/acre (all)

40 Trees/acre (oaks)

Stocking = 101%

300

30

0

Oak stocking = 70%

2

6

Dbh class (in.)

10

14

18

22

26

30

Dbh class (in.) Red oaks

White oaks

Fig. 5.12.  Diameter distributions of oaks and all trees (inset graphs) in four relatively undisturbed oak forests of the Ozark Highlands of Missouri. (A) A 120 acre old-growth stand with trees up to 200–250 years old; human disturbance has been limited to periodic fires occurring more than 40 years ago. (B) A 330 acre old-growth stand with trees up to 140 years old (a few exceed 200 years). There is little evidence of disturbance during the last 40 years. (C) A secondgrowth 4000 acre forest dominated by 70–90-year-old oaks with some shortleaf pine. Stands were disturbed by fire, grazing and logging 45 years prior. (D) A second-growth 3000 acre forest disturbed by fire, grazing and logging 45 years prior. Intensity/recentness of disturbance (increasing from A to D) is reflected in the increasing number of oaks in the smaller diameter classes. (From Shifley et al., 1995.) All four forests form mosaics of xeric to xero-mesic associations dominated by various mixtures of oaks, hickories and shortleaf pine growing on well- to excessively drained cherty soils. Red oaks include black, scarlet, southern red and northern red oaks; white oaks include white, post and chinkapin oaks. Important subordinate species include flowering dogwood, blackgum, sassafras, elms and red maple.

216

Chapter 5

reverse J-shape is even more pronounced because of the high density of permanent subcanopy species such as flowering dogwood and blackgum (Fig. 5.12 inset graphs). Except during the stand initiation stage, the non-oaks are largely relegated to the subcanopy (Dey et al., 1996). The specific characteristics of diameter distributions in this ecosystem and their silvicultural maintenance largely depend on the species composition and density of stands (see Chapter 9, this volume).

Disturbance–Recovery Cycles When forest disturbance is limited to gap-scale events, stand development follows the sequence illustrated in Fig. 5.2. However, stand-initiating events that eliminate all or most of the overstorey can occur during any stage of stand development. These events return stands to the stand initiation stage of development. In contrast, incomplete stand-­ scale disturbances may eliminate only a portion of the overstorey and leave significant numbers of trees standing. Although incomplete stand-­scale disturbances change the stage of stand development, they do not return the stand to the stand initiation stage. Rather, they create a mixed stage of stand development. Mixed-stage stands resulting from natural events often form mosaics of younger trees developing in large canopy openings interspersed with patches of older trees. They often form irregularly spaced tree populations of variable size and age structure. Mixed-stage stands also can result from various types of timber harvesting. Stands in the mixed stage of development are distinguishable from stands in other stages by: (i) the proportion of the stand area that is disturbed, which is greater than gap scale; and (ii) stand density, which is below average maximum density (see Chapter 6, this volume). Relatively young trees may predominate in stands during the mixed stage. Low density, highly disturbed stands of all descriptions therefore fall into the mixed stage of development. Examples of stands in the mixed stage include oak savannahs (see Chapter 12), stands resulting from indiscriminate timber harvests (e.g. ‘high-grading’), and some designed silvicultural practices. The latter include heavily thinned stands, low-density shelterwoods and stands managed by group selection or single tree selection at low stand densities (see Chapters 8 and 9). Oak forests in the mixed stage of development are ubiquitous because the disturbance events that create them are so common.

Development of Natural Stands

The complete spectrum of forest developmental stages thus includes the mixed stage plus the four stages previously defined. Collectively they represent points in a potentially endless series of disturbance–­ recovery cycles initiated by stand-scale and gap-scale disturbances. These cycles follow specific sequences determined by the developmental stage of the stand at the time of disturbance and the type and spatial scale of disturbance (Fig. 5.13). Disturbance and recovery cycles provide a conceptual framework for ecological process and silvicultural practice. Silvi­ culture involves the planned application of controlled disturbances to create and maintain (at least temporarily) specific species compositions, age distributions and size distributions. An ecological understanding of disturbance–recovery cycles is central to achieving silvicultural objectives. However, silvicultural control of stand composition and structure is often complicated by unpredictable natural disturbance events that lie beyond the direct control of the silviculturist. Even when forests are intensively managed, unwelcome disturbances are a part of silvicultural reality. Frequent, stand-initiating disturbances can maintain a stand in the stand initiation stage indefinitely. Such disturbance regimes were common and extensive in many regions of North America before European settlement. For example, in the Forest– Prairie Transition Region of south-western Wisconsin, plant communities described as ‘oak scrub’ failed to develop beyond the sapling stage because of frequent wildfires that recurred for centuries (Marks, 1942; Grimm, 1984; Chumbley et al., 1990). As long as these fires persisted, the stands of oak scrub persisted. The fires arrested succession, and thus maintained stands in the stand initiation stage of development. But by the early 20th century, wildfires were controlled and the oak scrubs quickly developed into closed canopy forests (Marks, 1942; Curtis, 1959). An incomplete stand-scale disturbance will transform most stands into the mixed stage of development. Exceptions are young, even-aged stands that are in the stand initiation stage of development. They remain in the stand initiation stage of development following incomplete stand-scale disturbances because the existing trees and the new trees recruited following the disturbance are all within the same age class. If a stand is already in the mixed stage of development, frequent incomplete standscale disturbances (such as the fires that historically maintained oak savannahs) may keep it in that state indefinitely.

217

Stand initiation stage

Stem exclusion stage

Understorey reinitiation stage

Complex stage

Mixed stage

Type of disturbance Gap-scale disturbance Incomplete stand-scale disturbance Stand-initiating disturbance Fig. 5.13.  Types of forest disturbances and their relation to stages of stand development and disturbance–recovery cycles. Stands in all developmental stages are subject to gap-scale disturbances originating from the natural mortality of individual trees (dotted arrows). As average tree size (and thus gap size) increases with increasing stand age, stands progress towards the uneven-aged state (complex stage) via the gap-wise replacement of main canopy trees by subcanopy trees and reproduction. However, stand-scale disturbances can occur at any stage of stand development. When stand-initiating disturbances eliminate all or most of the overstorey, stands abruptly return to the stand initiation stage (solid arrows). Disturbances that eliminate only a fraction of the overstorey, but leave a significant number of trees standing, are termed incomplete stand-scale disturbances. These disturbances produce a mixed stage of stand development comprised of both new and older trees that develop into multi-tiered stands of irregular age structure (dashed arrows). The mixed stage may advance to the complex stage in the absence of further stand-initiating or incomplete stand-scale disturbances, return to the stand initiation stage after a stand-scale disturbance, or remain in the mixed stage as a result of further incomplete stand-scale disturbances.

Not all stands progress through the stages of development according to the illustrated sequence (Fig. 5.13). For example, oak chaparral and other shrub-like oak communities may not conform to

218

the five-stage model presented. Disturbances are of many types and intensities, and follow many temporal-spatial patterns. Even distinguishing between gap-scale and stand-scale disturbances is artificial

Chapter 5

because real-world disturbance scales are continuous. Disturbances also may affect only some parts of a stand, or two types of disturbances (e.g. windthrow and disease-induced mortality) may affect different parts of a stand simultaneously. Some forests are also more resilient than others (i.e. they have a greater capacity to return to their previous state after disturbance). Consequently, oak forests differ in their disturbance recovery rates and in related changes in stand structure and species composition. The 35-year history of an oak–hickory stand in the Ozark Highlands of Missouri provides an example of disturbance and recovery in that ecoregion. The stand is part of a 160,000-acre forest that has been managed by the single-tree selection system since exploitative timber harvesting reduced average forest-wide stand densities to low levels in the early 1950s (Loewenstein et al., 1995). The recovery of this forest is illustrated by data from four 0.2-acre plots in a single stand that was inventoried every 5 years from 1957 to 1992. During that time, numbers of trees ≥ 5 inches dbh doubled and stand basal area increased from 33 to 73 ft2/ acre (Fig. 5.14A and B). This increase occurred even though 17 ft2 of basal area was harvested between 1972 and 1977. During this 35-year period, oaks maintained a nearly constant proportion (48%) of total basal area. Changes in stand structure and species composition are described by changes in basal area and numbers of trees per acre for all species combined and for individual species or groups (Fig. 5.14C and D). Although the number of trees and basal area were reduced by the 1972–1977 timber harvest, the stand quickly resumed its pre-harvest ­trajectory of increasing basal area and number of trees per acre (Fig. 5.14). However, within the overall pattern of stand development, there was great variation among species in recovery responses. Whereas numbers of trees and basal areas of all species increased between 1957 and 1972, the 1972–1977 timber harvest reduced the number of large black and scarlet oaks proportionately more than other species. Black and scarlet oak basal areas nevertheless remained relatively constant from 1977 to 1992 (Fig. 5.14D). In contrast, the white oaks and shortleaf pine increased in basal area relative to black and scarlet oaks. The continual increase in the basal area of white oak on the study plots paralleled forest-wide changes that

Development of Natural Stands

occurred during the same period (Loewenstein, 1996; Wang, 1997). Patterns of recovery from disturbance were also evident from changes in diameter distributions. Even though the distributions of all species combined retained a reverse J-shape during the 35-year period, the distributions of the oaks did not (Fig. 5.15). From 1957 to 1972, the oak diameter distributions formed two or more peaks. Oaks in the smallest (6 inch) dbh class nevertheless were more numerous than in any other class throughout the observed period. For the oaks, reverse J-shaped distributions became increasingly prominent with time. Over time, the number of white oaks increased in the smaller diameter classes and by 1992 white oak dominated the 6–8 inch dbh classes (Fig. 5.15). This change reflects white oak’s shade tolerance and its capacity in this ecosystem to sustain rates of ingrowth into successively larger diameter classes in numbers sufficient to maintain a reverse J-shaped diameter distribution. The reduction in stand density to 47 ft2 of basal area/acre between 1972 and 1977 was an important factor in sustaining white oak ingrowth and thus in perpetuating an unevenaged stand structure. The less shade-tolerant black and scarlet oaks decreased in abundance in the smaller diameter classes. Stand density, diameter distributions and species composition in this uneven-aged stand thus changed continually during the 35-year period. The forest’s resilience is reflected in its rapid rate of recovery from exploitative timber harvests in the 1950s and a silviculturally designed harvest in the 1970s. Since then, oaks have maintained their dominance, stand basal area has continued to increase and a reverse J-shaped diameter distribution consistent with sustaining an uneven-aged stand structure has developed. Comprehensive analyses of this forest have similarly indicated that continued application of the single-tree or group selection method is likely to sustain the oak’s dominance (Loewenstein, 1996; Wang, 1997). However, successful application of the method in this and ecologically similar forests will require reducing stocking to about 50 ft2 of basal area/acre every 15–20 years in order to sustain adequate rates of oak recruitment into the overstorey (Larsen et al., 1999). In effect, this requires using silvicultural control of stand structure and density to suspend stands in the mixed stage of

219

(A)

(B)

200

90 Basal area (ft2/acre)

180

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100

160 140 120 100

80 70 60 50 40

80 1955

1965

1975

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1995

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2005

1965

1975

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100

Basal area (ft2/acre)

90

(02)

80 (92)

70

(72) (87)

60

(67)

50 40 30

(77)

100

120

140

2005

(02) Shortleaf pine (72)

30 Black and scarlet oaks

20

160

180

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Trees/acre

0

(92)

(57) (72)

10

(02)

(72)

(02) White oak

(57)

(57)

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1995

50 40

Basal area (ft2/acre)

(C)

1985

Year

Hickories

0

20

40

60

80

100

Trees/acre

Fig. 5.14.  Change in number and basal area of trees ≥ 5 inches dbh during a 45-year period in an oak stand managed by the single-tree selection method in the Ozark Highlands of Missouri. The stand is dominated by black, scarlet and white oaks; black oak site index ranges from approximately 55 to 65 ft, an index age of 50 years. A large reduction in stand density by exploitative harvesting before 1957 placed the stand in the mixed stage of development (Fig. 5.13). This sequence of stand development illustrates recovery from that disturbance and subsequent response to a single-tree selection harvest between 1972 and 1977. Number of trees (A) and basal area (B) increased during all intervals except 1972–1977 when the timber harvest removed some trees 15 inches dbh and larger. (C) Trajectories over time in the relation between basal area and number of trees for all species combined. The plotted line traces changes from 1957 to 2002 in 5-year increments (year shown in parentheses). The stand was in the mixed stage of development during the 45-year period, and low stand basal area facilitated recruitment of trees into the overstorey. However, by 2002 the increase in number of trees has nearly ceased. (D) Trajectories for the four species groups. The timber harvest reduced the number and basal areas of black/scarlet oaks and shortleaf pine. Although drought-induced mortality after 1977 further reduced the number and basal area of black and scarlet oaks, by 1992 values were similar to those in 1962. Both the number and the basal area of white oaks (white and post oaks combined) continually increased over time. See also Fig. 5.15. (Data courtesy of Pioneer Forest, Salem, Missouri.)

development. From this it should not be inferred that oak forests are generally amenable to unevenaged silviculture. On the contrary, the intrinsic oak regeneration characteristics of an ecosystem

220

largely determine its capacity to sustain unevenaged oak populations and thus its suitability to uneven-aged management (see Chapters 3 and 9, this volume).

Chapter 5

Trees/acre

Trees/acre

Trees/acre

Trees/acre

Trees/acre

All trees

All oaks 1962

60 40

40

20

20

0

0 1972

60

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0 1982

40

20

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0 1992

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0 2002

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20 6

10 14 18 22 26 Dbh (in.) Black and scarlet oak

2002

60

40 0

1992

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40

60

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60

40

60

1972

60

40

60

1962

60

0

6

10

14

22

Dbh (in.) White and post oak

References Abrams, M.D. and Downs, J.A. (1990) Successional replacement of old-growth white oak by mixed mesophytic hardwoods in southwestern Pennsylvania. Canadian Journal of Forest Research 20, 1864–1870. https://doi.org/10.1139/x90-250 Abrams, M.D. and Nowacki, G.J. (1992) Historical variation in fire, oak recruitment, and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119, 19–28. https://doi. org/­10.2307/2996916 Abrams, M.D. and Scott, M.L. (1989) Disturbancemediated accelerated succession in two Michigan

Development of Natural Stands

18

26

Fig. 5.15.  Forty-year change in diameter distributions in an uneven-aged oak forest in the Ozark Highlands of Missouri managed by the single-tree selection method. A large reduction in stand density by exploitative harvesting prior to 1957 placed the stand in the mixed stage of development. This sequence of stand development illustrates recovery from that disturbance and subsequent response to a single-tree selection harvest between 1972 and 1977. The diameter distributions for all species (left-hand column of graphs) maintained a reverse J-shape until 2002. By 1992, white oaks dominated the 6–8 inch dbh classes. The number of black and scarlet oaks in the smaller diameter classes increased between 1957 and 1972 but thereafter declined. See also Fig. 5.14. (Data courtesy of Pioneer Forest, Salem, Missouri.)

forest types. Forest Science 35, 42–49. https://doi. org/10.1093/forestscience/35.1.42 Batista, W.B. and Platt, W.J. (1997) An old-growth definition for southern mixed hardwood forests. USDA Forest Service General Technical Report SRS-9. USDA Forest Service, Southern Research Station, Asheville, North Carolina. https://doi.org/10.2737/SRS-GTR-9 Beck, D.E. and Hooper, R.M. (1986) Development of a southern Appalachian hardwood stand after clearcutting. Southern Journal of Applied Forestry 10, 168–172. https://doi.org/10.1093/sjaf/10.3.168 Bormann, F.H. and Likens, G.E. (1979) Pattern and Process in a Forested Ecosystem. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4612-6232-9

221

Chumbley, C.A., Baker, R.G. and Bettis, E.A. III (1990) Midwestern holocene paleoenvironments revealed by floodplain deposits in northeastern Iowa. Science 249, 272–274.https://doi.org/10.1126/science.249.4966.272 Clatterbuck, W.K. and Hodges, J.D. (1988) Development of cherrybark oak and sweet gum in mixed, evenaged bottomland stands in central Mississippi, U.S.A. Canadian Journal of Forest Research 18, 12–18. https://doi.org/10.1139/x88-003 Clements, F.E. (1916) Plant succession: an analysis of the development of vegetation. Carnegie Institution of Washington Publication 242. Carnegie Institution of Washington, Washington, DC. https://doi.org/10.5962/ bhl.title.30488 Curtis, J.T. (1959) The Vegetation of Wisconsin. University of Wisconsin Press, Madison, Wisconsin. Dey, D.C. (1991) A comprehensive Ozark regenerator. PhD dissertation, University of Missouri, Columbia, Missouri. Dey, D.C., Ter-Mikaelian, M., Johnson, P.S. and Shifley, S.R. (1996) Users guide to acorn: a comprehensive Ozark regeneration simulator. USDA Forest Service General Technical Report NC-180. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10244 (accessed 1 July 2018). Egler, F.E. (1954) Vegetation science concepts I. Initial floristic composition. A factor in old-field vegetation management. Vegetatio 4, 412–417. https://doi.org/ 10.1007/BF00275587 Gevorkiantz, S.R. and Scholz, H.F. (1948) Timber yields and possible returns from the mixed-oak farmwoods of southwestern Wisconsin. USDA Forest Service Lake States Forest Experiment Station Publication 521. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Gingrich, S.F. (1971) Management of young and intermediate stands of upland hardwoods. USDA Forest Service Research Paper NE-195. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/23703 (accessed 1 July 2018). Greenberg, C.H., McLeod, D.E. and Loftis, D.L. (1997) An old-growth definition for western and mixed mesophytic forests. USDA Forest Service General Technical Report SRS-16. USDA Forest Service, Southern Research Station, Asheville, North Carolina. https://doi.org/10.2737/SRS-GTR-16 Grimm, E.C. (1984) Fire and other factors controlling the Big Woods vegetation of Minnesota in the midnineteenth century. Ecological Monographs 54, 291–311. https://doi.org/10.2307/1942499

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Harper, J.L. (1977) Population Biology of Plants. Academic Press, London. https://doi.org/10.1017/ S0376892900005774 Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of Amercian Foresters, Bethesda, Maryland. Hibbs, D.E. (1982) Gap dynamics in a hemlock–hardwood forest. Canadian Journal of Forest Research 12, 522–527. https://doi.org/10.1139/x82-081 Hibbs, D.E. (1983) Forty years of forest succession in central New England. Ecology 64, 1394–1401. https:// doi.org/10.2307/1937493 Hilt, D.E. (1985) Where has all my yellow-poplar gone? Northern Journal of Applied Forestry 2, 67–69. https://doi.org/10.1093/njaf/2.3.67a Jenkins, M.A. and Parker, G.R. (1997) Changes in down dead wood volume across a chronosequence of silvicultural openings in southern Indiana forests. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 162–169. Available at: https://www.fs.usda.gov/treesearch/pubs/15655 (accessed 1 July 2018). Johnson, P.S. and Sander, I.L. (1987) Quantifying regeneration potentials of Quercus forests in the Missouri Ozarks. USDA Forest Service General Technical Report NC-120, Vol. 1. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 377–385. Available at: https://www. fs.usda.gov/treesearch/pubs/10184 (accessed 1 July 2018). Johnson, R.L. and Deen, R.T. (1993) Prediction of oak regeneration in bottomland forests. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 146–155. https://doi. org/10.2737/SE-GTR-84 Johnson, R.L. and Krinard, R.M. (1983) Regeneration in small and large sawtimber sweetgum–red oak stands following selection and seed tree harvest: 23-year results. Southern Journal of Applied Forestry 7, 176–184. https://doi.org/10.1093/sjaf/7.4.176 Johnson, R.L. and Krinard, R.M. (1988) Growth and development of two sweetgum–red oak stands from origin through 29 years. Southern Journal of Applied Forestry 12, 73–78. https://doi.org/10.1093/sjaf/12.2.73 Larsen, D.R., Loewenstein, E.F. and Johnson, P.S. (1999) Sustaining recruitment of oak reproduction in uneven-aged stands in the Ozark Highlands. USDA Forest Service General Technical Report NC-203. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10267 (accessed 1 July 2018). Lockhart, B.R., Ezell, A.W., Hodges, J.D. and Clatterbuck, W.K. (2006) Using natural stand development patterns in artificial mixtures: a case study with cherrybark oak and sweetgum in east-central Mississippi,

Chapter 5

USA. Forest Ecology and Management 222, 202– 210. https://doi.org/10.1016/j.foreco.2005.09.029 Loewenstein, E.F. (1996) An analysis of the size- and age-­ structure of a managed uneven-aged oak forest. PhD dissertation, University of Missouri, Columbia, Missouri. Loewenstein, E.F., Garrett, H.E., Johnson, P.S. and Dwyer, J.P. (1995) Changes in a Missouri Ozark oak–hickory forest during 40 years of uneven-aged management. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 159–164. Available at: https://www.fs.usda.gov/ treesearch/pubs/12750 (accessed 1 July 2018). Loftis, D.L. (1983) Regenerating red oak on productive sites in the southern Appalachians: a research approach. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 144–150. Available at: https://www.srs.fs.usda. gov/pubs/1767 (accessed 1 July 2018). Loftis, D.L. (1990) Predicting post-harvest performance of advance red oak reproduction in the southern Appalachians. Forest Science 36, 908–916. https:// doi.org/10.1093/forestscience/36.4.908 Marks, J.B. (1942) Land use and plant succession in Coon Valley, Wisconsin. Ecological Monographs 12, 114–133. https://doi.org/10.2307/1943275 Martin, W.H. (1992) Characteristics of old-growth mixed mesophytic forests. Natural Areas Journal 12, 127–135. Meyer, J. (1986) Management of old growth forests in Missouri. Missouri Department of Conservation Habitat Management Series 3. Missouri Department of Conservation, Jefferson City, Missouri. Murphy, P.A. and Nowacki, G.J. (1997) An old-growth definition for xeric pine and pine-oak woodlands. USDA Forest Service General Technical Report SRS7. USDA Forest Service, Southern Research Station, Asheville, North Carolina. https://doi.org/10.2737/ SRS-GTR-7 O’Hara, K.L. (1986) Developmental patterns of residual oaks and oak and yellow-poplar regeneration after release in upland hardwood stands. Southern Journal of Applied Forestry 10, 244–248. https://doi.org/ 10.1093/sjaf/10.4.244 Oliver, C.D. (1978) The development of northern red oak in mixed stands in Central New England. Yale University School Forestry and Environment Science Bulletin 91. Yale University, New Haven, Connecticut. Oliver, C.D. (1980) Even-aged development of mixedspecies stands. Journal of Forestry 78, 201–203. https://doi.org/10.1093/jof/78.4.201 Oliver, C.D. (1981) Forest development in North America following major disturbances. Forest Ecology and Management 3, 153–168. https://doi.org/10.1016/03781127(80)90013-4 Oliver, C.D. (1997) Hardwood forest management in the United States: alternatives for the future. In: Proceedings

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of the 25th Annual Hardwood Symposium. National Hardwood Lumber Association, Memphis, Tennessee, pp. 45–58. Oliver, C.D. and Larson, B.C. (1996) Forest Stand Dynamics. Wiley, New York. Oliver, C.D. and Stephens, E.P. (1977) Reconstruction of a mixed species forest in central New England. Ecology 58, 562–572. https://doi.org/10.2307/1939005 Oliver, C.D., Clatterbuck, W.K. and Burkhardt, E.C. (1990) Spacing and stratification patterns of cherrybark oak and American sycamore in mixed, even-aged stands in the southeastern United States. Forest Ecology and Management 31, 67–79. https://doi.org/10.1016/03781127(90)90112-O Parker, G.R. (1989) Old-growth forests of the central hardwood region. Natural Areas Journal 9, 5–11. Pretzsch, H. (2009) Forest dynamics, growth and yield: from measurement to model. Springer, Berlin. https:// doi.org/10.1007/978-3-540-88307-4 Reice, S.R. (1994) Nonequilibrium determinants of biological community structure. American Scientist 82, 424–435. Rogers, R. (1983) Spatial pattern and growth in a Missouri oak–hickory stand. PhD dissertation, University of Missouri, Columbia, Missouri. Runkle, J.R. (1985) Disturbance regimes in temperate forests. In: Pickett, S.T.A. and White, P.S. (eds) The Ecology of Natural Disturbance and Patch Dynamics. Academic Press, San Diego, California, pp. 17–33. https://doi.org/10.1016/B978-0-12-554520-4.50007-1 Sampson, T.L. (1983) A stocking guide for northern red oak in New England. MSc. thesis, University of New Hampshire, Durham, New Hampshire. Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc. nal.usda.gov/download/CAT86200555/PDF (accessed 1 July 2018). Shifley, S.R., Roovers, L.M. and Brookshire, B.L. (1995) Structural and compositional differences between old-growth and mature second-growth forests in the Missouri Ozarks. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 23–36. Available at: https:// www.fs.usda.gov/treesearch/pubs/4301 (accessed 1 July 2018). Smith, D.M. (1986) The Practice of Silviculture, 8th edn. Wiley, New York. Sousa, W.P. (1984) The role of disturbance in natural communities. Annual Review of Ecology and Systematics 15, 353–391. https://doi.org/10.1146/ annurev.es.15.110184.002033 Spetich, M.A. (1995) Characteristics and spatial pattern of old-growth forests in the Midwest. PhD dissertation, Purdue University, West Lafayette, Indiana.

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Spetich, M.A., Shifley, S.R. and Parker, G.R. (1999) Regional distribution and dynamics of coarse woody debris in Midwestern old-growth forests. Forest Science 45, 302–313. https://doi.org/10.1093/ forestscience/45.2.302 Stoll, J. and Frey, B.R. (2016) Stand development patterns for young planted oak stands on bottomland hardwood restoration sites. USDA Forest Service e-General Technical Report SRS-212. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 369–376. Available at: https://www.fs. usda.gov/treesearch/pubs/50707 (accessed 1 July 2018). Stout, S.L. (1991) Stand density, stand structure, and species composition in transition oak stands of northwestern Pennsylvania. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 194–206. Available at: https://www.fs.usda.gov/treesearch/pubs/13499 (accessed 1 July 2018). Trimble, G.R., Jr and Tryon, E.H. (1966) Crown encroachment into openings cut in Appalachian hardwood stands. Journal of Forestry 64, 104–108. https://doi. org/10.1093/jof/64.2.104 Tyrell, L.E., Nowacki, G.J., Crow, T.R., Buckley, D.S., Nauertz, E.A., Niese, J.N., Rollinger, J.L. and Zasada, J.C. (1998) Information about old growth for selected forest type groups in the eastern United States. USDA Forest Service General Technical Report NC-197. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota.

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Available at: https://www.fs.usda.gov/treesearch/pubs/ 10261 (accessed 1 July 2018). Wang, Z. (1997) Stability and predictability of diameter distributions in a managed uneven-aged oak forest. PhD dissertation, University of Missouri, Columbia, Missouri. Ward, J.S. and Stephens, G.R. (1994) Crown class transition rates of maturing northern red oak (Quercus rubra L.). Forest Science 40, 221–237. https://doi. org/10.1093/forestscience/40.2.221 Ward, J.S., Anagnostakis, S.L. and Ferrandino, F.J. (1999) Stand dynamics in Connecticut hardwood forests, the old series plots (1927–1997). Connecticut Agricultural Experiment Station Bulletin 959. Connecticut Agricultural Experiment Station, New Haven, Connecticut. https:// doi.org/10.5962/bhl.title.51287 Waring, R.H. and Schlesinger, W.H. (1985) Forest Ecosystems Concepts and Management. Academic Press, Orlando, Florida. Weigel, D.R. and Johnson, P.S. (1999) Planting red oak under oak/yellow-poplar shelterwoods: a provisional prescription. USDA Forest Service General Technical Report NC-210. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. https://doi.org/10.2737/NC-GTR-210 White, P.S. and Pickett, S.T.A. (1985) Natural disturbance and patch dynamics: an introduction. In: Pickett, S.T.A. and White, P.S. (eds) The Ecology of Natural Disturbance and Patch Dynamics. Academic Press, San Diego, California, pp. 3–13. https://doi. org/10.1016/B978-0-08-050495-7.50006-5

Chapter 5

6



Self-thinning and Stand Density

Introduction Self-thinning is the natural process whereby numbers of trees per unit area decrease as average tree size increases over time. It is a process intrinsic not only to oak forests but to all forest and plant communities whose composition and structure are influenced by competition for growing space. Whereas self-thinning is a process, the term stand density refers to various expressions of the absolute or relative amounts of an attribute of tree populations (e.g. numbers of trees or stand basal area) per unit land area. The two concepts are closely connected and together rank among the most important in forest ecology and silviculture. The concepts in this chapter are presented as measurements (metrics) of stand structure, growing space and species composition. More comprehensive overviews of these metrics are available elsewhere (e.g. Pretzsch, 2009).

Self-thinning The principle of self-thinning is most easily described by the temporal changes that occur in the numbers of trees in undisturbed even-aged stands. However, self-thinning also occurs in uneven-aged stands. According to this principle, the finite growing space of a stand is occupied by progressively fewer trees as average tree size increases with stand age. Trees at a competitive disadvantage die from crowding and suppression as stands approach the limit of the number of trees of a given average size that can coexist within an area. As stands reach the stem exclusion stage of development (Chapter 5, this volume), tree crowns expand to fill the available growing space. Crown expansion continues until an upper limit of tree crowding is reached. Thereafter, stands follow a relatively predictable course of density-dependent tree mortality as numbers of trees per unit area decrease with increasing average tree size. It is generally assumed that the combined effects of crown expansion and

tree mortality are compensatory so that canopy closure is always maintained except in the presence of ‘irregular’ mortality. Air pollution, high winds, flooding, epidemic insect and disease outbreaks, and other factors may cause the latter. Reineke’s model Reineke’s model for defining average maximum stand density expresses the negative relation between number of trees per unit area and average stand diameter in undisturbed, even-aged stands (Reineke, 1933). Plotting the logarithm of number of trees over the logarithm of mean stand diameter produces a straight line. The relation is given by: log(N) = a′ + b[log(Dq )] [6.1] where N is number of trees per unit area, Dq is the quadratic mean stand diameter (i.e. the dbh of the tree of average basal area), and aʹ and b are constants for a given species or group of species, where the constant b defines the slope of the line. The non-linear analogue of Equation 6.1 is given by: N = a(Dq )b [6.2] The relation has often been used to describe the average maximum limits of stand density and, by extension, to provide a relative measure or index of stand density (Reineke, 1933). Similar models of self-thinning based on the relation between numbers of trees and tree height are used in European forestry. Such models can be empirically derived by regression analysis and other statistical methods (Weller, 1987b) using data from temporary or permanent field plots from undisturbed stands encompassing a wide range of average stand diameters within a given forest type. Data from permanent plots with repeated measurements are preferred because periodic mortality is actually observed, which reduces assumptions about the self-thinning process (Zeide, 1987). Stands selected to define a line

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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or limit of average maximum stand density should be at or near the upper limits of stand density with respect to their average diameter. The resulting line showing number of trees per acre by quadratic mean stand dbh is sometimes interpreted as a self-thinning line, or line of 100% relative density (Fig. 6.1). The line provides a useful definition of the upper limits of stand density because the number of trees per unit area and mean dbh are highly correlated. Reineke (1933) postulated that the coefficient b, which determines the slope of the self-thinning line (Equations 6.1 and 6.2), is static and assumes a value close to −1.605 for all tree species. However, for even-aged, upland oak forests in the eastern USA dominated by white, black, scarlet and chestnut oaks, the estimated slope coefficient was −1.5 based 3000 2000

Trees/acre

1000 600 400 200 100 60

2

3

4 6 8 12 Average dbh (in.)

From Schnur (1937)

16 20

From Gingrich (1971)

Fig. 6.1.  Self-thinning lines (lines of average maximum stand density) for even-aged upland oak forests in the eastern USA based on the Reineke model. Axes are plotted in logarithmic scale. The dotted line is derived from Schnur’s (1937) stand table for mixed upland oak stands widely distributed across the eastern USA. The solid line is derived from Gingrich’s (1971) stand table for mixed upland oak stands in the Central Hardwood Region. The arrow from a hypothetical disturbed stand represented by the dot on the graph illustrates a typical trajectory of convergence with the Gingrich-based self-thinning line. Trajectories of stands below the self-thinning line generally move slightly downwards from left to right. The downward trend results from competition-induced mortality, which occurs even in stands below average maximum density. After convergence with the self-thinning line and in the absence of further disturbance, the trajectory of stand change continues along the self-thinning line. The slope coefficient is −1.50 for the Schnur (1937) self-thinning line and −1.57 for the Gingrich (1971) self-thinning line.

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on temporary plot data from undisturbed stands ranging in site index from 50 to 80 ft (Fig. 6.1). Moreover, data from permanent plots in similar oak stands in the Central Hardwood Region produced a self-thinning line with a slope coefficient of −1.57 (Fig. 6.1). A value of −1.42 was reported for sessile oak stands monitored for more than 60 years in Germany. This value differed from European beech (−1.79) and two species of conifers (−1.66 and −1.59) (Pretzsch and Biber, 2005). The larger (i.e. less negative) self-thinning values suggest that sessile oak may be more self-tolerant and thereby self-thin at a slower rate. Despite the variation among species in the coefficient b, the principle of self-thinning with increasing mean tree size has been firmly established. Over time, stands lying below the self-thinning line will grow and move towards the line. On approaching the self-thinning line, stand development trajectories converge to approximately the line and follow it from upper left (younger stands) to lower right (older stands) (Fig. 6.1). However, densitydependent mortality does not occur among all trees in a stand with equal probability; it is concentrated among the suppressed trees. The overall rate of mortality thus is greatest during the stem exclusion stage of stand development, which is when a large proportion of trees succumb to suppression. Although silvicultural practices and site quality are usually assumed to have no effect on maximum stand density as expressed by Reineke’s model, studies in southern pine indicate those assumptions are not universally true (Weller, 1990; VanderSchaaf, 2004; VanderSchaaf and Burkhart, 2007). The self-­ thinning line also defines the stand density index (see the section on ‘Stand density index diagrams’ later in this chapter). The −3/2 rule Another approach to defining the self-thinning line is based on the relation between average total plant biomass and number of plants per unit area in singlespecies populations undergoing density-dependent mortality (Yoda et  al., 1963). The power function model, similar to Equation 6.2, is used to describe the relation. However, in this case the model expresses the relation between average plant dry weight (biomass), w, and number of plants per unit area (N) such that: w = aN b [6.3] where a and b are coefficients that are usually estimated by regression from experimental data or field

Chapter 6

observations. Alternatively, the relation can be expressed as total plant weight (W) per unit area by: W = a′N b ′ 

[6.4]

where coefficients aʹ and bʹ are related to a and b in Equation 6.3. When the −3/2 power relation is displayed graphically, N is usually shown on the horizontal axis, in contrast to Reineke’s model, where N is shown on the vertical axis. Self-thinning for the −3/2 power relation thus graphically proceeds from lower right (younger stands) to upper left (older stands) along the self-thinning line (Fig. 6.2). Numerous studies have shown that b, the slope coefficient in Equation 6.3, approximates –1.5 (and equivalently, bʹ = –0.5 in Equation 6.4) for many plant species including herbs, shrubs and trees (Yoda et al., 1963; Harper, 1977; Miyanishi et al., 1979; White, 1985; Weller, 1987a). The relation consequently has become known variously as the ‘–3/2 power law of self-thinning’, the ‘self-thinning rule’ and the ‘–3/2 rule’. However, the relation is herein referred to as a rule rather than a law because of its

Mean dry weight of tree bole (kg)

600.0 200.0 100.0 50.0

Self-thinning line

20.0 10.0 5.0 3.0 1.0 0.3 100

200

500

1000 2000 4000 7000

Trees/acre Fig. 6.2.  A self-thinning line (line of average maximum stand density) for normally stocked, even-aged, upland oak forests in the eastern USA based on the relation between average dry weight of tree bole (inside bark) and number of trees per acre. The relation is conceptually similar to Reineke’s model (Fig. 6.1), but differs in format. (Adapted from Schnur, 1937.) The arrow from a hypothetical disturbed stand represented by the dot on the graph illustrates a possible trajectory of convergence with the self-thinning line. As stand biomass increases and the number of trees per acre decreases over time, the stand trajectory moves upwards and to the left along the self-thinning line.

Self-thinning and Stand Density

demonstrated lack of generality (Sprugel, 1984; Weller, 1987a; Zeide, 1987; Norberg, 1988; Lonsdale, 1990) and the absence of a supporting theory (Hutchings, 1983). Discrepancies between observed slope values and –1.5 nevertheless have been interpreted as experimental error because of the coefficient’s presumed generality (Miyanishi et  al., 1979; White, 1981). The rule also is purported to be applicable to species mixtures and single-species stands (Westoby, 1984; White, 1985) and to be independent of environmental factors (Yoda et  al., 1963; White and Harper, 1970), although the latter point has been disputed (Zeide, 1987). The –3/2 rule can be interpreted geometrically. The rule assumes that plant weight, w, is proportional µ to plant volume, v, which in turn is proportional to any linear plant dimension on which volume depends raised to the third power: v µ w = aN b [6.5] If we select crown diameter (Cd) as a linear dimension of interest, then (Cd)3 µ aNb. To conceptualize the relation geometrically, it is convenient to consider (Cd)3 proportional to a cylinder representing the ‘exclusive space’ of a tree (Norberg, 1988). Then (Cd)2, which is proportional to the cylinder’s cross-sectional area, can be used to represent crown area. Further, the cylinder’s height is assumed proportional to crown diameter. This three-dimensional space conceptually envelopes the tree, extending downwards from the top of the crown to its corresponding ‘exclusive ground area’ and into its soil space. The volume of exclusive space also can be viewed as a hexagonal column, which conceptually allows for symmetrical packing of trees without producing crown overlap or unoccupied area as in circular crown areas (Fig. 6.3). The explanatory value of this simplified geometric view and its relation to the −3/2 rule is apparent from the geometric relation between the volume of a cylinder and its diameter (i.e. volume is equal to the cylinder’s squared diameter raised to the 3/2 power). Note that this relation only holds when the cylinder’s height is proportional to its diameter. To satisfy the geometric analogy for the –3/2 power rule, the thinning-rule model must provide a measure of (Cd)2. N, the number of trees per unit area (Equation 6.3), provides such a measure. Because the reciprocal of N represents the area occupied by the average tree, N is related to crown area and thus crown diameter squared, (Cd)2. For

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average plant biomass per unit of ground area. Coefficient a thus increases with increasing density, or packing, of plant parts within a tree’s exclusive space. Coefficient a also has been theorized to be related to the mass of mechanical tissue (bolewood in the case of trees) required to support a unit area of canopy (Givnish, 1986).

D

H

Relation between Reineke’s model and the −3/2 rule The diameter of a tree raised to some power between 2 and 3 equals its volume. For many species, the value of the exponent has been shown to be near 2.5 (Yoda et  al., 1963). Reineke’s model and the −3/2 rule therefore are related by the approximate relation between average tree volume, v, and tree diameter, D, where: v = D2.5 [6.6] From Equation 6.2 it then follows that: D2.5 µ N 2.5 / b [6.7]

Fig. 6.3.  The ‘exclusive space’ of closely packed trees. D is the diameter of the ‘exclusive ground area’ associated with each tree’s crown area and H is tree height. The thinning rule theory implies that the ratio of D:H remains constant throughout stand development. (Redrawn from Norberg, 1988.)

every unit increase in crown area, the exclusive space (volume) of a tree increases by 3/2. So, given a finite amount of growing space, the number of trees (N) in that space must decrease at a rate of –3/2 per unit increase in crown area. To conform to this geometric model, however, a tree must maintain the same heightto-diameter ratio during self-thinning (Fig.  6.3). Accordingly, the various tree structures, including bole and crown, must remain proportionately similar during self-thinning (Yoda et  al., 1963). Such constancy of proportions is known as isometry or geometric similarity (McMahon and Bonner, 1983; Norberg, 1988). Under the −3/2 power rule, coefficient b (Equation 6.3) is assumed to be −3/2 for all species. In contrast, coefficient a varies among species and determines the intercept (or elevation) of the thinning lines. This coefficient has been termed the ‘packing constant’ (Norberg, 1988) because it reflects the proportion of space occupied by plant biomass and the

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When b = −1.605 in Equation 6.1 (Reineke’s postulated constant), the relation becomes: v = N -1.56 [6.8] where the exponent approximates −3/2 (Zeide, 1985). In postulating a constant of −1.605, Reineke was implying, intentionally or not, that the relation between a tree’s growing space and its diameter is not constant (i.e. not isometric). To be consistent with the assumption of constancy of tree proportions (isometry) inherent in the −3/2 power rule, the Reineke model must assume a slope constant of -2. Oak forests and the −3/2 rule Knowledge of the limiting relation between numbers of trees and volume per tree in oak stands is silviculturally useful. The relation can be used as a standard against which other stands can be measured. The −3/2 power rule attempts to describe this limiting relation in general terms for a wide range of plant communities. But to what extent do oak forests conform to the −3/2 power rule? Evaluation of the rule can be divided into two questions. First, does the mathematical form of the model (i.e. the power function) adequately express the relation? Secondly, is the slope coefficient of −3/2 universally applicable? If the answer to the

Chapter 6

(Canadell and Rodà, 1991). Secondly, most oak stands are comprised of a mixture of species. This confounds the effects of competition within and among species, which have funda­mentally different explanations in relation to the thinning rule (Zeide, 1985; Norberg, 1988; Weller, 1989). Variation in wood density among species can also introduce error into coefficient estimates. Thirdly, yield tables available to evaluate self-­thinning relations for oak have been smoothed by hand-fit curves or other unspecified methodologies. As such, they represent models, not data (Weller, 1987a). This obscures variation in the original data and may introduce other possible errors (Lonsdale, 1990). Despite these problems, it is of interest to evaluate the −3/2 rule in relation to existing yield tables. Based on three oak yield tables for the midwestern and eastern USA, the line representing the relation

first question is no, then the second becomes irrelevant. However, rejection of −3/2 as a universal slope coefficient does not, by itself, negate the utility of the model form. Evaluating conformance of oak forests to the −3/2 power rule is complicated by three factors. The first is limited observational data. Oak yield tables are the best comprehensive sources of information, but only a few yield tables report total tree volume. Most yield tables include only merchantable bole volume, which must substitute for total tree volume and biomass. However, bole volume or mass is unlikely to be a constant fraction of total tree mass (Sprugel, 1984). Although information on the allocation of biomass to below- and above-ground portions of oaks is limited, there is evidence that allocation varies greatly with site quality and between trees of coppice and non-coppice origin

Average tree volume (ft3)

(A)

Average tree volume (ft3)

(C)

(B)

100.0

10.0 b = –3/2

b = –3/2

1.0

0.1 100

b = –1.68

b = –1.83

1,000

10,000 100

1,000

10,000

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0.1 100

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10,000

Fig. 6.4.  Self-thinning lines for oak stands derived from published yield tables compared with the −3/2 thinning rule line. (A) From yield tables for even-aged, upland oak stands in the central and eastern states. (From Schnur, 1937.) (B) From yield tables for mixed hardwood stands dominated by northern red oak in south-western Wisconsin. (From Gevorkiantz and Scholz, 1948.) (C) From yield tables for mixed oak stands in Connecticut. (From Frothingham, 1912.) Heavy lines are fit by linear regression to table values (shown by dots); b is the slope coefficient. Each regression fit is based on the model: log(v) = a + log[b(N)] where v is the volume of the average tree, N is stand density, and log is the logarithm to the base 10. Regression estimates are averaged across the site classes given in each yield table.

Self-thinning and Stand Density

229

230

4000 Stand volume (ft3/acre)

between mean tree volume and number of trees produced slope coefficients that ranged from −1.7 to −2.2 (Fig. 6.4). Those estimates were based on substituting bole volume for w in Equation 6.3. The yield table values fit the power function with negligible error (Fig. 6.4). The tenuous conclusions from the above empirical evidence indicate that self-thinning lines in oak forests: (i) show varying proximity to the theoretical −3/2 slope; and (ii) are nominally, if not statistically, more negative than −3/2. Other investigators have concluded that self-thinning lines based on yield tables are likely to have slopes steeper than −3/2 because only portions of trees (boles) are represented, and because they include only trees above some minimum size (Harper, 1977; Lonsdale, 1990). The latter factor may explain, in part, why the yield tables for the Connecticut oak stands (Fig. 6.4C), limited to stems 2 inches dbh and larger, produce a steeper thinning line than either the upland oak or the Wisconsin yield tables, which included stems 0.6 inch dbh and larger (Fig. 6.4A and B). More recent data from permanent plots in unthinned upland oak stands representing three site classes in the oak–hickory region produced selfthinning lines quite different from those derived from earlier yield tables (Gingrich, 1971). The newer data suggest that each site class produces a separate concave-downward curve when both axes are transformed to logarithmic scale (Fig. 6.5). This pattern is consistent with those derived from Douglas-fir yield tables based on long-term observations from permanent sample plots (Curtis, 1982; Zeide, 1987). However, it is possible that at least part of the concave pattern could be caused by incomplete utilization of growing space in younger oak stands (e.g. stands with less than 500 ft3/acre of volume and more than 1200 trees/acre in Fig. 6.5). Yield tables for English oak stands in England also show that the self-thinning line plotted on loglog scale does not form a straight line. The slope coefficient for fully stocked stands up to 100 years old is −1.48, which approximates to the hypothetical value of −3/2 (White and Harper, 1970). However, in stands between 100 and 150 years old, the coefficient shifts to −1. This shift implies that bole volume per unit of ground area remains constant for English oak with continued tree growth and selfthinning (Harper, 1977; Norberg, 1988). Accordingly, the volume of bolewood lost to mortality would be compensated by the gap filling of survivors. A slope of −1 also could indicate stagnating height growth

2000 1000 500 200 100 60 100

200 300

500 1000 1500 Trees/acre

Site index 55 ft Site index 75 ft

3000

Site index 65 ft –3/2 rule

Fig. 6.5.  Stand volume in relation to stand density for three site index classes in unthinned (normal) stands in upland hardwoods dominated by mixed oaks in the Central Hardwood Region. (Adapted from Gingrich, 1971: Table 1.) The stands represent mixtures of black, white, scarlet and chestnut oaks. The self-thinning line (dotted) is assumed by the self-thinning rule model (i.e. a slope coefficient of −0.5 (bʹ in Equation 6.4)). Other lines represent table values based on models derived from permanent plots.

in old stands, or root competition and soil physical constraints on root expansion (Norberg, 1988). The emerging evidence from permanent plot data collectively indicates that the thinning line as expressed by the −3/2 thinning rule does not form a straight line over the life of a stand. If we accept that evidence, the power function equation generally, and thus the −3/2 slope coefficient specifically, cannot realistically describe self-thinning in oak stands. Moreover, there is evidence that the relation is not independent of site effects (Zeide, 1987). Conformity of tree growth to the slope coefficient of −3/2 requires that trees maintain geometric similarity and constant proportions, or isometry, among their various components as they grow. Alternatively elastic similarity occurs when tree components proportionately change with increasing tree size (allometry) (McMahon and Bonner, 1983). Constant ratios among tree dimensions such as crown diameter and bole diameter in relation to changing overall tree size thus represent geometric similarity for those dimensions. For a species or species group to intrinsically conform to the −3/2

Chapter 6

rule, ratios among tree components must remain constant (isometric) as tree size increases. A test of isometry is provided by the ratio of crown diameter to dbh in relation to changing tree size. These relations are defined by tree-area equations for oaks in stands at average maximum density. The equations indicate that the crown diameter:dbh ratio for forest-grown trees declines from about 30:1 for trees of small diameter to about 17:1 for trees of large diameter (Fig. 6.6d and e). For open-grown trees, ratios of crown diameter to dbh vary even more (Fig. 6.6a–c). In either case, small oaks have proportionately more crown area than large oaks. Such proportionate changes in tree dimensions are not consistent with geometric similarity and hence the −3/2 rule. In fact, bole diameters must increase at a proportionately faster rate than crown area (and correlatively crown mass) to prevent trees from collapsing under their own weight (McMahon, 1973). True isometry in oaks or any other tree species therefore is unlikely. Moreover, predictable changes in crown diameter:dbh ratios, by themselves, provide an alternative basis for defining a self-thinning line and corresponding measures of relative density, as discussed later in this chapter. Self-thinning consequently appears to be heavily influenced by tree geometry, which is continually changing to meet requirements for structural resistance to bole breakage as crown mass increases (McMahon, 1973). Moreover, trees grow with great

Crown diameter:dbh ratio

45

a

40

b

35

c

30

d e

Stand Density and Stocking

25

Terminology

20 15

physical plasticity to take advantage of their changing competition environment (Sorrensen-Cothern et al., 1993). For example, one side of a tree crown may expand into the gap created by the death or removal of one of its neighbours, resulting in an expanded but asymmetrical crown. Trees that survive self-thinning acquire new resources (space, light, soil moisture and nutrients) as a consequence of spatial adjustments resulting from the death of neighbouring trees and differential growth rates among competing survivors. Associated with these newly acquired resources are changes in the allocation of growth to the various parts of trees, which thereby influence their proportions, shape and competitive relations with neighbours (Sorrensen-Cothern et al., 1993). The capacity of oaks to fill irregular canopy spaces, and thus their conformity to allometric growth, may be further reinforced by their upward spreading (decurrent) crowns and weak apical control of lateral branching. Despite apparent limitations of the −3/2 selfthinning rule to describe the underlying geometric relations for trees in even-aged oak stands, the general pattern of rapidly decreasing numbers of trees with increasing mean size is well established. Self-thinning formulae such as the −3/2 rule and Reineke’s model describe stand-level changes in the number of trees with increasing tree size. These formulae are useful expressions of a relatively predictable process that has practical silvicultural value for defining limits of stand density in relation to average tree size. In turn, those limits can be used as a standard or index for expressing the relative density of any other stand. This leads to the subject of stand density and how it can be measured and expressed in oak forests.

0

4

8

12 16 Dbh (in.)

20

24

Fig. 6.6.  Crown diameter:dbh ratios in relation to dbh. (a) Open-grown pin oak. (From Krajicek, 1967.) (b) Open-­grown upland oaks and hickories. (From Krajicek et al., 1961.) (c) Open-grown bur oak. (From Ek, 1974.) (d) Northern red oak in stands at average maximum density. (From McGill et al., 1991.) (e) Upland oaks and hickories in stands at average maximum density. (From Gingrich, 1967.)

Self-thinning and Stand Density

Silviculturists are often interested in three related measures of stand density: (i) absolute density; (ii) relative density; and (iii) stocking. These measures can be used to describe a stand relative to some standard of comparison or to some condition that meets a silvicultural objective. Silvicultural decisions are often based on such measures of stand density, and the desired condition of the stand after treatment is usually described by these measures. Although the terms absolute density, relative density and stocking have not always been used consistently, some general conventions and definitions have been established

231

(Walker, 1956; Bickford et al., 1957; Gingrich, 1964; Ernst and Knapp, 1985; Kershaw et al., 2016). Absolute density (or simply density in common usage) is a quantitative, objective measure of one or more physical characteristics of a forest stand expressed per unit area. Measures of absolute density are expressed quantitatively as a tree count, area, volume or mass. Ecologists usually use the term density to refer exclusively to the number of individuals per unit area. In forestry, however, the term can refer to any of several measures of site occupancy, including number of trees, basal area or volume per unit area. Measures of density are usually restricted to trees larger than some minimum size, usually expressed as a minimum dbh. Specifying this minimum size is important because absolute density usually differs with differences in the minimum measured tree size. Measures of relative density provide additional information by comparing an absolute density to a reference value. An example of a measure of relative density is the ratio of the number of trees of a given species per acre to the total number of trees per acre. Expressed as a percentage, this value has long been used by ecologists to define the relative density of a species within a specified area. Forest regeneration and growth can be greatly affected by relative stand density. Consequently, silviculturists have developed various methods of expressing relative density. Virtually all silvicultural definitions of relative density involve ratios. For example, Reineke’s (1933) stand density index provides a reference line describing the maximum number of trees per acre for stands of a given quadratic mean stand dbh (Fig. 6.1). The maximum number of trees decreases rapidly as the mean stand diameter increases. For any given stand, the observed number of trees and quadratic mean dbh can be used to compute the ratio (or percentage) of the maximum (reference) number of trees indicated by the stand density index (Reineke, 1933; Schnur, 1937). In their application to oak forests in North America, most measures of relative density are designed to compare one or more absolute measures of stand density to a standard. The standard is often based on an observed maximum absolute density for undisturbed natural stands at a comparable stage of development. But it also may be based on other limits or reference conditions. For example, crown competition factor (CCF) estimates stand density relative to the cumulative tree crown area per acre required to fully utilize available growing space (Krajicek et al., 1961). Other common measures of

232

relative oak stand density are based on tree-area ratios or stocking per cent (Chisman and Schumacher, 1940; Gingrich, 1967; Ernst and Knapp, 1985). The method of French ‘normes’ compares the observed number of trees and the mean height of dominant trees to both the maximum density and the minimum number of trees necessary to maintain dbh growth below 2 mm (0.08 inch)/year, which by European standards is considered most desirable for veneer production (Oswald, 1982). Other measures of relative density that generally have not been applied to oak forests, but could be, include: (i) Curtis’s (1982) relative density index (references observed basal area per acre to that of an undisturbed stand with the same quadratic mean diameter); (ii) Wilson’s (1946) relative spacing index (references the observed number of trees per acre to the number of trees in an undisturbed stand having the same dominant height); and (iii) Drew and Flewelling’s (1977) relative density index (references number of trees per unit area to the volume of the average tree). The latter method is analogous to the graphical format for expressing the −3/2 power rule discussed earlier. Comprehensive reviews of measures of relative density include those by Curtis (1970) and Stout and Larson (1988). Stocking is a subjective term describing the observed level of stand density with respect to a specific silvicultural goal (Bickford et al., 1957; Gingrich, 1964). The terms overstocked, understocked and fully stocked describe stocking adequacy relative to that silvicultural goal. Accordingly, a stand may be overstocked (too dense) for one silvicultural objective and fully (i.e. appropriately) stocked for another, or may be overstocked at one age and understocked at another. In contrast to the subjective term stocking, the term stocking per cent is a measure of relative density specifically associated with the Gingrich-style stocking diagram. This diagram combines measures of absolute and relative density into a single graphical format (Gingrich, 1967). Stocking per cent is a widely used measure of relative stand density in North American oak silviculture. It is based on the relation between tree size and associated growing space requirements discussed later in this chapter. The word stocking is often used incorrectly to refer to stocking per cent (a measure of relative density). This sometimes creates confusion because, as discussed later, full stocking is synonymous with complete utilization of growing space, which covers stocking percentages ranging from about 60% to 100% on the Gingrich stocking diagram.

Chapter 6

Normal stocking is a term used to describe undisturbed even-aged stands that are at or near maximum density for their age. Normally stocked stands are characterized by a lack of gaps in the forest canopy and a relatively uniform spacing between stems. Basal area and cubic foot volume are at or near their maximum for a given stand age and site quality. Normally stocked stands (sometimes simply called normal stands) usually are identified subjectively based on these criteria. Observations of the number, basal area and volume of trees per acre in normally stocked stands across a wide range of stand age and site quality classes have been used to develop normal yield tables. These tables specify the expected maximum basal area and maximum cubic foot volume for unmanaged stands of a given age and site class. In addition to their application to yield estimation, the tabulated values can be used as reference conditions to estimate the relative density of other stands. Maximum and minimum growing space There are limits to the amount of growing space a tree of a given bole diameter can occupy. Although this may seem self-evident, the concept is central to quantifying stand density and stocking per cent in oak stands. The actual amount of space that a tree occupies is difficult to measure because it includes crowns and roots that overlap in three dimensions with other trees. Fortunately, for many silvicultural purposes, a tree’s growing space can be adequately estimated as a circular area, or tree area, representing the crown. In this context, tree area is interpreted geometrically as a tree’s area of influence or potential influence concentric to the tree bole; it is also highly correlated with dbh. Estimates of the maximum area that a tree of a given dbh can occupy are usually developed from crown and dbh measurements of open-grown trees. In contrast, estimates of the minimum area that a tree requires are usually developed from measurements of tree diameters in normally stocked stands. Trees that are open-grown throughout their lives develop the largest crowns possible for their dbh and species. Consequently, open-grown trees have often been used to estimate the maximum area a tree of given species and dbh can occupy. There is a high correlation between bole diameter and crown area of open-grown trees. This relation has led to the development of equations for estimating the crown areas of open-grown trees from dbh for various oaks and associated species in several regions in the eastern

Self-thinning and Stand Density

USA. The results have shown that the relation between maximum crown width and bole diameter is often linear or nearly linear (Krajicek et al., 1961; Krajicek, 1967; Ek, 1974). An example is the maximum crown width equation applicable to oaks and hickories in the Central Hardwood Region, which is given by: CWmax = 3.12 + 1.829D [6.9] where CWmax is the estimated crown width (ft) of an open-grown upland oak or hickory, and D is tree dbh (inches) (Krajicek et al., 1961). Assuming tree crowns are circular, squaring both sides of Equation 6.9 and multiplying by π/4 defines maximum crown area (CA) in relation to dbh so that: CAmax = 7.645 + 8.964D + 2.627 D2 [6.10] CAmax therefore is the approximate circular crown area (ft2 in vertical projection) of an open-grown upland oak or hickory. Maximum crown width equations also have been derived for other species and regions (Table 6.1). An exponent in the diameter term of some equations indicates non-linearity in the relation. As in the derivation of Equation 6.10, equations in Table 6.1 can be similarly expressed as crown area. Graphical presentation of equations facilitates comparisons among species. For example, open-grown black walnut trees have larger crowns than oaks and hickories for a given diameter, whereas shortleaf pines have smaller crowns. The maximum crown width of sugar maple may be larger or smaller than that of oaks and hickories, depending on dbh (Fig. 6.7). Assuming that maximum crown width equations adequately express the maximum amount of aboveground growing space that a tree of a given diameter can occupy, we can estimate the fewest trees of a given dbh required to completely occupy an acre (i.e. (43,560 ft2)/CAmax). Alternatively, the maximum tree area for all the trees on any acre can be calculated by summing their individual maximum crown areas (estimated from dbh using Equation 6.10 and Table 6.1). When the sum of the maximum crown areas equals the area of an acre (43,560 ft2), the acre (or stand) is said to have a maximum tree-area ratio (TARmax) of 100%. This represents the condition where the crowns of the trees that are present would fully occupy the horizontal growing space if the crowns were maximally extended. Maximum tree-area ratio (TARmax) therefore is a relative measure of stand density that uses maximum crown width equations to estimate the percentage of an area (e.g. an acre) that could be utilized by the trees that are present if their

233

Table 6.1.  Equations for estimating open-grown crown widths from dbh for oaks and some associated s­ pecies. Species (location)

Maximum crown widtha

Source

American elm (Wisconsin) American basswood (Wisconsin) Black cherry (Wisconsin) Black oak (Wisconsin) Black walnut (unspecified) Black walnut (Wisconsin) Bur oak (Wisconsin) Green ash (Wisconsin) Jack pine (Quebec) Loblolly pine (unspecified) Northern red oak (Wisconsin) Oaks and hickories (Iowa) Pin oak (unspecified) Red maple (Wisconsin) Shagbark hickory (Wisconsin) Shortleaf pine (Missouri) Sugar maple (Wisconsin) Sugar maple (eastern USA) Sweetgum (unspecified) White oak (Wisconsin)

2.829 + 3.456D0.8575 0.135 + 3.703D0.7307 0.621 + 7.059D0.5441 4.504 + 2.417D 4.873 + 1.993D 4.901 + 2.480D 0.942 + 3.539D0.7952 4.755D0.7381 1.736 + 2.036D 4.78 + 1.56D 2.850 + 3.782D0.7968 3.12 + 1.829D 9.06 + 1.525D 4.776D0.7656 2.360+ 3.548D0.7986 2.852 + 1.529D 0.868 + 4.150D0.7514 12.08 + 1.32D 2.65 + 1.975D 3.689 + 1.838D

Ek (1974) Ek (1974) Ek (1974) Ek (1974) Krajicek (1967) Ek (1974) Ek (1974) Ek (1974) Vezina (1963) Roberts and Ross (1965) Ek (1974) Krajicek et al. (1961) Krajicek (1967) Ek (1974) Ek (1974) Rogers (1983) Ek (1974) Smith and Gibbs (1970) Krajicek (1967) Ek (1974)

a Crown width in feet given tree dbh (D) in inches; corresponds to CWmax in text. Assuming tree crowns are circular in cross section, maximum crown area in ft2 is equal to (CWmax)2∙π/4. For multi-species stands the equations can be applied to individual trees and summed for total crown area per acre.

20

reducing TARmax below 100% by thinning will, at least temporarily, result in unutilized growing space. Calculating TARmax can be simplified by dividing equations for open-grown crown areas (e.g. Equation 6.10 or Table 6.1) by 435.6 ft2, the area comprising 1% of an acre. For Equation 6.10, TARmax for oaks and hickories is given by:

10

TARmax = 0.0176 + 0.0206D + 0.00603D2 [6.11]

Maximum crown width (ft)

60 50 40 30

0 0

4

8

12 16 20 24 28 Dbh (in.)

Oak and hickory Sugar maple

Shortleaf pine Black walnut

Fig. 6.7.  Estimated open-grown crown widths of oaks and hickories and three associated species in relation to bole diameter (dbh) (see also Table 6.1). (Adapted from Ek, 1974 (sugar maple); Krajicek, 1967 (black walnut); Krajicek et al., 1961 (oak and hickory); Rogers, 1983 (shortleaf pine).)

crowns were extended as far as possible given their species and dbh. When TARmax is less than 100%, the trees present would not utilize the available growing space even if their crowns were fully extended. Consequently,

234

where TARmax is the maximum percentage of an acre that a tree of a given dbh (D) can occupy. The sum of TARmax for all the trees on an acre is sometimes referred to as crown competition factor (CCF) (Krajicek et  al., 1961) and is calculated by summing TARmax for individual trees as follows: CCF = ∑(0.0176 + 0.0206Di + 0.00603Di 2 ) [6.12] = 0.0176N + 0.0206 å Di + 0.00603 å Di 2  [6.13] where summations (∑) are over all trees per acre, Di is the dbh of tree i and N is the number of trees per acre. Note that ∑Di2 is equal to the stand basal area in square feet per acre divided by π/576.1 A CCF of 100 (or equivalently, ∑TARmax = 100%) therefore is usually interpreted as the approximate lowest density at which a stand fully utilizes above-ground

Chapter 6

TARmin = c0 + c1D + c2D2 [6.14] where TARmin is the estimated minimum percentage of an acre required by a tree of a given dbh (D) in a normally stocked forest. Unlike the maximum treearea coefficients, the coefficients for the minimum tree-area equation (c0, c1 and c2) are not derived from measurements of crown diameters for individual trees. Instead, they are estimated by regression by assuming the sum of the tree areas for all trees on an acre of undisturbed, normally stocked forest is equal to 100% of an acre or 43,560 ft2 (Chisman and Schumacher, 1940; Gingrich, 1967). Minimum tree area then can be expressed directly as a percentage of an acre. This expression has been termed stocking per cent (S%) (Gingrich, 1967) and is given by: S% = ∑(b0 + b1Di + b2Di 2 ) [6.15]

When stocking percentage equations are ex­pressed on a per acre basis (e.g. Equation 6.15 or 6.16), equations for minimum tree area in square feet can be derived by multiplying each term in the equation by 435.6 (the number of square feet in 1% of an acre). Although stocking percentage is usually the relative density measure of choice, rescaling to square feet facilitates comparing the estimated tree areas representing maximum and minimum growing space (Fig. 6.8). Other factors being equal, the closer a tree’s crown is to its maximum size for its dbh, the faster the tree’s diameter and gross volume growth. The minimum tree-area ratio is reportedly independent of stand age and site quality (Chisman and Schumacher, 1940; Gingrich, 1967), and can be applied to mixed as well as to pure stands. The methodology for deriving minimum tree-area equations is described in more detail by others (Chisman and Schumacher, 1940; Gingrich, 1967; Roach, 1977; Ernst and Knapp, 1985; Stout and Nyland, 1986). A potential deficiency of all equations for estimating minimum tree-area ratios is the necessarily subjective process of selecting stands that are considered to be at maximum density (i.e. normally stocked) and used to calibrate equations estimating minimum tree-area ratios. Oak and associated forest types are seldom comprised of a single species. In applying stocking equations, it is therefore important to recognize differences in tree-area ratios among species. In developing stocking

3000 2500 Crown area (ft2)

growing space. Stands with CCF below 100 are certain to have canopy gaps. CCF values near 200 have been observed for undisturbed oak–hickory stands (Krajicek et al., 1961). Just as trees have a maximum area they can occupy, they also have a minimum tree area that is necessary for good physiological function and survival. However, minimum tree area is derived quite differently from its maximum tree-area counterpart. Unlike maximum tree area, which can be estimated from open-grown trees, minimum tree area is difficult to observe directly for individual trees. Minimum tree area requirements nevertheless can be estimated from data obtained from undisturbed, normally stocked, even-aged stands. Estimation is based on deriving minimum tree-area ratio (TARmin) equations that express tree growing space requirements for normally stocked stands (Chisman and Schumacher, 1940). Like TARmax, TARmin expresses tree area as a percentage of an acre. Just as maximum tree area is a linear function of diameter and diameter squared (Equations 6.9, 6.10 and 6.11), a tree’s minimum tree area can be similarly expressed by:

2000 1500 1000

[6.16]

500

where summations (∑) are over all trees per acre, S% is the percentage of an acre filled by the minimum tree areas of all trees on that acre, Di is the dbh of tree i, N is the number of trees per acre, and b0, b1 and b2 are coefficients (usually estimated by regression). The stocking percentage represented by a single tree of given dbh can be derived by solving Equation 6.16 with N = 1 and D = dbh.

0

= b0 N + b1 å Di + b2 å Di 2 

Self-thinning and Stand Density

Maximum crown area

Minimum crown area 2

6

10 14 18 22 26 30 Dbh (in.)

Fig. 6.8.  Estimated maximum and minimum tree areas in relation to bole diameter (dbh) for upland oaks and hickories in the Central Hardwood Region. The area between the two lines represents the approximate biological range of tree areas. (From Gingrich, 1967.)

235

equations, coefficients for individual species can be derived by incorporating species-specific terms into stocking equations so that Equation 6.15 expands to the more general form: ns nt nt æ ö S% = å ç b0 j N j + b1 j åDij + b2 j åDij2 ÷  j =1 è i =1 i =1 ø

[6.17]

where the outer summation (∑) is over all species (j = 1 to ns); the inner summations are over all trees (i = 1 to nt) of species j; b0j, b1j and b2j are coefficients specific to species j; Nj is the number of trees of species j; and Dij is the diameter of tree i of species j (Roach, 1977). Stated more simply: S% computes the stocking per cent for each tree in a stand inventory based on the tree’s dbh and the appropriate species-specific equation from Table 6.2, weights the individual-tree stocking estimates to a per acre basis, and then sums stocking values for all trees to yield a tree-based estimate of stocking per acre. When practical, this is the most accurate and preferred method of deriving stocking per cent. In forests such as the oak–hickory type of the Central Hardwood Region, the minimum tree-area ratios of the predominant species do not differ significantly (Krajicek et al., 1961; Gingrich, 1967). A single set of coefficients therefore can be used to represent the major species of the forest type. In other forest types, tree-area ratios differ appreciably among species (Roach, 1977; Stout and Nyland, 1986; Zhang et  al., 1995). When such differences occur, separate coefficients for individual species or species groups can improve the accuracy of relative

density equations. This is the case in the Allegheny hardwood forests of Pennsylvania, which are comprised of mixed stands of black cherry, yellow-poplar, red maple, white ash, sugar maple, black birch, yellow birch, American beech, oaks and other species. In those forests, analysis of species-specific tree-area ratios identified three species groups with significantly different tree-area ratios (Stout and Nyland, 1986; Zhang et  al., 1995). Stocking equations based on tree-area ratios also have been derived for northern red oak and various forest types of the eastern USA that often include oaks (Table 6.2). The silvicultural value of tree-area ratios and stocking percentage for defining relative density is reinforced by their demonstrated independence of stand age and site quality (Chisman and Schumacher, 1940; Gingrich, 1967). They also have been shown to be influenced little by variation in stand structure (Gingrich, 1967). Minimum tree-area ratio equations and equivalent stocking percentage equations can be used to calculate an approximate average upper limit of stand density corresponding to that for normally stocked stands. This upper limit, which can be graphically expressed as a line on a stand density diagram, has been termed average maximum density or average maximum competition (Ernst and Knapp, 1985). This line is generally interpreted as the level of density that stands tend to return to in the absence of disturbance (Gingrich, 1967; Ernst and Knapp, 1985). Nevertheless, stand density is likely to fluctuate around this line due to variation in weather,

Table 6.2.  Equations for estimating stocking per cent (minimum tree-area ratio expressed as a percentage of an acre) by species group. Species (location) Upland oaks and hickories (Ohio, Kentucky, Missouri, Iowa) Northern red oak (Wisconsin) Sugar maple, American beech (Allegheny Plateau) Black cherry, yellow-poplar (Allegheny Plateau) Red maple, American basswood, white ash (Allegheny Plateau) Black walnut (unspecified) Shortleaf pine (Missouri)

Equation for estimating stocking per centa 2

Source

−0.00507N + 0.01698ΣD + 0.00317ΣD

Gingrich (1967)

0.02476N + 0.004182ΣD + 0.00267ΣD2 −0.003082N + 0.006272ΣD + 0.00469ΣD2

McGill et al. (1991, 1999) Stout and Nyland (1986)

0.02794N + 0.01545ΣD + 0.000871ΣD2

Stout and Nyland (1986)

−0.01798N + 0.02143ΣD + 0.001711ΣD2

Stout and Nyland (1986)

0.01646N + 0.01347ΣD + 0.002757ΣD2 0.008798N + 0.009435ΣD + 0.00253ΣD2

Schlesinger (unpublished)b Rogers (1983)

a N is number of trees per acre, ΣD is sum of diameters (inches/acre), and ΣD2 is sum of squared diameters (inches2/acre). Note that ΣD2 is equal to basal area (ft2/acre) divided by 0.005454. Minimum tree area in ft2 (TARmin) can be obtained by multiplying the calculated stocking per cent by 435.6, the number of square feet in 1% of an acre. b Unpublished equation by Richard C. Schlesinger, USDA Forest Service, Columbia, Missouri, 1977.

236

Chapter 6

7

110

6 Ave A

100

Ov

90

ers

80

Fu

lly

70

sto

50 40

CUn

de

rst

ke

d

dia

me

ter

ke

d

(in

.) 3

ed

40

30

oc

toc

4

ck

B

60

ree

50

Basal area (ft2/acre)

Stand density diagrams are graphical representations of the equations and variables that define relative stand density. In practical application, measures of relative density are often more convenient when displayed as diagrams than as equations. The most widely used type of stand density diagram in North American oak and hardwood silviculture is the one developed by Gingrich (1967). However, stand density index diagrams based on Reineke’s model also have been developed for oaks and other species (Williams, 2003; Shaw, 2006; VanderSchaaf and Burkhart, 2007; Vacchiano et al., 2008).

et

11 0

Stand density diagrams

rag

5

60 St oc 70 kin g 80 pe r c 90 en t 10 0

minor outbreaks of insects and disease, and other factors associated with ‘regular’ tree mortality.

20 1

The Gingrich diagram

Self-thinning and Stand Density

5 7 9 11 13 Hundred trees/acre 15

140 130

A

120

15

13 Aver 12 age tree 11 dia 10 me ter Ov 9 ers (in. toc ) 8 ked 7 11

0

110

Ful

90

ly s

toc

80

50

CU n

70 oc kin

B

der

sto

cke

d

St

60

ked

60

70

8 gp 0 9 er ce 0 nt

10

0

100

50

Basal area (ft2/acre)

Gingrich’s (1967) stand density diagram is based on minimum tree-area ratio equations. The equations, in turn, are based on data from white, black, scarlet and chestnut oak stands in the Central Hardwood Region. Gingrich’s diagram is sometimes called a stocking diagram or a stocking chart because it incorporates measures of absolute density, relative density and stocking percentage into one graph. The measures of absolute density used are number of trees per acre (horizontal axis) and basal area per acre (vertical axis). Quadratic mean stand diameter (i.e. the diameter of the tree of average basal area) is also shown in relation to basal area and trees per acre. For any given stand, observed values of basal area and trees per acre can be plotted to determine stocking per cent directly from the diagram (Fig. 6.9). Gingrich’s diagram graphically defines the line representing average maximum stand density in relation to basal area, trees per acre and mean stand diameter. The line is based on the minimum tree-area equation for upland oaks and hickories (Equation 6.16, Table 6.2). On the diagram, the line is labelled 100% stocking or A-level stocking (Fig. 6.9). It could also be called the ‘line of imminent competition-induced mortality’ (Drew and Flewelling, 1977). Similarly, the maximum treearea equation (Equation 6.11) defines the fewest number of trees of a given diameter sufficient to completely occupy all the growing space on an acre. On the diagram, this reference line is labelled B-level stocking. Over a wide range of stand

3

40 50 100 150 200 250 300 350 400 Trees/acre Fig. 6.9.  Relation of basal area, number of trees and average tree diameter to stocking per cent in upland oak stands in the Central Hardwood Region. Values are for trees ≥ 2 inches dbh. Lines of average tree diameter correspond to quadratic mean stand diameter. Upper panel is for stands with quadratic mean diameters from 3 to 7 inches dbh; lower panel is for stands with quadratic mean diameters from 7 to 15 inches dbh. The area between curves A and B represents the range of stocking where trees fully utilize growing space. (From Gingrich, 1967.)

237

­ ensities, B-level occurs between 57% and 59% of d A-level stocking. The trees in stands below B-level stocking are too small and too few for their crowns to fully occupy the available growing space. Another line, labelled C-level stocking, defines the relative density at which a stand on an average site requires about 10 years to attain B-level stocking. The steep slope of the C-level line reflects the relatively rapid rate of increase in stocking per cent (and basal area) in stands with small mean diameters. Unless residual stand densities are reduced to levels below C-level stocking, the canopy gaps created by thinning quickly close through crown expansion and correlated dbh growth. Qualitative levels of stocking (overstocked, fully stocked and understocked) are also identified on the stocking diagram. Stands are overstocked when they fall above A-level, fully stocked when they are between A- and B-levels, and understocked when they are below B-level. Stands within the fully stocked range therefore are considered capable of completely utilizing the available growing space. In the absence of disturbances, stands that are overstocked, fully stocked or understocked are all expected to follow trajectories that over time gradually move towards 100% stocking (A-level). Although stocking per cent can be calculated directly from the associated tree-area ratio equation (Table 6.2), the stocking diagram facilitates rapid estimation when basal area and number of trees per acre are known. Experienced observers can link visual images of canopy conditions with an approximate stocking per cent (Fig. 6.10). The stocking diagram illustrates the inadequacy of basal area alone as an expression of relative stand density. If basal area and stocking per cent were equivalent measures of density, the stocking per cent lines would parallel the horizontal basal area lines – thereby signifying isometry between tree crown area and basal area. Instead, the slope of the stocking per cent lines shows that trees can endure more crowding as they increase in diameter. This relation also implies that trees of large diameter require less space to support a unit of basal area than trees of small diameter. The density diagram is designed for rapid estimation of stocking per cent from two easily obtained measures of stand density: basal area and trees per acre. Note, however, that the stocking per cent equation (Equation 6.16 and Table 6.2) includes three terms: (i) number of trees per acre (N); (ii) sum of diameters per acre (∑D); and (iii) sum of

238

(A)

(B)

(C)

Fig. 6.10.  Representative canopy cover of oak stands in the Ozark Highlands for three levels of stocking: (A) 100%; (B) 80%; and (C) 60%. Photos for 60% and 80% stocking were taken immediately after thinning. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

diameters squared per acre (∑D2). The latter value also is equivalent to basal area measured in ft2/acre divided by 0.005454. The density diagram incorporates only number of trees and basal area with no direct measure of sum of diameters. Nevertheless, it is important to consider both ∑D and ∑D2 when

Chapter 6

estimating stocking per cent because together they account for variation in stand structure. This can be seen from the relation between the variance (σ2) in tree diameters and the difference or ‘discrepancy’ between arithmetic mean stand diameter and quadratic mean stand diameter: s 2 = Dq2 − D2 [6.18] where D is the arithmetic mean stand diameter (i.e. ∑D ) and Dq is the dbh of the tree of average basal area N (i.e. ∑ D ). The greater the variance in tree diameters, N the greater the difference between arithmetic mean stand diameter and quadratic mean stand diameter. Moreover, if either D or Dq is given, the other can be algebraically derived from knowledge of σ2. A valuable property of the stocking per cent equation (Equation 6.16 and Table 6.2) therefore is the indirect incorporation of information about the variance of tree diameters via inclusion of ∑D and ∑D2. The loss of accuracy in calculating stocking per cent that results from not considering ∑D would not be significant if all the trees were of equal or nearly equal diameter. But this is seldom if ever the case. When Gingrich (1967) derived the stocking per cent lines shown on his diagram, he incorporated estimates of the variance of tree diameters. He based these estimates on the observed negative linear relation between σ2 and Dq. The stocking per cent lines shown on the density diagram therefore reflect average or ‘representative’ stand diameter distributions that change as the arithmetic mean stand diameter changes. Stocking per cent derived directly from the oak stocking per cent equation may be as much as 4% greater than indicated by the stocking diagram (Fig. 6.9) when the range of tree diameters is small. Where there is a wide range of diameters (e.g. in uneven-aged stands), stocking per cent calculated from the equation may be as much as 6% lower than that indicated by the stocking diagram. Such differences are usually of little practical importance when making management decisions. Not all density diagrams based on tree-area ratios incorporate estimates of the variance of diameters. Developers of stocking diagrams may wittingly or unwittingly assume that variance in diameters, and thus stand structure, is inconsequential in estimating tree-area ratios. When information on individual tree diameters is available, the minimum tree-area equation or stocking per cent equation (Table 6.2) will produce estimates that are more accurate than estimates read from the 2

Self-thinning and Stand Density

stocking diagram. Stocking values can be computed for each sampled tree in a stand, weighted to a per acre basis, and summed to estimate total stocking per cent or stocking per cent by species group without using the Gingrich stocking diagram. Gingrich’s stand density diagram was developed from observations in forests comprised largely of white, black, scarlet and chestnut oaks. Other oaks may have different growing space requirements and thus different A-level and B-level stocking lines. For example, northern red oak attains 100% stocking at substantially higher levels of absolute density than those represented by Gingrich’s equation. Alternative stocking equations and stand density diagrams therefore have been developed for northern red oak (Sampson, 1983; Sampson et al., 1983; McGill et al., 1991, 1999; Stout, 1991) (Fig. 6.11). Based on the equation of McGill and others (Table 6.2), a stocking per cent of 100 for northern red oak equates to stocking percentages ranging from 120 to 140 on the Gingrich diagram (Fig. 6.9). Assuming the associated tree-area equations are accurate, white, black, scarlet and chestnut oaks and hickories in the Central Hardwood Region require 20–40% more growing space per tree than that required for northern red oak in Wisconsin. Two other stand density diagrams based on treearea ratios have relevance to oak forests in the eastern USA. One is the stand density diagram for Allegheny hardwoods developed by Roach (1977). Those forests often include northern red oak as an associated species. The related stand density diagram is unique in that it accommodates adjustments in A-level stocking associated with changes in species composition. Because of the relatively narrow crowns of black cherry, white ash and yellow-poplar, the reference line for A-level stocking increases as the percentage of those species increases. A stand density diagram based on tree-area ratios also has been developed for southern bottomland hardwood forests that include oaks as component species (Goelz, 1995). The diagram is based on the stand tables of Putnam and others (1960) for mixed bottomland stands in the lower Mississippi Valley, lower Piedmont and southern Coastal Plain. In those forests, cherrybark, laurel, Nuttall, overcup, pin, Shumard, water, willow and swamp chestnut oaks are commonly associated with other bottomland species. Despite the widespread use of stand density diagrams based on tree-area ratios, some of their properties and assumptions are not widely understood

239

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or appreciated. Some characteristics worth remembering are presented below:

er

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Fig. 6.11.  Relation of basal area, number of trees and average tree diameter to stocking per cent in northern red oak stands in Wisconsin. Values are for trees ≥ 2 inches dbh. Lines of average tree diameter correspond to the dbh of the tree of average basal area or quadratic mean diameter. Upper panel is for stands with quadratic mean diameters from 3 to 7 inches dbh; lower panel is for stands with quadratic mean diameters from 7 to 15 inches dbh. The area between curves A and B represents the range of stocking where trees fully utilize growing space. (From McGill et al., 1991.)

240

●● Crown closure for a stand is assumed to occur when the sum of the computed open-grown tree crown areas per acre equals 43,560 ft2, i.e. ∑TARmax = 43,560 ft and CCF = 100. This is assumed to be the point of crown closure where inter-tree competition begins. However, it is difficult to perceive of a situation where this assumption would be literally true. Irregularities in tree spacing virtually ensure that tree crowns will form overlaps and gaps. The assumption nevertheless provides a quantifiable and demonstrably useful reference line that estimates the minimum number of trees necessary to occupy the available growing space. ●● On a good site, trees of a given diameter may be younger than trees of the same diameter on a poor site. Trees of the same size nevertheless are assumed to require the same amount of growing space, regardless of their ages (Krajicek et  al., 1961; Gingrich, 1967). ●● Stand density can exceed 100% stocking due to the approximate nature of stocking relationships and the plasticity of tree crowns. This gives rise to the term ‘overstocked’. However, in the absence of disturbance, stands are expected to gradually move towards the line of A-level stocking regardless of where they initially fall on the density diagram. ●● According to current silvicultural theory, oak stands maintained in the ‘fully stocked’ zone will produce approximately the same total gross volume or biomass growth regardless of where they are maintained within that zone. However, other factors being equal, individual trees in oak stands maintained near B-level stocking will grow faster in diameter and usually produce greater total merchantable board foot volume than trees in stands maintained near A-level. However, for oak stands grown exclusively for total wood fibre or biomass, thinning to reduce stocking is not necessarily advantageous. Also, the net merchantable growth per acre of stands containing a large proportion of black cherry (Nowak, 1996) or yellow-­ poplar (Trimble, 1968; Beck, 1986) may decrease with decreasing residual stand density. For those species, post-thinning increases in crown expansion, crown density and bole growth may be insufficient to compensate for growth loss due to the removal of trees (Nowak, 1996). The general rule of equal stand growth across the ‘fully

Chapter 6

stocked’ zone therefore may not hold for some species mixes that commonly include oaks. ●● There is evidence, based on long-term forest growth studies, that gross volume growth increases with increasing stand density up to a stand’s maximum biological limits of density (Zeide, 2001). Accordingly, there would be no such thing as an excessive number of trees – as implied by the labelling of the ‘overstocked’ zone of the Gingrich and similar stocking charts. In a general biological context, the concept of ‘overstocking’ therefore may be misleading. Nevertheless, the use of thinning to increase the rate of residual stand growth to more quickly obtain merchantable timber products other than wood fibre generally holds. ●● Tree-area ratio equations assume that interactions among species do not affect tree area. However, it would seem plausible that co-occurring species that appreciably differ in how they utilize site resources would more efficiently ‘share’ a unit of space than co-occurring species with very similar resource requirements (McGill et  al., 1999). If this were true, we might expect TARmin for two species with very different resource requirements to be smaller (and therefore their joint absolute maximum densities to be larger) when the two species are interacting than when they are growing apart. For example, northern red oak stands with a subcanopy of sugar maple have higher average maximum absolute densities (measured in basal area and trees per acre) than pure red oak stands (McGill et al., 1999). This suggests that minimum tree-area ratios for a given species may change as stand composition changes. ●● Stands at identical stocking per cent may develop differently, depending on their structure and history. For example, the growth of a previously undisturbed stand reduced to 70% stocking by removing the largest trees is likely to be different from a similar stand that was thinned to the same stocking per cent by removing mostly small trees. The general direction of the response should be similar for both stands: increased stocking and basal area, and decreased number of trees. But rates of stand growth and increases in stocking would be expected to differ substantially because of differences in stand structure and composition. Stocking per cent, therefore, does not define a unique stand condition even within the same forest type and site class. Use of stocking per cent or other measures of stand density to make accurate growth predictions thus requires considering additional stand characteristics.

Self-thinning and Stand Density

●● The labels understocked, fully stocked and overstocked on the Gingrich stocking guide (Fig. 6.9) apply to the implied objective of growing oak sawtimber. However, other management objectives can be addressed readily in the stocking guide framework because the stocking graphic incorporates the full range of forest structures for stands that have attained a 3 inch mean diameter. For example, stocking targets for oak savannah and woodland management (Chapter 12, this volume, Fig. 12.7) can be described using a Gingrich stocking guide, but target stocking levels for savannahs are typically less than 30%, open woodlands between 30% and 55%, and closed woodlands between 55% and 75%. Likewise relative aesthetic ratings related to stand structure can be outlined using the stocking guide framework (e.g. see Chapter 13, this volume). ●● Typically the basal area and number of trees for all species combined are used for stocking estimates. Stocking values can be plotted for a single year or they can be plotted over time for multiple years to illustrate patterns of stand dynamics (see examples later in this chapter). It also may be informative to plot stocking by species-groups as well. For example in Chapter 5, this volume, Figs 5.5, 5.8 and 5.14 illustrate change over time for the entire stand and for separate species groups plotted in the stocking guide framework (basal area on vertical axis and number of trees on the horizontal axis). In those figures, however, the usual lines showing stocking per cent are omitted to avoid obscuring the trend lines and labels. Stand density index diagrams Stand density index (SDI) represents another graphical format used to express the relative degree of tree crowding. It also can be used to indicate how a stand is likely to change over time in the absence of disturbance. SDI is derived from the Reineke (1933) model (discussed earlier in this chapter) and relates the number of trees per acre and their mean diameter to a reference condition. It is defined as the number of trees per acre expected at an index (or reference) stand diameter of 10 inches, where diameter, Dq, is the quadratic mean stand diameter (i.e. the diameter of the tree of average basal area) (see Equation 6.18). When a stand’s Dq is not 10 inches, SDI for a stand of any given Dq can be derived from Equation 6.1 (i.e. Reineke’s model) by letting Dq equal 10 and solving for aʹ. In turn, this 241

the number of trees per acre and the quadratic mean stand diameter to estimate the SDI of any stand and compare it to the self-thinning line or to another reference line. SDI also can be computed directly by substituting the observed number of trees per acre and quadratic mean stand diameter (Dq) into Equation 6.23. The SDI of an observed stand also can be expressed relatively as a ratio of the observed SDI to the SDI of the self-thinning line or lines that define the upper limit of stand density (Weller, 1990; VanderSchaaf and Burkhart, 2007). While stand density index and its corresponding diagram define the upper boundaries of stand density (i.e. the minimum growing space needed by an average tree), it provides no information about the lower limits of density for full utilization of growing space as does the Gingrich model. Fig. 6.12 shows stand density index lines for SDI values of 125, 216 and 250. At a Dq of 10 inches, the number of trees associated with each line is equal to the SDI value. Any one SDI line represents stand conditions that correspond to about the same level of within-stand competition. For example, a stand with Dq = 5 inches and 720 trees/acre, a second with Dq = 10 inches and 250 trees/acre, and a third with Dq = 15 inches and 130 trees/acre all fall along the

value is substituted back into Equation 6.1, that is log(N) = aʹ + b[log(Dq)]. Then letting: Dq = 10, log(10) = 1 [6.19] log(SDI) = a′ + b(1) 

[6.20]

a′ = log(SDI) − b 

[6.21]

log (N) = (log (SDI) − b) + b[log (Dq )]  = log (SDI) − b[1 − log (Dq )]

[6.22]

Rearranging Equation 6.22 to solve for SDI in terms of N and Dq results in: log (SDI) = log (N) + b[1 − log (Dq )] [6.23] As discussed with the Reineke equation (Equation. 6.1), the value of coefficient b for upland oak forests is approximately −1.6. From these relations, a stand density index chart can be constructed showing numbers of trees per acre on the vertical axis and quadratic mean stand diameter, Dq, on the horizontal axis, with both scaled logarithmically (Fig. 6.12). The resulting chart shows the self-thinning line based on Equation 6.1, together with a series of parallel lines from Equation 6.22 (one for each SDI calculated at convenient intervals). The SDI chart is a convenient way to use 1000 900 800 700 600 500

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Fig. 6.12.  A stand density index (SDI) diagram for upland hardwoods dominated by oak in southern Ohio. (Adapted from Williams, 2003.) By convention, each SDI line shows the number of trees associated with a quadratic mean stand diameter of 10 inches. The A-line and B-line are derived from the corresponding lines on Gingrich’s (1967) stand density diagram (see Fig. 6.9). The maximum density line represents the average maximum stand density reported by Williams (2003). According to Gingrich’s (1967) stocking diagram, stands below SDI 125 do not fully utilize growing space. Williams (SDI = 250) and Gingrich (SDI = 216) differ in their estimates of maximum stand density.

242

Chapter 6

SDI = 250 index line. All three stands represent about the same level of competition or growing space utilization. The lower the SDI value, the more growing space that is available to the average tree. Williams (2003) suggested using stand density index in upland hardwood stands as an alternative to the Gingrich-style stocking guide. To do this, he developed linear regression equations describing the relation between SDI, stocking per cent (S) and crown competition factor (CCF): SDI = −0.0359 + 1.2525 (CCF) [6.24] SDI = −0.4144 + 2.1639(S) 

[6.25]

S = −0.1022 + 0.5807(CCF) 

[6.26]

The line for A-level stocking in Gingrich’s diagram corresponds to 100% stocking. Therefore Equation 6.25 can be used to calculate the SDI value corresponding to A-level stocking for the Gingrich stocking diagram, i.e. SDI = 216. The line for B-level stocking on Gingrich’s diagram is the line of imminent crown closure and corresponds to the maximum tree area as defined by Gingrich (Equation 6.11). It is also equivalent to a CCF value of 100, a stocking per cent of approximately 58, and a SDI value of 125 (Figs 6.9 and 6.12). Williams (2003) considered an SDI = 250 to be maximum density, which corresponds to a Gingrich-derived stocking per cent of 115. Based on the assumptions in the Gingrich stocking guide (Fig. 6.9), stands with SDI values below 125 would be considered understocked and those with values above 216 would be overstocked. The SDI of a stand is determined from the number of trees per acre (N) and their quadratic mean stand diameter (Dq). Although these values can be obtained by counting trees and measuring their diameters on sample areas, both values can be determined by tree counts alone using fixed-area circular plots combined with point sampling. Using the same plot centre, trees first can be counted on fixed-area plots and that number then expanded to estimate number of trees per acre (N). Then from the same location, basal area per acre (BA) can be obtained from a tree count based on point sampling and expanding the count by the corresponding basal area factor (Avery and Burkhart, 2001). When number of trees per acre and basal area (ft2/acre) are known, the quadratic mean stand diameter, Dq (the tree of average basal area), can be calculated by the following equation: Dq = 24

BA  pN

Self-thinning and Stand Density

[6.27]

where BA is basal area in ft2/acre and N is trees per acre. SDI was developed for even-aged stands of a single species, or for a group of similar species in the case of mixed oak stands. However, some investigators describe methods to compute SDI metrics for stands of mixed species (e.g. Woodall et al., 2005; Ducey and Knapp, 2010; Rivoire and Le Moguedec, 2012). The methods of Woodall et  al. (2005) and of Ducey and Knapp (2010) account for an inverse relationship between the specific gravity of a species’ wood and the species’ maximum SDI. Species that produce wood with relatively high specific gravity (e.g. oaks) typically have maximum stand SDI values that are relatively low (i.e. fewer trees of a given size are required to fully occupy the growing space of a stand). Other diagrams Other variations of stand density diagram include some that are applicable to oak forests. An example is Oswald’s (1982) diagram for English (pedunculate) oak and sessile oak in France. This diagram is a variant of Reineke’s (1933) stand density index. However, mean tree diameter as shown on the horizontal axis in the Reineke model is replaced by the mean height of the 100 tallest trees/ha (Fig. 6.13). Another example is the stand density diagram for upland hardwoods developed by Kershaw and Fischer (1991), which is based on a format developed for Douglas-fir (Drew and Flewelling, 1977). It incorporates the same principles as the Reineke and Gingrich diagrams, but shows trees per acre in relation to mean board foot volume per tree. It differs from other oak stand density diagrams by directly incorporating information on merchantable products. Density diagrams and stand growth In addition to their conventional use in managing stand density, density diagrams can be used to show how different stand attributes simultaneously change through time. This application can be illustrated by plotting actual or projected changes in stand basal area and trees per acre on the density diagram. Some growth and yield simulation models provide this as a graphic display option. Stand growth can be displayed in a variety of

243

Trees/ha

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M ax im um

bi ol og ic al de ns it y

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Density index 140 120 100 85 70

Fig. 6.13.  A stand density diagram for sessile and English oaks (non-coppice stands) in France. (Redrawn from Oswald, 1982.) Stand density is expressed as the relation between trees per hectare (upperstorey trees ≥ 7 cm in diameter at 1.4 m) and the mean height of the 100 largest-diameter trees/ha. The uppermost line represents maximum biological stand density. Densities below the lowermost line result in annual ring widths exceeding 2 mm, which European foresters regard as undesirable for oak veneer logs. In this diagram, stand densities are indexed to the number of trees per hectare in stands with dominant trees averaging 35 m tall. Density indices between 70 and 140 identify the operational range for residual stand densities; the usual after-thinning range for oaks is between 70 and 100. French foresters refer to these indices as ‘normes’.

f­ormats and units, depending on silvicultural objectives (Fig. 6.14). In the absence of disturbance and at densities below 100% stocking on the Gingrich guide (i.e. below the A-level stocking line), we would expect stands to increase in basal area and decrease in number of trees per acre. Similarly, stands above 100% stocking would be expected to gradually decrease in number of trees without substantially increasing in basal area. In either case, the expectation is for stands to move through time towards the 100% stocking line. But because the reference line of maximum density is only an approximation of an average state that fluctuates, the growth of any given stand can be expected to vary about the line after reaching it. A relation between relative density and stand growth is implicit in oak stocking diagrams. Gross

244

volume growth is assumed to be maximum and nearly constant when stands are fully stocked (i.e. maintained between A- and B-level reference lines). Except as noted above, merchantable board foot volume growth is often maximized by maintaining stand density near B-level. Stocking charts also can be used in conjunction with growth and yield models to compare stand growth responses to various combinations of initial basal area and trees per acre (Leary and Stanfield, 1986; Goelz, 1991). Although the relative density lines shown on stand density diagrams are independent of site quality and stand age, rates of stand growth are not. Consequently, when growth is plotted on a density diagram, the results pertain to specific site and stand conditions. Analysis of estimated oak growth rates based on Dale’s (1972) growth and yield model applied to a wide range of relative density classes showed that the model predicted maximum net cubic-foot volume yield at basal areas as much as 20 ft2/acre below B-level on the Gingrich stand density diagram (Leak, 1981). Nevertheless, stands maintained below B-level are likely to incur defects in bole quality associated with the resulting increase in epicormic branching (Dale, 1972; Sonderman, 1985). Stand density diagrams and equations therefore should be considered guides rather than rigid rules applicable to all situations. Absolute measures of stand density, by themselves, insufficiently express the degree of crowding experienced by trees. Stand density diagrams overcome this problem by incorporating measures of tree size (e.g. volume, height, dbh) along with one or more absolute measures of stand density (e.g. number of trees or basal area per acre). For example, a stand with 70 ft2 of basal area and an average dbh of 3 inches looks very different from one with the same basal area that averages 15 inches dbh. In the latter, there are likely to be canopy gaps surrounded by trees with large, fully expanded crowns. In contrast, trees in the stand with an average dbh of 3 inches are more crowded (i.e. relative density is higher) and the canopy is tightly closed. Tree size thus influences tree crowding and therefore self-thinning. Gaps between tree crowns also may be more important to tree survival than growth. These gaps may occur even in stands of high average density. The associated spatial heterogeneity in crown canopy cover and stand density may need to be accounted for if

Chapter 6

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Fig. 6.14.  Seventeen-year growth of an upland oak stand in Missouri (shown by arrows) displayed on three types of stand density diagrams. To better illustrate stand growth, only part of each diagram is shown. (A) Gingrich’s (1967) diagram for oak–hickory forests of the Central Hardwood Region. (B) A Reineke diagram of stand density index for upland oaks of eastern USA. An index of 100 represents average maximum density (the dotted self-thinning line in Fig. 6.1). (Adapted from Schnur, 1937.) (C) A diagram based on the relation between average tree volume and number of trees per acre for upland oak stands of eastern USA. A stand volume index of 100 represents average maximum density. This diagram follows the format in Fig. 6.4. (Adapted from Schnur, 1937.)

accurate characterization of stand density is important (Zeide, 2005). An understanding of the principles of self-thinning and stand density is prerequisite to knowledgeable application of silvicultural systems. The silviculture of North American oaks is, and has been, largely centred on the manipulation of the structure, composition and regeneration of natural stands. From an ecological perspective, a silvicultural system therefore represents a planned disturbance or series of disturbances designed to achieve specific goals. In ­subsequent

Self-thinning and Stand Density

chapters the principles discussed in this chapter are applied to silvicultural methods for oak forests.

Note 1

  Because area, A, of a circle is pi (π) times its radius squared, r2, and radius is one-half of the circle’s diameter, d, then A = π r2 = π(d/2)2 = πd2/4. This result divided by 122 = 144 expresses basal area in square feet from diameter measured in inches. Combining the constants p p 2 d2 = d = .0054541539 d2. results in 4 ⋅ 144 576

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References Avery, T.E. and Burkhart, H.E. (2001) Forest Measure­ ments, 5th edn. McGraw Hill, Boston, Massachusetts. Beck, D.E. (1986) Thinning Appalachian pole and small sawtimber stands. In: Clay Smith, H. and Eye, M.C. (eds) Guidelines for Managing Immature Appalachian Hardwood Stands, 28–30 May, Morgantown, West Virginia. Society of American Foresters Publication 86-02. Society of American Foresters, Bethesda, Maryland, pp. 85–98. Bickford, C.A., Baker, F.S. and Wilson, F.G. (1957) Stocking, normality, and measurement of stand density. Journal of Forestry 55, 99–104. https://doi.org/10.1093/ jof/55.2.99 Canadell, J. and Rodà, F. (1991) Root biomass of Quercus ilex in a montane Mediterranean forest. Canadian Journal of Forest Research 21, 1771–1778. https://doi.org/10.1139/x91-245 Chisman, H.H. and Schumacher, F.X. (1940) On the tree-area ratio and certain of its applications. Journal of Forestry 38, 311–317. https://doi.org/10.1093/jof/ 38.4.311 Curtis, R.O. (1970) Stand density measures: an interpretation. Forest Science 16, 403–414. https://doi. org/10.1093/forestscience/16.4.403 Curtis, R.O. (1982) A simple index of stand density for Douglas-fir. Forest Science 28, 92–94. https://doi. org/10.1093/forestscience/28.1.92 Dale, M.E. (1972) Growth and yield predictions for upland oak stands 10 years after initial thinning. USDA Forest Service Research Paper NE-241. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.5962/bhl.title. 97587 Drew, J.T. and Flewelling, J.W. (1977) Some recent Japanese theories of yield–density relationships and their application to Monterey pine plantations. Forest Science 23, 517–534. https://doi.org/10.1093/forest science/23.4.517 Ducey, M.J. and Knapp, R.A. (2010) Rapid assessment of relative density in mixed-species stands of the northeastern United States. International Journal of Forestry Research vol. 2010, Article ID 212068, 8 pp. https://doi.org/10.1155/2010/212068 Ek, A.R. (1974) Dimensional relationships of forest and open grown trees in Wisconsin. University of Wisconsin Forestry Research Note July. University of Wisconsin, Madison, Wisconsin. Ernst, R.L. and Knapp, W.H. (1985) Forest stand density and stocking: concepts, terms, and the use of stocking guides. USDA Forest Service General Technical Report WO-44. USDA Forest Service, Washington, DC. Frothingham, E.H. (1912) Second-growth hardwoods in Connecticut. USDA Forest Service Bulletin 96. US Government Printing Office, Washington, DC. https:// doi.org/10.5962/bhl.title.66435

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Gevorkiantz, S.R. and Scholz, H.F. (1948) Timber yields and possible returns from the mixed-oak farmwoods of southwestern Wisconsin. USDA Forest Service Lake States Forest Experiment Station Publication 521. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gingrich, S.F. (1964) Criteria for measuring stocking in forest stands. In: Proceedings of the 1964 Society of American Foresters National Convention, 27 September–1 October, Denver, Colorado. Society of American Foresters, Bethesda, Maryland, pp. 198–201. Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Gingrich, S.F. (1971) Management of young and intermediate stands of upland hardwoods. USDA Forest Service Research Paper NE-195. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/23703 (accessed 1 July 2018). Givnish, T.J. (1986) Biomechanical constraints on selfthinning in plant populations. Journal of Theoretical Biology 119, 139–146. https://doi.org/10.1016/S00225193(86)80069-8 Goelz, J.C.G. (1991) Generation of a new type of stocking guide that reflects stand growth. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 240–247. Available at: https://www.fs.usda.gov/treesearch/pubs/789 (accessed 1 July 2018). Goelz, J.C.G. (1995) A stocking guide for southern bottomland hardwoods. Southern Journal of Applied Forestry 19, 103–104. https://doi.org/10.1093/sjaf/19.3.103 Harper, J.L. (1977) Population Biology of Plants. Academic Press, London. Hutchings, M. (1983) Ecology’s law in search of a theory. New Scientist 16, 765–767. Kershaw, J.A., Jr and Fischer, B.C. (1991) A stand density management diagram for sawtimber-sized mixed upland central hardwoods. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 414–428. Available at: https://www.fs.usda.gov/treesearch/pubs/13519 (accessed 1 July 2018). Kershaw, J.A., Jr, Ducey, M.J., Beers, T.W. and Husch, B. (2016) Forest Mensuration, 5th edn. Wiley, Chichester, UK. Available at: https://onlinelibrary.wiley.com/doi/ book/10.1002/9781118902028 (accessed 1 July 2018). Krajicek, J.E. (1967) Maximum use of minimum acres. In: Proceedings of the 9th Southern Forest Tree Improvement Conference, 8–9 June, Knoxville, Tennessee. Southern Forest Tree Improvement Committee, Knoxville, Tenessee, pp. 35–37. https://doi.org/10.5962/bhl. title.81087

Chapter 6

Krajicek, J.E., Brinkman, K.A. and Gingrich, S.F. (1961) Crown competition – a measure of density. Forest Science 7, 35–42. https://doi.org/10.1093/forestscience/ 7.1.35 Leak, W.B. (1981) Do stocking guides in the eastern United States relate to stand growth? Journal of Forestry 79, 661–664. https://doi.org/10.1093/jof/79.10.661 Leary, R.A. and Stanfield, D. (1986) Stocking guides made dynamic. Northern Journal of Applied Forestry 3, 139–142. https://doi.org/10.1093/njaf/3.4.139 Lonsdale, W.M. (1990) The self-thinning rule: dead or alive? Ecology 71, 1373–1388. https://doi.org/10.2307/ 1938275 McGill, D., Martin, J., Rogers, R. and Johnson, P.S. (1991) New stocking charts for northern red oak. University of Wisconsin Forestry Research Notes 277. University of Wisconsin, Madison, Wisconsin. McGill, D.W., Rogers, R., Martin, A.J. and Johnson, P.S. (1999) Measuring stocking in northern red oak stands in Wisconsin. Northern Journal of Applied Forestry 16, 144–150. https://doi.org/10.1093/njaf/16.3.144 McMahon, T. (1973) Size and shape in biology. Science 179, 1201–1204. https://doi.org/10.1126/science.179. 4079.1201 McMahon, T.A. and Bonner, J.T. (1983) On Size and Life. Scientific American Books, New York. Miyanishi, K., Hoy, A.R. and Cavers, P.B. (1979) A generalized law of self-thinning in plant populations. Journal of Theoretical Biology 78, 439–442. https:// doi.org/10.1016/0022-5193(79)90342-4 Norberg, R.A. (1988) Theory of growth geometry of plants and self-thinning of plant populations: geometric similarity, elastic similarity, and different growth modes of plant parts. American Naturalist 131, 220– 256. https://doi.org/10.1086/284787 Nowak, C.A. (1996) Wood volume increment in thinned, 50- to 55-year-old, mixed-species Allegheny hardwoods. Canadian Journal of Forest Research 26, 819–835. https://doi.org/10.1139/x26-091 Oswald, H. (1982) Silviculture of oak and beech high forests in France. In: Malcolm, D.C., Evans, J. and Edwards, P.N. (eds) Broadleaves in Britain. Proceedings of a symposium held at the University of Technology, Lough­ borough, 7–9 July. Institute of Chartered Foresters, Loughborough, Leicestershire, UK pp. 31–39. Pretzsch, H. (2009) Forest Dynamics, Growth and Yield: From Measurement to Model. Springer, Berlin. https://doi.org/10.1007/978-3-540-88307-4 Pretzsch, H. and Biber, P. (2005) A re-evaluations of Reineke’s rule and stand density index. Forest Science 51, 304–320. https://doi.org/10.1093/forestscience/51.4.304 Putnam, J.A., Furnival, G.M. and McKnight, J.S. (1960) Management and Inventory of Southern Hardwoods. USDA Forest Service Agriculture Handbook 181. USDA Forest Service, Washington, DC. Available at: https://www.fs.usda.gov/treesearch/pubs/28908 (accessed 1 July 2018).

Self-thinning and Stand Density

Reineke, L.H. (1933) Perfecting a stand-density index for even-aged forests. Journal of Agricultural Research 46, 627–638. Available at: https://naldc.nal.usda.gov/ download/IND43968212/PDF (accessed 1 July 2018). Rivoire, M. and Le Moguedec, G. (2012) A generalized self-thinning relationship for multi-species and mixedsize forests. Annals of Forest Science 69, 207–219. https://doi.org/10.1007/s13595-011-0158-z Roach, B.A. (1977) A stocking guide for Allegheny hardwoods and its use in controlling intermediate cuttings. USDA Forest Service Research Paper NE-373. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/14498 (accessed 1 July 2018). Roberts, E.G. and Ross, R.D. (1965) Crown area of freegrowing loblolly pine and its apparent independence of age and site. Journal of Forestry 63, 462–463. Rogers, R. (1983) Guides for thinning shortleaf pine. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 217–225. Available at: https:// www.srs.fs.usda.gov/pubs/1767 (accessed 1 July 2018). Sampson, T.L. (1983) A stocking guide for northern red oak in New England. MSc. thesis, University of New Hampshire, Durham, New Hampshire. Sampson, T.L., Barrett, J.P. and Leak, W.B. (1983) A stocking chart for northern red oak in New England. University of New Hampshire Agricultural Experiment Station Research Report 100. University of New Hampshire, Durham, New Hampshire. Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc. nal.usda.gov/download/CAT86200555/PDF (accessed 15 January 2019). Shaw, J.D. (2006) Reineke’s stand density index: where are we and where do we go from here? In: Proceedings of the Society of American Foresters 2005 National Convention. Society of American Foresters, Bethesda, Maryland. Available as a CD-ROM. Smith, H.C. and Gibbs, C.B. (1970) A guide to sugarbush stocking based on the crown diameter/dbh relationship of open-grown sugar maples. USDA Forest Service Research Paper NE-171. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/23780 (accessed 1 July 2018). Sonderman, D.L. (1985) Stand density – a factor affecting stem quality of young hardwoods. USDA Forest Service Research Paper NE-561. USDA Forest Service, North­ eastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/21735 (accessed 1 July 2018). Sorrensen-Cothern, K.A., Ford, E.D. and Sprugel, D.G. (1993) A model of competition incorporating plasticity

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through modular foliage and crown development. Ecological Monographs 63, 277–304. https://doi. org/10.2307/2937102 Sprugel, D.G. (1984) Density, biomass, productivity, and nutrient-cycling changes during stand development in wave-regenerated balsam fir forests. Ecological Mono­ graphs 54, 165–186. https://doi.org/10.2307/1942660 Stout, S.L. (1991) Stand density, stand structure, and species composition in transition oak stands of northwestern Pennsylvania. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 194–206. Available at: https://www.fs.usda.gov/treesearch/pubs/13499 (accessed 1 July 2018). Stout, S.L. and Larson, B.C. (1988) Relative stand density: why do we need to know? USDA Forest Service General Technical Report INT-243. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, pp. 73–79. Stout, S.L. and Nyland, R.D. (1986) Role of species composition in relative density measurement in Allegheny hardwoods. Canadian Journal of Forest Research 16, 574–579. https://doi.org/10.1139/x86-099 Trimble, G.R., Jr (1968) Multiple stems and single stems of red oak give same site index. Journal of Forestry 66, 198. https://doi.org/10.1093/jof/66.3.198a Vacchiano, G., Motta, R., Long, J.N. and Shaw, J.D. (2008) A density management diagram for Scots pine (Pinus sylvestris L.): a tool for assessing the forest’s protective effect. Forest Ecology and Management 255, 2542– 2554. https://doi.org/10.1016/j.foreco.2008.01.015 VanderSchaaf, C.L. (2004) Can planting density have an effect on the maximum size-density line of loblolly and slash pine? In: Doruska, P.F. and Radtke, P. (compilers) Proceedings of the Northeastern Mensurationist Organization and Southern Mensurationists Joint Conference. Department of Forestry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, pp. 115–126. Available at: filebox.vt.edu/users/cvanders/ (accessed 1 April 2007). VanderSchaaf, C.L. and Burkhart, H.E. (2007) Comparison of methods to estimate Reineke’s maximum sizedensity relationship species boundary line slope. Forest Science 53, 435–442. https://doi.org/10.1093/ forestscience/53.3.435 Vezina, P.E. (1963) More about the crown competition factor. Forestry Chronicle 39, 313–317. https://doi. org/10.5558/tfc39313-3 Walker, N. (1956) Growing stock volumes in unmanaged and managed forests. Journal of Forestry 54, 378– 383. https://doi.org/10.1093/jof/54.6.378 Weller, D.E. (1987a) A re-evaluation of the -3/2 power rule of plant self-thinning. Ecological Monographs 57, 23–43. https://doi.org/10.2307/1942637 Weller, D.E. (1987b) Self-thinning exponent correlated with allometric measures of plant geometry. Ecology 68, 813–821. https://doi.org/10.2307/1938352 248

Weller, D.E. (1989) The interspecific size-density relationship among crowded plant stands and its implications for the −3/2 power rule of self-thinning. American Naturalist 133, 20–41. https://doi.org/10.1086/284899 Weller, D.E. (1990) Will the real self-thinning rule please stand up? – A reply to Osawa and Sugita. Ecology 71, 2004–2007. https://doi.org/10.2307/1937389 Westoby, M. (1984) The self-thinning rule. Advances in Ecological Research 14, 167–225. https://doi.org/ 10.1016/S0065-2504(08)60171-3 White, J. (1981) The allometric interpretation of the selfthinning rule. Journal of Theoretical Biology 89, 475– 500. https://doi.org/10.1016/0022-5193(81)90363-5 White, J. (1985) The thinning rule and its application to mixtures of plant populations. In: White, J. (ed.) Studies on Plant Demography: a Festschrift for John L. Harper. Academic Press, New York, pp. 291–309. White, J. and Harper, J.L. (1970) Correlated changes in plant size and number in plant populations. Journal of Ecology 58, 467–485. https://doi.org/10.2307/ 2258284 Williams, R.A. (2003) Use of stand density index as an alternative to stocking percent in upland hardwoods. Northern Journal of Applied Forestry 20, 137–142. https://doi.org/10.1093/njaf/20.3.137 Wilson, F.G. (1946) Numerical expression of stocking in terms of height. Journal of Forestry 44, 758–761. https://doi.org/10.1093/jof/44.10.758 Woodall, C.W., Miles, P.D. and Vissage, J.S. (2005) Determining maximum stand density index in mixed species stands for strategic-scale stocking assessments. Forest Ecology and Management 216, 367– 377. https://doi.org/10.1016/j.foreco.2005.05.050 Yoda, K., Kira, T., Ogawa, H. and Hozumi, K. (1963) Self-thinning in overcrowded pure stands under cultivated and natural conditions. Journal of Biology 14, 107–129. Zeide, B. (1985) Production of unmanaged bottomland hardwoods in Arkansas. In: Proceedings of the Central Hardwood Forest Conference V. University of Illinois, Urbana-Champaign, Illinois, pp. 118–124. Zeide, B. (1987) Analysis of the 3/2 power law of selfthinning. Forest Science 33, 517–537. https://doi. org/10.1093/forestscience/33.2.517 Zeide, B. (2001) Thinning and growth: a full turnaround. Journal of Forestry 99(1), 20–24. https://doi.org/10.1093/ jof/99.1.20 Zeide, B. (2005) How to measure stand density. Trees – Structure and Function 19, 1–14. https://doi.org/ 10.1007/s00468-004-0343-x Zhang, L., Oswald, B.P., Green, T.H. and Stout, S.L. (1995) Relative density measurement and species composition in the mixed upland hardwood forests of North Alabama. USDA Forest Service General Technical Report SRS-1. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 467–472. Available at: https://www.fs.usda.gov/ treesearch/pubs/355 (accessed 1 July 2018). Chapter 6

7



Fire and Oak Forests

Introduction There is a long history of fire determining the distribution of major vegetation types (i.e. prairie, savannah, woodland and forest) in North America and around the world (Scott et al., 2014). Oak ecosystems are strongly associated with recurrent fire, and oak savannahs result when fire occurs frequently (e.g. every 2–3 years). Many oak species are adapted to persist and even increase in dominance under a disturbance regime where fire is a dominant factor. Oak species that can tolerate drought are able to survive and prosper on fire-prone sites across the landscape where summer droughts are common and severe droughts are recurring, with 20–40 year periodicity (Cwynar, 1977; Stambaugh and Guyette 2004; Guyette et  al., 2006a; Hoss et  al., 2008; Flatley et  al., 2013). Prescribed fire is increasingly used to restore the historical role of fire in creating and maintaining oak savannahs and woodlands (see Chapter 12, this volume) and to increase oak regeneration success in sustaining oak forests (see Chapter 8, this volume). There are several attributes of a fire regime that are within the decision space of land managers. Our lack of knowledge of fire regime attributes for specific ecosystems often leads to a simplified approach to prescribed fire management by applying fire dogmatically (e.g. every 3 years and always in the spring without variation). This approach limits ecosystem heterogeneity and, hence, biodiversity by not taking advantage of the complex interactions among fire regime attributes and their variable effects on vegetation that modify forest species composition and size structure at multiple spatial and temporal scales. Managers must consider a set of attributes that describe a fire in order to better understand fire effects on vegetation, fauna, soil, air, water, other ecosystem components and processes. No two fires are alike, because they vary in behaviour and effect both spatially within a single fire and temporally among multiple fire years. Consequently, modelling

fire effects based on multiple attributes improves our ability to predict the outcomes of prescribed fires and to extrapolate that knowledge to other ecosystems, locales and environments. Many oak species are well adapted to fire, and prescribed fire is increasingly used in silvicultural prescriptions to increase oak regeneration potential and relative competitiveness. Integrating oak physiology and ecology in an oak life cycle analysis, with knowledge of the silvics of major competitors and understanding of fire effects on vegetation, is fundamental to developing prescriptions that produce desired results. There are certain stages in the life cycle of an oak where fire can be deleterious to oak regeneration establishment and development. For example, an ill-timed fire may prevent oak recruitment into the overstorey. At certain stages in tree and stand development, fire may seriously degrade oak health, productivity and value for forest products. However, fire may also provide ecological and conservation benefits, increase ecosystem function, and increase ecosystems goods and services. Recurring fire is essential in the restoration of oak savannah and woodland ecosystems, but fire must be applied knowledgably to recruit oaks and sustain their presence in the overstorey while controlling competing vegetation, including non-native invasive species.

Attributes of Oak Fire Regimes Knowing the attributes of a fire regime – or of any disturbance regime – is essential for understanding how vegetation will respond to disturbances and for designing silvicultural prescriptions that integrate this knowledge with other factors to efficiently obtain the desired response in vegetation composition and structure. Fire intensity, frequency, season, extent, type and severity are important attributes of the fire regime that influence vegetation response to fire and hence landscape character (Davies, 2013; Scott

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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et al., 2014). Managers can exert various degrees of control over these fire regime attributes within the constraints and limits of staff, budget, time, conflict with other resource objectives, regulatory laws, statutory liabilities, size of land ownership, and acceptance of practices by adjacent landowners and communities. Fire intensity Fire intensity is one measure of fire behaviour that can be quantified and correlated with fire effects such as vegetation response to burning. Intensity is commonly defined as the rate of heat release per unit time per unit length of the moving fire front, and it is the product of the low1 heat of fuel combustion, weight of fuel consumed per unit area and fire rate-of-spread (Plate 2) (Byram, 1959). Rateof-spread largely determines fire intensity, because the two fuel components that contribute to fire intensity are nearly constant or vary within a narrow range. Thus, managers can achieve desired fire intensities by selecting the places and choosing the conditions under which they will burn. By doing so, they determine the factors that influence rate-ofspread. Rate-of-spread, and thus fire intensity, are affected by: (i) fuel structure (fuel type, amount, continuity, moisture content); (ii) weather (wind speed, precipitation, relative humidity); (iii) climate (vegetation productivity; seasonal patterns in lightning, drought and plant dormancy); and (iv) physical features of the land (topography, elevation, slope, aspect, soil type). Commonly reported fire line intensities for prescribed fires in oak systems range from an average of 100 kW/m (i.e. kilowatts per metre of fireline length) in dormant season burns to 300 kW/m in growing season burns to 1300 kW/m in dormant season burns in commercially thinned stands with higher fuel loading (Sparks et al., 1999; Kolaks et  al., 2007). Flame lengths may average from 1.6–3.2 ft. In hardwood forests, the type of leaf litter influences fire intensity, even among the oak species. Leaf litter from turkey, sand post, southern red, bluejack, white, post and blackjack oaks are more flammable and increase fire intensity compared with water and laurel oak (Kane et  al., 2008; Kreye et al., 2013; Varner et al., 2016). The invasion of fire-sensitive species such as red maple, sugar maple, American beech, elm and ironwood into oak stands reduces flammability of the litter layer, making it less likely that these sites will burn (Nowacki and Abrams, 2008). The increased biomass

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of tallgrasses and forbs in savannahs and woodlands not only adds to the loading of flammable fuels but also creates the physical structure that suspends oak leaf litter, thus increasing its rate of drying and flammability. Fire frequency Fire frequency is the number of fires that occur over a given time and place (Fig. 7.1). Hence, temporal and spatial scales are key determinants of the frequency of fire in a specific area (Stambaugh et  al., 2016). Fires are least frequent and their occurrence most variable at the tree scale, and most frequent and consistent in occurrence at the landscape, regional and global scales. The occurrence of a fire is predicated on all elements of the fire triangle (oxygen, fuel, heat/ignition) being in place in adequate amounts to initiate and sustain fire. The frequency of fire is determined by fuel, climate, topography and human factors. The production of fuels (especially fine fuels, e.g. 1-h timelag fuels2; Scott and Burgan, 2005), their accumulation and availability are important factors in fire occurrence. Climate, geology and soils are major determinants of site productivity that determine the potential for fuel production. Climate also affects fuel loading, availability and flammability in a number of ways. Climates with seasonal dry periods, droughts and periods of plant dormancy increase fuel inputs (e.g. leaf fall and shedding of other plant parts), fuel availability through concentration of fuels on the ground, and fuel flammability by regulating moisture content. Fuels accumulate to a greater extent in cool, dry climates due to low decay rates. Fine fuels are most dynamic in their response to climate, and they greatly influence fire ignition and spread. Climate also defines seasons of lightning activity, especially in relation to fine fuel availability and flammability (the annual burning window). Arid climates (deserts) have high fuel receptivity (low moisture content) but low fuel productivity and continuity, and hence low fire frequency. Temperate and tropical rainforests have high fuel productivity but low fuel availability for ignition. Lightning, if prevalent in these systems, often occurs with heavy precipitation. Moreover, live fuels dominate and fuel moisture contents are high, thus limiting fire ignition and spread. Fire frequency is potentially highest in temperate climates that support moderate vegetation production, have seasonal drought, lightning activity without rain and periods of dormancy

Chapter 7

1700

1850

1900

1950

COMPOSITE ALL SERIES MIN SCARS = 1 MIN SAMP = 1

19 80

1 1 95 1 95 2 19 956 5 58

19 10 19 19 17 20 19 19 25 19 28 31 19 34 19 42

18 19 99 02

1 18 869 71 18 78

4

1800

1 18 816 18 1 18 1 82 26 1 82 7 18 831 9 18 33 18 31 35 1 18 1838837 39 1 18 841 18 1845 43 18 46 48 18 1850 51 18 1856 58

18 0

1 178 17 782 1 84 17 91

5

1750

17 17 1767 65 68

17 5

1 17 732 33 17 39

16 82

16 91 16 16 95 98 17 17 05 08 17 14 17 20

16 72

Fire and Oak Forests

25 1 5 18 21 32 22 14 34 19 15 28 33 31 23 29 27 24 13 12 20 2 7 30 16 10 9 8 11 6 4 17 3

2000

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Fig. 7.1.  A composite fire chronology of Brickyard Hill Conservation Area, Missouri. (From Stambaugh et al., 2006a.) This fire history represents a commonly observed change in fire frequency associated with changes in human history and land use that has played itself out from Native American occupation through European immigration as experienced throughout the range of oak in North America, albeit there is variation in fire history from site to site. The x-axis is calendar years and y-axis (right side) is a list of sample numbers. Horizontal bars correspond to each sample number and represent the period of record for that tree. Bold vertical bars indicate the year of fire injury. Pith dates (centre of tree) are represented by short, thin vertical lines at the left end of horizontal bars, while inside ring dates (pith absent, hollow centre) are represented by short, thin diagonal lines. Bark years (outside of tree) are represented by short, thin vertical lines at the right end of horizontal bars while outside ring dates (bark missing, partially decomposed) are represented by short, thin diagonal lines. A composite fire-scar chronology with all fire years is given at the bottom of the plot.

in vegetation (Davies, 2013; Scott et al., 2014). In North America and throughout the world, humans are the major source of ignitions. Before the modern era of fire suppression, fires were relatively more frequent in the places that humans roamed and inhabited (Pyne, 1982; Williams, 1989; Whitney, 1994; Pyne et  al., 1996; Delcourt and Delcourt, 1997; Cronan, 2003; Scott et al., 2014). The history of anthropogenic fire is dynamic and complex (see the section ‘The History of Fire and Oaks’ later in this chapter). During the past 300 years or longer, the occurrence of human-caused fire has been dependent upon: (i) population density (probability of ignition); (ii) surface fuel production (probability of fire ignition and spread); (iii) fuel fragmentation by grazing, agriculture and urban development (probability of fire spread); and (iv) changing cultural values as societies transform from subsistence to high technology economies (fire use to fire suppression) (Guyette et al., 2002). Fire season The season that fires occur has an important influence on the response of vegetation, both from an individual survival standpoint and from a population viability perspective. Seasonal variation in weather and fuel conditions is greatest in temperate regions, and historically fine fuels were most available and flammable for burning in the spring and autumn seasons. The probability of winter fires is greater in the south-eastern region of the USA, where permanent snow cover through the winter months is uncommon. Late summer fires are common in arid regions with Mediterranean climates (e.g. southern California), or in maritime, continental and subtropical climates (e.g. Florida). Lightning is an important source of fire ignitions and has its greatest activity in the late spring to summer months. Lightning strikes accompanied by precipitation and occurring in green fuels with high moisture content are less likely to result in ignition that spreads; this is characteristic of much of the US eastern oak region. In contrast, dry lightning storms in xeric forests of the south and west are more likely to result in wildfires, especially in years of drought. Fires that burn in the autumn, winter or early spring seasons are often referred to as dormant-season fires in the fire history literature, because the tree is not actively growing in diameter when scarred. Dormant-season fires are strong indications that fires are of human origin and not

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the result of lightning (Brose et al., 2013a; Aldrich et al., 2014; Stambaugh et al., 2014a; Lafon et al., 2017). Seasonality can be determined by the position of the fire scar within the anatomy of the annual ring, or its location between rings. In this instance, the dormant season in oaks begins in July–August when diameter growth ceases. In contrast, dormant season defined by leaf drop and the onset of winter may begin in October–December depending on location. This difference in definitions of dormant season causes confusion in debates over seasonality and prescribed fire. The seasonality of fire in relation to the biology, ecology and life cycle of plants affects a species’ persistence or dominance after fires. A plant’s ability to survive a fire is somewhat dependent on its physiological activity at the time of burning. Plants that are actively growing, which many are in late spring and summer seasons, are more susceptible to injury or death by high temperatures experienced during a fire (Whelan, 1995; Davies, 2013). It is often noted that fires in the growing season cause greater injury or higher mortality in the reproduction cohort of oaks and other hardwood species (Glitzenstein et al., 1995). The mode of reproduction (sexual or asexual), location of reproductive structures (above or below ground) and stage of reproductive development (mature versus immature) at the time of a fire are important determinants of a species’ ability to persist regardless of the fate of individuals. Reproductive structures located below ground (e.g. dormant buds, rhizomes, tubers and stolons) are better protected from the heat of fire because, in general, soil is a poor conductor of heat (Iverson and Hutchinson, 2002). Species that are able to store viable seed in the seed bank over years, especially those that have a chemical or thermal requirement to break dormancy, have greater regeneration potential following a fire. Prolific and annual seed producers, and species with serotinous seed-bearing structures are more likely to seed and colonize a recently burned area. Species that quickly reach reproductive maturity relative to the fire interval are more likely to build up a seed bank or have a supply of seed for regeneration. The species with seeds that are readily dispersed by wind, water or animals are more likely to colonize recently burned areas, especially those of large extent. Trees with rapid juvenile growth rates are better able to develop sufficient bark thickness to protect their cambium from fire or to grow tall enough to place their crown and reproductive structures above the

Chapter 7

heat of a fire (Midgley and Bond, 2013). Lowintensity fires in seasons when ambient air temperatures are high are able to attain lethal temperatures more readily than similar fires in cooler, dormant seasons. However, higher temperatures in summer are often accompanied with higher fuel moisture content, increased precipitation and higher relative humidity that act to inhibit fire ignition and spread in many climatic regions. Seasonal variation in precipitation and temperature, in conjunction with plant life cycles that promote the accumulation of dry, fine fuels, increases the availability, continuity and flammability of fuels, and hence, increases the probability of fire ignition and the rate of spread. Fire extent Fire spatial extent has both direct and indirect effects on vegetation composition and structure dynamics, as well as implications for ecosystem process and function that provide ecological feedback on vegetation change. There is little quantitative documentation on the extent of fires in the period before European immigration and settlement, but one can safely assume that fires, especially in drought years, burned over large areas before rural development and conversion to agriculture fragmented and altered the fuels landscape. Once ignited by lightning or humans, fires could burn tens of thousands of acres until extinguished by a change in weather or an encounter with a natural barrier to fire spread such as a waterway or a topographic change. Topography influences various attributes of a fire regime including fire frequency and size (Plate 3). Frequent fires – sometimes annual fires – were characteristic of the once extensive prairies in the eastern USA (Transeau, 1935; Anderson, 2006). In the past, prairies occurred most often on level to gently rolling terrain (e.g. < 4% slope) where fires could spread rapidly and extensively. As topography becomes more dissected, topographic roughness increases and fire frequency and size decreases (Guyette et al., 2006a; Stambaugh and Guyette, 2008a). Steeply dissected topography creates landscapes that have more natural barriers to fire spread such as waterways and protected mesic north-to-east slopes, which either physically oppose the advance of fire, or modify fire weather, fuel dynamics (e.g. continuity, loading and moisture) and site hydrology to restrict fire spread. Although fires can burn rapidly and more intensely up exposed, xeric, south-west slopes, the rate of fire

Fire and Oak Forests

spread – especially that of low-intensity backfires – is reduced on north-east slopes due to cooler and moister conditions, increased fuel moisture, changes in fuel loading and flammability, and increased tree density, among other factors. With less frequent fire on protected and mesic sites, trees increase in dominance and density, and forests develop complex vertical structure with the formation of a midstory (or subcanopy) and shrub/tree understorey (Fig 5.1). Over time, shade-tolerant species such as maples, elms, hackberry and ironwood increase in dominance on oak-dominated sites while the more flammable grasses and heliotrophic forbs are greatly diminished. Litter from shade-tolerant trees and shrubs has relatively low flammability, and it decomposes quickly to create a fuel bed of matted leaves. These conditions act to reduce the probability of fire ignition, the fire intensity, and the rate of spread. This mesophication that occurs in the absence of fire, or in areas of infrequent fire, creates a positive feedback that promotes succession and forest development towards more fire-resistant conditions (Nowacki and Abrams, 2008) (Figs 12.5 and 12.6, this volume). Fire type Three types of fire behaviour are recognized based on where in the fuel profile the fire is burning: (i)  subsurface below ground; (ii) on the surface burning through litter and low vegetation; or (iii) in the crowns of trees. Most historical fires in oak forests are believed to have been surface fires. Crown fires are more likely in areas where vertical fuel structure and loading is continuous and complex enabling the fire to ‘ladder’ from the surface into tree crowns. Crown fires are most likely in years of moderate to extreme drought when fuel moisture and weather conditions favour fire ignition and spread, and when weather and topography promote convection of heat into the tree canopy. Crown fires arise from surface fires but can become self-propagating through tree crowns depending on weather, topography and horizontal continuity of flammable fuels in tree canopies. Open oak woodlands and savannahs (see Chapter 12, this volume) are less likely to experience crown fires due to discontinuous fuels in the crown layer and a relative lack of vertical fuel structure and loading (ladder fuels). Crown fires occur where fire suppression has allowed midstorey canopies to infill with highly flammable vegetation such as eastern redcedar, western conifers, yaupon and mountain laurel.

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Fire severity Fire severity refers to the direct, short-term effects of fire on ecological and social systems (Davies, 2013; Midgley and Bond, 2013). Direct physical effects on ecosystems include loss, damage or changes to vegetation, fuels, water, soils, air, biodiversity, wildlife populations and other ecosystem components that are valued by society. Fire severity is commonly measured by remote sensing or ground surveys conducted before and after the fire (Stambaugh et  al., 2015). Fire severity is most commonly reported in terms of effects on vegetation and fuels. Typical measures include: (i) fuel consumption; (ii) crown scorch and bark char (i.e. injury, wounding) on tree boles; (iii) tree sprouting; and (iv) mortality (see Key and Benson, 2006). Fire severity is a function of fire intensity and duration of heating, and is influenced by weather, fuels, topography and fire behaviour. Hence, the historical fires that burned across large landscapes and over days or weeks were mixed severity fires. Mixed severity fires at the scale of the landscape arise because fire severity is higher during hot, dry, windy weather and when fires burn on steep south-west aspects, but it is lower on evenings with high humidity, on cool days with light precipitation, and on mesic, north-east aspects. Fire scarring on the boles of oak trees is more common on southern than northern slopes (e.g. Stevenson et al., 2008; Kinkead et al., 2017). Fire severity is also influenced by fuel type, loading, moisture content, structure and proximity to live vegetation. Fire intensity in fine, flashy fuels such as grasses and forbs can be high, but the duration of high temperatures is relatively brief, hence fire severity may be low, especially if the subject of interest is a large live white oak (white, post or bur oak) tree with thick bark. In contrast, fire burning through heavier fuels can smoulder and linger for longer periods of time, resulting in more damage to residual trees, especially when heavy fuels are burning against the trunk of a large tree, even an oak. Historically, mixed severity fires produced a landscape mosaic of diverse natural communities (prairie, savannah, woodland and forest) as fire interacted with topography, weather and fuels (Plate 3). Low-intensity fires may result in moderate to high fire severity in terms of damage to trees. However, damage is generally localized and severity is often low in relation to fuel consumption, and fire injury and mortality in larger trees (Regelbrugge and Smith, 1994; Smith and Sutherland, 2006; Kinkead et al.,

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2017). Low-intensity prescribed fires usually have a minor effect on the density of overstorey trees (> 10 inches dbh), commonly causing < 5% mortality after a single fire, and 10–20% after multiple frequent fires in a decade (Hutchinson et al., 2005a; Dey and Fan, 2009; Elliott and Vose, 2010; Fan and Dey, 2014; Arthur et  al., 2015). Repeated annual fires that maintain a low volume of surface fuels result in less overstorey tree mortality than periodic fires (e.g. every 4 years) with greater fuel loads (Knapp et al., 2017). In addition to tree mortality, fire severity also expresses itself in ecological and economic ways through wounding the lower bole of trees. The percentage of trees scarred by low-intensity prescribed burns has been observed to average 19% (trees > 6 inches dbh) after two prescribed fires in the Missouri Ozarks, but in stands that were thinned by commercial harvest that reduced stand stocking to 40%, bole scarring increased to 32% presumably due to higher fuel loading from logging slash that produced higher fire intensity (Kinkead et al., 2017). In comparison, estimates of the number of overstorey trees scarred by any one fire ranged from 10% to 50% in the era before European settlement (Stambaugh et al., 2018). The height of charring on the bole after a fire is significantly related to tree mortality (Arthur et  al., 2015). Char heights may be < 40 inches in typical dormant season prescribed fires in eastern hardwoods, and many wounds are small, covered by intact bark and are effectively compartmentalized in oak trees (Smith and Suther­ land, 1999). As the severity of fire injury to the boles of red oak increases, the loss of merchantable volume and product value also increases. The amount of loss is significantly and positively related to the initial size of the fire wound and the time since fire injury (Loomis, 1974; Stambaugh and Guyette, 2008b; Guyette et  al., 2012a; Marschall et  al., 2014). However, scars that are < 20 inches in height result in minor volume and value loss in red oak for up to 14 years after fire injury (see the section ‘Fire Effects on Tree Quality, Volume and Value’ later in this chapter) (Marschall et al., 2014). Fire severity in relation to vegetation depends on individual plant attributes that promote survival and persistence after burning. Fire damage to plants is influenced by traits that confer protection from lethal temperatures during burning (e.g. thick bark, tall stature) or that promote survival by underground vegetative buds and reproductive structures that are able to sprout after a fire top-kills the aerial

Chapter 7

portion of the plant. Buried seeds, such as acorns, that are preferred by animals who cache their food in the ground are often protected from lethal temperatures by the insulating properties of the soil. A plant’s life stage (i.e. seed, juvenile, mature adult) and physiological activity (i.e. actively growing tissues or not) at the time of burning influences the degree of fire severity. Actively growing plants are more vulnerable to fire injury and mortality than dormant plants. Many oak species have traits that help them to survive and persist in a frequent fire regime, but there are certain stages in their life cycle where they are vulnerable to fire mortality (i.e. as exposed seeds and small seedlings) (Arthur et  al., 2012). Also, not all oak species are equal in their ability to resist or tolerate fire. Due to thin bark, red oak species, and in particular scarlet oak, are more likely to be scarred by low-intensity fires than white oak species (Kinkead et al., 2017), and white oak species are more resistant to wood decay than are red oak species (Forest Products Laboratory, 1967). Compared with historical fire regimes, the severities of modern prescribed fires are more uniform. In deference to safety and public health concerns, prescribed fires typically burn in the low to moderate range with burns conducted over short periods of time (usually 1 day) under low to moderate fire weather conditions. Typically, fire scars to the lower bole of oak trees are small in size (Smith and Sutherland, 1999). Loss of volume and value can be minimized by harvesting in a timely manner (e.g, within 15–20 years after injury), before decay encroaches into the log scaling cylinder of the lower bole.

The History of Fire and Oaks Paleo-history When land plants appeared on the face of earth, the fire triangle was complete: fuel, oxygen, heat (lightning, volcanoes). And since that time 400 million years ago (Belcher et al., 2013), fire has been a major force shaping the earth’s vegetation. The presence of charcoal in sediments was widespread around the world at a time when the first forests had expanded to cover large areas. The level of fire fluctuated over time with changes in climate, O2 levels, CO2 levels and vegetation. The climate of continental land masses, beginning with Pangea, were characterized by annual wet and dry seasons that promoted the production of vegetation (fuel) and provided an opportunity for fires. Quercus first appeared in the fossil record in North America between 50 and 55 million years bp

Fire and Oak Forests

(Crepet and Nixon, 1989a, b; Patterson, 2006), and by the end of the Paleogene (~23 million years bp) oaks were widespread in the northern hemisphere (Nixon, 2006; Patterson, 2006). Oaks were present in mixed-mesic hardwood forests that replaced boreal conifers in the early Holocene (approximately 12,000 years bp), expanding their distribution and abundance under favourable climate-driven fire regimes (Foster et  al., 2002; Camill et  al., 2003; Faison et al., 2006; Patterson, 2006). Early in the Holocene, climate was the main driver of fire activity and firemediated changes in vegetation before human settlement, and when human populations were low (Guyette et al., 2002; Marlon et al., 2008; Munoz et  al., 2010; Abrams and Nowacki, 2015). The relationship between major climate variables (e.g. annual precipitation and mean maximum temperature) and the probability of fire has been demonstrated in statistical models used to understand fire dynamics in ecosystems located in different climate zones, and to predict fire frequency for both historical (c.1650–1850) (Plate 4) and future periods (Guyette et al., 2012b, 2014, 2017). A thermal maximum and period of high aridity occurred during the Holocene period approximately 8000–3000 bp in North America, the timing of which varies geographically (Baker et  al., 1992; Camill et al., 2003; Faison et al., 2006; Nelson et al., 2006). During this period of warm, dry climate, there were shorter-term climatic variations. It was during this time that oaks rose to their greatest distribution, abundance and dominance in North America, especially where humans used fire to manage the landscape for their social and economic benefit (see Chapter 1, this volume) (Day, 1953; Delcourt, 1979; Pyne, 1982; Winkler, 1985; Baker et  al., 1992, 1996; Delcourt et al., 1998; Foster et al., 2002; Camill, et al., 2003; Cronon, 2003; Delcourt and Delcourt, 2004; Ander­ son, 2006; Mensing, 2015). A variable fire regime interacting with climate, soils and landform resulted in landscapes dominated by prairie grasses and forbs or by savannahs, woodlands and forests dominated by oak, hickory, chestnut or pine. Specific combinations of fire regime attributes, in particular fire frequency and intensity, favoured prairies over woodlands, pine over oak, and oak over northern hardwoods. Oaks prospered during periods of higher fire activity during interglacial periods when climates were drier and warmer, and they fell to low abundance and persisted in refugia during glacial maxima (Abrams, 2002, Camill et al., 2003; Mensing, 2015). The correlation between levels of charcoal and oak pollen percentages

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in ancient lake sediments is significantly positive, with highs and lows in fire activity and oak pollen abundance varying temporally across the distribution of oak in North America in response to changing climate, dynamic fire regimes, differing topography and soils, and fluctuating human populations and cultures (Baker et al., 1996; Foster et al., 2002; Camill et al., 2003; Faison et al., 2006; Nelson et al., 2006). It was also during this time period that humans migrated and rapidly settled across North America and transformed paleo fire regimes dominated by lightning ignitions into anthropogenic fire regimes (Delcourt and Delcourt, 1998; Foster et al., 2002; Nelson et al., 2006; Patterson, 2006). In North America, humans were widely distributed at the beginning of the Holocene, about 10,000–15,000 bp, with dates of occupation varying by region of the continent (Delcourt et al., 1998; Goebel et al., 2008; Mensing, 2015). Historical to modern era Before European migration to North America, increasing populations of Native Americans have been linked to increased fire occurrence since at least the middle Holocene (c.6000 years bp) (see Chapter 1, this volume; Winkler, 1985; Delcourt and Delcourt, 1998; Delcourt et  al., 1998; Foster et  al., 2002; Abrams and Nowacki, 2008). First, hunter-gatherer societies used fire in agriculture and horticulture to manage native animals, plants and trees (including oaks). Then with the development and introduction of domesticated crop species, more sedentary agricultural societies used fire to culture squash, beans and maize on small family plots and in large fields surrounding villages. They also used fire to maintain prairies and savannahs for grazing herds of native ungulates (Anderson, 2005; Abrams and Nowacki, 2008; Lightfoot et al., 2013; Tushingham and Bettinger, 2013). For millennia, human-set fires largely determined fire regimes as Native Americans settled throughout North America. The relationship between increasing oak dominance and historical human land use, including fire, is strong throughout the latter Holocene before European immigration (Delcourt and Delcourt, 1997; Abrams, 2002; Dey, 2002; Black et al., 2006). The exact timing of increases in oak abundance and dominance varied by site-­ specific fire history and fire regime attributes (Winkler, 1985; Guyette et al., 2002; Nelson et al., 2006; Stambaugh and Guyette, 2008a). Coinciding with the arrival of Europeans to North America, the dramatic changes in fire regimes and

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land use over the past 400 years resulted in rapid shifts in forest vegetation species composition and size structure (Winkler, 1985; Foster et  al., 2002; Patterson, 2006). Soon after landfall, European diseases spread rapidly and decimated Native American populations (Dobyns, 1983; Ubelaker, 1988; Verano and Ubelaker, 1992). In regions where native populations declined precipitously, fire occurrence often decreased, and extended fire-free periods (e.g. 50–100 years) have been observed in several eastern fire histories (Guyette et  al., 2003; Hart and Buchanan, 2012; Brose et al., 2015). This period of low Native American population density in many places came to an end when total population in a region increased due to the migration of tribes pushed westward by the advance of European settlement, and then due to displacement of native populations by European settlers. From that time forward, fire frequency increased with increasing population density until the beginning of modern fire suppression in the 20th century. Today, we are living in the era of fire exclusion, which represents a most novel set of circumstances in the history of disturbance regimes in North American oak ecosystems. Fire-free periods in many places have been longer since the start of modern fire suppression than any other time in the past 400 years (Stambaugh and Guyette, 2006; Aldrich et  al., 2010; Stambaugh et  al., 2011b, 2018). This progression of shifting populations, cultures, land uses, economies and fire activity is a common history throughout North America, especially in the range of oak–pine ecosystems (e.g. Greenlee and Langenheim, 1990; Frost, 1998; Guyette et al. 2002; McEwan et  al., 2007). It is widely accepted that long-term fire regimes throughout areas of oak–pine dominance are strongly influenced, even dominated by anthropogenic ignitions. This does not diminish the role that lightning plays in certain regions in maintaining the presence of fire on the landscape in the absence of human occupation or activity, but lightning alone cannot account for the dendrochronological record of fire frequency in oak ecosystems (e.g. Harmon, 1982; Greenlee and Langenheim, 1990; Loope and Anderton, 1998; Aldrich et  al., 2014). The history of fire leading up to the modern day fire-suppression era, in conjunction with other land use activities such as logging and agriculture, set the stage for oak–pine regeneration and dominance once fire was excluded as a common, widespread disturbance, and forests concurrently began recovery from timber exploitation and overgrazing practices.

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Changes over time in fire attributes Fire attributes have varied over the last thousand years in response to climate and human influences. The following sections describe temporal changes in fire attributes that have been important in shaping forest conditions for oaks and associated species. Changes over time in fire severity Over the past millennia in the USA, individual fires commonly burned with low intensity in mixed oak– hardwood and oak–pine forests, based on studies of charcoal in sediments of bogs and lakes, and soil profiles (Hart et al., 2008; Fesenmyer and Christensen, 2010; Hart and Buchanan, 2012; Lafon et al., 2017). Low-intensity fires such as these are often low in severity, causing little mortality in large overstorey oaks and pines (Horney et al., 2002; Hutchinson et  al., 2005a; Fry, 2008; Arthur et  al., 2012; Dey and Kabrick, 2015). Fire histories derived from treering-dated fire scars over the past 500 years give testimony to a mixed severity fire regime across oak– pine landscapes. Based on the percentage of trees scarred in a given fire year, it has been shown that the fire regimes included frequent low severity fires and less frequent moderate to high severity fires (Fulé and Covington, 1994; Stephens, 1997; Kaib, 1998; Purcell and Stephens, 2005; Stambaugh and Guyette, 2006; Aldrich et  al., 2010; Stambaugh et al., 2014a, 2018; Marschall et al., 2016). Surface fires of low to moderate severity seldom caused stand replacement, but they often left a variable density overstorey of multi-aged large trees (Dey and Guyette, 2000; Guyette et al., 2006a, 2016; Stambaugh and Guyette, 2006; Stambaugh et  al., 2009, 2014a, 2018; Marschall et al., 2016). Before the modern fire suppression era, low-intensity fires typically burned in the dormant season when they were less likely to injure the cambium on larger trees because of the insulation of thicker bark, reduced tree physiological activity, high relative humidity and low water stress. Moderate to high severity fires burned with a periodicity (e.g. 10–40 years) that is often associated with cycles of severe and multi-year droughts in a region (Stambaugh et al., 2005, 2014a, 2016, 2018). Across the USA, the historical fire landscape was commonly one of mixed severity with moderate to high severity fires occurring at finer scales within a matrix of frequent, widespread, low severity fires. Fire patch size and location were determined by the interaction of weather, topography and fuels.

Fire and Oak Forests

The variability in fire severity during the Native American period resulted in diverse natural oak communities occurring across the landscape (Stambaugh et  al., 2014a). Fire severity declined and was more consistently low in the European settlement period when, in many places, fire frequency was at its highest in the past 500 years (Stambaugh et al., 2009, 2014a; Allen and Palmer, 2011; Brose et  al., 2013a). Low severity fires in oak ecosystems are common in frequent fire regimes (e.g. fire occurring less than every 5 years) (Aldrich et al., 2010; Knapp et al., 2015). Increased fire frequency: (i) inhibits accumulation of large fuels; (ii) reduces total fuel loading; (iii) reduces complex fuel structure; and (iv) promotes development of fine grass and forb fuels in the more open woodlands (Fig. 7.2). An abundance of fine grass– forb fuels promulgates a frequent fire regime, because those fuels are able to dry out quickly, ignite readily and carry fire rapidly. However, frequent grass fires typically cause less damage to residual overstorey trees than less frequent fires burning hardwood litter and woody fuels (Purcell and Stephens, 2005; Stambaugh et  al., 2014a). After a fire, fine fuels recover to equilibrium levels rapidly (e.g. there is 75% recovery of fine fuel loading within 4 years after a fire in Central Hardwood oak forests) (Stambaugh et al., 2006b). Hence, no fire for 4 years allows near maximum fine fuel accumulation in oak forests and closed woodlands. In the current fire suppression era, high severity fires are more probable when wildfires burn out of control under high fire-danger weather and severe drought in forested landscapes characterized by dense forests with a complex fuel structure and high loading of woody fuels (Stam­ baugh and Guyette, 2006; Stambaugh et al., 2009, 2014b). Changes over time in fire season Dendrochronological analyses of fire scars covering the past 400 years in oak–pine ecosystems in North America show the majority of fires (often > 90% of fires) burned in the dormant season (September/ October through to March/April and outside the period of active diameter growth). Dormant-season fires prevailed in: (i) the Ozark Highlands of Missouri and Arkansas (Guyette and Spetich, 2003; Stam­ baugh et al., 2005; Guyette et al., 2006a; Stambaugh and Guyette, 2006); (ii) the Cross Timbers Region in Oklahoma and Texas (Stambaugh et  al., 2009, 2011b, 2014b, 2016; DeSantis et al., 2010; Allen and Palmer, 2011); (iii) the Allegheny Plateau and Ridge

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(A)

(B)

(C)

Fig. 7.2.  Fuel models are used to predict fire behaviour, and they vary among the various oak natural community types ranging from (A) oak savannah (fuel model 3, or GR8 or GR9; tallgrass, heavy, coarse continuous grass (grass 3–5 ft for GR8; 5–8 ft for GR9), subhumid to humid climate); (B) open oak woodland (fuel model 2, or TU3; moderate litter load, timber–grass–shrub, humid climate); to (C) closed oak woodland and oak forest (fuel model 9, or TL6; dead and down woody fuel (broadleaf litter) beneath forest canopy, moderate load) (Anderson, 1982; Scott and Burgan, 2005). (Photographs courtesy of USDA Forest Service, Northern Research Station.)

and Valley of Pennsylvania (Brose et al., 2013a, 2015; Marschall et al., 2016; Stambaugh et al., 2018); (iv) the Interior Low Plateau of Tennessee (Stambaugh et  al., 2016); (v) the Allegheny and Cumberland Plateaus of Ohio and Kentucky (McEwan et  al., 2007); (vi) the central and southern Appalachian Highlands (Shumway et al., 2001; Hoss et al., 2008; Aldrich et al., 2010, 2014; Flatley et al., 2013); and (vii) the Superior Uplands and Central Lowlands of Wisconsin (Wolf, 2004; Sands and Abrams, 2011; Guyette et al., 2016). On more mesic sites, primarily in the eastern USA, growing-season fires prevailed in the late spring to early summer after initiation of diameter growth; late summer fires were rare but did occur in drought years (Stambaugh and Guyette, 2006; McEwan et  al., 2007; Aldrich et  al., 2010; Brose et al., 2013a; Lafon et al., 2017; Stam­baugh et al., 2018). Dormant-season fires are characteristic of anthropogenic fire regimes, and the seasonality

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of fires in oak systems remained fairly consistent among historic periods over the past 400 years. The proportion of growing-season fires was higher in the southern Coastal Plain pine–oak forests and woodlands (Stambaugh et  al., 2011a) and in oak woodlands and savannahs in Mediterranean climatic zones in the western USA where dry lightning storms occur when sufficient fine fuels are cured to ignite and carry fire (Purcell and Stephens, 2005). Changes over time in fire size Large fires burned periodically in the past, and they continue to do so today. Mean fire size and the frequency of large fires have changed over time. In the Native American historic period, smaller fires were most common, but individual fires occasionally burned large areas (i.e. hundreds of thousands of acres in North American oak–pine ecosystems, especially in

Chapter 7

drought years) (Cwynar, 1977; Purcell and Stephens, 2005; Guyette et  al., 2006b, 2016; Clark et  al., 2007; Brose et  al., 2013a; Lafon et  al., 2017). In arid regions, where low precipitation typically limits fuel loading, large fires were more likely in drought years that were preceded by several years of above average precipitation. The precipitation increased production of biomass and subsequently of fuels (McEwan et al., 2007; Scasta et al., 2016). Collectively, fires occasionally burned millions of acres a year in oak–pine regions (Anderson, 2005; Guyette et al., 2006a; Lafon et al., 2017). Larger fires burned with some periodicity every 10–40 years in synchrony with regional drought cycles (Guyette et al., 2006a; Hoss et  al., 2008; Flatley et  al., 2013). But even low-intensity fires on plains’ landforms could burn millions of acres when they were not suppressed and human population density was low – as it was during periods of Native American depopulation (Stam­ baugh and Guyette, 2008a). Then, fire size was strongly influenced by topography, with large fires occurring commonly on the plains and topographically smooth land surfaces where they could spread rapidly through grasslands and savannahs. Mean fire size decreased in topographically rough, dissected areas dominated by woodlands and forests. In general, mean fire size decreased in the early European settlement period due to landscape and fuel fragmentation caused by changes in land use. However, the cumulative area burned annually remained large due to the many small fires that were set in an ignition-saturated landscape (Aldrich et  al., 2010; Hart and Buchanan, 2012). The link between drought and fire was weakened during the European settlement period when human-set fires burned in both wet and dry years (Guyette et  al., 2006a; McEwan et  al., 2007; Flatley et  al., 2013; Stambaugh et  al., 2018). In the modern era, most fires are suppressed when small in size (< 100 acres), but occasional large fires occur in severe drought years. In 2011, the Bastrop Fire Complex in Texas burned > 34,000 acres during a severe drought year when all fires that year burned 4,000,000 acres in the state. Large fires also burned throughout Oklahoma and Kansas in 2011. Similarly, severe drought in 2016 contributed to fires burning > 119,000 acres throughout the south-eastern USA, including the infamous Chimney Tops 2 fire in Tennessee that burned > 17,000 acres and the town of Gatlinburg. Most recently, the 2017 Thomas fire in California burned > 281,000 acres contributing to a total area burned that exceeded > 505,000 acres. Large fires are uncommon in oak

Fire and Oak Forests

forests today, but when they occur in xeric oak regions, severe fires can be stand replacing (i.e. they kill the overstorey trees and reset affected stands to the stand initiation stage of development; see Fig. 5.13, this volume) (Stambaugh et al., 2014b). Changes over time in fire frequency Fire frequency in oak systems has changed significantly over the past 400 years. Many researchers have divided this historical period into Native American depopulation, initial–early European settlement, European regional development and modern fire suppression eras (Guyette et  al., 2002; McEwan et al., 2007; Aldrich et al., 2010; Stambaugh et al., 2011a; Brose et  al., 2013a). Due to a lack of tree ring samples that date back much before 1600, dendrochronological fire history studies have not yet fully documented the role of fire before European diseases influenced Native American fire regimes. Fires have always been frequent in oak–pine regions but fire regime attributes were dynamic (Guyette et al., 2012b; Lafon et al., 2017). Temporal and spatial variability in fire frequency was generally highest in the Native American historic period (Guyette et  al., 2006b). Periods of frequent to annual low severity fires were common, being intermixed with periodic higher severity fires and infrequent, extended fire-free periods lasting 30–100 years (Guyette et al., 2003; Brose et al., 2015). Fire frequency was linked to the dynamism of human population density, movement, conflict and economy, from the site to landscape levels. In most published oak–pine fire histories, fire frequency increased to its highest level beginning with European settlement when it was consistent and nearly annual in occurrence (Guyette and Spetich, 2003; Purcell and Stephens, 2005; Stambaugh et al., 2006a; Clark et al., 2007; Engbring et al., 2008; DeSantis et al., 2010; Flatley et al., 2013; Brose et al., 2015; Stambaugh et al., 2017a). This period of high fire frequency has been depicted as a wave that has rolled westward from the Atlantic seaboard as Europeans moved towards the Great Plains (Stambaugh et al., 2018). Some exceptions to this trend of increasing fire frequency with European settlement following Native American depopulation have been reported, noting that frequent fire continued unabated between Native American and European periods (Hoss et al., 2008; McClain et al., 2010; Lafon et al., 2017). Between 1900 and the 1950s, modern fire suppression began in most places, and currently we are essentially in a

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novel fire-free period. Despite thousands of humancaused fires in most states each year, most fires are suppressed when they are quite small, virtually eliminating fire from the landscape until that infrequent year of severe drought when catastrophic fires burn at high intensity and severity over large landscapes. The history of fire over the past 500 years is a history also of oak dominance. Periods of frequent low-intensity fires inhibited development of oak competitors, reduced stand density, and promoted competitive oak regeneration. Less frequent but more severe fires or other intermediate-scale disturbances that resulted in overstorey mortality created large gap and patch openings in the canopy. Occasional fire-free periods of extended duration allowed oak regeneration, predominantly large vigorous oak advance reproduction, to recruit into overstorey canopy openings. As the oaks grew in size, they increased their resistance to fire injury when fires resumed (McClaran and Bartolome, 1989; Cutter and Guyette, 1994; McClain et  al., 2010; Stambaugh et  al., 2011a, b, 2014a). This variability in fire frequency and severity within what was on average a frequent fire regime enabled oaks to dominate more than 100 million acres in the USA. It resulted in continuous low levels of oak recruitment into the overstorey, accompanied by episodic pulses of cohort recruitment in extended fire-free periods, especially when it followed a more severe fire in a droughty period. This process created oak savannahs, woodlands and forests with irregular tree age structures and complex tree size structures (Dey and Guyette, 2000; Purcell and Stephens, 2005; Stambaugh et al., 2005, 2011a, b, 2014a; Hoss et  al., 2008; DeSantis et  al., 2010; McClain et al., 2010; Allen and Palmer, 2011).

Fire and the Oak Regeneration Problem Quercus has been a dominant and widely distributed genus throughout eastern North America for thousands of years, and it rose substantially in prominence through anthropogenic fire with the advent of human colonization of the region (e.g. Williams, 1989; Abrams, 2002; McWilliams et al., 2002). With European settlement, fire regimes became more consistent, frequent and ubiquitous on the eastern US landscape from about the 1850s to the 1930s (Guyette et al., 2002, 2012b; Arthur et al., 2012). With European migration westward, a wave of fire rolled westward from the eastern

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seaboard across North America until it reached the tallgrass prairie region (see Division 250 in Plate 1 and Chapter 1, this volume, Table 1.2) (Stambaugh et  al., 2018). European immigrants cleared and burned forests for agriculture, livestock grazing and forest products. The origin of many of today’s mature oak forests is from forest disturbances operating during the period of dramatic cultural changes in the latter 19th and early 20th centuries (see section above, ‘Changes over time in fire attributes’). Forest disturbances were collectively frequent, of low to moderate intensity, and widespread (Pyne, 1982; Williams, 1989; Whitney, 1994). Timber was selectively harvested (i.e. high-graded in contemporary terms) or commercially clearcut (i.e. cutting only the merchantable timber) (Clark, 1993). Forests were treated as open range for livestock in many regions and burned annually to improve forage in the understorey. Introduced tree diseases caused the loss of dominant species such as the American chestnut (Fig. 1.10, this volume). Marginal agricultural land was abandoned where it was not economical or sustainable for pasture or crops, and it was allowed to revert to forest. There was a drastic reduction in wildlife populations that consume acorns or browse oak reproduction such as the wild turkey, white-tailed deer and passenger pigeon (Dickson, 1992; Ellsworth and McComb, 2003; Rooney and Waller, 2003). In areas of charcoal production for the manufacture of iron, forests were coppiced repeatedly on short rotations, which favoured hardwood species able to sprout prolifically such as the oaks. From 1860 to 1920, much of the forest in the eastern USA was harvested on an unprecedented scale (Williams, 1989), and then repeatedly burned, sometimes with catastrophic results. Thus, commercial logging in conjunction with increasing fire on the landscape and high levels of domestic livestock grazing (Pyne, 1982; Guyette et al., 2002, 2012b) set the stage for the proliferation of oak forests when these disturbances subsided. Oaks were able to persist and develop under this regime of moderate to frequent disturbances that created low-density woodland structures, caused indiscriminate shoot dieback of advance reproduction among all species, and increased mortality in species sensitive to fire. Historically, fire maintained savannah and open woodland structures in which oaks were favoured (Fig. 7.3) (Curtis, 1959; Nelson, 2010). However, with European settlement, new land uses also were creating relatively

Chapter 7

Fig. 7.3.  Oak seedling sprouts are able to grow to be vigorous competitors under a regime of frequent fire in open stands. This is because sufficient light supports rapid recovery of sprouts after a fire that are then able to increase root and shoot biomass during the several years between fires. Through a repeated cycle of growth and top-kill from fire, oak sprouts are promoted while their competitors are weakened and reduced in density. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

open environments (e.g. forest clearings, recovering old fields or partial forest canopies) where light was sufficient to promote the development of vigorous, persistent oak sprouts. Meanwhile, disturbances such as fire and grazing kept oak competitors in check. In many cases, fire was the ubiquitous background disturbance that favoured oak reproduction development in these open forests or woodlands. Young and small-diameter oak trees and advance reproduction have a high capacity to sprout after cutting, burning or browsing (Fig. 2.25, this volume). This capacity to sprout vigorously increases exponentially as the basal diameter, a surrogate for size of root system, increases in oak seedlings and saplings (Sander, 1971; Loftis, 1990a; Dey and Parker, 1997). Oak advance reproduction can produce sprouts repeatedly if there is sufficient light and time between disturbances that top-kill the oak reproduction. Through repeated disturbances and resprouting, young oaks are able to build large root reserves as

Fire and Oak Forests

they preferentially allocate biomass to root growth and as understorey light levels increase due to canopy disturbances (e.g. repeated thinning or tending) (Brose, 2008). These factors increase oak’s ability to dominate regeneration during extended (10–30 years) disturbance-free periods, especially when the intensity of the final disturbance is sufficient to reduce overstorey density and create opportunities for recruitment of regeneration into the overstorey. Because historical patterns of land use and disturbance that favoured oak regeneration were so widespread, oaks became dominant after the commence­ment of widespread fire suppression around the 1930s provided them with the opportunity to thrive on a wide variety of sites over much of North America. Oaks proliferated even on the mesic and productive sites where oak regeneration and sustainability is most problematic today. The importance of this history is that much of the current oak forest was regenerated in the early 1900s, at the time that

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professional forest management and research were beginning in the USA. It took decades of stand development under new, low-disturbance regimes before contemporary oak regeneration problems developed and became apparent. The prevailing sentiment then was that oaks, which were dominant in the overstorey, would continue to be dominant when stands were regenerated. There were a few early reports in the literature of oak regeneration failures, but it was not until well after the 1930s

that concern for oak regeneration and sustaining oak timber supply became a priority (Clark, 1993). Since the early 20th century, the predominant methods for harvesting in eastern hardwoods were selective cutting (high-grading) or single-tree selection (McGee, 1972; Clark, 1993). By the 1960s, it had become obvious that these harvest methods favoured shade-tolerant species to replace oak in the absence of fire (Fig. 7.4). Thus, scientists at that time began advocating even-aged management, particularly

Fig. 7.4.  Single tree gaps or small group openings that form from natural mortality or single-tree selection cutting in a mature oak forest do not increase light to reproduction substantially. Consequently, oak reproduction remains small with low regeneration potential and survival probability over time. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

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clearcutting, for regenerating oak and other desired shade-intolerant species (e.g. Roach and Gingrich, 1968) and as a way to convert cutover, low-quality, hardwood forests into productive, diverse forests again (McGee, 1972). Early results from experimental clearcuts done in the 1930s showed that oak could dominate regeneration and sustain stocking into maturity (Kuenzel and McGuire, 1942; Liming and Johnston, 1944; Bey, 1964), but after decades of fire suppression and encroachment of shade-tolerant species in forest understories, oaks were often replaced by other species in clearcuts and group openings (Elliott et  al., 1997, Weigel and Parker, 1997; Morrissey et al., 2010). Since the 1950s, it has been increasingly observed that clearcutting eastern oak forests was resulting in stands being dominated by, in particular, yellow-poplar and red maple (Lorimer, 1984; Clark, 1993). In the west, oak forests were increasingly replaced by Douglas-fir, other western conifers and shrubs (Purcell and Stephens, 2005). The decline of oaks across North America and succession to other species was being noted (e.g. Abrams, 1998; Fei and Steiner, 2007; Fei et  al., 2011). On millions of acres where oaks are currently dominant in the overstorey (Plate 1 and Fig. 15.27, this volume), the regeneration potential of oaks is decreasing with time under modern disturbance regimes as overstorey oaks age and decline in stump sprouting capacity and acorn production ability, forest understories become increasingly dominated by shade-tolerant species, and oak advance reproduction fails to survive and grow in the shade of dense forests (Fig. 7.4) (Downs, 1944; Downs and McQuilken, 1944; Gysel, 1957; Goodrum et al., 1971; Weigel and Peng, 2002).

Fire in the Life Cycle of Oaks The potential impact of fire on oak trees depends in large part on the life cycle stage of an oak tree when it is burned. The following sections discuss fire effects on oaks in four developmental stages. Period of flowering, pollination and acorn production Many factors affect flowering, fertilization and acorn production including the exogenous variables, weather, site productivity (fertility and moisture), stand density (competing vegetation), insects and disease, and endogenous tree characteristics such as species, vigour, crown size and genetics (Downs and McQuilken, 1944; Beck, 1977; Sork et  al., 1993;

Fire and Oak Forests

Greenberg, 2000) (see Chapter 2, this volume). Few of these factors can be managed directly to promote acorn production. However, one important management action that can be applied in young stands to improve acorn production is the selection, at a young age, of individual trees to be the future acorn producers. Selected trees should include a diversity of oak species, and stand density should be managed to give those trees adequate growing space for maximum crown development. However, selecting good acorn producers in young stands that have yet to bear seed is problematic. In older stands, managers can identify the good acorn producers, provide growing space through stand thinning, and plan for their long-term retention from one rotation to the next to provide continuity in acorn production for wildlife and as a sustained source of regeneration. Prescribed fire is not effective for developing the stand density and species composition necessary to enhance acorn production in stands dominated by large saplings, poles or sawtimber trees. Fire is a blunt tool that is unable to selectively remove undesirable trees that are similar in size to the crop trees, especially if the trees to be removed are of the same species as the crop trees. As a stand grows, more intense fires are needed to kill increasingly larger trees. This increases the risk of either killing or severely scarring the crop trees and potentially compromising their vigour and longevity in the stand. Also, moderate to high intensity fires may damage the crown of crop trees, thus reducing their growth, vigour and health. Conclusive research is lacking on fire effects on oak flowering, pollination and seed production, but there appears to be limited shortterm benefit to burning, thinning, or burning and thinning to promote acorn production (Bellocq et al., 2005; Lombardo and McCarthy, 2008). Weevils are major insect predators of acorns and destroy much of the crop in years of low to average production (Gibson, 1982; Galford et  al., 1991; Lombardo and McCarthy, 2008). Many species of acorn weevils overwinter in the soil and litter as larvae or adults, and it has been speculated that burning to reduce leaf litter habitat and thermal insulation might reduce weevil populations, as would direct fire mortality of weevils if burning were timed with emergence (Gibson, 1964; Wright, 1986). However, there have been no definitive reports of the successful use of fire to control acorn weevil populations (Lombardo and McCarthy, 2008). Most studies have only assessed fire effects on weevil populations at the stand scale (i.e. < 40 acres). The distance that weevils

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are able to disperse varies by species (Pélisson et al., 2013), but even the least mobile weevil seems able to disperse into recently burned stands from adjacent oak forests. The potential to reduce weevil populations probably requires the application of frequent fire on the landscape level. In summary, there appear to be few benefits associated with prescribed fires or wildfires occurring when oaks are flowering or shedding pollen. Nor does fire offer benefits in the retention and management of oak trees identified as future acorn producers. Period of acorn germination and seedling establishment Sound acorns that fall to the ground have the potential to germinate and produce seedlings, but there are a number of threats to regeneration success in this process. Acorns lying on the surface of the forest floor are exposed to predation, damage and desiccation (García et al., 2002). Acorns are recalcitrant seed, and loss of viability begins when moisture content falls below 45% for white oak species and 25% for red oak species (Korstian, 1927). Acorns buried in mineral soil by small mammals or birds are better protected from predation and desiccation than seed lying on the surface (Fig. 7.5) (García et al., 2002; Kostel-Hughes et al., 2005), and buried acorns are insulated from the heat of surface fires (Iverson and Hutchinson, 2002). Seeds located on the soil surface under leaf litter and other more matted and

(A)

decomposed organic layers are also better able to maintain seed moisture and have higher germination rates. However, increasing litter depth reduces seedling emergence (Barrett, 1931; García et al., 2002; Kostel-Hughes et al., 2005). Prescribed burning before a good acorn crop can be used to reduce deep leaf litter (e.g. > 2 inches) and to increase understorey available light by killing vegetation in the midstorey. Both outcomes are beneficial to oak seedling emergence and early growth (Wang et al., 2005; Royse et al., 2010; Brose et al., 2013b, 2014). However, seasonal timing of fire is important in relation to seed fall and litter cover. It is desirable to have some depth (0.75–1.5 inches) of continuous leaf litter cover to shelter acorns from desiccation through the winter, especially in areas that lack permanent snow cover in winter (Barrett, 1931). Hence, autumn fires in the year of a good acorn crop may leave acorns exposed during the winter. Burning will need to be repeated (e.g. < 5–6 years) to maintain optimal litter cover for oak regeneration because yearly leaf fall input rapidly accumulates (Stambaugh et  al., 2006b). However, burning should be temporarily withheld following a good acorn crop to allow for the establishment of oak seedlings that can vigorously sprout after the next fire (Brose et al., 2014). Acorns mixed in loose leaf litter and young oak seedlings (i.e. < 3 years old) are more vulnerable to being killed by burning, and most of the acorn crop can be destroyed in a lowintensity dormant-season fire (Fig. 7.5) (Johnson,

(B) (B)

Fig. 7.5.  Acorns are generally highly susceptible to mortality when they experience a low-intensity fire, but their location is important in determining their probability of survival. Acorns on or mixed in the leaf litter are vulnerable to mortality. (A) Northern red oak acorns lying mixed within the surface leaf litter experienced 90% mortality, but (B) those buried beneath older, matted, moist or frozen organic layers, or buried in mineral soil were better insulated from the fire and able to survive and germinate. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

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1974; Auchmoody and Smith, 1993; Cain and Shelton, 1998; Greenberg et al., 2012). Acorns do not remain viable in the seed bank for more than about 6 months before they either germinate or are consumed. In contrast, the seed bank may be thick with viable seed from trees and shrubs that compete with oak. Competitors may be consistent, prolific seed producers in and around the stand, and they may have seed that can remain viable for years (e.g. 4–7 years for yellow-poplar, 2 years for red maple, 2–3 years for black birch, > 100 years for Rubus) (Burns and Honkala, 1990; Schuler et al., 2010). Prescribed fire can reduce the supply of viable seed of oak competitors such as black birch, yellow-­ poplar, red maple and grapevines (Vitis spp.) that occur in the seed bank, but repeated fires are needed to be effective, especially if seed-bearing trees occur in or near the stand to continuously add to the seed bank (Schuler et  al., 2010). The initial fire may stimulate germination of some oak competitors, but a second fire the following year can cause high mortality of those new seedlings, and hence, reduce competition. Removal of seed trees of undesirable competitors in conjunction with a regime of prescribed burning can help to deplete competitor seed bank supply. Period of seedling development Many investigators are exploring the role of prescribed fire in fostering oak regeneration. Lowintensity fires are capable of causing death of the entire cambium on smaller diameter trees of any hardwood species. The susceptibility to cambial death and top-­ kill by a single fire is nearly equal for seedlings and smaller sapling-sized stems (< 4 inches at the root collar), almost regardless of species because the bark is thin for all trees in these size classes (e.g. Harmon, 1984; Hengst and Dawson, 1994; Dey and Hartman, 2005). Prescribed fire can cause high mortality in young oak advance reproduction with small root systems (Johnson, 1974, Dey and Hartman, 2005; Fan et  al., 2012). Tree mortality due to burning increases with decreasing tree diameter and is highest in the seedling size class. Many hardwood species are able to produce vegetative sprouts after girdling of the stem by one fire. However, large oak seedlings and saplings are better able to persist with repeated burning than their major competitors (Brose et al., 2013b). Oaks have several advantages over their competitors that help them survive and recover from fire

Fire and Oak Forests

including preferential allocation of carbohydrates to the root systems and a cluster of adventitious buds at the root collar, which is often located in the soil where the buds and roots are protected from the heat of fire by the insulating qualities of mineral soil (Fig. 7.6) (Kruger and Reich, 1997; Brose and Van Lear, 1998, 2004; Iverson and Hutchinson, 2002; Iverson et  al., 2004, 2008; Keyser et  al., 2017). Also, bark thickness of many oak species is thicker at given diameters and accumulates at a faster rate with diameter growth than species such as red maple, conferring greater protection to cambial tissue from the heat of fire. Even among the oaks though, there is variation in bark growth and characteristics that affect fire resistance such as the difference between the fire-resistant bur oak and the relatively firesensitive scarlet oak (Scowcroft, 1965; Hengst and Dawson, 1994; Dey and Hartman, 2005). Presumably, the relative abundance and competitiveness of oak advance reproduction can be increased by prescribed burning (Brose et al., 2013b), but this outcome is not guaranteed in all stands (Alexander et  al., 2008; Green et  al., 2010; Schweitzer et  al., 2016). On many sites, decades of fire suppression has allowed abundant shade-tolerant advance reproduction and midstorey trees to develop beneath an oak canopy. In many such cases, repeated prescribed fires alone will not be sufficient to develop adequate advance oak reproduction to replace the oaks in the overstorey. Red maple, in particular, is a common, aggressive competitor of oak on many sites. It often dominates the advance reproduction and midstorey in mature oak forests, especially on dry-mesic to mesic sites. Reversing the mesophication of oak forests (Nowacki and Abrams, 2008) by maple invasion using fire alone is a long-term process (Figs 12.5 and 12.6, this volume). Small red maple seedlings suffer high mortality from fire (Iverson et al., 2008), but larger seedlings and saplings are prolific sprouters if topkilled by fire. As red maples become larger, sapling and pole-sized trees are increasingly resistant to top-kill by fire (Chiang et  al., 2005; Blankenship and Arthur, 2006; Schweitzer et al., 2016; Keyser et  al., 2017). As a shade-tolerant species, maples benefit from increases in understorey light that result from overstorey thinning or midstorey removal. They are a persistent competitive problem in the process of developing large oak advance reproduction, and the competitive status of oaks is not always improved by burning alone (Alexander et al., 2008; Green et al., 2010; Arthur et al., 2015; Keyser et al., 2017). Even though repeated fires over a decade or

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Fig. 7.6.  When buried in the soil, the root collar and set of dormant buds on oak seedlings are often protected from fire damage. Acorns are protected when buried by wildlife, and they further benefit from their hypogeal form of germination, whereby the cotyledons remain in the seedcoat located underground where they provide energy for shoot growth and emergences above ground (solid black line in photo). In contrast, seed of major competitors such as red maple and yellow-poplar exhibits epigeal germination in which the cotyledon emerges from the seed above the ground to form the first photosynthetic leaves. Their location above the ground makes them highly susceptible to damage and mortality by surface fire. From left to right are seedlings of white oak (WO), northern red oak (RO), yellow-poplar (YP) and red maple (RM). (Photograph courtesy of USDA Forest Service, Northern Research Station.)

more can significantly reduce the density of red maple and other oak competitors, their abundance is so high that the well-established and often larger survivors of prescribed fires are able to overwhelm small oak reproduction. Inconsistent reports on the effectiveness of fire to control red maple reproduction and increase oak dominance are often related to differences in the initial size structure of red maples, other shade-tolerant competitors and oaks. Initiating prescribed fire in closed-canopy stands with small oak advance reproduction is risky due to the potential for high mortality of oak reproduction (Alexander et al., 2008). Fire may also aggravate the intensity of competition by promoting the germination and establishment of light-seeded species such as black birch and yellow-poplar that are otherwise inhibited by litter cover (Keyser et  al., 2017). These species can dominate canopy gaps and open patches (Thomas-Van Gundy et al., 2014). However, they remain suppressed under an intact mature forest canopy because they are shade intolerant, leaving them vulnerable to mortality from subsequent fires.

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Having large oak advance reproduction is often recognized as the key to sustaining oak stocking in forests, woodlands and savannahs; new oak seedlings are not competitive and not all mature oak trees are capable of producing stump sprouts (see Chapter 2, this volume) (Dey et al., 1996; Weigel and Peng, 2002; Dey, 2014). Large oak advance reproduction (e.g. > 0.5 inches in basal diameter) are uncommon in modern oak forests, particularly on mesic sites. A common starting point for contemporary forest management is a fully stocked oak-­dominated forest with a shade-tolerant midstorey that permits < 10% of full sunlight to penetrate to the forest floor. Oak advance reproduction, if they exist, are small (e.g. 6 inches tall and < 0.2 inches in basal diameter). Their survival is precarious after newly germinated seedlings exhaust the energy stored in their cotyledons, unless they receive sufficient light to conduct photosynthesis and grow. Developing large oak advance reproduction often focuses on increasing available light to oak reproduction by managing stand structure (Schweitzer and Dey, 2011; Arthur et  al., 2012; Brose et  al., 2013b; Dey, 2014). Oak root

Chapter 7

and shoot growth response to increasing light varies by species, as does that of their major competitors (Gottschalk, 1994; Dillaway et  al., 2007; Paquette et al., 2007; Brose, 2008; Rebbeck et al., 2011). A serious conundrum is that releasing oak advance reproduction also invigorates competing vegetation, which must be controlled to promote oak development. Prescribed fire is an effective tool for reducing midstorey density, largely by top-killing saplings that are < 4 inches in basal diameter (Waldrop et al., 1992; Green et al., 2010; Hutchinson et al., 2012b; Arthur et al., 2015; Schweitzer et al., 2016; Kinkead et al., 2017). As tree dbh increases from 4 to 10 inches the ability of low-intensity, dormant-season fires to top-kill trees decreases, but such fires may reduce density of midstorey trees by a third (Blankenship and Arthur, 2006; Hutchinson et al., 2012b). Repeated fires in closed-canopy forests eventually reduce the number of sprouts per sapling and the capacity for resprouting in the low light understorey environment (Outcalt and Brockway 2010; Hutchinson et al., 2012b; Arthur et al., 2015). Removal of a midstorey canopy can increase understorey light to 10–20% of full sunlight (Fig. 7.7) (Lorimer et al., 1994; Ostrom and Loewenstein, 2006; Motsinger et al., 2010; Brose, 2011; Schweitzer and Dey, 2015). The increase in available light associated with midstorey removal benefits oak advance reproduction, survival and growth (Loftis, 1990b; Lorimer et al., 1994; Lhotka and Loewenstein, 2008, 2009; Brose, 2011), but the increase is ephemeral, lasting only 3–5 years (Lockhart et al., 2000; Miller et al., 2004; Schweitzer and Dey, 2015). Increases in understorey light also promote the growth of shade-tolerant advance reproduction (Parker and Dey, 2008). Frequent prescribed burns can sustain the moderate light levels by repeatedly top-­ killing the seedling and sapling sprouts. However, this increases the risk of mortality in small oak advance reproduction. Increasing the length of time between fires allows oak seedling sprouts time to recover height growth and continue building root mass, but it also gives competitors time to recover. Repeated burning over 10 years or more may increase the absolute or relative density of larger oak advance reproduction (Hutchinson et  al., 2012a; Waldrop et  al., 2016), but not always (Blankenship and Arthur, 2006; Arthur et al., 2015). Many attempts to use prescribed fire to increase abundance of large oak advance reproduction have made progress towards that goal, especially on sites with xeric, dry and intermediate moisture

Fire and Oak Forests

regimes. In many cases, however, large numbers of competitors remain, signalling that further investment is needed to shift the balance towards oak dominance in regeneration (Green et  al., 2010; Hutchinson et  al., 2012b; Arthur et  al., 2015; Waldrop et al., 2016; Iverson et al., 2017; Keyser et al., 2017). The optimal fire frequency varies by site conditions, overstorey density, species composition, size structure of competing vegetation, and size of oak reproduction. Annual burning over decades may eliminate all advance reproduction, though oaks are some of the most persistent species (Waldrop et al., 1992; Regelbrugge and Smith, 1994; Fan and Dey, 2014; Arthur et  al., 2015; Knapp et  al., 2015). Increasing the time between fires may increase fire intensity and cause higher mortality in small seedlings than annual burning due to accumulation of fine fuel between fires (Hutchinson et al., 2005a; Stambaugh et al., 2006b; Knapp et al., 2017). Growth of oak advance reproduction is increased by higher levels of available light following midstorey removal, but oak growth continues to increase with further increases in light (e.g. > 30% of full sunlight) obtained by thinning or shelterwood harvesting to reduce overstorey density (Gottschalk, 1994; Gardiner and Hodges, 1998; Brose, 2008, Hutchinson et al., 2012a). Interestingly, light environments under historical oak woodlands have been estimated to be between 25% and 50% of full sunlight (Blizzard et al., 2013; Hanberry et al., 2014a; Dey et al., 2017). Root growth in young oak advance reproduction is minimal at 15% of full sunlight but near maximum in chestnut oak and white oak at moderate light levels (e.g. 50% full sunlight), and in black oak and northern red oak at higher light levels (e.g. 90% full sunlight) (Fig. 7.8) (Brose, 2008). Increase in understorey light from < 3% full sunlight to 20% by midstorey removal also promotes the growth of shade-tolerant species, but when the overstorey is thinned (e.g. by a shelterwood harvest) the drought-sensitive, shade-tolerant species are limited in their ability to acclimate photosynthetically due, in part, to higher vapour pressure deficits and increased water stress (Parker and Dey, 2008). As understorey light increases above about 20% of full sunlight, oaks are able to achieve significantly greater rates of net photosynthesis over their shadetolerant competitors. Unfortunately, the growth of shade-intolerant competitors such as yellow-poplar and birches also increases, and the intensity of their competition increases as the stand becomes more open (Thomas-Van Gundy et al., 2014). The combination

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(A)

(B)

(C)

Fig. 7.7.  Low-intensity prescribed fires done in the spring (March/April) are effective in eliminating or significantly reducing density of the hardwood understorey in mature oak forests (A). Annual (B) or periodic (every 2–3 years) fires (C) over 10 years top-kill saplings that form the midstorey and inhibit redevelopment of the midstorey by causing mortality of understorey stems and repeated top-killing of survivors. Overstorey density is relatively unaffected by these burning regimes and growth of hardwood sprouts is reduced by shade of the closed overstorey canopy. Periodic burning allows for higher persistence and coverage of woody sprouts and herbaceous species in the understorey compared with annual burning. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

of released shade-tolerant advance reproduction and rapid growth of pioneer species in openings, following group selection harvesting, clearcutting or final shelterwood removal, have led to widespread failures in oak regeneration (Hix and Lorimer, 1991; Weigel and Parker, 1997; Jenkins and Parker, 1998; Harmer et  al., 2005; Morrissey et  al., 2010; Schweitzer and Dey, 2011). Fire after thinning or shelterwood harvesting can be used to help shift species composition of advance regeneration in favour of oak species. Fire may initially promote the germination

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and seedling establishment of shade-intolerant, pioneer species such as yellow-poplar and birches (Thomas-­ Van Gundy et  al., 2014), subsequent fires are effective at killing small seedlings of these species and shade-tolerant competitors, and top-killing larger seedlings and saplings (Brose and Van Lear, 1998; Brose et al., 1999; Ward and Brose, 2004; Iverson et al., 2008). Hence, combining shelterwood harvesting with prescribed fire usually benefits oaks. The shelterwood regeneration method combined with prescribed burning is a commonly recommended

Chapter 7

Fig. 7.8.  Root development in chestnut oak seedlings after 4 years of being planted as acorns in a mixed-oak forest that was (from left to right) uncut (5% of full sunlight), or treated by a shelterwood preparatory thinning (15% of full sunlight), first-stage shelterwood harvest (40% of full sunlight, or final shelterwood removal cut (90% full sunlight) according to Brose (2008). (Photograph courtesy of USDA Forest Service, Northern Research Station.)

for regenerating and sustaining oak forests, especially on xeric to dry-mesic sites (see Chapter 8, this volume) (Brose et  al., 2008, 2013b, 2014; Dey, 2014). Shelterwood and prescribed fire prescriptions for oak regeneration vary by site productivity to account for more abundant and aggressive competition on the more productive sites (Hutchinson et al., 2012b; Iverson et al., 2017; Keyser et al., 2017). Ideally, adequate oak advance reproduction should be present before this process begins. Otherwise, prescribed fire can be used as a site preparation practice to manage litter depth, suppress competitors arising from the seed bank, and control undesirable advance reproduction in the understorey and ­ midstorey before the next good acorn crop falls (see above) or oak underplanting is done (Dey et al., 2008a, 2012) (see Chapter 10, this volume). Oak advance reproduction is often small, and the goal of the shelterwood system is to incrementally increase the amount of light to encourage oak growth, especially oak root growth (Fig. 7.8) (Brose, 2008, 2011). The first shelterwood harvest reduces overstorey density by 25–50% to provide 30% or more of full sunlight (Fig. 10.5, this volume). At this point during the shelterwood harvest, the shade-tolerant

Fire and Oak Forests

midstorey should have been reduced by prescribed fire, mechanical means or herbicide. Higher overstorey density is retained on higher quality sites to retard the growth of fast-growing species such as yellowpoplar (Loftis, 1990b). Prescribed fire to control competing vegetation at this time may cause unnecessary mortality of small oak advance reproduction. Normally there is a 3 year ± release of oak from the shelterwood harvest alone before the competing vegetation starts closing in on the oak. Oak reproduction may be large enough to consider a lowintensity prescribed fire in the dormant season, or oak can be given another release by the final removal of the shelterwood. Clearing harvest slash from around the base of desirable trees reduces risk of mortality or bole damage (Brose and Van Lear, 1999). Prescribed fire after the final harvest is effective in controlling competing vegetation and may be repeated as necessary to maintain and increase oak dominance. When oak reproduction becomes large (e.g. > 0.75 inches in diameter, Brose et  al., 2006), higher fire intensity in the early growing season preferentially benefits oaks over their competitors (Brose, 2010). When sufficient numbers of large oak reproduction are free to grow, prescribed

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burning is ceased to allow recruitment of oaks and associates to form a closed-canopy stand. Period of recruitment into the overstorey Repeated fire, if frequent enough, keeps the overall population of seedlings and saplings in a perpetual cycle of top-kill and sprouting, unable to recruit into the overstorey (Waldrop et al., 1992; Peterson and Reich, 2001; Knapp et al., 2017). In a frequent fire regime, a sufficient fire-free period is necessary for individual stems of reproduction to develop resistance to fire mortality and top-kill so they can grow into the overstorey. This period is largely determined by the time it takes for bark thickness to increase sufficiently to protect the cambium of desirable trees against future fires. It may take 10–30 years for oaks to gain resistance to fire top-kill (Harmon, 1984; Signell et al., 2005; Dey and Kabrick, 2015; Dey et al., 2017). Fire-free periods of one to three decades occurred infrequently but were not uncommon in the historical Native American period throughout North American oak forests, woodlands and savannahs (Fig. 7.1). If timber production is the overriding objective, there may not be any further role for fire through the rest of the rotation until it is time to prepare for regeneration again. Bark thickness and the rate of bark accumulation varies by: (i) species; (ii) rate of diameter growth; (iii) source of reproduction (i.e. new seedling or stump sprout); (iv) intensity of competing vegetation; (v) overstorey density; and (vi) site quality (Varner et  al., 2016). For example, oak stump sprouts can reach critical diameters associated with sufficiently thick bark in 15–20 years when growing in the open on average quality sites in Missouri (e.g. site index 65 ft white oak, base age 50) (Dey et al., 2008b). Stump sprouts of white oak species generally take longer than red oak species to reach a given diameter, but white oaks may have thicker bark at given diameters and more rapid bark accumulation rates than red oaks, depending on species (Hengst and Dawson, 1994; Dey et  al., 2008b). In contrast, it takes 25–30 years for codominant white oak saplings growing in clearcut stands on sites of average quality in Missouri to reach diameters associated with resistance to top-kill from low-intensity dormant-season fires (Shifley and Smith, 1982; Arthur et al., 2012; Dey et al., 2017). Growth rates of reproduction beneath an overstorey decrease as overstorey density increases, and

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basal areas as low as 20 ft2/acre can significantly reduce the growth rate of red oak species (Vickers et al., 2014), thus lengthening the time it takes to develop fire resistance. Overstorey basal areas greater than 60 ft2/acre, near B-level stocking, can suppress oak stump sprouts so much that they remain less than 0.5 inches dbh 10 years after stand thinning (Dey et al., 2008b). Successful oak regeneration culminates with an adequate number of oaks in the upper crown classes in stands at canopy closure. However, continued recruitment of oaks into the overstorey still can be a precarious process with few certainties. On xeric sites of below average productivity, oak saplings are better able to maintain dominance without any silvicultural intervention as stands mature (Hilt, 1985; Morrissey et al., 2008; Steiner et al., 2018). In contrast, oaks growing on high-quality, mesic sites often are unable to compete with other species, and during the period of stand development from crown closure to maturity, the continued presence of oaks in the overstorey requires some form of silvicultural intervention (Ward, 2009). Oaks that are dominant at crown closure are most likely (e.g. 75% likelihood for northern red oak) to be dominant or codominant at maturity, but more than half of codominant oaks are destined to die or drop into lower crown classes (Fig. 7.9) (Ward and Stephens, 1994; Wang and Nyland, 1996; Zenner et al., 2012). Crop tree thinning around oaks during the stem exclusion stage of development is necessary to maximize the proportion of oak stocking at maturity (Miller et al., 2007; Ward, 2009). Thinning around oak crop trees increases diameter growth, bark thickness, fire resistance, crown development and acorn production. Prescribed fires usually kill or top-kill some trees, but fire is a poor tool for thinning stands at crown closure. Prescribed fires may kill desirable trees, scar desired crop trees, and they can be difficult to conduct because of low fine fuel loading and unfavourable microclimate in the understorey of these dense stands. Mechanical cutting or herbicide applications rather than fire, are preferred thinning methods.

Fire Effects on Ground Flora When managing closed-canopy forests to restore oak savannahs and woodlands, the focus is not normally on securing competitive oak regeneration, but rather, developing open, woody vegetation structure that promotes high quality native ground flora and provides quality wildlife habitat (see Chapter 12, this

Chapter 7

Crown class transitions for northern red oak from stand age 25 to 55 years

Initial crown class age 25

Suppressed

Intermediate

Codominant

Dominant

20

0

40

60

80

100

Suppressed

Dead

Probability of future crown class age 55 (%) Dominant

Codominant

Intermediate

Fig. 7.9.  In the development of even-aged hardwood forests, oaks must compete successfully to remain in the upper crown class strata all along to be dominant or codominant at maturity. This example of crown class transition for northern red oak from stand age 25 to 55 years is from mixed-oak forests regenerated by clearcutting in Connecticut. Without silvicultural intervention, dominant oaks at age 25 had an 86% chance of being in the dominant or codominant crown classes at age 55 (value of 86% is total outcome for dominant trees that stayed dominant (74%) plus dominants that became codominant trees (12%)). In contrast, only 44% of the oaks that were codominant at age 25 were in the dominant or codominant crown classes at age 55 (value of 44% is total outcome for codominant trees that became dominant (26%) plus those that remained codominant (18%)). Mortality was high for oaks that were initially in the intermediate or suppressed crown classes. (Adapted from Ward and Stephens, 1994.)

volume). Ground flora and wildlife habitat objectives drive the timing and sequence of silvicultural practices intended to develop the desired tree size structure and ground flora. Fire is different from other silvicultural tools because it is a recurring disturbance process that is essential to the existence, maintenance and functioning of these fire-dependent ecosystems. A common beginning point in oak savannah and woodland restoration (see Chapter 12, this volume) is a fully stocked mature oak forest with complex woody vertical structure. Modern forests have two to three times the tree density that former oak woodlands once had (Hanberry et al., 2014b, c), and woody cover significantly reduces floral species richness and productivity (Ratajczak et  al., 2012). Therefore,

Fire and Oak Forests

reducing stand density is a first step, as is reducing litter and fuel loads that have accumulated over decades of fire suppression. Acorn production capacity is not normally a limiting factor that needs to be considered, and future acorn crops that occur during restoration will inevitably add to the pool of oak advance reproduction over the decades. The general approach to restoration includes reducing stand density and periodically applying fire, which is a disturbance regime that inherently promotes accumulation of large oak advance reproduction. The details of how tree structure and fire are managed depends largely on desired future conditions of the ground flora, and the prescription is modified by: (i) initial stand conditions; (ii) physical environment; (iii) site quality; (iv) presence of invasive

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species; (v) initial floristic composition and structure; and (vi) the physiology and ecology of key indicator native flora. Native ground flora Knowing the historical and ecological role that fire played in determining the distribution and character of fire-dependent oak savannah and woodland communities, it may seem logical to assume that all one needs do to restore them is to begin a regimen of repeated burning. However, there is evidence that prescribed burning either annually or periodically (i.e. every 4 years) in the dormant season for over 60 years may still leave the overstorey canopy largely intact (Knapp et  al., 2015). During the modern period of fire suppression, many oak forests have experienced mesophication (Nowacki and Abrams, 2008), by which maturing trees have developed increased fire resistance due to thicker bark, and fine fuels have declined in flammability and availability. Increased concerns for human safety and health also limit the intensity and severity of prescribed burning that can be applied to affect woody structure. When trying to restore oak savannahs from mature forest conditions, the use of prescribed fire alone generally increases herbaceous species coverage, richness and diversity but improvements are small in magnitude due to the shade from a closed-canopy overstorey (Taft, 2003, 2005; Ralston and Cook, 2013; Lettow et al., 2014). Repeated low-intensity, dormant-season fires, whether annual or periodic, do not normally affect overstorey tree density at the stand scale or larger (Waldrop et al., 1992; Knapp et  al., 2015). Thus, improvements in the ground flora community are also ephemeral unless burning is repeated to control the sprouting and regrowth of trees and shrubs in the understorey. Density of understorey trees and shrubs have increased over decades of fire suppression; 20–40 years of annual summer burning may be needed to eliminate most of the understorey woody species (Waldrop et al., 1992; Knapp et al., 2015). Also, an important factor that governs the initial response of ground flora to burning is the abundance of propagules in the seed and bud bank. These may be depauperate and degraded from decades of agricultural land use or from years under heavy forest shade and deep litter (Ralston and Cook, 2013). Restoration of severely degraded sites may require artificial regeneration of the ground flora by seeding and planting. Guidelines exist for actively managing the recovery of ground

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flora through artificial regeneration (e.g. Packard and Mutel, 1997). Heavy tree canopy cover is a major limiting factor contributing to low diversity and productivity of ground flora (i.e. grasses, forbs and legumes) (Zenner et  al., 2006; Peterson and Reich, 2008; Ratajczak et al., 2012; Vander Yacht et al., 2017). Thinning the overstorey to reduce its density (i.e. removing larger diameter trees that are resistant to fire treatments) often increases herbaceous richness and coverage by increasing light levels at the forest floor (Hutchinson, 2006; Zenner et  al., 2006; Waldrop et  al., 2008; Kinkead et  al., 2017). Retaining a moderate density overstorey may benefit cool-season grasses, sedges and shade-tolerant forbs, but any tree cover inhibits warm-season prairie grasses and forbs that thrive best in the open (Peterson et al., 2007). The increasing species richness and coverage in the ground flora is accelerated when stand basal area is held less than 60 ft2/acre, which is below B-level stocking (i.e. where growing space is unoccupied by trees and thus available to ground flora in treeless openings) (Fig. 7.10; see also Figs 6.9 and 12.7, this volume) (Vander Yacht et al., 2017). However, these gains in ground flora restoration may be ephemeral, because an abundance of woody sprouts can rapidly form a dense midstorey that shades out the ground flora once again. However, thinning or harvesting trees in a way that leaves a variable density overstorey maximizes heterogeneity and increases diversity (Peterson et  al., 2007; Peterson and Reich, 2008; Vander Yacht et al., 2017). Thinning alone is no surrogate for fire when it comes to other ecosystem processes and functions such as nutrient cycling, litter dynamics, plant regeneration and competition, and community development (Hutchinson, 2006; Phillips and Waldrop, 2008). Restoration of a healthy, productive, diverse ground flora community can be accelerated by thinning the overstorey from below and implementing prescribed burning, which provides substantial increases in cover, species richness, diversity and plant productivity compared with burning or thinning alone. Repeated burning is effective in preventing the dominance of sprouting trees and shrubs after harvesting (Hutchinson et al., 2005b; Waldrop et al., 2008; Lettow et al., 2014; Kinkead et al., 2017). There are treatments that can precede harvesting or be done in conjunction with harvesting to reduce density, limit dominance of tree and shrub sprouts, or control an invasive species problem. Reducing

Chapter 7

(A)

(B)

Fig. 7.10.  Savannahs (A) usually support the highest level of plant diversity compared with other community structures such as prairie, woodland and forest. Heterogeneity of woody structure increases the diversity of microhabitats that promote plant diversity. Savannahs occur below the B-level of forest stocking (Fig. 6.9, this volume) and therefore have openings of full sunlight that favour high light-demanding species that are often found in prairie systems. Closed woodlands (B) occur in the fully-stocked area of the stocking chart and hence, have uniform tree canopies that fully occupy the site and cast more homogeneous shade on the ground. This reduces the diversity of ground flora and favours more shade-tolerant species. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

the regeneration potential of undesirable competing vegetation before thinning or harvesting the overstorey takes advantage of overstorey shade to reduce response and vigour of undesirable vegetation. Combinations of fire, herbicide and mechanical practices (e.g. cutting, scarification and mastication) can be used to minimize the response of undesirable vegetation after overstorey harvest. Maintain­ ing higher levels of overstorey canopy cover (e.g. > 70%) or density (e.g. > 60 ft2/acre) can suppress the growth of woody sprouts or invasive species in the understorey through shading (Dey and Hartman, 2005; Kinkead et  al., 2017), but this would also delay the restoration of savannah ground flora, which requires more sunlight. In the end, rapid reduction of the overstorey to meet long-term structure objectives for woodlands and savannahs promotes the greatest response in ground flora (Zenner et  al., 2006). A chart has been developed that is useful for managing stand density to produce desired crown cover in upland oak savannahs depending on overstorey tree size and density (Fig. 12.7, this volume) (Law et  al., 1994). Models have been developed that are useful for estimating available light in the understorey from overstorey crown cover, density, basal area or stocking for upland oak– hickory forests in the Missouri Ozarks Figs. 10.2 and 12.11, this volumes) (Blizzard et al., 2013; Dey et  al., 2017). These can be used to manage stand density to provide light levels necessary to promote desirable ground flora.

Fire and Oak Forests

In addition to using fire to restore and maintain the woody structure of savannah ecosystems, the restoration of fire as a disturbance that shapes the ground flora community is important. Fire promotes germination and growth of herbaceous plants by: (i) removing litter that acts as a physical barrier to germination and seedling establishment; (ii) preparing a receptive seedbed for colonization by wind and animal dispersed seed; (iii) breaking chemical and thermal seed dormancy by producing heat and smoke; (iv) releasing nutrients tied up in the litter; and (v) increasing light and temperature at ground level. Fire frequency, season, intensity and other attributes of the fire regime can be determined by the manager and various combinations can dramatically alter the direction of plant dominance and community succession. Each woody and herbaceous species is unique, but some generalities in how functional groups of plants respond to changes in the fire regime are worth noting. A fire in any season increases species richness and cover of ground flora to some extent, but the positive effects are temporary, and repeated fires are usually needed to sustain the desirable ground flora (Abrahamson and Abrahamson, 1996; Kuddes-Fischer and Arthur, 2002; Bowles et  al., 2007; Glasgow and Matlack, 2007; Vander Yacht et al., 2017). Annual fires increase grass dominance and biennial fires promote forb species richness and cover, especially in open environments such as prairies, savannahs and open woodlands (Anderson

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et al., 1999; Haywood et al., 2001; Peterson et al., 2007; Nelson, 2010; Burton et  al., 2011). Fires separated by 3–5 years or longer favour trees, shrubs and vines (Briggs et al., 2002; Peterson et al., 2007; Haywood, 2009; Burton et al., 2010). Ground flora diversity is relatively lower in prairies where tallgrasses can dominate or in forests where woody species are the major competitors than in savannahs and open woodlands where there is the greatest heterogeneity in environmental conditions that supports high plant diversity (Towne and Kemp, 2003; Peterson et  al., 2007; Peterson and Reich, 2008; Haywood, 2009). Controlling the season of burning can help promote specific plant functional groups. Burning in the spring (March–April) favours warm-season grasses and forbs, may promote flowering and biomass production of late summer flowering species, and diminishes survival, growth and vigour of coolseason grasses and forbs (Glen-Lewin et al., 1990; Howe, 1994; Copeland et  al., 2002; Taft, 2003; Towne and Kemp, 2003; Peterson and Reich, 2008). Promoting the dominance of warm-season grasses may actually decrease total plant diversity due to the ability of warm-season grasses to suppress all subdominant vegetation (Biondini et  al., 1989; Copeland et  al., 2002). Compared with winter or spring fires, summer burns (mid-July–early August) can: (i) increase cool-season grass and forb diversity, cover and density; (ii) reduce to a greater extent woody trees, shrubs and vines; and (iii) increase perennials that are able to flower before the summer burn (Waldrop et  al., 1992; Howe, 1994; Haywood et  al., 2001; Haywood, 2009; Nelson, 2010). Summer burns done during a time of warmseason grasses active growth can reduce their dominance, thus releasing subdominant vegetation and increasing total species richness (Biondini et  al., 1989). The outcomes of autumn (September–October) fires are somewhat inconsistent but show a tendency to: (i) decrease cool-season exotic grasses; (ii) reduce woody cover; (iii) increase perennial forb cover; and (iv) either increase or have no effect on warm- and cool-season grasses and forbs (Biodini et  al., 1989; Howe, 1994; Copeland et  al., 2002; Towne and Kemp, 2003; Bowles et al., 2007; Weir and Scasta, 2017). Dormant season (December– February) fires have the least impact on herbaceous or woody species, even when repeated for decades (Waldrop et al., 1992; Hutchinson, 2006; Haywood, 2009; Weir and Scasta, 2017). Consistency in application of prescribed fire tends to create homogeneity

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in the vegetation community. Therefore it can be beneficial to vary the frequency, intensity and season of burning to sustain plant species diversity and provide a variety of habitats for wildlife, insects, pollinators and other taxa (Howe, 1994; Hiers et al., 2000; Peterson and Reich, 2008; Nelson, 2010). Invasive species With over 5000 plant species introduced into North America, non-native invasive species (NNIS) are increasingly a threat to oak management and restoration (Oswalt and Oswalt, 2011; Kurtz, 2013; Miller et  al., 2013). No longer are they merely a problem in urban areas; they are rapidly expanding into rural forest lands. There are some 300 troublesome NNIS in eastern forests and grasslands, 50 of which are considered critical and 23 are of global concern (Oswalt and Oswalt, 2011; Miller et  al., 2013). Many of the more common and problematic NNIS prosper in open environments and are adapted to fire. Thus, prescriptions to sustain or restore oak forests, woodlands and savannahs that use regeneration methods such as the group selection, clearcut, shelterwood or seed tree in combination with prescribed burning can potentially promote the rapid colonization and invasion by NNIS (Rebbeck, 2012; Phillips et  al., 2013). Also, natural disturbances such as windstorms, wildfires and insect and disease outbreaks that cause intermediate to catastrophic forest damage may provide entry points for NNIS in forest landscapes. Fire may promote NNIS Fire can promote NNIS colonization and spread, or it can be a useful method for control, depending on the timing and severity of fire and the phenology, physiology, mode of reproduction and ecology of the invading NNIS (Huebner, 2006; DiTomaso et  al., 2006; Zouhar et  al., 2008; Rebbeck, 2012; Miller et  al., 2013). NNIS often have traits that help them prosper in a post-fire environment (Plate 5). The ability to self-pollinate allows them to reproduce in low density or sparse populations. Some species are prolific seed producers and seed can be dispersed by the wind and/or birds, which maximizes the number of seeds disseminated for opportunistic colonization of ephemerally favorable sites. The seed of some NNIS can remain viable in the seed bank for years or decades. Thus, seed can accumulate to densities far exceeding those of

Chapter 7

annual crops and remain ready for release by a future disturbance. Chemically or thermally induced seed dormancy ensures that germination is likely to occur after a fire when the post-fire environment is favourable for establishment and early growth. Reproductive structures such as rhizomes, caudices, bulbs, corms and root crown buds are commonly buried in mineral soil, or located under moist duff (partially decomposed organic matter on the forest floor) and, hence, protected from fire injury. Thus, after a fire, NNIS are immediately on site and capable of rapid vegetative growth when resources are most available. Rapid early growth is characteristic of many NNIS as they are adapted to high light environments following moderate to high severity disturbances. This promotes early dominance and acquisition of resources for development towards maturity and completion of their life cycle. Monitoring to control NNIS Monitoring for early detection of NNIS is key to controlling invasive populations when they are still small. Aggressive eradication at this stage is necessary to avoid the development of an expensive problem that can thwart other management goals. NNIS monitoring and control are essential elements of contemporary sustainable management programmes. A prudent approach to NNIS control is to deal with threats before initiating major disturbances such as prescribed fire that may accelerate expansion and dominance of the NNIS. Early treatment of existing NNIS in and around a management unit takes advantage of overstorey shade that can help limit the response of shade-intolerant NNIS. Complications arise when considering NNIS control treatment impacts on desired native flora, because an important control strategy is to promote the establishment and dominance of native species, thereby increasing the competitive pressure on NNIS. Fire to control NNIS Fire can be used alone or in combination with other practices to control some NNIS. The timing and severity of fires are key to controlling NNIS, and determine the fate of native species as well. The easiest NNIS to control are annuals that produce seed after the fire season, whose seed is readily exposed directly to fire’s flames and does not persist in the seed bank. Late spring to early summer fires are most likely to control NNIS annuals that

Fire and Oak Forests

set seed later in the summer. Biennial and perennial NNIS are more difficult to control. Severe fires are needed to kill reproductive structures in organic or mineral soil layers. Few NNIS are controlled with a single fire. It takes consecutive, repeated fires to stop seed production by killing existing individuals and to eliminate plants that arise from the seed bank or from vegetative structures, which often are stimulated by the initial fire. Scheduling fires several years apart only allows NNIS to add seed to the seed bank, or build energy reserves in belowground structures. Burning followed by herbicide application can be an effective approach (DiTomaso et al., 2006). The fire kills current vegetation, stimulates germination, converts large plants into small concentrated sprout clumps through top-kill and sprouting, and removes debris that facilitates herbicide application. Herbicides can be effective at killing plants that sprout prolifically from large underground bud banks and stored energy reserves. The succulent growth of seedling sprouts and germinants readily absorbs herbicides, increasing their efficacy. Fire effects on native species must also be considered in planning prescribed fire regimes to ensure natives are not adversely impacted and that their response to fire is vigorous. Dominance of native species after fire can help to suppress NNIS establishment or recovery. Native species as invasives Several native plant species can become invasive and form dense canopies or dense ground cover after silvicultural treatments, thus inhibiting oak regeneration (Plate 6). For example, native ferns such as eastern bracken fern, hayscented fern and New York fern may proliferate after harvesting and burning and cause oak regeneration failure (Engelman and Nyland, 2006). These ferns have adaptations that facilitate their regeneration following disturbances such as fire, including a bank of spores in the forest floor and rhizomes buried deep in the soil. Also, high deer populations can overbrowse forest understories, removing preferred species and providing opportunities for native ferns to increase substantially in dominance (Horsley and Marquis, 1983; Fredericksen et  al., 1998; Engelman and Nyland, 2006). In the Appalachian Mountains, native mountain laurel and Rhodo­ dendron species have come to dominate oak–pine forest understories in the long-term absence of fire,

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and they inhibit regeneration of oak species that cannot tolerate the low light conditions under the shrub canopy (Phillips and Murdy, 1985; Brose and Waldrop, 2006, 2010). In some cases, oak–pine forests are converted to shrub lands or fern glades as the oak/pine overstorey dies or is harvested. In the Great Plains and West, disruption of historical disturbance regimes by fire exclusion and overgrazing has promoted the encroachment of native Juniperus species (Schmidt and Leatherberry, 1995; Hanberry et al., 2012), or Douglas-fir and other western conifers (Purcell and Stephens, 2005) into oak savannahs and woodlands, which makes it difficult to regenerate oak and sustain these ecosystems. For similar reasons, yaupon and other shrubs and junipers have invaded oak savannahs and woodlands causing oak regeneration and recruitment failures (Stambaugh et al., 2011b; Sparks et al., 2012).

Fire Effects on Tree Quality, Volume and Value Overstorey mortality Surface fires in hardwood forests are capable of killing mature trees of any species if the intensity and duration of heating is sufficient to cause death of the cambium and foliage. Temperatures (e.g. at 10 inches above the ground) in low-intensity, dormant-season (e.g. March–April) fires can average 300°F (150°C) to more than 400°F (200°C) (Cole et  al., 1992; Hengst and Dawson, 1994; Franklin et al., 1997; Iverson and Hutchinson, 2002). This is hot enough to kill living organisms and vascular plant tissue and to cause tree mortality by stem girdling (Dey and Hartman, 2005; Elliott and Vose, 2005; Hutchinson et  al., 2005a). In mixed-oak forests, relatively high percentages of overstorey trees may be scarred on the lower bole from lowintensity fires but mortality of overstorey trees (> 4.5 inches dbh) is usually less than 5% basal area or less than 8% of stem density after single or repeated low-intensity fires over 10 or more years (Plate 7) (Regelbrugge and Smith, 1994; Hutchinson et  al., 2005a, 2012b; Smith and Sutherland, 2006; Fan and Dey, 2014; Knapp et al., 2015; Kinkead et al., 2017). Higher fire intensity and increased exposure to lethal temperatures are needed to kill trees larger than 10 inches; this may occur locally during lowintensity fires where accumulations of fuels occur near the base of individual trees (Brose and Van Lear, 1999).

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Variations in tree response to fire can be attributed to differences in bark characteristics among species and size classes. There are many properties of a tree’s bark that influence its ability to insulate the cambium from the heat of a fire: (i) thickness; (ii) texture; (iii) thermal conductivity; (iv) specific heat; and (v) thermal diffusivity. However, it is bark thickness that largely determines the degree of protection of the cambium from lethal temperatures (Vines, 1968; Hengst and Dawson, 1994; VanderWeide and Hartnett, 2011). As trees grow, small increases in bark thickness provide exponentially greater protection from fire damage (Hare, 1965; Pausas, 2015). Guyette and Stambaugh (2004) found that the probability of fire scarring and the percentage of bole circumference scarred were significantly and negatively related to tree diameter, bark thickness, radial growth rate and tree age in post oak (dbh range 4–28 inches). Sutherland and Smith (2000) reported that the probability of young oaks surviving a fire (i.e. not top-killed) increases significantly at the sapling size (2–4 inches dbh), the period when the bark starts to achieve sufficient thickness to prevent top-kill, depending on species. Similarly, Guyette and Stam­baugh (2004) observed that post oak trees greater than 4 inches dbh were likely to survive low-intensity fires without top-kill. There is, however, a substantial variation in bark thickness, rate of bark growth on the lower bole, and bark texture among species (Harmon, 1984; Hengst and Dawson, 1994; Sutherland and Smith, 2000). In general, upland species have thicker bark than bottomland species for similar sized trees (Hengst and Dawson, 1994; Sutherland and Smith, 2000). Within Quercus, bark thickness at a given diameter and the rate of bark thickening with diameter growth is greater in white oak group species (Quercus section Quercus) than red oak group species (Quercus section Lobatae). Resistance to scarring decreases in upland oaks from post oak and bur oak > white oak > black oak > southern red oak and scarlet oak (Scowcroft, 1965; Hengst and Dawson, 1994; Stevenson et  al., 2008, Kinkead et  al., 2017). Species with inherently thin bark include American beech, flowering dogwood, black cherry, maple and hickory. The rate of bark thickening during growth is important because faster growth rates allow trees to sooner reach critical thresholds of bark thickness that are associated with protection of the cambium and survival. Eastern cottonwood and yellow-poplar are both thin-barked, fire-sensitive species when trees are small, but they have rapid rates of bark

Chapter 7

growth and become resistant to fire scarring as they mature (Hengst and Dawson, 1994; Wiedenbeck and Schuler, 2014). In contrast, silver maple has a slow rate of bark growth throughout its life and is vulnerable to fire injury even as a large tree. Species that have smooth bark texture such as water oak are more vulnerable to fire injury than are deeply fissured, rough-textured species such as chestnut oak and bur oak. The bark of southern yellow pines confers a high degree of resistance to fire scarring (Stevenson et  al., 2008; Schweitzer et  al., 2016; Kinkead et  al., 2017). Once a tree is scarred by a fire, it is more vulnerable to additional scarring in future fires because the bark is thin on the callus wood forming over the original scar.

Timber quality and value Land managers are increasingly pursuing restoration and sustainable management of oak savannahs, woodlands and forests using prescribed fire in combination with other forestry practices. The reintroduction of fire into hardwood forests is at times controversial due to the potential negative effects of fire on timber volume, quality and value (Marschall et al., 2014; Stambaugh et al., 2017b). Debates can be vigorous over the merits of reintroduction of prescribed fire for habitat restoration because of the potential for fire injury to cause substantial loss of wood volume, quality and value (Hepting, 1941; Gustafson, 1944; Burns, 1955; Loomis, 1974). Improvements in timber quality and decreases in the amount of cull in forests since the beginning of fire suppression beginning in the 1900s–1950s were strong testimony to the benefits of keeping fire out of the woods. However, it is also recognized that the loss of fire from oak and oak–pine ecosystems is a major contributor to the loss of savannahs and woodlands, and a problem managers are struggling with in sustaining oak–pine forests. In this era of ecosystem restoration, using fire to restore native communities puts emphasis on ecological benefits such as increased native plant diversity and improved habitat quality for species that prosper in woodlands and savannahs. However, age-old concerns about fire damage to trees and forests remain and should be considered when planning management approaches and silvicultural prescriptions for restoring and sustaining these highly valued oak forest, woodland and savannah ecosystems. Ecologically sound plans for introduction of prescribed fire regimes intended

Fire and Oak Forests

to restore woodland and savannah habitats are not always socially acceptable or economically viable. Bole wounding and wood decay Low-intensity prescribed fires can kill cambial tissue at the base of overstorey tree boles and create wounds, but not all trees within the boundaries of a given fire are wounded (Fig. 7.11) (Smith and Sutherland, 1999). Whether a tree is wounded or not depends largely on fire behaviour (i.e. temperature, flame length and duration of heating) at any one location within the burn unit, and characteristics of affected trees including species, size and bark thickness. Numerous fire history studies, which sample the full range of ages in a stand to document fire occurrence as evidenced by scars in tree rings, report that most fires in oak ecosystems are low intensity and scar on average about 10% of trees, but 60% or more of the trees may be scarred by moderate to high-intensity fires (e.g. Stambaugh et al., 2014a). In mixed oak–hardwood forests, the observed proportion of trees scarred in long-term frequent prescribed fire studies ranges from less than 20% to 70% of surviving trees (> 4.5 inches dbh), depending on tree species and size, slope, aspect, fire season, fire frequency and fuel loading (Kinkead et  al., 2017; Knapp et  al., 2017; Stam­ baugh et al., 2017b). The threat of scarring and scar size is substantially reduced in larger, thick-barked tree species. The probability of scarring is higher on southern aspects and steeper upper slopes and ridgetops where fire intensity may be higher than on mesic sites and flat terrain (Iverson et al., 2004; Stevenson et al., 2008; Kinkead et al., 2017). Growing-season fires have a greater potential to cause scarring because plants are physiologically active and ambient temperatures are closer to lethal temperatures that cause plant tissue necrosis (Loomis and Paananen, 1989). A regime of annual burning often results in less scarring than periodic fires (e.g. every 4–5 years) because fuel loading is kept low and fine fuel continuity is patchy (Paulsell, 1957; McEwan et  al., 2007; Stambaugh et al., 2014a; Knapp et al., 2017). When overstorey thinning is conducted to increase residual tree growth, to promote oak regeneration, or to develop woodland/savannah structure, subsequent fires usually increase the percentage of trees scarred due to increased fuel loading from the thinning (Kinkead et al., 2017), but the timing can be important. For example, Iverson et al. (2004) found that burning immediately after thinning in upland

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(A)

(B)

(C)

(D)

Fig. 7.11.  Illustrated here is a gradient in severity of fire injuries and tree responses. (A) A fire injury occurs near the centre of this oak cross section at a time when the tree was small in diameter and relatively fast growing as evidenced by tree-ring widths. The wound rapidly healed and practically no degrade to forest products resulted. (B) In contrast, this small-diameter tree has suffered repeated fire injury from which it has not been able to recover, in part, due to its suppression and slow growth in the understorey of a mature forest. (C) Small fire injuries on the lower bole and buttress root of this dominant oak rapidly healed with little to no degradation to tree health or forest product value. (D) This dominant oak has been repeatedly burned for over 20 years, and once severely wounded, it has been unable to heal completely. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

mixed-oak forests, before the newly added fuels had cured, actually reduced fire temperature, rate of spread and, hence, intensity. When heavy fuels from shelterwood harvesting are allowed to cure a few years prior to prescribed burning, then severe

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mortality and bole damage are likely where heavy slash is within 3 ft of the boles of oaks, hickories and yellow-poplar (Brose and Van Lear, 1999). Open fire scars provide opportunities for wooddecaying fungi to colonize and infect trees. Large

Chapter 7

scars with exposed wood that remain open and moist for long periods provide good environments for fungal colonization and development. However, fire scars are often small and the bark commonly remains intact, covering the injury after low-intensity fires in upland oak forests of the Central Hardwood region (Smith and Sutherland, 1999). Loss of volume and value in fire-scarred oak trees may be relatively minor in the short term (< 10 years), but with time, advanced decay can result in substantial value losses (Stambaugh and Guyette, 2008b; Marschall et al., 2014). Although larger diameter trees are less likely to scar, when they do suffer wounding that results in open-faced scars, the potential is high for loss to decay over the ensuing decades (Loomis, 1974). If younger, vigorous trees are able to rapidly enclose the open wound in a relatively short period of time, then the loss to decay will be minimized. Pole and small sawtimber trees are at risk of losing substantial volume and value in the lower log when they suffer open-faced scars because typically they will remain in the stand for decades before reaching maturity. This allows advanced decay to develop. Considering that about one-third of the total standing tree volume may be in the lowest 8 ft log, fire injury leading to wood decay at the base of a tree can have significant effects on merchantable volume and value. Even where timber production is not the primary management concern (e.g. in woodland and savannah restoration), the longevity of mature overstorey trees may be compromised by advance decay in the boles of fire-scarred trees, because such trees are more susceptible to stem breakage and blowdown during wind and ice storms (Guyette and Stambaugh, 2004).

ribs) can be faster than on other portions of the bole (Smith and Sutherland, 1999). However, frequent fires decrease diameter growth in most species, and wounds may remain open for decades if fires are frequent enough (< 5 or 10 years) (Scowcroft, 1965; Kinkead et al., 2017; Stambaugh et al., 2017b). Compartmentalization is a process whereby trees are able to establish a protective boundary zone surrounding cells injured by fire (Smith, 2015). The boundary is the result of the formation of tyloses and production of waxes, gums and resins to form a barrier to further cell desiccation and microbial infection. The ability to compartmentalize injuries varies by species. The birches are less effective at compartmentalizing stem wounds than maples and oaks (Sutherland and Smith, 2000). Oak species, especially those in the white oak group, have an unusual ability to rapidly compartmentalize fire injuries (Smith and Sutherland, 1999; Sutherland and Smith, 2000). Smith and Sutherland (1999) found that low-­ intensity dormant-season fires produced relatively small scars (scorch height < 40 inches above the ground) that were often concealed by intact bark and were effectively and rapidly compartmentalized in black oak and chestnut oak trees (dbh range 4–22 inches). Decay resistance of the heartwood varies by species and is important to retarding decay that originates from fire scarring. Species of the white oak group, black locust, catalpa, black cherry, redcedar and bald cypress have heartwood that is resistant to very resistant to decay (Forest Products Laboratory, 1967). Red oak group species, hickories, maples, sweetgum, yellow-poplar, birches, eastern cottonwood and American beech have slight to no resistance to heartwood decay.

Defence against decay

Scar size and time since wounding

A tree’s diameter growth rate determines how long an open fire scar will permit entry of decay-causing organisms into the tree’s stem. Trees with faster rates of diameter growth are able to close open wounds sooner, thus minimizing the time the wound face is available for fungal colonization. By sealing the wound, the tree also creates an unfavourable anaerobic environment for wood-decay organisms, most of which are aerobic (Sutherland and Smith, 2000; Smith, 2015). High rates of diameter growth more rapidly restore structural support in the tree’s bole and vascular cambial functioning after fire scarring of the bole (Smith and Sutherland, 2006; Smith, 2015). Growth near the area of injury (wound wood

Fungi that infect tree boles through logging or fire scars can cause substantial loss of value and degrade timber quality over several decades (Hesterberg, 1957). Stambaugh and Guyette (2008b) found that one-third of the volume can be defective in white oak, black oak and scarlet oak butt logs within 25 years after the trees received a fire scar. The proportion of butt log that was defective after fire scarring increased with increasing size of fire scar (from 155 to 930 square inches) and decreased with increasing tree diameter (from about 8 to 22 inches dbh) at time of scarring. Wider scars take a longer time for a tree to close by diameter growth. Stambaugh et al. (2017b) observed

Fire and Oak Forests

279

that fire scars in mature white oak averaged 3.5 inches in width and took on average 10 years to close in a Missouri oak woodland managed by prescribed burning, but larger scars (9 inches wide) took up to 24 years to close. Fire frequency has an effect on potential scar sizes, with the percentage of trees scarred and scar size decreasing in annual fire regimes compared with fires repeated every 4–5 years (Scowcroft, 1965; Stambaugh et  al., 2014b; Knapp et  al., 2017). Due to increased fuel loads, burning in thinned stands increases not only the percentage of trees scarred but also increases average scar size in oaks (Kinkead et al., 2017). Marschall et  al. (2014) reported that both the value and the volume loss to decay and lumber degrade in black oak, northern red oak and scarlet oak butt logs increased with increasing prescribed fire severity and initial size of fire scar (as represented by scar height and scar depth, i.e. the distance from outer bark circumference to internal bole surface in open wounds). Most of the devaluation in the butt log resulted from declines in lumber grade and not from volume loss. However, they found that volume loss averaged only 4% and value loss averaged 10% after 14 years from fire injury. They concluded that where less than 20% of the bole circumference was scarred and scar heights were less than 20 inches, the value loss would be insignificant within 15 years of scarring. Harvesting the most severely injured trees within 5 years also limits value loss. Loomis (1974) reported also that value and volume loss increased with increasing fire scar size (wound width and length), time since wounding and tree diameter at the time of scarring. Similar evidence of the extent of fire injury was noted by Smith and Sutherland (1999) who measured scorch height on oak boles and found heights were generally less than 40 inches after low-intensity prescribed fires in Ohio. They observed that within 2 years of the fire, most wounds were near the ground, covered by intact bark, small in size and effectively compartmentalized. Thus, losses due to wood decay can be minimized if fire intensity is low and scarred trees are harvested before advanced decay enters the log scaling cylinder. The stage of stand development and tree size at the time of fire scarring may influence the probability that decay will substantially reduce wood volume or value by the time the tree is harvested. Fire scars on small-diameter trees that survive the injury are necessarily small in size because they are limited by tree size. Closure of the wound is rapid if the tree

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is vigorous and free to grow; this minimizes the likelihood of fungal infection and value loss is negligible (Loomis, 1974). If scars do lead to decay in smaller trees, the trees may be able to compartmentalize the decay column in the centre of the bole, where wood quality is lower to begin with and where loss can be minimized during the manufacture of the log (Loomis, 1974). Large-diameter trees are better protected from fire scarring by their thick bark, and wounds tend to be small and low on the bole in low-intensity fires. These trees are merchantable and may be removed in a timber harvest soon after the fire (within 10 or 15 years) should fire injury occur, thus minimizing decay development that results in value loss (Loomis, 1974; Marschall et  al., 2014; Wiedenbeck and Schuler, 2014). In larger sawtimber, injuries on trees generally occur on the large end of the butt log and therefore they are often outside of the scaling cylinder where defect is removed in the slabwood resulting in little, if any, decrease in product recovery and value (Marschall et al., 2014). However, if a large tree is fire scarred and decay develops, then the potential for volume and value loss from decay is increased because the decay may advance throughout the entire existing bole at the time of scarring (Loomis, 1974). The rate of decay and extent of value loss depends, in part, on time and the decay resistance of the heartwood of a given species (Forest Products Laboratory, 1967). Fire scarring of pole-sized and small sawtimber trees that will remain in the stand for 30 years or more are at greatest risk of advanced decay and significant loss of volume and value by the time they are harvested. Pole-sized and small sawtimber trees can sustain large-sized scars that take time to heal, during which time they are prone to fungal infections, especially on mesic sites, where fungi populations thrive and moist scar surfaces may be more receptive to infection. Also, prolonged moisture in scars is more likely to occur when scars are in contact with the ground or when they are shaped such that they trap water.

Notes 1

  Caloric content of wildland fuel reduced by the latent heat absorbed when the water of the reaction is vaporized. 2   A 1-hour timelag fuel particle is one that can adjust to changes in equilibrium moisture content on the scale of hours to a day. Examples of 1-hour timelag fuels include fine twigs, dead herbaceous plant material such as grasses, and litter.

Chapter 7

References Abrahamson, W.G. and Abrahamson, C.R. (1996) Effects of fire on long-unburned Florida uplands. Journal of Vegetation Science 7, 565–574. https://doi. org/10.2307/3236306 Abrams, M.D. (1998) The red maple paradox: what explains the widespread expansion of red maple in eastern forests? Biosience 48, 355–363. https://doi. org/10.2307/1313374 Abrams, M.D. (2002) The postglacial history of oak forests in eastern North America. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 34–45. Abrams, M.D. and Nowacki, G.J. (2008) Native Americans as active and passive promoters of mast and fruit trees in the eastern USA. The Holocene 18(7), 1123–1137. https://doi.org/10.1177/0959683608095581 Abrams, M.D. and Nowacki, G.J. (2015) Exploring the early Anthropocene burning hypothesis and climate–fire anomalies for the eastern U.S. Journal of Sustainable Forestry 34(1–2), 30–48. https://doi.org/10.1080/105 49811.2014.973605 Aldrich, S.R., Lafon, C.W., Grissino-Mayer, H.D., DeWeese, G.G. and Hoss, J.A. (2010) Three centuries of fire in montane pine–oak stands on a temperate forest landscape. Applied Vegetation Science 13, 36–46. https:// doi.org/10.1111/j.1654-109X.2009.01047.x Aldrich, S.R., Lafon, C.W., Grissno-Mayer, H.D. and DeWeese, G.G. (2014) Fire history and its relations with land use and climate over three centuries in the central Appalachian Mountains, USA. Journal of Biogeography 41, 2093–2104. https://doi.org/10.1111/jbi.12373 Alexander, H.D., Arthur, M.A., Loftis, D.L. and Green, S.R. (2008) Survival and growth of upland oak and co-occurring competitor seedlings following single and repeated prescribed fires. Forest Ecology and Management 256(5), 1021–1030. https://doi.org/10. 1016/j.foreco.2008.06.004 Allen, M.S. and Palmer, M.W. (2011) Fire history of a prairie/forest boundary: more than 250 years of frequent fire in a North American tallgrass prairie. Journal of Vegetation Science 22, 436–444. https:// doi.org/10.1111/j.1654-1103.2011.01278.x Anderson, H.E. (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service General Technical Report INT-122. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, 22 pp. https://doi.org/10.2737/ INT-GTR-122 Anderson, M.K. (2005) Tending the Wild: Native Ameri­ can Knowledge and the Management of California’s Natural Resources. University of California Press, Berkeley, California. Anderson, R.C. (2006) Evolution and origin of the Central grassland of North America: climate, fire, and mam-

Fire and Oak Forests

malian grazers. Journal of the Torrey Botanical Society 133(4), 626–647. https://doi.org/10.3159/1095-5674 (2006)133[626:EAOOTC]2.0.CO;2 Anderson, R.C., Fralish, J.L. and Baskin, J.M. (1999) Savannas, Barrens, and Rock Outcrop Plant Com­ munities of North America. Cambridge University Press, Cambridge, 470 pp. Arthur, M.A., Alexander, H.D., Dey, D.C., Schweitzer, C.J. and Loftis, D.L. (2012) Refining the oak–fire hypothesis for management of oak-dominated forests of the eastern United States. Journal of Forestry 110, 257–266. https://doi.org/10.5849/jof.11-080 Arthur, M.A., Blankenship, B.A., Schörgendorfer, A., Loftis, D.L. and Alexander, H.D. (2015) Changes in stand structure and tree vigor with repeated prescribed fire in an Appalachian hardwood forest. Forest Ecology and Management 340, 46–61. https://doi.org/10.1016/j. foreco.2014.12.025 Auchmoody, L.R. and Smith, H.C. (1993) Survival of northern red oak acorns after fall burning. USDA Forest Service Research Paper NE-678. USDA Forest Service, Northeastern Forest Experiment Station, Radnor, Pennsylvania, 5 pp. https://doi.org/10.2737/ NE-RP-678 Baker, R.G., Maher, L.J., Chumbley, C.A. and Van Zant, K.L. (1992) Patterns of Holocene environmental change in the Midwestern United States. Quaternary Research 37, 379–389. https://doi.org/10.1016/ 0033-5894(92)90074-S Baker, R.G., Bettis, III, E.A., Schwert, D.P., Horton, D.G., Chumbley, C.A., Gonzalez, L.A. and Reagan, M.K. (1996) Holocene paleoenvironments of northeast Iowa. Ecological Monographs 66(2), 203–234. https://doi.org/10.2307/2963475 Barrett, L.I. (1931) Influence of forest litter on the germination and early survival of chestnut oak, Quercus montana Willd. Ecology 12, 476–484. https://doi. org/10.2307/1928993 Batek, M.J., Rebertus, A.J., Schroeder, W.A., Haithcoat, T.L., Compas, T.L.E. and Guyette, R.P. (1999) Reconstruction of early nineteenth-century vegetation and fire regimes in the Missouri Ozarks. Journal of Biogeography 26, 397–412. Beck, D.E. (1977) Twelve-year acorn yield in southern Appalachian oaks. USDA Forest Service Research Note SE-244. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, 8 pp. Available at: https://www.fs.usda.gov/treesearch /pubs/9237 (accessed 1 July 2018). Belcher, C.M., Collinson, M.E. and Scott, A.E. (2013) A 450-million-year history of fire. In: Belcher, C.M. (ed.) Fire Phenomena and the Earth System: an Interdisciplinary Guide to Fire Science. Wiley-Blackwell, Oxford, pp. 229– 249. https://doi.org/10.1002/9781118529539.ch12 Bellocq, M.I., Jones, C., Dey, D.C. and Turgeon, J.J. (2005) Does the shelterwood method to regenerate oak forests affect acorn production and predation?

281

Forest Ecology and Management 205, 311–323. https://doi.org/10.1016/j.foreco.2004.10.013 Bey, C.F. (1964) Advance oak reproduction grows fast after clearcutting. Journal of Forestry 62, 339–340. https://doi.org/10.1093/jof/62.5.338 Biondini, M., Steuter, A.A. and Grygiel, G.E. (1989) Seasonal fire effects on the diversity patterns, spatial distribution and community structure of forbs in the Northern Mixed Prairie. Vegetatio 85, 21–31. https:// doi.org/10.1007/BF00042252 Black, B.A., Ruffner, C.M. and Abrams, M.D. (2006) Native American influences on the forest composition of the Allegheny Plateau, northwest Pennsylvania. Canadian Journal of Forest Research 36(5), 1266– 1275. https://doi.org/10.1139/x06-027 Blankenship, B.A. and Arthur, M.A. (2006) Stand structure over nine years in burned and fire-excluded oak stands on the Cumberland Plateau, Kentucky. Forest Ecology and Management 225, 134–145. https://doi. org/10.1016/j.foreco.2005.12.032 Blizzard, E.M., Kabrick, J.M., Dey, D.C., Larsen, D.R., Pallardy, S.G. and Gwaze, D.P. (2013) Light, canopy closure, and overstory retention in upland Ozark forests. In: USDA Forest Service e-General Technical Report SRS-GTR-175. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 73– 79. Available at: https://www.fs.usda.gov/treesearch/ pubs/43478 (accessed 1 July 2018). Bowles, M.L., Jacobs, K.A. and Mengler, J.L. (2007) Longterm changes in an oak forest’s woody understory and herb layer with repeated burning. The Journal of the Torrey Botanical Society 134(2), 223–237. https://doi. org/10.3159/1095-5674(2007)134[223:LCIAOF]2.0. CO;2 Briggs, J.M., Knapp, A.K. and Brock, B.L. (2002) Expan­ sion of woody plants in tallgrass prairie: a fifteen-year study of fire and fire-grazing interactions. The American Midland Naturalist 147(2), 287–294. https://doi. org/10.1674/0003-0031(2002)147[0287:EOWPIT]2.0. CO;2 Brose, P.H. (2008) Root development of acorn-origin oak seedlings in shelterwood stands on the Appalachian Plateau of northern Pennsylvania: 4-year results. Forest Ecology and Management 255, 3374–3381. https://doi.org/10.1016/j.foreco.2008.02.021 Brose, P.H. (2010) Long-term effects of single prescribed fires on hardwood regeneration in oak shelterwood stands. Forest Ecology and Management 260, 1516– 1524. https://doi.org/10.1016/j.foreco.2010.07.050 Brose, P.H. (2011) A comparison of the effects of different shelterwood harvest methods on the survival and growth of acorn-origin oak seedlings. Canadian Journal of Forest Research 41, 2359–2374. https:// doi.org/10.1139/x11-143 Brose, P.H. and Van Lear, D.H. (1998) Responses of hardwood advance regeneration to seasonal prescribed fires in oak-dominated shelterwood stands.

282

Canadian Journal of Forest Research 28, 331–339. https://doi.org/10.1139/x97-218 Brose, P. and Van Lear, D. (1999) Effects of seasonal prescribed fires on residual overstory trees in oakdominated shelterwood stands. Southern Journal of Applied Forestry 23(2), 88–93. https://doi.org/10.1093/ sjaf/23.2.88 Brose, P.H. and Van Lear, D.H. (2004) Survival of hardwood regeneration during prescribed fires: the importance of root development and root collar location. USDA Forest Service General Technical Report SRS73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 123–127. Available at: https://www.fs.usda.gov/treesearch/pubs/6509 (accessed 1 July 2018). Brose, P.H. and Waldrop, T.A. (2006) Fire and the origin of Table Mountain pine – pitch pine communities in the southern Appalachian Mountains, USA. Canadian Journal of Forest Research 36, 710–718. https://doi. org/10.1139/x05-281 Brose, P.H. and Waldrop, T.A. (2010) A dendrochronological analysis of a disturbance–succession model for oak–pine forests of the Appalachian Mountains, USA. Canadian Journal of Forest Research 40, 1373–1385. https://doi.org/10.1139/X10-077 Brose, P., Van Lear, D. and Cooper, R. (1999) Using shelterwood harvests and prescribed fire to regenerate oak stands on productive upland sites. Forest Ecology and Management 113, 125–141. https://doi. org/10.1016/S0378-1127(98)00423-X Brose, P.H., Schuler, T.M. and Ward, J.S. (2006) Responses of oak and other hardwood regeneration to prescribed fire: what we know as of 2005. In: USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service Northern Research Station, Newtown Square, Pennsylvania, pp. 123–135. Available at: https://www.fs.usda.gov/treesearch/ pubs/18437 (accessed 1 July 2018). Brose, P.H., Gottschalk, K.W., Horsley, S.B., Knopp, P.D., Kochenderfer, J.N., McGuinness, B.J., Miller, G.W., Ristau, T.E., Stoleson, S.H. and Stout, S.L. (2008) Prescribing regeneration treatments for mixed-­ oak forests in the Mid-Atlantic region. USDA Forest Service General Technical Report NRS-33. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 100 pp. https://doi.org/10.2737/ NRS-GTR-33 Brose, P.H., Dey, D.C., Guyette, R.P., Marschall, J.M. and Stambaugh, M.C. (2013a) The influences of drought and humans on the fire regimes of northern Pennsylvania, USA. Canadian Journal of Forest Research 43, 757–767. https://doi.org/10.1139/cjfr-2012-0463 Brose, P.H., Dey, D.C., Phillips, R.J. and Waldrop, T.A. (2013b) A meta-analysis of the fire-oak hypothesis: does prescribed burning promote oak reproduction in Eastern North America? Forest Science 59(3), 322–334. https://doi.org/10.5849/forsci.12-039

Chapter 7

Brose, P.H., Dey, D.C. and Waldrop, T.A. (2014) The fireoak literature of eastern North America: synthesis and guidelines. USDA Forest Service General Technical Report NRS-135. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 98 pp. https://doi.org/10.2737/NRS-GTR-135 Brose, P.H., Guyette, R.P., Marschall, J.M. and Stambaugh, M.C. (2015) Fire history reflects human history in the Pine Creek Gorge of north-central Pennsylvania. Natural Areas Journal 35(2), 214–223. https://doi.org/10.3375/043.035.0202 Burns, P.Y. (1955) Fire scars and decay in Missouri oaks. University of Missouri, College of Agriculture Research Bulletin 642. University of Missouri Press, Columbia, Missouri, 8 pp. Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. Volume 2, Hardwoods. USDA Forest Service Agriculture Handbook 654. USDA Forest Service, Washington, DC. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Burton, J.A., Hallgren, S.W. and Plamer, M.W. (2010) Fire frequency affects structure and composition of xeric oak forests of eastern Oklahoma. Natural Areas Journal 30, 370–379. https://doi.org/10.3375/043. 030.0401 Burton, J.A., Hallgren, S.W., Fuhlendorf, S.D. and Leslie, D.M., Jr (2011) Understory response to varying fire frequencies after 20 years of prescribed burning in an upland oak forest. Plant Ecology 212, 1513–1525. https://doi.org/10.1007/s11258-011-9926-y Byram, G.M. (1959) Combustion of forest fuels. In: Davis, K.P. (ed.) Forest Fire: Control and Use. McGraw-Hill, New York, pp. 61–89. Cain, M.D. and Shelton, M.G. (1998) Viability of litterstored Quercus falcata Michx. acorns after simulated prescribed winter burns. International Journal of Wildland Fire 8, 199–203. Available at: https://www.fs. usda.gov/treesearch/pubs/872 (accessed 1 July 2018). Camill, P., Umbanhowar, C.E., Jr, Teed, R., Geiss, C.E., Aldinger, J., Dvorak, L., Kenning, J., Limmer, J. and Walkup, K. (2003) Late-glacial and Holocene climatic effects on fire and vegetation dynamics at the prairie– forest ecotone in south-central Minnesota. Journal of Ecology 91, 822–836. https://doi.org/10.1046/ j.1365-2745.2003.00812.x Chiang, J., Arthur, M.A. and Blankenship, B.A. (2005) The effect of prescribed fire on gap fraction in an oak forest on the Cumberland Plateau. Journal of the Torrey Botanical Society 132, 432–441. https://doi. org/10.3159/1095-5674(2005)132[432:TEOPFO] 2.0.CO;2 Clark, F.B. (1993) An historical perspective of oak regeneration. In: Loftis, D. and McGee, C.E. (eds) Oak Regeneration: Serious Problems, Practical Recommendations. USDA Forest Service, General Technical Report SE-84. Southeastern Forest

Fire and Oak Forests

Experiment Station, Asheville, North Carolina, pp. 3–13. https://doi.org/10.2737/SE-GTR-84 Clark, S.L., Hallgren, S.W., Engle, D.M. and Stahle, D.W. (2007) The historic fire regime on the edge of the prairie: a case study from the Cross Timbers of Oklahoma. In: Masters, R.E. and Galley, K.E.M. (eds) Proceedings of the Tall Timbers 23rd Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 40–49. Cole, K.L., Klick, K.F. and Pavlovic, N.B. (1992) Fire temperature monitoring during experimental burns at Indiana Dunes National Lakeshore. Natural Areas Journal 12, 177–183. Copeland, T.E., Sluis, W. and Howe, H.F. (2002) Fire season and dominance in an Illinois tallgrass prairie restoration. Restoration Ecology 10(2), 315–323. https:// doi.org/10.1046/j.1526-100X.2002.02023.x Crepet, W.L. and Nixon, K.C. (1989a) Earliest megafossil evidence of Fagaceae: phylogenetic and biogeographic implications. American Journal of Botany 76(6), 842–855. https://doi.org/10.1002/j.1537-2197. 1989.tb15062.x Crepet, W.L. and Nixon, K.C. (1989b) Extinct transitional Fagaceae from the Oligocene and their phylogenetic implications. American Journal of Botany 76(10), 1493–1505. https://doi.org/10.1002/j.1537-2197.1989. tb15131.x Cronan, W. (2003) Changes in the Land: Indians, Colonists, and the Ecology of New England, revised edition. Hill and Wang, New York. Curtis, J.T. (1959) The Vegetation of Wisconsin. University Wisconsin Press, Madison, Wisconsin. Cutter, B.E. and Guyette, R.P. (1994) Fire frequency on an oak–hickory ridgetop in the Missouri Ozarks. American Midland Naturalist 132, 393–398. https:// doi.org/10.2307/2426595 Cwynar, L.C. (1977) The recent fire history of Barron Township, Algonquin Park. Canadian Journal of Botany 55, 1524–1538. https://doi.org/10.1139/b77-180 Davies, G.M. (2013) Understanding fire regimes and the ecological effects of fire. In: Belcher, C.M. (ed.) Fire Phenomena and the Earth System: an Interdisciplinary Guide to Fire Science. Wiley-Blackwell, Oxford, pp. 97–124. https://doi.org/10.1002/9781118529539.ch6 Day, G.M. (1953) The Indian as an ecological factor in the northeastern forest. Ecology 34, 329–343. https:// doi.org/10.2307/1930900 Delcourt, H.R. (1979) Late Quaternary vegetation history of the eastern Highland Rim and adjacent Cumberland Plateau of Tennessee. Ecological Monographs 49(3), 255–280. https://doi.org/10.2307/1942485 Delcourt, H.R. and Delcourt, P.A. (1997) Pre-Columbian Native American use of fire on southern Appalachian landscapes. Conservation Biology 11(4), 1010–1014. https://doi.org/10.1046/j.1523-1739.1997.96338.x Delcourt, P.A. and Delcourt, H.R. (1998) The influence of prehistoric human-set fires on oak–chestnut forests

283

in the southern Appalachians. Castanea 63(3), 337–345. Delcourt, P.A. and Delcourt, H.R. (2004) Prehistoric Native Americans and Ecological Change. Cambridge University Press, Cambridge. https://doi. org/10.1017/CBO9780511525520 Delcourt, P.A., Delcourt, H.R., Ison, C.R., Sharp, W.E. and Gremillion, K.J. (1998) Prehistoric human use of fire, the Eastern Agricultural Complex, and Appalachian oak–chestnut forests: paleoecology of Cliff Palace Pond, Kentucky. American Antiquity 63(2), 263–278. https://doi.org/10.2307/2694697 DeSantis, R.D., Hallgren, S.W. and Stahle, D.W. (2010) Historic fire regime of an upland oak forest in southcentral North America. Fire Ecology 6(3), 45–61. https://doi.org/10.4996/fireecology.0603045 Dey, D.C. (2002) Fire history and postsettlement disturbance. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 46–59. Dey, D.C. (2014) Sustaining oak forests in eastern North America: regeneration and recruitment, the pillars of sustainability. Forest Science 60(5), 926–942. https:// doi.org/10.5849/forsci.13-114 Dey, D.C. and Fan, Z. (2009) A review of fire and oak regeneration and overstory recruitment. USDA Forest Service General Technical Report NRS-P-46. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 2–20. Available at: https:// www.fs.usda.gov/treesearch/pubs/17288 (accessed 1 July 2018). Dey, D.C. and Guyette, R.P. (2000) Anthropogenic fire history and red oak forests in south-central Ontario. The Forestry Chronicle 76(2), 339–347. https://doi. org/10.5558/tfc76339-2 Dey, D.C. and Hartman, G. (2005) Returning fire to Ozark Highland forest ecosystems: effects on advance regeneration. Forest Ecology and Management 217, 37–53. https://doi.org/10.1016/j.foreco.2005.05.002 Dey, D.C. and Kabrick, J.M. (2015) Restoration of Midwestern oak woodlands and savannas. In: Stanturf, J.A. (ed.) Restoration of Boreal and Temperate Forests, 2nd edn. CRC Press, Boca Raton, Florida, pp. 401–428. Dey, D.C. and Parker, W.C. (1997) Morphological indicators of stock quality and field performance of red oak (Quercus rubra L.) seedlings underplanted in a central Ontario shelterwood. New Forests 14, 145–156. https://doi.org/10.1023/A:1006577201244 Dey, D.C., Johnson, P.S. and Garrett, H.E. (1996) Modeling the regeneration of oak stands in the Missouri Ozark Highlands. Canadian Journal of Forest Research 26, 573–583. https://doi.org/10.1139/ x26-066 Dey, D.C., Jacobs, D., McNabb, K., Miller, G., Baldwin, V. and Foster, G. (2008a) Artificial regeneration of major

284

oak (Quercus) species in the eastern United States – a review of the literature. Forest Science 54(1), 77–106. https://doi.org/10.1093/forestscience/54.1.77 Dey, D.C., Jensen, R.G. and Wallendorf, M.J. (2008b) Single-tree selection reduces survival and growth of oak stump sprouts in the Missouri Ozark Highlands. In: USDA Forest Service General Technical Report NRS-P-24. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 26–37. Available at: https://www.fs.usda.gov/treesearch/ pubs/13910 (accessed 1 July 2018). Dey, D.C., Gardiner, E.S., Schweitzer, C.J., Kabrick, J.M. and Jacobs, D.F. (2012) Underplanting to sustain future stocking of oak (Quercus) in temperate deciduous forests. New Forests 43, 955–978. https://doi. org/10.1007/s11056-012-9330-z Dey, D.C., Kabrick, J.M. and Schweitzer, C.J. (2017) Silviculture to restore oak savannas and woodlands. Journal of Forestry 115(3), 202–211. https://doi. org/10.5849/jof.15-152 Dickson, J.G. (ed.) (1992) The Wild Turkey Biology and Management. Stackpole Books, Mechanicsburg, Pennsylvania. Dillaway, D.N., Stringer, J.W. and Rieske, L.K. (2007) Light availability influences root carbohydrates, and potentially vigor, in white oak advance regeneration. Forest Ecology and Management 250, 227–233. https://doi.org/10.1016/j.foreco.2007.05.019 DiTomaso, J.M., Brooks, M.L., Allen, E.B., Minnich, R., Rice, P.M. and Kyser, G.B. (2006) Control of invasive weeds with prescribed burning. Weed Technology 20(2), 535–548. https://doi.org/10.1614/WT-05-086R1.1 Dobyns, H.F. (1983) Their Numbers Became Thinned: Native American Population Dynamics in Eastern North America. University of Tennessee Press, Knoxville, Tennessee, 378 pp. Downs, A.A. (1944) Estimating acorn crops for wildlife in the southern Appalachians. Journal of Wildlife Manage­ment 8(4), 339–340. https://doi.org/10.2307/ 3796028 Downs, A.A. and McQuilken, W.E. (1944) Seed production of southern Appalachian oaks. Journal of Forestry 42(12), 913–920. https://doi.org/10.1093/jof/42.12.913 Elliott, K.J. and Vose, J.M. (2005) Effects of understory prescribed burning on shortleaf pine (Pinus echinata Mill.)/mixed-hardwood forests. Journal of the Torrey Botanical Society 132(2), 236–251. https://doi.org/ 10.3159/1095-5674(2005)132[236:EOUPBO]2.0.CO;2 Elliott, K.J. and Vose, J.M. (2010) Short-term effects of prescribed fire on mixed oak forests in the southern Appalachians: vegetation response. Journal of the Torrey Botanical Society 137(1), 49–66. https://doi. org/10.3159/09-RA-014.1 Elliott, K.J., Boring, L.R., Swank, W.T. and Haines, B.R. (1997) Successional changes in plant species diversity and composition after clearcutting a southern Appala­chian watershed. Forest Ecology and

Chapter 7

Management 92, 67–85. https://doi.org/10.1016/ S0378-1127(96)03947-3 Ellsworth, J.W. and McComb, B.C. (2003) Potential effects of passenger pigeon flocks on the structure and composition of presettlement forests of eastern North America. Conservation Biology 17(6), 1548–1558. https://doi.org/10.1111/j.1523-1739.2003.00230.x Engbring, B.L., Heitzman, E. and Spetich, M.A. (2008) Ridgetop fire history of an oak–pine forest in the Ozark Mountains of Arkansas. Southeastern Naturalist 7(1), 49–60. https://doi.org/10.1656/1528-7092(2008)7 [49:RFHOAO]2.0.CO;2 Engelman, H.M. and Nyland, R.D. (2006) Interference to hardwood regeneration in northeastern North America: assessing and countering ferns in northern hardwood forests. Northern Journal of Applied Forestry 23(3), 166–175. https://doi.org/10.1093/njaf/23.3.166 Faison, E.K., Foster, D.R., Oswald, W.W., Hansen, B.C.S. and Doughty, E. (2006) Early Holocene openlands in southern New England. Ecology 87(10), 2537–2547. https://doi.org/10.1890/00129658(2006)87[2537:EHOISN]2.0.CO;2 Fan, Z. and Dey, D.C. (2014) Effects of prescribed fire on upland oak forest ecosystems in Missouri Ozarks (Project NC-F-06-02). USDA Forest Service General Technical Report SRS 198. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 119–127. Available at: https://www.fs.usda.gov/ treesearch/pubs/47208 (accessed 1 July 2018). Fan, Z., Zhongqiu, M., Dey, D.C. and Roberts, S.D. (2012) Response of advance reproduction of oaks and associated species to repeated prescribed fires in upland oak–hickory forests, Missouri. Forest Ecology and Management 266, 160–169. https://doi.org/10.1016/j. foreco.2011.08.034 Fei, S. and Steiner, K.C. (2007) Evidence for increasing red maple abundance in the eastern United States. Forest Science 53(4), 473–477. https://doi.org/10.1093 /forestscience/53.4.473 Fei, S., Kong, N., Steiner, K.C., Moser, W.K. and Steiner, E.B. (2011) Change in oak abundance in the eastern United States from 1980 to 2008. Forest Ecology and Management 262, 1370–1377. https://doi.org/10.1016/j. foreco.2011.06.030 Fesenmyer, K.A. and Christensen, N.L., Jr (2010) Reconstructing Holocene fire history in a southern Appalachian forest using soil charcoal. Ecology 91(3), 662–670. https://doi.org/10.1890/09-0230.1 Flatley, W.T., Lafon, C.W., Grissno-Mayer, H.D. and LaForest, L.B. (2013) Fire history, related to climate and land use in three southern Appalachian landscapes in the eastern United States. Ecological Applications 23, 1250–1266. https://doi.org/10.1890/ 12-1752.1 Forest Products Laboratory (1967) Comparative decay resistance of heartwood of native species. USDA Forest Service Research Note FPL-0153. USDA

Fire and Oak Forests

Forest Service, Forest Products Laboratory, Madison, Wisconsin, 2 pp. Available at: https://www.fs.usda. gov/treesearch/pubs/5731 (accessed 1 July 2018). Foster, D.R., Clayden, S., Orwig, D.A., Hall, B. and Barry, S. (2002) Oak, chestnut and fire: climatic and cultural controls of long-term forest dynamics in New England, USA. Journal of Biogeography 29, 1359–1379. https://doi.org/10.1046/j.1365-2699.2002.00760.x Franklin, S.B., Robertson, P.A. and Fralish, J.S. (1997) Small-scale fire temperature patterns in upland Quercus communities. Journal of Applied Ecology 34, 613–630. https://doi.org/10.2307/2404911 Fredericksen, T.S., Ross, B., Hoffman, W., Lester, M., Beyea, J., Morrison, M.L. and Johnson, B.N. (1998) Adequacy of natural hardwood regeneration on forestlands in northeastern Pennsylvania. Northern Journal of Applied Forestry 15(3), 130–134. https:// doi.org/10.1093/njaf/15.3.130 Frost, C.C. (1998) Presettlement fire frequency regimes of the United States: a first approximation. In: Pruden, T.L. and Brennan, L.A. (eds) Fire in Ecosystem Manage­ ment: Shifting the Paradigm from Suppression to Prescription. Tall Timbers Fire Ecology Conference Proceedings, No. 20, Tall Timbers Station, Tallahassee, Florida, pp. 70–81. Fry, D.L. (2008) Prescribed fire effects on deciduous oak woodland stand structure, northern Diablo Range, California. Rangeland Ecology and Management 61(3), 294–301. https://doi.org/10.2111/07-113.1 Fulé, P.Z. and Covington, W.W. (1994) Fire regime disruption and pine–oak forest structure in the Sierra Madre Occidental, Durango, Mexico. Restoration Ecology 2(4), 261–272. https://doi.org/10.1111/j.1526-100X. 1994.tb00058.x Galford, J., Auchmoody, L.R., Smith, H.C. and Walters, R.S. (1991) Insects affecting establishment of northern red oak seedlings in central Pennsylvania. In: USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Radnor, Pennsylvania, pp. 271– 280. Available at: https://www.fs.usda.gov/treesearch/ pubs/13504 (accessed 1 July 2018). García, D., Bañuelos, M.-J. and Houle, G. (2002) Differential effects of acorn burial and litter cover on Quercus rubra recruitment at the limit of its range in eastern North America. Canadian Journal of Botany 80, 1115–1120. https://doi.org/10.1139/b02-102 Gardiner, E.S. and Hodges, J.D. (1998) Growth and biomass distribution of cherrybark oak (Quercus pagoda Raf.) seedlings as influenced by light availability. Forest Ecology and Management 108, 127–131. https://doi.org/10.1016/S0378-1127(98)00220-5 Gibson, L.P. (1964) Biology and life history of acorn-infesting weevils of the genus Conotrachelus (Coleoptera: Curculionidae). Annals of the Entomological Society of America 57, 521–526. https://doi.org/10.1093/ aesa/57.5.521

285

Gibson, L.P. (1982) Insects that damage northern red oak acorns. USDA Forest Service Research Paper NE-492. USDA Forest Service, Northeastern Forest Experiment Station, Broomall, Pennsylvania, 6 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/15046 (accessed 1 July 2018). Glasgow, L.S. and Matlack, G.R. (2007) Prescribed burning and understory composition in a temperate deciduous forest, Ohio, USA. Forest Ecology and Manage­ment 238, 54–64. https://doi.org/10.1016/j. foreco.2006.08.344 Glen-Lewin, D.C., Johnson, L.A., Jurik, T.W., Akey, A., Leoschke, M. and Rosburg, T. (1990) Fire in central North American grasslands: vegetative reproduction, seed germination, and seedling establishment. In: Collins, S.L. and Wallace, L.L. (eds) Fire in North American Tallgrass Prairies. University of Oklahoma Press, Norman, Oklahoma, pp. 28–45. Glitzenstein, J.S., Platt, W.J. and Streng, D.R. (1995) Effects of fire regime and habitat on tree dynamics in north Florida longleaf pine savannas. Ecological Monographs 65(4), 441–476. https://doi.org/10.2307/2963498 Goebel, T., Waters, M.R. and O’Rouke, D.H. (2008) The late Pleistocene dispersal of modern humans in the Americas. Science 319, 1497–1502. https://doi.org /10.1126/science.1153569 Goodrum, P.H., Reid, V.H. and Boyd, C.E. (1971) Acorn yields, characteristics, and management criteria of oaks for wildlife. Journal of Wildlife Management 35, 520–532. Gottschalk, K.W. (1994) Shade, leaf growth and crown development of Quercus rubra, Quercus velutina, Prunus serotina and Acer rubrum seedlings. Tree Physiology 14, 735–749. https://doi.org/10.1093/ treephys/14.7-8-9.735 Green, S.R., Arthur, M.A. and Blankenship, B.A. (2010) Oak and red maple seedling survival and growth following periodic prescribed fire on xeric ridgetops on the Cumberland Plateau. Forest Ecology and Management 259, 2256–2266. https://doi.org/10.1016 /j.foreco.2010.02.026 Greenberg, C.H. (2000) Individual variation in acorn production by species of southern Appalachian oaks. Forest Ecology and Management 132, 199–210. https://doi.org/10.1016/S0378-1127(99)00226-1 Greenberg, C.H., Keyser, T.L., Zarnoch, S.J., Connor, K., Simon, D.M. and Warburton, G.S. (2012) Acorn viability following prescribed fire in upland hardwood forests. Forest Ecology and Management 275, 79– 86. https://doi.org/10.1016/j.foreco.2012.03.012 Greenlee, J.M. and Langenheim, J.H. (1990) Historic fire regimes and their relation to vegetation patterns in the Monterey Bay area of California. The American Midland Naturalist 124(2), 239–253. https://doi. org/10.2307/2426173 Gustafson, R.O. (1944) Cull as determined from basal wounds in Kentucky highlands timber. Journal of Forestry 42(3), 181–184.

286

Guyette, R.P. and Spetich, M.A. (2003) Fire history of oak– pine forests in the lower Boston Mountains, Arkansas, USA. Forest Ecology and Management 180, 463–474. https://doi.org/10.1016/S0378-1127(02)00613-8 Guyette, R.P. and Stambaugh, M.C. (2004) Post-oak fire scars as a function of diameter, growth, and tree age. Forest Ecology and Management 198, 183–192. https://doi.org/10.1016/j.foreco.2004.04.016 Guyette, R.P., Muzika, R.M. and Dey, D.C. (2002) Dynamics of an anthropogenic fire regime. Ecosystems 5, 472–486. Available at: https://www.fs.usda.gov/ treesearch/pubs/14040 (accessed 1 July 2018). Guyette, R.P., Dey, D.C. and Stambaugh, M.C. (2003) Fire and human history of a barren–forest mosaic in southern Indiana. American Midland Naturalist 149, 21–34. https://doi.org/10.1674/0003-0031(2003)149 [0021:FAHHOA]2.0.CO;2 Guyette, R.P., Spetich, M.A. and Stambaugh, M.C. (2006a) Historic fire regime dynamics and forcing factors in the Boston Mountains, Arkansas, USA. Forest Ecology and Management 234, 293–304. https://doi. org/10.1016/j.foreco.2006.07.016 Guyette, R.P., Dey, D.C., Stambaugh, M.C. and Muzika, R.M. (2006b) Fire scars reveal variability and dynamics of eastern fire regimes. In: USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 20–39. Available at: https:// www.fs.usda.gov/treesearch/pubs/18430 (accessed 1 July 2018). Guyette, R., Marschall, J., Stambaugh, M. and Stevenson, A. (2012a) Validating time-dependent tree defect models and determining lumber yield changes for prescribed fire injured oak trees. Final Report Cooperative Agreement 327. Missouri Department of Conservation, Jefferson City, Missouri, 45 pp. Guyette, R.P., Stambaugh, M.C., Dey, D.C. and Muzika, R.M. (2012b) Predicting fire frequency with chemistry and climate. Ecosystems 15, 322–335. https://doi. org/10.1007/s10021-011-9512-0 Guyette, R.P., Thompson, F.R., Whittier, J., Stambaugh, M.C. and Dey, D.C. (2014) Future fire probability modeling with climate change data and physical chemistry. Forest Science 60(5), 862–870. https:// doi.org/10.5849/forsci.13-108 Guyette, R.P., Stambaugh, M.C., Dey, D.C., Marschall, J.M., Saunders, J. and Lampereur, J. (2016) 350 years of fire–climate–human interactions in a Great Lakes sandy outwash plain. Forests 7, 189. https:// doi.org/10.3390/f7090189. Guyette, R., Stambaugh, M.C., Dey, D. and Muzika, R.M. (2017) The theory, direction and magnitude of ecosystem fire probability as constrained by precipitation and temperature. PLoS ONE 12(7), e0180956. https://doi.org/10.1371/journal.pone.0180956 Gysel, L.W. (1957) Acorn production on good, medium, and poor oak sites in southern Michigan. Journal

Chapter 7

of Forestry 55, 570–574. https://doi.org/10.1093/ jof/55.8.570 Hanberry, B.B., Dey, D.C. and He, H.S. (2012) Regime shifts and weakened environmental gradients in open oak and pine ecosystems. PLoS ONE 7(7), e41337. https://doi.org/10.1371/journal.pone.0041337 Hanberry, B.B., Jones-Farrand, D.T. and Kabrick, J.M. (2014a) Historical open forest ecosystems in the Missouri Ozarks: reconstruction and restoration targets. Ecological Restoration 32(4), 407–416. https:// doi.org/10.3368/er.32.4.407 Hanberry, B.B., Dey, D.C. and He, H.S. (2014b) The history of widespread decrease in oak dominance exemplified in a grassland–forest landscape. Science of the Total Environment 476–477, 591–600. https://doi. org/10.1016/j.scitotenv.2014.01.048 Hanberry, B.B., Kabrick, J.M. and He, H.S. (2014c) Densification and state transition across the Missouri Ozarks landcape. Ecosystems 17, 66–81. https://doi. org/10.1007/s10021-013-9707-7 Hare, R.C. (1965) The contribution of bark to fire resistance of southern trees. Journal of Forestry 63, 248– 251. https://doi.org/10.1093/jof/63.4.248 Harmer, R., Boswell, R. and Robertson, M. (2005) Survival and growth of tree seedlings in relation to changes in the ground flora during natural regeneration of an oak shelterwood. Forestry 78(1), 21–32. https://doi.org/10.1093/forestry/cpi003 Harmon, M. (1982) Fire history of the westernmost portion of Great Smoky Mountains National Park. Bulletin of the Torrey Botanical Club 109(1), 74–79. https://doi.org/10.2307/2484470 Harmon, M.E. (1984) Survival of trees after low-intensity surface fires in the Great Smoky Mountains National Park. Ecology 65, 796–802. https://doi.org/10.2307/ 1938052 Hart, J.L. and Buchanan, M.L. (2012) History of fire in eastern oak forests and implications for restoration. In: USDA Forest Service General Technical Report NRS-P-102. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 34–51. Available at: https://www.fs.usda.gov/ treesearch/pubs/42130 (accessed 1 July 2018). Hart, J.L., Horn, S.P. and Grissino-Mayer, H.D. (2008) Fire history from soil charcoal in a mixed hardwood forest on the Cumberland Plateau, Tennessee, USA. Journal of the Torrey Botanical Society 135(3), 401– 410. https://doi.org/10.3159/08-RA-013.1 Haywood, J.D. (2009) Eight years of seasonal burning and herbicidal brush control influence sapling longleaf pine growth, understory vegetation, and the outcome of an ensuing wildfire. Forest Ecology and Management 258, 295–305. https://doi.org/10.1016/j. foreco.2009.04.016 Haywood, J.D., Harris, F.L., Grelen, H.E. and Pearson, H.A. (2001) Vegetative response to 37 years of seasonal burning on a Louisiana longleaf pine site.

Fire and Oak Forests

Southern Journal of Applied Forestry 25, 122–130. https://doi.org/10.1093/sjaf/25.3.122 Hengst, G.E. and Dawson, J.O. (1994) Bark properties and fire resistance of selected tree species from the central hardwood region of North America. Canadian Journal of Forest Research 24, 688–696. https://doi. org/10.1139/x94-092 Hepting, G.H. (1941) Prediction of cull following fire in Appalachian oaks. Journal of Agricultural Research 62(2), 109–120. Hesterberg, G.A. (1957) Deterioration of sugar maple following logging damage. USDA Forest Service Station Paper 51. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota, 58 pp. https://doi.org/10.5962/bhl.title.81548 Hiers, J.K., Wyatt, R. and Mitchell, R.J. (2000) The effects of fire regime on legume production in longleaf pine savannas: is a season selective? Oecologia 125, 521–530. https://doi.org/10.1007/s004420000469 Hilt, D.E. (1985) Species composition of young central hardwood stands that develop after clearcutting. In: Proceedings of 5th Central Hardwood Forest Conference. University Illinois, Champaign-Urbana, Illinois, pp. 11–14. Hix, D.M. and Lorimer, C.G. (1991) Early stand development on former oak sites in southwestern Wisconsin. Forest Ecology and Management 42, 169–193. https://doi.org/10.1016/0378-1127(91)90023-O Horney, M., Standiford, R.B., McCreary, D., Tecklin, J. and Richards, R. (2002) Effects of wildfire on blue oak in the northern Sacramento Valley. In: USDA Forest Service General Technical Report PSW 184. USDA Forest Service, Pacific Southwest Research Station, Albany, California, pp. 261–267. Available at: https:// www.fs.usda.gov/treesearch/pubs/26128 (accessed 1 July 2018). Horsley, S.B. and Marquis, D.A. (1983) Interference by weeds and deer with Allegheny hardwood reproduction. Canadian Journal of Forest Research 13, 61–69. https://doi.org/10.1139/x83-009 Hoss, J.A., Lafon, C.W., Grissino-Mayer, H.D., Aldrich, S.R. and DeWeese, G.G. (2008) Fire history of a temperate forest with an endemic fire-dependent herb. Physical Geography 29(5), 424–441.Available at: https://www.tandfonline.com/doi/abs/10.2747/02723646.29.5.424 (accessed 1 July 2018). Howe, H.F. (1994) Response of early- and late-flowering plants to fire season in experimental prairies. Ecological Applications 4(1), 121–133. https://doi. org/10.2307/1942122 Huebner, C.D. (2006) Fire and invasive exotic plant species in eastern oak communities: an assessment of current knowledge. In: USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 218–232. Available at: https://www. fs.usda.gov/treesearch/pubs/18451 (accessed 1 July 2018).

287

Hutchinson, T.F. (2006) Fire and the herbaceous layer in eastern oak forests. In: USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 136–149. Available at: https://www.fs.usda.gov/treesearch/pubs/18438 (accessed 1 July 2018). Hutchinson, T.F., Sutherland, E.K. and Yaussy, D.A. (2005a) Effects of repeated prescribed fires on the structure, composition, and regeneration of mixedoak forests in Ohio. Forest Ecology and Management 218, 210–228. https://doi.org/10.1016/j.foreco.2005. 07.011 Hutchinson, T.F., Boerner, R.E.J., Sutherland, S., Sutherland, E.K., Ortt, M. and Iverson, L.R. (2005b) Prescribed fire effects on the herbaceous layer of mixedoak forests. Canadian Journal of Forest Research 35, 877–890. https://doi.org/10.1139/x04-189 Hutchinson, T.F., Long, R.P., Rebbeck, J., Sutherland, E.K. and Yaussy, D.A. (2012a) Repeated prescribed fires alter gap-phase regeneration in mixed-oak forests. Canadian Journal of Forest Research 42, 303– 314. https://doi.org/10.1139/x11-184 Hutchinson, T.F., Yaussy, D.A., Long, R.P., Rebbeck, J. and Sutherland, E.K. (2012b) Long-term (13-year) effects of repeated prescribed fires on stand structure and tree regeneration in mixed-oak forests. Forest Ecology and Management 286, 87–100. https://doi. org/10.1016/j.foreco.2012.08.036 Iverson, L.R. and Hutchinson, T.F. (2002) Soil temperature and moisture fluctuations during and after prescribed fire in mixed-oak forests, USA. Natural Areas Journal 22(4), 296–304. Available at: https://www. fs.usda.gov/treesearch/pubs/22433 (accessed 1 July 2018). Iverson, L.R., Yaussy, D.A., Rebbeck, J., Hutchinson, T.F., Long, R.P. and Prasad, A.M. (2004) A comparison of thermocouples and temperature paints to monitor spatial and temporal characteristics of landscape-scale prescribed fires. International Journal of Wildland Fire 13, 311–322. https://doi.org/10.1071/ WF03063 Iverson, L.R., Hutchinson, T.F., Prasad, A.M. and Peters, M.P. (2008) Thinning, fire and oak regeneration across a heterogeneous landscape in the eastern US: 7-year results. Forest Ecology and Management 255, 3035–3050. https://doi.org/10.1016/j.foreco.2007.09.088 Iverson, L.R., Hutchinson, T.F., Peters, M.P. and Yaussy, D.A. (2017) Long-term response of oak– hickory regeneration to partial harvest and repeated fires: influence of light and moisture. Ecosphere 8(1), e01642. https://doi.org/10.1002/ecs2.1642 Jenkins, M.A. and Parker, G.R. (1998) Composition and diversity of woody vegetation in silvicultural openings of southern Indiana forests. Forest Ecology and Management 109, 57–74. https://doi.org/10.1016/ S0378-1127(98)00256-4

288

Johnson, P.S. (1974) Survival and growth of northern red oak seedlings following a prescribed burn. USDA Forest Service Research Note NC-177. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, 3 pp. Available at: https://www.fs.usda. gov/treesearch/pubs/11354 (accessed 1 July 2018). Kaib, J.M. (1998) Fire history in riparian canyon pine–oak forests and the intervening desert grasslands of the southwest borderlands: a dendroecological, historical, and cultural inquiry. MSc. thesis, University of Arizona, Flagstaff, Arizona, 234 pp. Kane, J.M., Varner, J.M. and Hiers, J.K. (2008) The burning characteristics in Southeastern oaks: discriminating fire facilitators from fire impeders. Forest Ecology and Management 256, 2039–2045. https://doi. org/10.1016/j.foreco.2008.07.039 Key, C.H. and Benson, N.C. (2006) Landscape assessment (LA). In: USDA Forest Service General Technical Report RMRS-GTR-164-CD. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, pp. LA-1–55. Available at: https://www.fs. usda.gov/treesearch/pubs/24066 (accessed 1 July 2018). Keyser, T.L., Arthur, M. and Loftis, D.L. (2017) Repeated burning alters the structure and composition of hardwood regeneration in oak-dominated forests of eastern Kentucky, USA. Forest Ecology and Management 393, 1–11. https://doi.org/10.1016/j.foreco.2017.03.015 Kinkead, C.S., Stambaugh, M.C. and Kabrick, J.M. (2017) Mortality, scarring, and growth in an oak woodland following prescribed fire and commercial thinning in the Ozark Highlands. Forest Ecology and Management 403, 12–26. https://doi.org/10.1016/j. foreco.2017.07.028 Knapp, B.O., Stephan, K. and Hubbart, J.A. (2015) Structure and composition of an oak–hickory forest after over 60 years of repeated prescribed burning in Missouri, U.S.A. Forest Ecology and Management 344, 95–109. https://doi.org/10.1016/j.foreco.2015. 02.009 Knapp, B.O., Hullinger, M.A. and Kabrick, J.M. (2017) Effects of fire frequency on long-term development of an oak–hickory forest in Missouri, U.S.A. Forest Ecology and Management 387, 19–29. https://doi. org/10.1016/j.foreco.2016.07.013 Kolaks, J., Cutter, B.E., Loewenstein, E.F., Hartman, G.W. and Kabrick, J.M. (2007) The effect of thinning and aspect on fire behavior in the Missouri Ozarks of the Central Hardwood Region. In: Masters, R.E. and Galley, K.E.M. (eds) Proceedings of the 23rd Tall Timbers Fire Ecology Conference: Fire in Grassland and Shrubland Ecosystems. Tall Timbers Research Station, Tallahassee, Florida, pp. 193–201. Korstian, C.F. (1927) Factors controlling germination and early survival in oaks. Yale University School of Forestry Bulletin 19. Yale University Press, New Haven, Connecticut, 115 pp.

Chapter 7

Kostel-Hughes, F., Young, T.P. and Wehr, J.D. (2005) Effects of leaf litter depth on the emergence and seedling growth of deciduous forest tree species in relation to seed size. Journal of the Torrey Botanical Society 132, 50–61. https://doi.org/10.3159/10955674(2005)132[50:EOLLDO]2.0.CO;2 Kreye, J.K., Varner, J.M., Hiers, J.K. and Mola, J. (2013) Toward a mechanism for eastern North American forest mesophication: the role of litter drying across 17 species. Ecological Applications 23, 1976–1986. https://doi.org/10.1890/13-0503.1 Kruger, E.L. and Reich, P.B. (1997) Responses of hardwood regeneration to fire in mesic forest openings. I. Postfire community dynamics. Canadian Journal of Forest Research 27, 1822–1831. https://doi.org/10.1139/x97-136 Kuddes-Fischer, L.M. and Arthur, M.A. (2002) Response of understory vegetation and tree regeneration to a single prescribed fire in oak–pine forests. Natural Areas Journal 22(1), 43–52. Kuenzel, J.G. and McGuire, J.R. (1942) Response of chestnut oak reproduction to clear and partial cutting of overstory. Journal of Forestry 40(3), 238–243. https://doi.org/10.1093/jof/40.3.238 Kurtz, C.M. (2013) An assessment of invasive plant species monitored by the Northern Research Station Forest Inventory and Analysis program, 2005 through 2010. USDA Forest Service General Technical Report NRS-109. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 70 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/43136 (accessed 1 July 2018). Lafon, C.W., Naito, A.T., Grissno-Mayer, H.D., Horn, S.D. and Waldrop, T.A. (2017) Fire history of the Appalachian region: a review and synthesis. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southern Research Station, Asheville, North Carolina, 97 pp. Available at: https:// www.fs.usda.gov/treesearch/pubs/53589 (accessed 1 July 2018). Law, J.R., Johnson, P.S. and Houf, G. (1994) A crown cover chart for oak savannas. USDA Forest Service Technical Brief TP-NC-2. USDA Forest Service, North Central Forest Experiment Station, 4 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/10999 (accessed 1 July 2018). Lettow, M.C., Brudvig, L.A., Bahlai, C.A. and Landis, D.A. (2014) Oak savanna management strategies and their differential effects on vegetative structure, understory light, and flowering forbs. Forest Ecology and Management 329, 89–98. https://doi. org/10.1016/j.foreco.2014.06.019 Lhotka, J.M. and Loewenstein, E.F. (2008) Influence of canopy structure on the survival and growth of underplanted seedlings. New Forests 35, 89–104. https:// doi.org/10.1007/s11056-007-9063-6 Lhotka, J.M. and Loewenstein, E.F. (2009) Effect of midstory removal on understory light availability and the

Fire and Oak Forests

2-year response of underplanted cherrybark oak seedlings. Southern Journal of Applied Forestry 33(4), 171–177. https://doi.org/10.5849/sjaf.12-001 Lightfoot, K., Cuthrell, R.Q., Stiplen, C.J. and Hylkema, M.G. (2013) Rethinking the study of landscape management practices among hunter-gatherers in North America. American Antiquity 78(2), 285–301. https://doi.org/10.7183/0002-7316.78.2.285 Liming, F.G. and Johnston, J.P. (1944) Reproduction in oak–hickory forest stands of the Missouri Ozarks. Journal of Forestry 42(3), 175–180. https://doi.org/ 10.1093/jof/42.3.175 Lockhart, B.R., Hodges, J.D. and Gardiner, E.S. (2000) Response of advance cherrybark oak reproduction to midstory removal and shoot clipping. Southern Journal of Applied Forestry 24, 45–50. https://doi. org/10.1093/sjaf/24.1.45 Loftis, D.L. (1990a) Predicting post-harvest performance of advanced red oak reproduction in the southern Appalachians. Forest Science 36(4), 908–916. https://doi.org/10.1093/forestscience/36.4.908 Loftis, D.L. (1990b) A shelterwood method for regenerating red oak in the southern Appalachians. Forest Science 36(4), 917–929. https://doi.org/10.1093/ forestscience/36.4.917 Lombardo, J.A. and McCarthy, B.C. (2008) Silvicultural treatment effects on oak seed production and predation by acorn weevils in southeastern Ohio. Forest Ecology and Management 255, 2566–2576. https:// doi.org/10.1016/j.foreco.2008.01.017 Loomis, R.M. (1974) Predicting the losses in sawtimber volume and quality from fires in oak–hickory forests. USDA Forest Service Research Paper NC-104. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, 6 pp. Available at: https:// www.fs.usda.gov/treesearch/pubs/10625 (accessed 1 July 2018). Loomis, R.M. and Paananen, D.M. (1989) Appraising fire effects. In: USDA Forest Service North Central Forest Experiment Station Central Hardwood Notes Note 8.03. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11666 (accessed 1 July 2018). Loope, W.L. and Anderton, J.B. (1998) Human vs. lightning ignition of presettlement surface fires in coastal pine forests of the Upper Great Lakes. The American Midland Naturalist 140(2), 206–218. https://doi.org/ 10.1674/0003-0031(1998)140[0206:HVLIOP]2.0.CO;2 Lorimer, C.G. (1984) Development of the red maple understory in northeastern oak forests. Forest Science 30(1), 3–22. https://doi.org/10.1093/forestscience/30.1.3 Lorimer, C.G., Chapman, J.W. and Lambert, W.D. (1994) Tall understory vegetation as a factor in the poor development of oak seedlings beneath mature stands. Journal of Ecology 82(2), 227–237. https:// doi.org/10.2307/2261291

289

Marlon, J.R., Bartlein, P.J., Carcaillet, C., Gavin, D.G., Harrison, S.P., Higuera, P.E., Joos, F., Power, M.J. and Prentice, I.C. (2008) Climate and human influences on global biomass burning over the past two millennia. Nature Geoscience 1, 697–702. https://doi. org/10.1038/ngeo313 Marschall, J.M., Guyette, R.P., Stambaugh, M.C. and Stevenson, A.P. (2014) Fire damage effects on red oak timber product values. Forest Ecology and Management 320, 182–189. https://doi.org/10.1016/j. foreco.2014.03.006 Marschall, J.M., Stambaugh, M.C., Jones, B.C., Guyette, R.P., Brose, P.H. and Dey, D.C. (2016) Fire regimes of remnant pitch pine communities in the Ridge and Valley Region of central Pennsylvania, USA. Forests 7, 224. https://doi.org/10.3390/f7100224 McClain, W.E., Esker, T.L., Edgin, B.R., Spyreas, G. and Ebinger, J.E. (2010) Fire history of a post oak (Quercus stellata Wang.) woodland in Hamilton County, Illinois. Castanea 75(4), 461–474. https://doi. org/10.2179/09-007.1 McClaran, M.P. and Bartolome, J.W. (1989) Fire-related recruitment in stagnant Quercus douglasii populations. Canadian Journal of Forest Research 19, 580–585. https://doi.org/10.1139/x89-091 McEwan, R.W., Hutchinson, T.F., Long, R.P., Ford, D.R. and McCarthy, B.C. (2007) Temporal and spatial patterns in fire occurrence during the establishment of mixed-oak forests in eastern North America. Journal of Vegetation Science 18, 655–664. https://doi. org/10.1111/j.1654-1103.2007.tb02579.x McGee, C.E. (1972) From a defective hardwood stand to multiple use opportunity. Journal of Forestry 70(11), 700–704. https://doi.org/10.1093/jof/70.11.700 McWilliams, W.H., O’Brien, R.A., Reese, G.C. and Waddell, K.A. (2002) Distribution and abundance of oaks in North America. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 13–33. Mensing, S. (2015) The paleohistory of California oaks. USDA Forest Service General Technical Report PSW-251. USDA Forest Service, Pacific Southwest Research Station, Berkeley, California, pp. 35–47. Available at: https://www.fs.usda.gov/treesearch/ pubs/49912 (accessed 1 July 2018). Midgley, J.J. and Bond, W.J. (2013) Plant adaptations to fire: an evolutionary perspective. In: Belcher, C.M. (ed.) Fire Phenomena and the Earth System: an Interdisciplinary Guide to Fire Science. WileyBlackwell, Oxford, pp. 125–134. https://doi.org/10.1002/ 9781118529539.ch7 Miller, G.W., Kochenderfer, J.N. and Gottschalk, K.W. (2004) Effect of pre-harvest shade control and fencing on northern red oak seedling development in the central Appalachians. In: USDA Forest Service General Technical Report SRS-73. USDA Forest

290

Service, Southern Research Station, Asheville, North Carolina, pp 182–189. Available at: https://www.fs. usda.gov/treesearch/pubs/6538 (accessed 1 July 2018). Miller, G.W., Stringer, J.W. and Mercker, D.C. (2007) Technical guide to crop tree release in hardwood forests. University Tennessee Extension PB 1774. University of Tennessee Press, Knoxville, Tennessee, 23 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/14228 (accessed 1 July 2018). Miller, J.H., Lemke, D. and Coulston, J. (2013) The invasion of Southern forests by nonnative plants: current and future occupation, with impacts, management strategies, and mitigation approaches. In: USDA Forest Service General Technical Report SRS-178. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 397–456. Available at: https://www.fs.usda.gov/treesearch/pubs/44217 (accessed 1 July 2018). Morrissey, R.C., Jacobs, D.F., Seifert, J.R., Fischer, B.C. and Kershaw, J.A. (2008) Competitive success of natural oak regeneration in clearcuts during the stem exclusion stage. Canadian Journal of Forest Research 38, 1419–1430. Available at: http://www. nrcresearchpress.com/doi/10.1139/X08-018#. Wr0JtP4Uncs (accessed 1 July 2018). Morrissey, R.C., Jacobs, D.F., Seifert, J.R. and Kershaw, J.A. (2010) Overstory species composition of naturally regenerated clearcuts in an ecological classification framework. Plant Ecology 208, 21–34. https://doi. org/10.1007/s11258-009-9683-3 Motsinger, J.R., Kabrick, J.M., Dey, D.C., Henderson, D.E. and Zenner, E.K. (2010) Effect of midstory and understory removal on the establishment and development of natural and artificial pin oak advance reproduction in bottomland forests. New Forests 39, 195–213. https://doi.org/10.1007/s11056-009-9164-5 Munoz, S.E., Gajewski, E.K. and Peros, M.C. (2010) Synchronous environmental and cultural change in the prehistory of the northeastern United States. Proceedings of the National Academy of Science 107, 22008–22013. https://doi.org/10.1073/pnas. 1005764107 Nelson, D.M., Hu, F.S., Grimm, E.C., Curry, B.B. and Slate, J.E. (2006) The influence of aridity and fire on Holocene prairie communities in the eastern prairie peninsula. Ecology 87(10), 2523–2536. https://doi.org/ 10.1890/0012-9658(2006)87[2523:TIOAAF]2.0.CO;2 Nelson, P.W. (2010) The Terrestrial Natural Communities of Missouri. Missouri Natural Areas Committee, Jefferson City, Missouri. Nixon, K.C. (2006) Global and neotropical distribution and diversity of oak (genus Quercus) and oak forests. In: Kappelle, M. (ed.) Ecology and Conservation of Neotropical Montane Oak Forests. Ecological Studies 185. Springer-Verlag, Berlin, pp. 3–13. https:// doi.org/10.1007/3-540-28909-7_1

Chapter 7

Nowacki, G.J. and Abrams, M.D. (2008) The demise of fire and ‘mesophication’ of forests in the eastern United States. BioScience 58(2), 123–138. https:// doi.org/10.1641/B580207 Ostrom, B.J. and Loewenstein, E.F. (2006) Light transmittance following midstory removal in a riparian hardwood forest. In: USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 265–268. Available at: https://www.fs.usda.gov/ treesearch/pubs/23392 (accessed 1 July 2018). Oswalt, S.N. and Oswalt, C.M. (2011) The extent of selected non-native invasive plants on southern forest lands. In: USDA Forest Service General Technical Report NRS-P-78. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 447–459. Available at: https://www.fs.usda.gov/ treesearch/pubs/38083 (accessed 1 July 2018). Outcalt, K.W. and Brockway, D.G. (2010) Structure and composition changes following restoration treatments of longleaf pine forests on the Gulf Coastal Plain of Alabama. Forest Ecology and Management 259, 1615–1623.https://doi.org/10.1016/j.foreco.2010.01.039 Packard, S. and Mutel, C.F. (1997) The Tallgrass Restoration Handbook. Island Press, Washington, DC, 463 pp. Paquette, A., Bouchard, A. and Cogliastro, A. (2007) Morphological plasticity in seedlings of three deciduous species under shelterwood under-planting management does not correspond to shade tolerance ranks. Forest Ecology and Management 241, 278–287. https://doi.org/10.1016/j.foreco.2007.01.004 Parker, W.C. and Dey, D.C. (2008) Influence of overstory density on ecophysiology of red oak (Quercus rubra) and sugar maple (Acer saccharum) seedlings in central Ontario shelterwoods. Tree Physiology 28, 797–804. https://doi.org/10.1093/treephys/28.5.797 Patterson, W.A., III (2006) The paleoecology of fire and oaks in eastern forests. USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 2–19. Available at: https://www.fs.usda.gov/treesearch/pubs/18429 (accessed 1 July 2018). Paulsell, L.K. (1957) Effects of burning on Ozark hardwood timberland. University of Missouri, College of Agriculture Research Bulletin 640. University of Missouri Press, Columbia, Missouri, 24 pp. Pausas, J.G. (2015) Bark thickness and fire regime. Functional Ecology 29, 315–327. https://doi. org/10.1111/1365-2435.12372 Pélisson, P.-F., Bernstein, C., Francois, D., Menu, F. and Venner, S. (2013) Dispersal and dormancy strategies among insect species competing for a pulsed resource. Ecological Entomology 38, 470–477. https://doi.org/10.1111/een.12038 Peterson, D.W. and Reich, P.B. (2001) Prescribed fire in oak savanna: fire frequency effects on stand struc-

Fire and Oak Forests

ture and dynamics. Ecological Applications 11(3), 914–927. https://doi.org/10.1890/1051-0761(2001)011 [0914:PFIOSF]2.0.CO;2 Peterson, D.W. and Reich, P.B. (2008) Fire frequency and tree canopy structure influence plant species diversity in a forest–grassland ecotone. Plant Ecology 194, 5–16. https://doi.org/10.1007/s11258-007-9270-4 Peterson, D.W., Reich, P.B. and Wrage, K.J. (2007) Plant functional group responses to fire frequency and tree canopy cover gradients in oak savannas and woodlands. Journal of Vegetation Science 18, 3–12. https:// doi.org/10.1111/j.1654-1103.2007.tb02510.x Phillips, D.L. and Murdy, W.H. (1985) Effects of rhododendron (Rhododendron maximum) on regeneration of southern Appalachian hardwoods. Forest Science 31(1), 226–233. https://doi.org/10.1093/ forestscience/31.1.226 Phillips, R.J. and Waldrop, T.A. (2008) Changes in vegetation structure and composition in response to fuel reduction treatments in the South Carolina Piedmont. Forest Ecology and Management 255, 3107–3116. https://doi.org/10.1016/j.foreco.2007.09.037 Phillips, R.J., Waldrop, T.A. and Stottlemyer, A.D. (2013) Occurrence and spread of nonnative invasive plants in stands treated with fire and/or mechanical treatments in the Upper Piedmont of South Carolina. In: USDA Forest Service General Technical Report SRS 175. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 525–531. Available at: https://www.fs.usda.gov/treesearch/pubs/ 43683 (accessed 1 July 2018). Purcell, K.L. and Stephens, S.L. (2005) Changing fire regimes and the avifauna of California oak woodlands. Studies in Avian Biology 30, 30–45. Available at: https://www.fs.usda.gov/treesearch/pubs/23829 (accessed 1 July 2018). Pyne, S.J. (1982) Fire in America. Princeton University Press, Princeton, New Jersey. Pyne, S.J., Andrews, P.L. and Laven, R.D. (1996) Introduction to Wildland Fire, 2nd edn. Wiley, New York. Ralston, J.L. and Cook, J. (2013) Impact of prescribed fire, timber removal, and the seed bank on understory plant diversity and canopy cover in an oak–pine barrens, central Wisconsin, USA. Ecological Restoration 31(4), 395–411. https://doi.org/10.3368/er.31.4.395 Ratajczak, Z., Nippert, J.B. and Collins, S.L. (2012) Woody encroachment decreases diversity across North American grasslands and savannas. Ecology 93(4), 697–703. https://doi.org/10.1890/11-1199.1 Rebbeck, J. (2012) Fire management and woody invasive plants in oak ecosystems. In: USDA Forest Service General Technical Report NRS-P-102. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 142–155. Available at: https://www.fs.usda.gov/treesearch/pubs/42137 (accessed 1 July 2018).

291

Rebbeck, J., Gottschalk, K. and Scherzer, A. (2011) Do chestnut, northern red, and white oak germinant seedlings respond similarly to light treatments? Growth and biomass. Canadian Journal of Forest Research 41, 2219–2230. https://doi.org/10.1139/ x11-124 Regelbrugge, J.C. and Smith, D.W. (1994) Postfire tree mortality in relation to wildfire severity in mixed oak forests in the Blue Ridge of Virginia. Northern Journal of Applied Forestry 11, 90–97. https://doi.org/10.1093/ njaf/11.3.90 Roach, B.A. and Gingrich, S.F. (1968) Even-aged silviculture for upland central hardwoods. USDA Forest Service, Agriculture Handbook 355. USDA Forest Service, Washington, DC, 39 pp. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Rooney, T.P. and Waller, D.M. (2003) Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and Management 181, 165–176. https://doi.org/10.1016/S0378-1127(03)00130-0 Royse, J., Arthur, M.A., Schörgendorer, A. and Loftis, D.L. (2010) Establishment and growth of oak (Quercus alba, Quercus prinus) seedlings in burned and fireexcluded upland forests on the Cumberland Plateau. Forest Ecology and Management 260, 502–510. https://doi.org/10.1016/j.foreco.2010.05.005 Sander, I.L. (1971) Height growth of new oak sprouts depends on size of advance reproduction. Journal of Forestry 69, 809–811. https://doi.org/10.1093/ jof/69.11.809 Sands, B.A. and Abrams, M.D. (2011) A 183-year history of fire and recent fire suppression impacts in select pine and oak forest stands of the Menominee Indian reservation, Wisconsin. The American Midland Naturalist 166(2), 325–338. https://doi.org/10.1674/ 0003-0031-166.2.325 Scasta, J.D., Weir, J.R. and Stambaugh, M.C. (2016) Droughts and wildfires in western U.S. rangelands. Rangelands 38(4), 197–203. https://doi.org/10.1016/ j.rala.2016.06.003 Schmidt, T.L. and Leatherberry, E.C. (1995) Expansion of eastern redcedar in the lower Midwest. Northern Journal of Applied Forestry 12, 180–183. https://doi. org/10.1093/njaf/12.4.180 Schuler, T.M., Thomas Van-Gundy, M., Adams, M.B. and Ford, W.M. (2010) Seed bank response to prescribed fire in the Central Appalachians. USDA Forest Service Research Paper NRS 9. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 9 pp. https://doi.org/10.2737/NRS-RP-9 Schweitzer, C.J. and Dey, D.C. (2011) Forest structure, composition, and tree diversity response to a gradient of regeneration harvests in the mid-Cumberland Plateau escarpment region, USA. Forest Ecology and Management 262, 1729–1741. https://doi. org/10.1016/j.foreco.2011.07.020

292

Schweitzer, C.J. and Dey, D.C. (2015) The conundrum of creating understory light conditions conducive to promoting oak reproduction: midstory herbicide and prescribed fire treatment. In: USDA Forest Service e–General Technical Report SRS–203. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 45–56. Available at: https://www.fs.usda. gov/treesearch/pubs/47482 (accessed 1 July 2018). Schweitzer, C.J., Dey, D.C. and Wang, Y. (2016) Hardwood–pine mixedwoods stand dynamics following thinning and prescribed burning. Fire Ecology 12(2), 85–104. https://doi.org/10.4996/fireecology.1202085 Scott, A.C., Bowman, D.M.J.S., Bond, W.J., Pyne, S.J. and Alexander, M.E. (2014) Fire on Earth. WileyBlackwell, Chichester, UK. Scott, J.H. and Burgan, R.E. (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service General Technical Report RMRS-GTR-153. USDA Forest Service, Rocky Mountain Research Station, Ft Collins, Colorado, 72 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/9521 (accessed 1 July 2018). Scowcroft, P.G. (1965) The effects of fire on the hardwood forests of the Missouri Ozarks. MSc. thesis, University of Missouri, Columbia, Missouri, 126 pp. Shifley, S.R. and Smith, W.B. (1982) Diameter growth, survival, and volume estimates for Missouri trees. USDA Forest Service Research Note NC-292. USDA Forest Service, North Central Forest Experimental Station, St Paul, Minnesota, 7 pp. Available at: https:// www.fs.usda.gov/treesearch/pubs/11197 (accessed 1 July 2018). Shumway, D.L., Abrams, M.D. and Ruffner, C.M. (2001) A 400-year history of fire and oak recruitment in an old-growth oak forest in western Maryland, USA. Canadian Journal of Forest Research 31, 1437–1443. https://doi.org/10.1139/x01-079 Signell, S.A., Abrams, M.D., Hovis, J.C. and Henry, S.W. (2005) Impact of multiple fires on stand structure and tree regeneration in central Appalachian oak forests. Forest Ecology and Management 218, 146–158. https://doi.org/10.1016/j.foreco.2005.07.006 Smith, K.T. (2015) Compartmentalization, resource allocation, and wood quality. Current Forestry Reports 1, 8–15. https://doi.org/10.1007/s40725-014-0002-4 Smith, K.T. and Sutherland, E.K. (1999) Fire-scar formation and compartmentalization in oak. Canadian Journal of Forest Research 29, 166–171. https://doi. org/10.1139/x98-194 Smith, K.T. and Sutherland, E.K. (2006) Resistance of eastern hardwood stems to fire injury and damage. USDA Forest Service General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 210–217. Available at: https://www.fs.usda.gov/treesearch/ pubs/18450 (accessed 1 July 2018).

Chapter 7

Sork, V.L., Bramble, J. and Sexton, O. (1993) Ecology of mast-fruiting in three species of North American deciduous oaks. Ecology 74, 528–541. https://doi. org/10.2307/1939313 Sparks, J.C., Masters, R.E., Engle, D.M., Payton, M.E. and Bukenhofer, G.A. (1999) Influence of fire season and fire behavior on woody plants in red-cockaded woodpecker clusters. Wildlife Society Bulletin 27(1), 124–133. Sparks, J.C., Stambaugh, M.C. and Keith, E.L. (2012) Restoring fire suppressed Texas oak woodlands to historic conditions using prescribed fire. In: USDA Forest Service General Technical Report NRS-P-102. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 127–141. Available at: https://www.fs.usda.gov/treesearch/pubs/ 42136 (accessed 1 July 2018). Stambaugh, M.C. and Guyette, R.P. (2004) Long-term growth and climate response of shortleaf pine at the Missouri Ozark forest ecosystem project. In: USDA Forest Service General Technical Report NE-316. USDA Forest Service, Northeastern Research Station, Newtown Square, Pennsylvania, pp. 448– 458. https://doi.org/10.2737/NE-GTR-316 Stambaugh, M.C. and Guyette, R.P. (2006) Fire regime of an Ozark wilderness area, Arkansas. American Midland Naturalist 156, 237–251. https://doi. org/10.1674/0003-0031(2006)156[237:FROAOW]2.0. CO;2 Stambaugh, M.C. and Guyette, R.P. (2008a) Predicting spatio-temporal variability in fire return intervals using a topographic roughness index. Forest Ecology and Management 254, 463–473. https://doi.org/10.1016/j. foreco.2007.08.029 Stambaugh, M. and Guyette, R. (2008b) Prescribed fire effects on the wood quality of three common oaks in the Ozark region, Q. coccinea, Q. velutina, Q. alba. Final Report. Missouri Department of Conservation, Jefferson City, Missouri, 23 pp. Stambaugh, M., Guyette, R. and Putnam, C. (2005) Fire in the pines: a 341-year fire and human history at Big Spring Pines Natural Area, Ozark National Scenic Riverways. ParkScience 23(2), 43–47. Stambaugh, M.C., Guyette, R.P., McMurry, E.R. and Dey, D.C. (2006a) Fire history at the eastern Great Plains margin, Missouri River loess hills. Great Plains Research 16, 149–159. Available at: https://www.fs. usda.gov/treesearch/pubs/55422 (accessed 1 July 2018). Stambaugh, M.C., Guyette, R.P., Grabner, K.W. and Kolaks, J. (2006b) Understanding Ozark forest litter variability through a synthesis of accumulation rates and fire events. In: USDA Forest Service General Technical Report RMRS-P-41. USDA Forest Service, Rocky Mountain Research Station, Ft Collins, Colorado, pp. 321–332. Available at: https://www.fs.usda.gov/ treesearch/pubs/25958 (accessed 1 July 2018).

Fire and Oak Forests

Stambaugh, M.C., Guyette, R.P., Godfrey, R., McMurry, E.R. and Marschall, J.M. (2009) Fire, drought, and human history near the western terminus of the Cross Timbers, Wichita Mountains, Oklahoma, USA. Fire Ecology 5(2), 51–65. https://doi.org/10.4996/ fireecology.0502051 Stambaugh, M.C., Guyette, R.P. and Marschall, J.M. (2011a) Longleaf pine (Pinus palustris Mill.) fire scars reveal new details of a frequent fire regime. Journal of Vegetation Science 22, 1094–1104. https://doi. org/10.1111/j.1654-1103.2011.01322.x Stambaugh, M.C., Sparks, J., Guyette, R.P. and Willson, G. (2011b) Fire history of a relict oak woodland in northeast Texas. Rangeland Ecology and Management 64,419–423.https://doi.org/10.2111/REM-D-10-00128.1 Stambaugh, M.C., Marschall, J.M. and Guyette, R.P. (2014a) Linking fire history to successional changes of xeric oak woodlands. Forest Ecology and Management 320, 83–95. https://doi.org/10.1016/j.foreco.2014. 02.022 Stambaugh, M.C., Sparks, J.C. and Abadir, E.R. (2014b) Historical pyrogeography of Texas, USA. Fire Ecology 10(3), 72–89. https://doi.org/10.4996/fireecology. 1003072 Stambaugh, M.C., Hammer, L.D. and Godfrey, R. (2015) Performance of burn-severity metrics and classification in oak woodlands and grasslands. Remote Sensing 7, 10501–10522. https://doi.org/10.3390/rs70810501 Stambaugh, M.C., Guyette, R.P., Marschall, J.M. and Dey, D.C. (2016) Scale dependence of oak woodland historical fire intervals: contrasting the barrens of Tennessee and Cross Timbers of Oklahoma. Fire Ecology 12(2), 65–84. https://doi.org/10.4996/ fireecology.1202065 Stambaugh, M.C., Creacy, G., Sparks, J. and Rooney, M. (2017a) Three centuries of fire and forest vegetation transitions preceding Texas’ most destructive wildfire: Lost Pines or lost oaks? Forest Ecology and Management 396, 91–101. https://doi.org/10.1016/j. foreco.2017.04.017 Stambaugh, M.C., Smith, K.T. and Dey, D.C. (2017b) Fire scar growth and closure rates in white oak (Quercus alba) and the implications for prescribed burning. Forest Ecology and Management 391, 396–403. https://doi.org/10.1016/j.foreco.2017.02.005 Stambaugh, M.C., Marschall, J.M., Abadir, E.R., Jones, B.C., Brose, P.H., Dey, D.C. and Guyette, R.P. (2018) Wave of fire: an anthropogenic signal in historical fire regimes across central Pennsylvania, U.S.A. Ecosphere 9(5):e02222. https://doi.org/10.1002/ecs2.2222 Steiner, K.C., Stein, B.S. and Finley, J.C. (2018) A test of delayed oak dominance hypothesis at mid-rotation in developing upland stands. Forest Ecology and Management 408, 1–8. https://doi.org/10.1016/j. foreco.2017.10.027 Stephens, S.L. (1997) Fire history of a mixed oak–pine forest in the foothills of the Sierra Nevada, El Dorado

293

County, California. In: USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Research Station, Albany, California, pp. 191–198. Available at: https://www.fs.usda.gov/ treesearch/pubs/28174 (accessed 1 July 2018). Stevenson, A.P., Muzika, R.M. and Guyette, R.P. (2008) Fire scars and tree vigor following prescribed fires in Missouri Ozark upland forests. In: USDA Forest Service General Technical Report NRS-P-24. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 525–534. Available at: https://www.fs.usda.gov/treesearch/pubs/14091 (accessed 1 July 2018). Sutherland, E.K. and Smith, K.T. (2000) Resistance is not futile: the response of hardwoods to fire-caused wounding. In: USDA Forest Service General Technical Report NE-274. USDA Forest Service, Northeastern Research Station, Newtown Square, Pennsylvania, pp. 111–115. Available at: https://www.fs.usda.gov/ treesearch/pubs/23085 (accessed 1 July 2018). Taft, J.B. (2003) Fire effects on community structure, composition, and diversity in a dry sandstone barrens. Journal of the Torrey Botanical Society 130(3), 170–192. https://doi.org/10.2307/3557552 Taft, J.B. (2005) Fire effects on structure, composition, and diversity in a south-central Illinois flatwoods remnant. Castanea 70(4), 298–313. https://doi.org/ 10.2179/0008-7475(2005)070[0298:FEOSCA]2.0.CO;2 Thomas-Van Gundy, M., Rentch, J., Adams, M.B. and Carson, W. (2014) Reversing legacy effects in the understory of an oak dominated forest. Canadian Journal of Forest Research 44, 350–364. https://doi. org/10.1139/cjfr-2013-0375 Towne, E.G. and Kemp, K.E. (2003) Vegetation dynamics from annually burning tallgrass prairie in different seasons. Journal of Range Management 56(2), 185–192. https://doi.org/10.2307/4003903 Transeau, E.N. (1935) The prairie peninsula. Ecology 16, 423–427. https://doi.org/10.2307/1930078 Tushingham, S. and Bettinger, R.L. (2013) Why foragers choose acorns before salmon: storage, mobility, and risk in aboriginal California. Journal of Antropological Archaeology 32, 527–537. https://doi.org/10.1016/j. jaa.2013.09.003 Ubelaker, D.H. (1988) North American Indian population size, a.d. 1500 to 1985. American Journal of Physical Anthropology 77(3), 289–294. https://doi.org/10.1002/ ajpa.1330770302 VanderWeide, B.L. and Hartnett, D.C. (2011) Fire resistance of tree species explains historical gallery forest community composition. Forest Ecology and Manage­ ment 261, 1530–1538. https://doi.org/10.1016/j. foreco.2011.01.044 Vander Yacht, A.L., Barrioz, S.A., Keyser, P.D., Harper, C.A., Buckley, D.S., Buehler, D.A. and Applegate, R.D. (2017) Vegetation response to canopy disturbance and season on burn during oak woodland and

294

savanna restoration in Tennessee. Forest Ecology and Management 390, 187–202. https://doi.org/ 10.1016/j.foreco.2017.01.029 Varner, J.M., Kane, J.M., Hiers, J.K., Kreye, J.K. and Veldman, J.W. (2016) Suites of fire-adapted traits of oaks in the southeastern USA: multiple strategies for persistence. Fire Ecology 12(2), 48–64. https://doi. org/10.4996/fireecology.1202048 Verano, J.W. and Ubelaker, D.H. (1992) Disease and Demography in the Americas. Smithsonian Institute Press, Washington, DC, 294 pp. Vickers, L.A., Larsen, D.R., Knapp, B.O., Kabrick, J.M. and Dey, D.C. (2014) The impact of overstory density on sapling height growth in the Missouri Ozarks: implications for interspecific differentiation during canopy recruitment. Canadian Journal of Forest Re­search 44, 1320–1330. https://doi.org/10.1139/cjfr-20140237 Vines, R.G. (1968) Heat transfer through bark, and the resistance of trees to fire. Australian Journal of Botany 16, 499–514. https://doi.org/10.1071/ BT9680499 Waldrop, T.A., White, D.L. and Jones, S.M. (1992) Fire regimes for pine–grassland communities in the southeastern United States. Forest Ecology and Management 47, 195–210. https://doi.org/10.1016/ 0378-1127(92)90274-D Waldrop, T.A., Yaussy, D.A., Phillips, R.J., Hutchinson, T.F., Brudnak, L. and Boerner, R.E.J. (2008) Fuel reduction treatments affect stand structure of hardwood forests in western North Carolina and southern Ohio, USA. Forest Ecology and Management 255, 3117–3129. https://doi.org/10.1016/j.foreco.2007. 11.010 Waldrop, T.A., Hagan, D.L. and Simon, D.M. (2016) Repeated application of fuel reduction treatments in the southern Appalachian Mountains, USA: implications for achieving management goals. Fire Ecology 12(2), 28–47. https://doi.org/10.4996/fireecology. 1202028 Wang, G.G., Van Lear, D.H. and Bauerle, W.L. (2005) Effects of prescribed fires on first-year establishment of white oak (Quercus alba L.) seedlings in the upper Piedmont of South Carolina, USA. Forest Ecology and Management 213, 328–337. https://doi.org/ 10.1016/j.foreco.2005.03.049 Wang, Z. and Nyland, R.D. (1996) Changes in the condition and species composition of developing evenaged northern hardwood stands in central New York. Northern Journal of Applied Forestry 13(4), 189–194. https://doi.org/10.1093/njaf/13.4.189 Ward, J.S. (2009) Intensity of precommercial crop tree release increases diameter growth and survival of upland oaks. Canadian Journal of Forest Research 39, 118–130. https://doi.org/10.1139/X08-165 Ward, J.S. and Brose, P.H. (2004) Mortality, survival, and growth of individual stems after prescribed burning in

Chapter 7

recent hardwood clearcuts. In: USDA Forest Service General Technical Report NE-316. USDA Forest Service, Northeastern Research Station, Newtown Square, Pennsylvania, pp. 193–199. Available at: https://www.fs.usda.gov/treesearch/pubs/22719 (accessed 1 July 2018). Ward, J.S. and Stephens, G.R. (1994) Crown class transition rates of maturing northern red oak (Quercus rubra L.). Forest Science 40(2), 221–237. https://doi. org/10.1093/forestscience/40.2.221 Weigel, D.R. and Parker, G.R. (1997) Tree regeneration response to the group selection method in southern Indiana. Northern Journal of Applied Forestry 14(2), 90–94. https://doi.org/10.1093/njaf/14.2.90 Weigel, D.R. and Peng, C.-Y.J. (2002) Predicting stump sprouting and competitive success of five oak species in southern Indiana. Canadian Journal of Forest Research 32, 703–712. https://doi.org/10.1139/x02-042 Weir, J.R. and Scasta, J.D. (2017) Vegetation responses to season of fire in tallgrass prairie: a 13-year case study. Fire Ecology 13(2), 137–142. https://doi.org/ 10.4996/fireecology.130290241 Whelan, R.J. (1995) The Ecology of Fire. Cambridge University Press, Cambridge, 346 pp. Whitney, G.G. (1994) From Coastal Wilderness to Fruited Plain. Cambridge University Press, Cambridge. Wiedenbeck, J.K. and Schuler, T.M. (2014) Effects of prescribed fire on the wood quality and marketability of four hardwood species in the central Appalachian region. In: USDA Forest Service General Technical Report NRS-P142. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 202–212. Available at: https://www.fs.usda.gov/ treesearch/pubs/47414 (accessed 1 July 2018).

Fire and Oak Forests

Williams, M. (1989) Americans and Their Forests: a Historical Geography. Cambridge University Press, Cambridge. Winkler, M.G. (1985) A 12,000-year history of vegetation and climate for Cape Cod, Massachusetts. Quaternary Research 23, 301–312. https://doi.org/10.1016 /0033-5894(85)90037-7 Wolf, J. (2004) A 200-year fire history in a remnant oak savannah in southeastern Wisconsin. The American Midland Naturalist 152(2), 201–213. https://doi.org/10.1674/00030031(2004)152[0201:AYFHIA]2.0.CO;2 Wright, S.L. (1986) Prescribed burning as a technique to manage insect pests of oak regeneration. In: Koonce, A.L. (ed.) Prescribed Burning in the Midwest: Stateof-the-art. University of Wisconsin, Stevens Point, Wisconsin, pp. 91–96. Zenner, E.K., Kabrick, J.M., Jensen, R.G., Peck, J.E. and Grabner, J.K. (2006) Responses of ground flora to a gradient of harvest intensity in the Missouri Ozarks. Forest Ecology and Management 222, 326–334. https://doi.org/10.1016/j.foreco.2005.10.027 Zenner, E.K., Heggenstaller, D.J., Brose, P.H., Peck, J.E. and Steiner, K.C. (2012) Reconstructing the competitive dynamics of mixed-oak neighborhoods. Canadian Journal of Forest Research 42, 1714–1723. https:// doi.org/10.1139/x2012-119 Zouhar, K., Smith, J.K. and Sutherland, S. (2008) Effects of fire on nonnative invasive plants and invisibility of wildland ecosystems. In: USDA Forest Service General Technical Report RMRS-42. USDA Forest Service, Rocky Mountain Research Station, Ft Collins, Colorado, pp. 7–31. Available at: https:// www.fs.usda.gov/treesearch/pubs/32669 (accessed 1 July 2018).

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8



Even-aged Silvicultural Methods

Introduction Two silvicultural systems of managing forests are generally recognized: (i) even-aged; and (ii) unevenaged (Smith et al., 1997). This chapter focuses on the methods used in the even-aged silviculture of oak forests. The complete implementation of either system can lead to a regulated forest theoretically capable of sustaining to perpetuity an even flow of timber products and other values. The traditional objective of even-aged management is to regulate a forest by managing the stands within it as a mosaic of different age classes. The trees in each stand are allowed to grow to a specific age called the rotation age. On reaching rotation age, a stand is renewed or regenerated by a final harvest that requires the application of one of several evenaged regeneration methods. Even-aged forest management is said to be based on area control because it relies on regulating forest yield by creating and maintaining stands of various age classes, with each class occupying an approximately equal area of the forest. A regulated even-aged forest thus consists of a balanced distribution of stand age classes that is maintained through time. This arrangement also sustains an even and continuous flow of wood products and a constant proportion of stands in each age class. Diameter frequency distributions of mature even-aged oak stands are often bell-shaped (i.e. ­normally distributed) (see Chapter 5, this volume, Fig. 5.4). There also is growing interest in maintaining forests in specified conditions for social and ecological reasons. Such considerations may require a perspective different from that considered by silviculturists in the past. Although there are only two types of management systems, three types of tree age distributions have been recognized: (i) even-aged; (ii) two-aged; and (iii) uneven-aged (Smith et al., 1997). Evenaged stands are defined as those where the difference in age between the oldest and youngest trees in the overstorey (Chapter 5, this volume, Fig. 5.1)

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does not exceed 20% of the rotation. Uneven-aged stands contain at least three age classes intermingled on the same area. Two-aged stands are comprised of two age classes of trees. In silvicultural practice, a new stand resulting from the final harvest of an even-aged stand is usually considered even-aged regardless of the actual distribution of tree ages in the new stand. This convention requires a flexible definition of the evenaged state. One definition employs the term cohort to refer to all the trees, arising anew or from advance (pre-established) reproduction, that originate from a silvicultural or natural event that produces a canopy gap or large opening in the forest (Oliver and Larson, 1996). Members of a cohort are considered even-aged regardless of their actual biological ages. This definition differs from that used in plant population biology, where cohort usually denotes membership in a group of plants originating from a single seed crop (Harper, 1977). The broader silvicultural definition facilitates reference to single- and multiple-cohort stands and thus many commonly encountered tree populations resulting from natural and silvicultural regeneration events (Chapter 5, this volume, Figs 5.2 and 5.13). Spatial scale also can be considered in determining whether a stand is even-aged or uneven-aged. For example, several cohorts of trees may occur within spatial scales smaller than that normally defined as a stand. The definition of the even-aged state therefore may vary depending on the spatial scale that is relevant to management objectives. Defining the uneven-aged state similarly depends on considerations of spatial scale (Chapter 9, this volume).

Natural Regeneration Methods Stands under even-aged management are regenerated at or near the end of the rotation by one of three silvicultural methods: (i) clearcutting; (ii) shelterwood; or (iii) seed tree methods. In their usual

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

application, the parent stand is completely removed in one or more steps that eventually leave the site free of overstorey shade. Overstorey removal allows new reproduction to become established and the advance reproduction, if present, to develop in full light. All three methods can be used for regenerating a wide range of species from shade tolerant to shade intolerant. However, their use is often necessary for regenerating shade-intolerant and mid-­ tolerant species such as the oaks. Also there are variations within each method that provide additional flexibility for attaining regeneration and other objectives. For example, intermediate cuttings or other treatments early in the life of an even-aged stand may be designed to encourage development of advance oak regeneration long before a regeneration harvest. Although even-aged regeneration methods are applied at the end of the rotation (and thus the end of the life of the parent stand), their application is intended to initiate the new stand.

Central Hardwood Region and elsewhere where oak advance reproduction intrinsically accumulates (Roach and Gingrich, 1968; Sander, 1977; Johnson, 1993; Kabrick et al., 2008, 2014). In mature oaks stands (Fig. 8.1A), such accumulation may be sufficient for replacing the parent stand after clearcutting. Regeneration success depends on a reproduction establishment period of a decade or more before the final harvest is made (Sander, 1971, 1977; Brose et al. 2008; Dey, 2014). Where regeneration guidelines are available, stand regeneration potential can be objectively evaluated from an inventory of the overstorey (from which stump (A)

The clearcutting method Clearcutting probably is the easiest to apply and most economically efficient of the regeneration methods. In its simplest application to oak forests, the method requires only the removal of the overstorey to release the oak reproduction beneath it. The success of the method depends on pre-existing (‘advance’) reproduction to replace the parent stand. New reproduction of non-oaks from seed stored in the forest floor, root sprouts, and newly arriving wind-­disseminated seeds also can become a part of the new stand. These non-oaks compete with oaks for growing space, and sometimes replace the oaks as stands develop. The simplicity of the clearcutting method derives from the one-step removal of the overstorey. Its economic efficiency obtains from the minimization of logging costs per unit of tree volume harvested. However, for environmental and social reasons, the method also is the most controversial of the regeneration methods. Consequently, before the method is applied, several factors should be weighed, including: (i) the ecosystem-specific suitability of clearcutting for meeting oak regeneration requirements; (ii) the predictability of the regeneration outcome; and (iii) economic, environmental and social considerations. Suitability to oak regeneration requirements Clearcutting has been used to successfully regenerate oaks in many of the drier oak forests of the

Even-aged Silvicultural Methods

(B)

Fig. 8.1.  (A) A mature even-aged mixed-oak stand in central Missouri (Central Hardwood Region). The stand is fully stocked and dominated by northern red, white and black oaks. Site index for red oak ranges from 65 to 70 ft at an index age of 50 years. Successfully regenerating such stands to oak by clearcutting depends on their oak regeneration potential. (B) An inventory of the oak advance reproduction coupled with the application of a regional regeneration guide (e.g. Dey et al., 1996b) provides an objective basis for predicting future stand composition and the utilization of growing space by oaks and other species. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

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sprouts originate) and the oak advance reproduction (Fig. 8.1B). Oak advance reproduction of the requisite size and spatial distribution must be present at the time of final harvest if oaks are to become a major part of the next stand (Sander et al., 1984; Dey, 2014). If stand regeneration potential is deemed adequate, all trees ≥ 2 inches dbh should be cut if the management objective is exclusively timber production. However, clearcutting a stand without carefully considering its regeneration potential may result in a new stand with few oaks. Where the oak regeneration potential is low, clearcutting will abruptly shift species composition from oak to non-oak. A common outcome is a mixture of less desirable species and poorly distributed stocking of oak stump sprouts (Fig. 8.2A). Cutting only trees with commercial value usually leaves stands of poor quality and undesirable ­species (Fig. 8.2B). The residual trees are likely to capture much of the growing space at the expense of more desirable but smaller reproduction. Where management requires retaining overstorey trees to meet non-timber objectives, retained trees should be selected by silvicultural design rather than by the logger (Smith et al., 1997). When properly applied, the method is called clearcutting with reserves and creates, at least temporarily, a two-aged stand (Helms, 1998). A few trees per acre can be retained for aesthetic purposes, den trees and snags for wildlife, and acorn production for wildlife. However, acorns from the retained oaks are unlikely to contribute to stand regeneration after clearcutting. Oaks established from seed after overstorey removal are rarely able to compete with oak advance reproduction, oak or other hardwood stump sprouts, and the flush of other woody and herbaceous vegetation. Immediately after clearcutting, the site may temporarily appear to be inadequate in tree reproduction and vegetative cover (Fig. 8.3A). However, within a year, a dense new growth of herbaceous and woody species quickly occupies the site. Some of this emergent vegetation originates from plants and root systems present before harvest. Other plants originate from the seed bank in the forest floor and from windblown and animal-dispersed seed from adjacent sites. The sudden flush of growth holds soil and nutrients in place even in steep terrain. Soil erosion and site deterioration after clearcutting are usually the result of improper logging road and skid trail design, which are largely avoidable (Chapter 4, this volume). Usually

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(A)

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Fig. 8.2.  (A) Twenty years after clearcutting, this stand in south-western Wisconsin (Central Hardwood Region) is largely comprised of northern red oak stump sprouts; site index for northern red oak is 65 ft. Stocking of oak stump sprouts is poorly distributed and the remaining stocking is poor quality black cherry and American elm. (B) A commercial clearcut 5 years after harvesting an oak stand in south-eastern Ohio (Central Hardwood Region). Although the residual trees are of poor quality, their crowns and the growing space they occupy will expand and hinder the development of the established reproduction. Some residual trees can be retained in clearcuts for non-timber objectives, but it should be by design rather than ‘loggers choice’. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

within 2 or 3 years, trees begin to dominate the site (Fig. 8.3B); by stand age 20, trees fully occupy the growing space (Fig. 8.3C). In the Ozark Highlands, 400–600 stems of oak advance reproduction/acre from 3 to 5 ft tall may provide adequate future oak stocking after overstorey removal, depending on site factors (Sander et al., 1984). However, oak stump sprouts can compensate for deficiencies in stocking from advance reproduction. Stump sprouts may be especially important in previously unthinned stands where

Chapter 8

(A)

(B)

(C)

Fig. 8.3.  (A) A clearcut oak stand in the Central Hardwood Region during the first year after harvest. New growth rapidly develops from advance reproduction of trees and shrubs, stump sprouts, seed stored in the forest floor and seed disseminated from surrounding vegetation. (B) Three years after clearcutting, this stand in the Ozark Highlands of Missouri (Central Hardwood Region) was well stocked with oak seedlings and seedling sprouts. (C) Twenty years after clearcutting, this stand in the Ozark Highlands was at 100% stocking and dominated by white, black and scarlet oaks. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

numerous small-diameter white oaks often comprise a subordinate canopy layer. Previously thinned stands are therefore likely to produce few stump sprouts if thinning has been properly applied by

Even-aged Silvicultural Methods

concentrating removals in the overtopped and intermediate crown classes and rotation ages are 80  years or longer. Oak sprouting declines with increasing tree age and diameter (Fig. 2.25, this volume). Clearcutting is not an effective regeneration method in all xeric oak forests. For example, black oak and white oak stands on droughty outwash sands in northern Lower Michigan often fail to regenerate after clearcutting even when there is abundant oak advance reproduction (Johnson, 1992). In that region, regeneration failures occur even where oak advance reproduction is five to ten times greater per unit area than under similar oak stands in the Ozark Highlands. The site index, composition, structure, stocking and yield of oak stands in both regions are similar. The failure of clearcutting to regenerate the Michigan forests appears to be related to: (i) the small size of the oak reproduction (mostly less than 1 ft tall); and (ii) its relatedly slow growth and high mortality after overstorey removal. The latter is associated with the post-clearcutting development of a dense mat of sedges (Carex spp.), which, in turn, kills most established oak reproduction (Johnson, 1992; Congdon, 1993). These failures suggest the need for regeneration prescriptions that increase light to levels sufficient for the development of oak advance reproduction without stimulating the development of sedges. Regeneration failures related to clearcutting these northern oak forests also may be related to the removal of the frost-protecting overstorey canopy. In contrast, populations of oak advance reproduction in the Missouri forests, although of lower density than those in Michigan, include hundreds of large stems/acre that, after clearcutting, can rapidly capture growing space (Kabrick et al., 2008). Moreover, woody plants are the primary competitors of oak reproduction in the Ozark forests and late spring frosts occur there less frequently than in the northern forest. The contrasting regeneration ecology of these superficially similar oak forests emphasizes the sensitivity of oak regeneration to factors that may not measurably affect the growth and yield of oak stands. Such differences in ecosystem reactions to clearcutting, and disturbances in general, emphasize the importance of recognizing ecological differences among oak forests (see Chapter 1, this volume). In mesic oak forests, clearcutting usually fails to restore oaks to their pre-harvest level of importance (McGee and Hooper, 1970; Johnson, 1976; Beck

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and Hooper, 1986; Loftis, 1988; Stanturf et  al., 1997; Jenkins and Parker, 1998). There, clearcutting often accelerates succession towards shadetolerant hardwoods such as sugar maple, red maple, American beech, or fast-growing shade-intolerant species such as yellow-poplar, white ash and black cherry (Abrams and Nowacki, 1992; Jenkins and Parker, 1998; Groninger and Long, 2008; Swaim et al., 2016). Even when oak advance reproduction is abundant, it typically is suppressed after overstorey removal by the growth of non-oak stump sprouts and other competition (McGee and Hooper, 1970; Beck and Hooper, 1986). For example, 20 years after clearcutting an oak–yellow-poplar stand on a very productive site (yellow-poplar site index 100+ ft) in the southern Appalachians, the stand was dominated by yellow-poplar and other non-oaks (Beck and Hooper, 1986). This conversion occurred even though there were more than 5000 stems/acre of oak advance reproduction (Fig. 8.4). Nevertheless, oaks sometimes succeed after clearcutting mesic forests if a large proportion of the overstorey oaks sprout (P.S. Johnson, 1975; Wendel, 1975), or where competition is not severe or is controlled (Johnson et al., 1989; Jacobs and Wray, 1992). A survey of 29 clearcut oak stands in the Ohio Valley 5–26 years after harvest showed that the amount of oak reproduction varied with time since clearcutting and site quality. The oak volume of the parent stands, a mixture of upland oaks (chestnut, white, black and scarlet oaks), comprised at least 60% of the total volume. On poor sites (oak site index 50–59 ft), dominant and codominant oaks became increasingly more abundant with stand age and accounted for 64% of all stems in stands 15 years old and older (Hilt, 1985a). On medium sites (oak site index 60–69 ft), oaks attained moderate importance after clearcutting and changed little with increasing stand age. On good sites (oak site index 70–80 ft), dominant and codominant oaks decreased with time and accounted for only 11% of stems in stands 15 years old and older, by which time yellow-poplar, black cherry and ash dominated the sites (Fig. 8.5). In this region, the compositional outcome is largely determined by the interaction of time and site quality: oaks ultimately emerge as dominants within two decades on poor sites (Hilt, 1985b). Yellow-poplar and other sitedemanding hardwoods ultimately emerge as dominants on the more productive sites. On sites of intermediate quality, oaks often regenerate to intermediate levels of importance.

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Stand age (years) Fig. 8.4.  Twenty-year change in stand composition after clearcutting a cove hardwood stand in the southern Appalachian Mountains of North Carolina (Central Hardwood Region). (A) All reproduction 4.5 ft tall. (B) Free-to-grow (not overtopped) reproduction. (From Beck and Hooper, 1986.) ‘Other’ species include black locust, eastern hemlock, white ash, black cherry, hickories, American basswood, blackgum, sassafras, sourwood and flowering dogwood. The oak group includes northern red, black, chestnut and white oaks. The parent stand was 53% oak and 33% yellow-poplar by volume.

Similar results were observed in clearcut oak stands in southern Illinois (Groninger and Long, 2008). Twenty years after clearcutting, stands were well stocked with trees. However, the oaks as a proportion of stand basal area increased from lower to upper slope positions and from cool (north-east) to hot (south-west) aspects. On the drier upper slopes, oaks accounted for 30% of stand basal area, and exceeded the combined basal area of yellow-poplar, sugar maple and black cherry. On mesic lower slopes with cool (north-east) aspects, oaks comprised about 5% of total basal area and on average were about 15 ft shorter than the yellow-poplars.

Chapter 8

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Fig. 8.5.  Change in the relative proportion of oaks in relation to site index in mixed oak stands in southeastern Ohio (Central Hardwood Region). (Adapted from Hilt, 1985a.) The oaks include white, black, scarlet and chestnut oaks. Other species present include red and sugar maples, black cherry, white ash, hickories, bigtooth aspen, flowering dogwood, sassafras and eastern hophornbeam. The maples and yellow-poplar dominated stands on the two better site classes by stand age 15.

The compositional and structural changes occurring after clearcutting are expressions of secondary succession. Each silvicultural ‘outcome’ represents a point along one of several possible trajectories or successional pathways. Poor (xeric) sites typically possess few pathways (i.e. future compositional and structural possibilities) because many species, especially those that are sensitive to soil moisture stress, are excluded from those ecosystems by their failure to initially colonize and/or by high mortality rates. In mesic ecosystems, successional pathways are more numerous because virtually all species, from those adapted to dry sites (xerophytes) to those that are more moisture demanding (mesophytes), are physiologically capable of surviving and growing there. It is thus the competition environment that largely excludes oaks from the mesic ecosystems. Conversely, the moisture-demanding non-oaks usually are excluded from xeric ecosystems (Wuenscher and Kozlowski, 1971; Abrams, 1992; Kabrick et al., 2014). However, the common presence of oaks in mesic ecosystems suggests that certain kinds or sequences of disturbances can favour the oaks. For example, the use of fire over many centuries by American Indians before European settlement, and after that the burning and other disturbances associated with early agriculture, probably influenced

Even-aged Silvicultural Methods

the composition of many of today’s mesic oak forests in eastern USA (Chapter 7, this volume; Abrams, 1992, 2005; Abrams and Nowacki, 2008). Based on the outcome of numerous reported trials, it is nevertheless apparent that clearcutting, by itself, often fails to maintain oaks at pre-harvest levels of stocking in mesic ecosystems. Despite its importance, the presumed requirement for the presence of oak advance reproduction may not be an irrevocable rule. This may be especially true in some mesic and hydric ecosystems. Silvicultural alternatives to relying on advance reproduction have been described for northern red oak in the driftless area of south-western Wisconsin and adjacent states (Johnson et al., 1989; Bundy et al., 1991; Jacobs and Wray, 1992). There, the site index of red oak in the more mesic ecosystems ranges from about 65 to 70 ft (Gevorkiantz and Scholz, 1948; Gevorkiantz, 1957). On those sites, thousands of red oak seedlings/acre may become established in a single year following a good acorn crop (Scholz, 1955; Johnson, 1974). Numbers of seedlings also can be increased by mechanical soil scarification timed to coincide with an acorn crop. Scarification effectively reduces competition (Bundy et al., 1991). It also facilitates direct contact between germinating acorns and a mineral seedbed, which can increase initial seedling establishment if done before leaf fall. Scarification in combination with direct-seeded northern red oak acorns produced similar increases in seedling establishment under a shelterwood in Pennsylvania (Zaczek et al., 1997). After overstorey removal, the growth of red oak reproduction on productive sites is potentially rapid and less dependent on the development of a large root system and a long establishment period than is the case for the more xerophytic oaks. Thus, large numbers of red oak seedlings and a favourable postharvest environment can combine to effect successful regeneration. However, unlike many other mesic ecosystems, the density of competing trees in driftless area stands is relatively low and often comprised of species such as black cherry, paper birch, elms and hickories (Johnson, 1976; Martin et al., 1992). After clearcutting, the rapid decline of nonoaks from the main canopy is similar to that which occurs in young red oak stands in New England (Oliver, 1978; Hibbs and Bentley, 1984). Soil scarification has been used in the driftless area to effectively reduce competition and simultaneously prepare a patchy seedbed of mineral soil (Bundy et al., 1991; Jacobs and Wray, 1992). One

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technique uses a tractor-mounted blade set 6 inches above ground. Careful application of the treatment during a good acorn crop results in mechanical breakage of low vegetation, including tree reproduction and shrubs, while simultaneously effecting only moderate soil disturbance. In one trial, the method increased the height and leaf area of red oak reproduction two growing seasons after treatment (1 year after clearcutting) (Bundy et al., 1991). A similar treatment consisting of mechanical uprooting of low vegetation during logging, or in a separate operation before or after logging, also has been proposed (Jacobs and Wray, 1992). Experience has shown that oak advance reproduction survives the treatment because of its deep taproot and capacity for sprouting after top injury. The recommended time to apply the treatment is in the autumn during a good seed year before acorns drop. Timing the treatment with acorn fall can increase numbers of seedlings as well as reduce competition. Applying herbicides to the understorey before clearcutting and concurrently with a good acorn crop also can facilitate oak regeneration. Even though the herbicide kills some of the oak advance reproduction, one trial resulted in about 1000 red oak stems/acre, 0.5 inches dbh and larger, 11 years after clearcutting; about 27% were codominant or larger (Johnson et al., 1989). Strategies for regenerating northern red oak in the driftless area are discussed in more detail by Jacobs and Wray (1992). In New England and the Lake States, northern red oak sometimes regenerates under pine stands if there is a nearby red oak seed source (Cline and Lockard, 1925; Isebrands and Crow, 1985). When the pine is harvested, the oak advance reproduction then may capture the site (Sampson et al., 1983). Such pine-to-oak successions may be facilitated by the dispersal of acorns by animals. Small mammals disperse red oak acorns up to about 60 ft (Sork, 1984) while blue jays disperse them up to 2.5 miles (Chapter 2, this volume, Fig. 2.14). Although most dispersed acorns are consumed by animals, even the small surviving proportion of blue jay-dispersed acorns can produce significant numbers of oak seedlings because of the large numbers of acorns that are dispersed, the favourable germination and growth environments they are carried to (Johnson and Webb, 1989), and their accumulation over time. In bottomland oak forests, success in regenerating oaks by clearcutting has been mixed, ranging from successful to unsuccessful (e.g. Bowling and Kellison, 1983; Gresham, 1985a, b). There, flooding may

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confound prediction of stand replacement after clearcutting, which may affect oak and non-oak advance reproduction differently. Physiological tolerance of bottomland oaks to inundation varies among species and tolerance is partially dependent on length of the flood period, depth of inundation and other factors (McKevlin, 1992; Allen et al., 2001; Lockhart et al., 2008; Kabrick et al., 2012). Several bottomland non-oaks such as water hickory, water tupelo, swamp tupelo, green ash and red maple are more physiologically tolerant of inundation than are the oaks (Hosner and Minckler, 1960, 1963; Hosner and Boyce, 1962; Broadfoot and Williston, 1973; McKnight et al., 1981; McKevlin, 1992; Allen et al., 2001; Lockhart et al., 2008). However, floods of short to moderate duration limited to the dormant season may favour oak reproduction. For example, water oak seedlings in an East Texas floodplain survived winter and early spring flooding because, unlike their shallow-rooted competitors, some of the oak seedlings germinated after flooding, survived physical damage and resisted uprooting during flooding, and resprouted when damaged (Streng et al., 1989). Consequently, oaks may comprise a relatively large proportion of the older, and thus larger, pool of advance reproduction in bottomlands because of their deep roots and relatively high survival and sprouting rates (Streng et al., 1989) (Chapter 3, this volume, Fig. 3.1). Like the upland oaks, it is the larger advance reproduction that is most likely to capture the growing space after clearcutting. The outcome largely depends on the species composition and size distribution of the advance reproduction. Different reactions to flooding among species and the mix of species present at the time of flooding thus may largely determine the regeneration outcome in bottomland oak forests. In some cases, bottomland oaks have successfully regenerated from seedlings established after clearcutting (Golden and Loewenstein, 1991; Golden, 1993; Nix and Lafaye, 1993). In two bottomland sites in South Carolina, cherrybark, Shumard and water oak seedlings established after clearcutting resulted in over 500 oaks/acre that attained canopy dominance 5 years after clearcutting (Nix and Lafaye, 1993). Success was attributed to a moderately good acorn crop followed by early winter logging in wet weather, which in turn scarified the soil and buried many acorns. Despite such successes, it would be silviculturally more prudent to rely on oak advance reproduction of the requisite size and number. One guideline for regenerating bottomland hardwoods

Chapter 8

deems clearcutting a viable option only if 200–500 seedlings/acre of desirable species are present as advance reproduction (McKevlin, 1992). The composition and structure of bottomland clearcuts at the end of the first decade after cutting may not reflect the potential of oaks to ultimately dominate stands. For example, water and swamp chestnut oaks collectively increased from 32 to 44% of total stand basal area from stand age 14 to 22 years in a bottomland forest in Mississippi. This gain was made at the expense of sweetgum, ironwood, pine, blackgum and other species, whose collective basal areas remained relatively unchanged, but whose survival rates were lower than the oaks during that period (Fig. 8.6). As a result, the oaks emerged as

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Fig. 8.6.  Stand development after clearcutting a bottomland oak–mixed hardwood stand in east-central Mississippi (Southern Pine–Hardwood Region). (A) Change in basal area with stand age. The oaks are predominantly water oak but include swamp chestnut oak; pine includes loblolly and spruce pines; other includes magnolia, elms, hickory and red maple. (B) Survival of oaks compared with the average of all other hardwoods. (Adapted from Bowling and Kellison, 1983.) The curves are based on the negative exponential rates (k) calculated from observed 22nd-year survival. Actual survival between stand ages 14 and 22 was not observed.

Even-aged Silvicultural Methods

the major dominant species by stand age 22 (see also Chapter 5, this volume, Fig. 5.5). The emergence of bottomland oaks from inferior to dominant crown classes is related to spacing between oaks and competitors. When cherrybark oak competes with sweetgum in Mississippi lowlands, height growth and thus the outcome of competition between the two species depends on the average spacing between dominant/codominant trees (Clatterbuck and Hodges, 1988). When spacing between trees is less than 18 ft, sweetgum initially grows faster than oak. However, by stand age 32, the oaks are significantly taller than the sweetgum under these competitive conditions. In contrast, where the spacing between dominant and codominant competitors is greater than about 18 ft, the height growth of oak is similar to that of competitors (Chapter 5, this volume, Fig. 5.10B). The faster growth of oak under the more competitive (‘restricted’) condition also resulted in greater average clear bole length of oak at stand age 40 (40 ft) than in the unrestricted mode (23 ft). The ultimate height growth advantage of oak in the restricted mode occurred even though the oaks were 3–6 ft shorter than sweetgum during the brushy stand initiation stage of stand development. These relations have been used to design prescriptions for bottomland plantings of oaks mixed with other flood-tolerant hardwoods. The latter are selected specifically for their effect on increasing oak height growth and clear bole length (Lockhart et al., 2008). Because site quality and competition vary greatly among different kinds of oak forests, ecological classification also is a potential silvicultural tool for assessing which stands are unlikely to regenerate (Kabrick et al., 2008). Defined ecological classes of forests can be used to distinguish among oak stands that appear to be similar (e.g. similar cover types) but that behave differently in their regeneration characteristics. But even with a defined ecological class, it is desirable to have a more specific basis for assessing an individual stand’s regeneration potential before clearcutting is applied because of the temporal and spatial variation in reproduction density and size. So how can the required reproduction characteristics be identified given that a stand’s regeneration potential is not realized until after the overstorey is harvested? Although experienced silviculturists may be able to assess the regeneration outcome from visual examination of the advance reproduction and overstorey, methods that are easier to document and convey are advantageous.

303

Regeneration models One method for objectively assessing regeneration potential involves the use of predictive regeneration models. Among the models applicable to clearcutting oak forests are those developed for the Ozark Highlands (Sander et al., 1976, 1984; Johnson and Sander, 1987; Dey, 1991; Dey et al., 1996b). The region is transitional to the Great Plains and includes the southern half of Missouri and extends into northern Arkansas and north-eastern Oklahoma (McNab and Avers, 1994). The upland forests there are typically dominated by various combinations of black, white, scarlet, northern red, southern red, post and blackjack oaks, and other hardwoods that are sometimes mixed with shortleaf pine and eastern redcedar (Braun, 1972). The site index for black oak and scarlet oak ranges from about 40 to 80 ft at an index age of 50 years (McQuilkin, 1974). The predominant associated hardwoods include hickories, sassafras, blackgum and flowering dogwood. Although shortleaf pine and eastern redcedar are frequently absent from individual stands, the associated hardwoods are usually present. In these relatively dry forests, oak reproduction typically accumulates beneath the canopy of the parent stand for several decades (Liming and Johnston, 1944). There, the shoots of oak reproduction repeatedly die back and the roots of survivors slowly attain large size. Typical of ecosystems where oak reproduction accumulates, the oak advance reproduction remains largely in a suppressed state until disturbance substantially reduces overstorey density. The reproduction present at the time of disturbance is an important component of the initial state of the new stand because of its potential for capturing growing space after disturbance. The oaks of the Ozark Highlands are ‘persistent’ (sensu Veblen, 1992) because their presence is sustained over successive generations. This persistence contrasts with the more mesic oak forests to the north and east and with the bottomland oak forests to the south. In those forests, dominance by oaks often may last only one generation because of successional replacement by long-lived, faster growing or more shade-tolerant hardwoods (R.L. Johnson, 1975; Loftis, 1990b; Nowacki et al., 1990). In contrast, the dominance of the non-oak hardwoods in the Ozark Highlands usually lasts for only two decades after overstorey removal. The high mortality rates and limited development of the non-oaks in this ecosystem relegates them to saplings and

304

r­eproduction in older stands (Braun, 1972; Dey, 1991). Even though these forests are often called oak–hickory forests, the hickories usually comprise a minor part of the overstorey. During the first decade after overstorey removal, the composition of the reproduction largely depends on the mix of seed and plants present when the disturbance occurs. Although this initial mix itself is relatively unpredictable, the non-oaks are quickly relegated to the subcanopy as competition intensifies and crowns close. Within two decades, the oaks have re-emerged as the dominant species. This pattern of stand development conforms to the ‘competitive sorting’ model described by Margalef (1963, 1968) and Peet (1992). However, in the Ozark Highlands the outcome is influenced by topography. The ascendence of oaks to dominance by competitive sorting is rapid and predictable on hot south-west-facing slopes and neutral south-east and north-west slopes. It is less certain on cool north-east slopes where the predisturbance accumulation of oak reproduction is less pronounced and shade-tolerant competitors are sometimes capable of replacing the oaks or slowing their re-emergence to dominance (Sander et al., 1984; Kabrick et al., 2008). The outcome can be expressed as the probability that a tree of given species and size on a given site will reach a future dominant or codominant crown position. This probability can be specified for each species and depends on their size at the time of clearcutting (Fig. 8.7). Even though the non-oaks of the Ozark Highlands are usually unimportant as dominant components of older stands, they nevertheless persist from one generation to the next. Their abundance and early rapid growth after clearcutting also interferes with the re-emergence of the oaks. The oaks and associated hardwoods nevertheless re-establish a ‘compositional equilibrium’ (sensu Veblen, 1992) within two decades of disturbance. The consistency of this pattern of stand redevelopment simplifies the prediction of changes in species composition and therefore modelling the regeneration process. In the Ozark Highlands, predicting changes in species composition after clearcutting is not a question of losing the oaks through successional displacement. Rather, it is one involving how the proportions of the various oak species may change and how quickly and completely the oaks will capture growing space in the regenerated stand.

Chapter 8

(A)

Fig. 8.7.  (A) Large oak advance reproduction such as this 7 ft tall seedling sprout in a Central Hardwood stand has a high dominance probability (i.e. a high chance of becoming a dominant or codominant tree after complete overstorey removal). (Photograph courtesy of USDA Forest Service, Northern Research Station.) (B) Dominance probabilities for advance reproduction (seedling or seedling sprout) of different species in the Ozark Highlands of Missouri (Central Hardwood Region). In these examples, dominance probability is the probability that a tree will attain an intermediate-orlarger crown class 21 years after clearcutting. In each case, probabilities are for 6 ft tall advance reproduction with 1 inch basal diameters growing on neutral aspects (south-east- or north-west-facing) mid-slopes. The five species groups shown are the predominant hardwoods within this ecoregion. Probabilities were generated by the regeneration model acorn (Dey et al., 1996b). (B)

0.6

Dominance probability

0.5 0.4 0.3 0.2 0.1 0.0

Hickory

Sassafras

advregen is a probabilistic individual-tree regeneration model applicable to the Ozark Highlands. It was developed to assess the adequacy of the oak, hickory and blackgum regeneration potential in stands considered for harvesting by clearcutting (Sander et al., 1984; Johnson and Sander, 1987). However, the model is applicable to any method of

Even-aged Silvicultural Methods

Blackgum

Dogwood

Oaks

regeneration requiring complete overstorey removal on areas of about one-third acre or larger. The model provides a simple ‘yes’ or ‘no’ answer to the question of whether a given stand will be adequately stocked 20 years after final harvest. The criterion for defining minimum adequacy of stocking assumes that by stand age 20 there must be at least 221

305

The regeneration model is based on estimates of the probability that a seedling or seedling sprout of a given initial size (basal diameter and height) will survive and grow to dominant or codominant crown class after clearcutting. For a given species, large initial diameters and heights are associated with high probabilities. The resulting estimates, called dominance probabilities, are generated by a predictive equation (Fig. 8.8). Dominance probabilities are extended to stand age 20 by assuming that trees retain their dominance at an annual negative exponential ‘retainment’ rate of 0.99 (Johnson and Sander, 1987). A ‘stocking value’ for each plot is then calculated from the reciprocal of the 20-year dominance probability and the binomial probability distribution (Sander et al., 1984; Johnson and Sander, 1987). If a stand’s mean stocking value equals or exceeds 30%, the stand is deemed adequately stocked. The advregen model is simple in concept and application because the definition of a dominance probability integrates growth and survival into a single value. The specified size of the inventory plot, which considers the future growing space requirements of trees, also simplifies prediction and field application. Although the model does not explicitly consider competition effects, such effects are implicit in the effects of topographic factors (advance reproduction) or site index (stump sprouts) on dominance probabilities. The limitations of advregen include: (i) the lack of specification of the composition and structure of the future stand; (ii) the use of a single stocking criterion; (iii) restriction to stands

dominant and codominant oaks/acre averaging 4.5 inches dbh (the average expected dbh of dominant and codominant trees in 20-year-old stands). That level of stocking equates to C-level stocking on Gingrich’s (1967) stocking chart (Chapter 6, this volume, Fig. 6.9). By definition, the trees in stands at C-level stocking are expected to fully utilize growing space within 10 years (i.e. by about stand age 30) assuming that trees are well distributed (Gingrich, 1967; see also Chapter 6, this volume). Application of the model is relatively simple and is facilitated by the advregen computer program or by referring to related tables (Sander et al., 1984). To use the model, the height and basal diameter of the largest stem of advance reproduction must be measured on 1/735-acre (4.3 ft radius) field plots and the slope position and aspect of each plot must be specified. A 1/735-acre plot equals the minimum growing space required for a tree 4.5 inches dbh as defined by Gingrich (1967) (Figs 6.8 and 6.9). Codominant and dominant trees averaging 4.5 inches dbh occur in stands averaging about 3 inches dbh (based on trees 1.6 inches dbh and larger). Because 3 inches is the smallest mean stand diameter shown on Gingrich’s (1967) stocking chart, it represents the earliest point in stand development (approximately stand age 20 on average sites) that the utilization of growing space by trees (stocking) can be practically determined. advregen also facilitates estimating the probable contribution of stump sprouts to future stocking. To do that, the diameters of overstorey oaks must be sampled and stand site index must be determined.

(A)

0.7 0.6 0.5 0.4 0.3

r(

0.1 5

ete

(B) 4

3

Heigh

t (ft)

306

2

1

sa ld iam

0.0

1.75 1.50 1.25 1.00 0.75 0.50 0.25

in. )

0.2

0

Ba

ability Dominance prob

0.8

Fig. 8.8.  Estimated dominance probabilities for oak advance reproduction in relation to initial (pre-harvest) size of reproduction in the Ozark Highlands of Missouri (Central Hardwood Region). Dominance probability is here defined as the probability that a seedling or seedling sprout of a given height and basal diameter immediately before clearcutting will be dominant or codominant 5 years later. (A) Probabilities for mid-slopes on neutral aspects (south-east- and north-west-facing slopes). (B) Probabilities for lower slopes on cool aspects (north-east-facing slopes). Probabilities for other aspect/slope combinations lie between the two response surfaces shown, which represent averages for black, white, scarlet, post and blackjack oaks. (Derived from a logistic regression model from Sander et al., 1984.)

Chapter 8

where there will be complete overstorey removal (i.e. where all trees 2 inches dbh and larger will be cut); (iv) regeneration stocking estimates that are limited to oaks, hickories and blackgum; and (v) a lack of generality of application to conditions not considered by the data from which the model was built. Model development and application are discussed in more detail elsewhere (Sander et al., 1984; Johnson and Sander, 1987). A similar regeneration model applicable to northern red oak reproduction in the southern Appalachians predicts 20th-year dominance probabilities from pre-harvest basal (ground line) diameter and site index. Probabilities increase with increasing basal diameter and decreasing site index across the site index range of 70–90 ft (Table 8.1). The smaller dominance probabilities associated with the higher site indexes are attributable to the increasing competition from yellow-poplar and other vegetation associated with increasing site quality. In application, the expected future numbers of dominant and codominant red oaks per unit area can be calculated by multiplying the observed number of seedlings in each of several basal diameter classes by the dominance probability for each class and summing those products across all diameter classes. Because northern red oak is usually a Table 8.1.  Twentieth-year dominance probabilitiesa for northern red oak advance reproduction in the southern Appalachians. (From Loftis, 1990a.) Basal diameterb of advance reproduction (in.)

70 ft

80 ft

90 ft

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1–1.5 1.6–2.0

0.01 0.02 0.04 0.06 0.09 0.13 0.17 0.21 0.25 0.29 0.38 0.46

0 0.01 0.02 0.03 0.04 0.06 0.09 0.12 0.15 0.18 0.29 0.41

0 0 0.01 0.01 0.02 0.03 0.04 0.06 0.08 0.11 0.19 0.34

Probability according to oak site indexc

a

The probability that a stem of red oak advance reproduction will be dominant or codominant 20 years after overstorey removal. b Measured at ground line. c Height in feet at an index age of 50 years based on the curves of Olson (1959).

Even-aged Silvicultural Methods

minor to moderately important component of the mixed mesophytic forests of the region, the model itself makes no assumptions about the adequacy of future red oak stocking. A model called acorn (AComprehensive Ozark Regenerator), like advregen, is applicable to the Ozark Highlands. It can be used to predict the composition and structure of stands 21 years after complete overstorey removal (Dey et al., 1996a, b). This probabilistic individual-tree, distanceindependent model simulates the development of oak stands including all the oaks native to the Ozarks and the four major associated hardwoods (hickories, blackgum, flowering dogwood and sassafras). acorn makes no assumptions about the stocking adequacy of the future stand. Instead, the model predicts the future distribution of tree diameters by species (Fig. 8.9). The output from acorn can be used as input for existing growth and yield models. This facilitates projecting stand growth and change through the next rotation. Like advregen, the application of acorn requires an inventory of the advance reproduction and overstorey together with information on site quality as expressed by slope position, aspect and site index. Because of its complexity, using the model requires a computer. In addition to the model’s application to ‘real’ stands, the simulator can be used to compare various hypothetical situations and assess the relative ease or difficulty of regenerating oaks over a wide range of stand conditions and ecological land types. The acorn model assumes that the height growth of reproduction, including advance reproduction and stump sprouts, is related to the apparent size of the root system rather than reproduction origin, per se. Accordingly, all forms of reproduction including new seedlings, seedling sprouts and stump spouts represent a continuum of growth potential determined by root size, which in turn is correlated with basal tree diameter. Rate of height growth at first increases rapidly with increasing initial basal diameter, reaches a maximum, and then decreases (Chapter 2, this volume, Fig. 2.28). For oak advance reproduction, the model also considers initial height. However, for various physiological and ecological reasons, initial size relations explain only a small to moderate proportion of the variance in the postharvest growth of reproduction. To produce realistic simulations of the size distributions of trees, acorn jointly employs the regression estimates of future tree heights and the normal

307

(A) 24

Data input: overstorey

Trees/acre

20 16 12 8 4 0

2.5

4.5

6.5

8.5 10.5 12.5 14.5 16.5 18.5

Other species

Fig. 8.9.  Graphical representation of input and output for the regeneration model acorn (A Comprehensive Ozark Regenerator) (Dey et al., 1996b). The model predicts, from a pre-harvest stand inventory (tree list), the 21st-year composition, structure and stocking of oak stands that are proposed for complete overstorey removal in the Ozark Highlands of Missouri (Central Hardwood Region). (A) Diameter distribution of overstorey trees. acorn requires an inventory of the diameters of overstorey trees (listed by species) and an estimate of stand site index. This facilitates predicting contributions to the new stand from stump sprouts originating from cut overstorey trees. The graph represents a mature black oak–white oak stand at 84% stocking (based on Gingrich, 1967) growing on a south-east-facing slope (black oak site index 63). The stand is dominated by black oak. (B) Distribution of basal diameters of advance (pre-harvest) reproduction. The model requires an inventory of the advance reproduction (a list of basal diameters and heights by species) to predict its contribution to the future stand. Information on the slope position and aspect of each reproduction sample plot or the stand as a whole also is required. In this example, white oak reproduction predominates. (C) Model output: the regenerated stand. Based on input from A and B, the model predicts the new stand will be dominated by white oak, which contributes 72% stocking of the total projected 94% at stand age 21. Other species include hickories, blackgum, sassafras and flowering dogwood. The computer model produces graphical output similar to that shown in (C) as well as tables. Output can be obtained by crown classes, source of reproduction (stump sprout or advance reproduction), and user-specified species or species-groups. (Adapted from Dey et al., 1996b.)

­ istribution of prediction errors about the estimate d (Dey, 1991). The prediction errors are used to estimate the probability that a seedling, seedling sprout or a stump sprout will attain a specified 21st-year

diameter based on their initial size and site-related factors. The resulting individual-tree probabilities facilitate predicting future diameter distributions of trees that survive and grow to stand age 21 based on

Dbh class (in.) Advance reproduction (genets/acre)

(B) 500

Data input: advance reproduction

400 300 200 100 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 Basal diameter class (in.)

(C)

24

Trees/acre

400

2*

300

Model output: regenerated stand at age 21

8

*Stocking % in dbh class 200

15

22

100 0

15

0.5

1.5

2.5

3.5

4.5

5.5

4

3

1

6.5

7.5

8.5

Dbh class (in.) White oak

308

Black oak

Chapter 8

a pre-harvest inventory of the advance reproduction and the overstorey. acorn is limited to predicting stand composition and structure at stand age 21, and like advregen is limited to predicting the outcome after complete overstorey removal. Also, contributions to future stocking from reproduction originating from seed cannot be accounted for. The latter eliminates prediction of two regionally important species, eastern redcedar and shortleaf pine. forcat is a regeneration model designed to simulate the development of clearcut stands on the Cumberland Plateau (Waldrop et al., 1986). The Cumberland Plateau is centred in eastern Tennessee and extends northwards into eastern Kentucky and southwards into northern Alabama (Smalley, 1979, 1982, 1984, 1986). This is a floristically diverse region that includes forests dominated by oaks, pines and numerous other hardwoods. Site index for upland oaks ranges from about 50 to 80 ft at an index age of 50 years. Variation in the region’s undulating to steep topography, together with soil depth and soil texture, largely controls site quality and therefore species composition, succession and regeneration dynamics. On the drier sites, oaks and hickories sometimes mixed with shortleaf pine, Virginia pine and eastern redcedar predominate. These stands occur on southerly facing slopes, dry ridges and thin soils. Common associated hardwoods include blackgum, red maple, sourwood, flowering dogwood and sassafras (Waldrop et al., 1986). Here, oak reproduction often accumulates naturally. Stand structure, composition and succession therefore are similar to the Ozark Highlands and other xeric oak forests of the Central Hardwood Region. The richer sites include north- and east-facing slopes, valley bottoms and coves. There, stands are often dominated by diverse mixtures of tolerant and intolerant mesophytes such as yellow-poplar, sugar maple, American beech, black cherry and white ash mixed with minor to moderate proportions of northern red, white and other oaks (Braun, 1972). Lush subcanopies of flowering dogwood, sourwood, bigleaf magnolia, umbrella magnolia, American hornbeam and other shade-tolerant species are common (Carpenter, 1976). Because oak advance reproduction usually does not accumulate under those conditions, regenerating oaks is problematic on these sites. forcat predicts future stand composition and structure from measurements of the dbh, height and species composition of the parent stand

Even-aged Silvicultural Methods

­ verstorey. The model can consider up to 33 tree o species and can simulate the composition and structure of stands of any age up to 100 years. The model user also can begin the simulation at any stand age beginning with the pre-harvest state or from subsequent years. Unlike acorn, forcat does not directly consider the presence of advance reproduction. Therefore, in application, forcat does not require measurements or counts of reproduction. Regeneration events are simulated by various model subroutines that probabilistically account for sprouting from harvested overstorey trees, seed production and germination. The model considers regional variation in temperature and soil moisture effects by constraining the maximum growth potential of each species based on growing-season degreedays and soil moisture days above specified critical thresholds. Site-specific effects are considered by providing separate values of a diameter growth constant for each species in each of 20 different ecological land types in the Mid-Cumberland Plateau as defined by Smalley (1982). Tree survival is predicted from tree age and expected diameter growth rate. The model also can simulate the effects of prescribed burning. In tests of forcat’s precision, predictions became increasingly more accurate with increasing stand age (up to 100 years). Its questionable ability to accurately predict the composition of young stands (e.g. at age 20) may be silviculturally problematic. forcat’s generalized structure makes it adaptable to a wide range of species, site and ecological conditions like those characteristic of the Cumberland Plateau. This flexibility is derived from the model’s lineage to foret, a gap model designed to predict long-term forest succession in Tennessee (Shugart and West, 1977). A regeneration model for the Allegheny Hardwood Region is applicable from the Allegheny Plateau of north-western Pennsylvania (ecoregion province 221a, Plate 1) northwards into New York where it is transitional to the Northern Hardwood Region (ecoregion province 211, Plate 1) (Marquis et al., 1992). To the south, the applicable area extends into West Virginia and Maryland (ecoregion provinces 221a and M221, Plate 1). Complete silvicultural guidelines, including the regeneration model, are available for three commonly occurring hardwood forest types within the region: (i) cherry– maple; (ii) beech–birch–maple; and (iii) oak–hickory (Marquis et al., 1992). The latter occur primarily on the drier sites. However, one or more species of oak

309

(including northern red, white, black, scarlet and chestnut oaks) are potential components of all three types. Important non-oaks include black cherry, red maple, sugar maple, American beech, yellow birch, sweet birch, hickories, white ash, yellow-poplar, pin cherry, striped maple, eastern hophornbeam and other species (Marquis et al., 1975). Serious regeneration problems often occur throughout the region because of heavy deer browsing and competition from ferns, grasses and shade-tolerant subcanopy tree species (Marquis, 1974, 1981; Horsley, 1982; Horsley and Marquis, 1983). Comprehensive guidelines are available for prescribing silvicultural treatments for stands within the region (Marquis et al., 1992). A computer program, silvah, which incorporates criteria for evaluating stand regeneration potential, facilitates application of the guidelines. silvah also produces printed output containing data summaries and stand diagnostics. More recent versions of silvah include criteria and guidelines specifically for regenerating mixed-oak forests in the region (Brose et al., 2008). Using the model requires pre-harvest counts of reproduction by species groups within sample plots of 6 ft radius. The counts are weighted by height and vigour classes and adjusted for interfering factors including intensity of deer browsing, soil characteristics and competition from noncommercial tree species and other plants. The resulting rating then is used to determine whether a reproduction plot is stocked (Marquis et al., 1992; Brose et al., 2008). For mixed-oak stands the regeneration potential is deemed adequate if at least 50% of plots are rated as stocked. Regeneration from sapling-size trees (0.5–6 inches dbh) and from stump sprouts also can be considered. Where regeneration potential is determined to be inadequate, prescriptions of enhancing oak regeneration stocking are provided in the guidelines. Several commercially valuable species groups are recognized including black cherry, yellow-poplar, eastern hemlock, pines, oaks and other ‘desirable’ species. Other tree species and some other understorey plants are categorized as ‘undesirable’ and are treated by the model as an ‘interfering’ factor in the regeneration of the commercially valuable species. There also is a provision for evaluating regeneration after final harvest. The silvah computer program projects regeneration adequacy, or alternatively the projection can be made by hand calculation using the tables and decision rules in Marquis et al. (1992) and in Brose et al. (2008).

310

Another regeneration model developed for mixedoak stands in the central Appalachians is linked to Gingrich’s (1967) stocking equations (Chapter 6, this volume). The model references the reproduction present in pre-harvest and young postharvest stands to minimum tree area (100% or A-level stocking) and maximum tree area (B-level stocking) based on tree height/crown-area relations (Fei et al., 2006, 2007). Because crown-area relations did not differ among species within the stands used to develop the model and related equations, the model does not require individual species’ equations or parameters. In field application, the method requires an inventory of reproduction to provide numbers of stems per acre and their average heights. These numbers then are used to calculate stocking per cent (sensu Gingrich, 1967) and the number of reproduction stems of a given average height needed to obtain B-level stocking. The model is unique in that it is the only one available, to date, that uses either preharvest or postharvest information to calculate stocking. Moreover, it determines stocking in the widely used context of the Gingrich crown-area equations and related stocking chart. However, the model makes no assumptions or predictions regarding changes in species composition or stand structure that might occur in the near future (e.g. by the time stands reach the stem exclusion stage of development; Chapter 5, this volume). Such changes are often a central issue in assessing the adequacy of reproduction at any point in time, and often as much or more so than overall stand stocking. A model for assessing regeneration in southern bottomland oak stands is based on scoring the advance reproduction and the overstorey. Trees are assigned a score of 1 to 3 (both advance reproduction and overstorey) within 1/100-acre plots (Johnson and Deen, 1993). Small advance reproduction is assigned a value of 1 whereas larger reproduction is assigned a value of 3; conversely, small overstorey trees are assigned a value of 3 and larger trees a value of 1 or 0, which reflects their stump sprouting potential (Table 8.2). Because the scoring system is relatively simple, an inventory of advance reproduction and the overstorey can be rapidly and efficiently obtained. Hand calculation of plot and stand stocking is relatively simple and an example tally sheet is presented in Johnson and Deen (1993). Based on assessments of reproduction immediately before and 1–3 years after complete overstorey removal in 118 plots in nine stands, 78% of plots were identified as correctly ­categorized

Chapter 8

Table 8.2.  Scoring system for assessing the adequacy of the regeneration potential of 1/100-acre plots in southern bottomland hardwood stands.a Overstorey tree dbh (in.)b

Advance reproduction height (ft)

Score

< 1.0

1.1–2.9

> 3.0

< 5.5

5.6–10.5

10.6–15.5

1

2

3

3

2

1

a

To determine if a plot is adequately stocked, each tree (reproduction and overstorey) within the plot is assigned the score given in the table. A plot is deemed adequately stocked with ‘desirable’ reproduction if the tree scores sum to 12 or more. Scores for overstorey trees reflect their stump sprouting potential. The system is based on studies in stands dominated by mixed oaks (willow, water, cherrybark, overcup, white, laurel, swamp chestnut, Shumard, southern red, Nuttall and northern red) and green ash in east central and southern Mississippi. (From Johnson and Deen, 1993.) b Includes trees >2 inches dbh.

by the pre-harvest scoring system. Because of the ephemeral nature of advance reproduction in bottomlands, the pre-harvest inventory should be made within 1 year of overstorey removal. Model development and characteristics are similar to the silvah regeneration model. The regen model (Loftis, 1989) was developed to estimate the composition of the codominant or dominant trees at canopy closure after clearcutting for mixed species forests. This model incorporates ‘expert’ opinion and empirical data where available. It requires an inventory of all sprouting species with a dbh and an advance reproduction survey by species and size class. Advance reproduction is assigned a competitive ranking ranging from 1 to 8 for each of the regeneration sources including stump sprouts, root sprouts and new seedlings. The rankings are used to select a predetermined number of stems that are likely to become the dominant or codominant trees at canopy closure of the regeneration cohort. Vickers and others (2011) found that the regen model produced accurate results in the Central Appalachians when the competitive rankings were informed with empirical data. Clatterbuck (2015) found reasonable performance of the regen model parameters from the Southern Appalachians in the Ridge and Valley province of Tennessee, but noted that local parameterization was needed to improve estimates. The shelterwood method The objective of the shelterwood method is to create favourable conditions for the establishment and development of tree reproduction of the desired species beneath the parent stand. Stand density is reduced to leave only those overstorey trees needed to produce adequate shade and protection for a

Even-aged Silvicultural Methods

new age class to develop (Helms, 1998). Control of overstorey density thus functions to control light and the development of understorey competition, moderate temperatures, reduce wind velocity and associated drying effects, and provide soil protection. The method can include a sequence of cuttings including: (i) a preparatory cut to facilitate crown expansion of residual trees and increase seed production; (ii) an establishment cut to prepare the seedbed for seedling establishment; and (iii) a removal cut to release established reproduction from the overstorey. A feature common to all stages of the shelterwood method is the retainment of the best trees for the final removal cut. There are several variants of the shelterwood method based on how cuttings are carried out spatially. In the uniform shelterwood method, trees are cut uniformly throughout the stand. In the group shelterwood method, trees are cut in groups or patches; in the strip shelterwood method, trees are cut in narrow strips (Helms, 1998). In the shelterwood with reserves method, trees are retained after reproduction is established to obtain goals other than regeneration. Sometimes the method also involves controlling understorey vegetation. All of these methods are potentially applicable to oak forests. Suitability to oak regeneration requirements The shelterwood method frequently has been advocated as a method suited to regenerating oaks (Korstian, 1927; Scholz, 1952; Sander, 1979a; Hannah, 1987; Beck, 1991; Smith et al., 1997). The method is potentially applicable in the troublesome mesic and hydric ecosystems because it focuses on controlling stand density near the end of the rotation – ­a critical period for reinforcing the accumulation of oak reproduction.

311

Controlling light and competition are key features of the shelterwood’s application to oaks. Light under unthinned hardwood stands may be only 1% of full sunlight (Dey and Parker, 1996, 1997). For northern red and many other oaks, the minimum amount of light required to produce carbohydrates sufficient to sustain existing tissues (i.e. the physiological compensation point) is about 2–5% of full sunlight (Hanson et al., 1987). However, maintaining positive rates of shoot growth requires light above 20% of full sunlight (Gottschalk, 1994). In red oak stands in Ontario, shelterwoods that reduce stand density to 70% and 50% crown cover increased light at seedling level to 25% and 50%, respectively (Dey and Parker, 1996). Thus, shelterwoods of appropriate density should in theory provide the light environment needed for adequate oak seedling growth. In northern climates such as the Lake States, the method also can be used to protect oak seedlings from frost damage (Teclaw and Isebrands, 1993a, b). However, effectively applying the shelterwood method to oak regeneration often involves more than controlling overstorey density. Reducing overstorey density often stimulates undesirable understorey vegetation and thus may defeat the objective of accumulating viable advance oak reproduction (Schuler and Miller, 1995). The timing of shelterwood cuts with acorn crops and a shelterwood period that assures adequate seedling development are also important but potentially complicating factors in applying the method. For these reasons, shelterwood prescriptions for oak forests need to be ecosystem specific. The following section presents some examples of the method’s application to oaks in several regions. Applications in oak forests Among the variants of the method, the uniform shelterwood method has been most commonly applied to oak forests. The method involves cutting uniformly throughout the stand to primarily remove overtopped and intermediate crown classes, and secondarily to remove other main canopy trees until the desired stand density is obtained (Fig. 8.10A). The desired shelterwood density and degree of understorey control may vary by forest type and site quality. The shelterwood can be removed in a single harvest or a series of harvests over intervals of time sufficient to obtain the desired reproduction development (Fig. 8.10B).

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(A)

(B)

Fig. 8.10.  (A) The uniform shelterwood method applied to a 90-year-old stand in the Ozark Highlands of Missouri (Central Hardwood Region); site index for black oak is 75 ft. The stand was thinned to 60% stocking largely through the removal of trees in suppressed and intermediate crown classes. The shelterwood is composed of northern red, white and black oaks. (B) Five years after reducing overstorey density to 60% stocking, a dense understorey of oak and other reproduction was present beneath this shelterwood in south-eastern Ohio (Central Hardwood Region); black oak site index is 65 ft. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

A prescription for applying the method was developed for productive sites (site index 70–90 ft) in mixed hardwood forests in the southern Appalachians (Loftis, 1983a, 1990a, b). These forests typically have dense subcanopies of tolerant hardwoods comprising 15–30% of total stand basal area. The prescription calls for a two-cut shelterwood in which stand density is reduced to 60–70% of average maximum stand basal density, depending on site index. This is accomplished by removing trees from the subcanopy by applying a herbicide, which also prevents their sprouting. The method thus eliminates the subcanopy and thereby increases light without creating large gaps in the main canopy. The two cuts used in this method are considered to be removal cuts because they are intended to enhance the survival and growth of the existing oak seedlings rather than to establish new ones (Loftis, 1990b). In stands

Chapter 8

of site indexes of 70, 80 and 90 ft, the recommended residual basal areas are 60%, 65% and 70% of the initial basal area, respectively. Reducing stand density below these limits increases the establishment of yellow-poplar seedlings to densities that are incompatible with regenerating red oak. Recommendations are to make the initial shelterwood cut at least 10 years before the end of the rotation. By applying this prescription, the survival rate (Chapter 3, this volume, Fig. 3.19A) and growth of red oak seedlings can be substantially increased. Although numbers of northern red oak seedlings under red oak stands in Appalachian forests commonly number 1000–2000/ acre (Tryon and Carvell, 1958; Loftis, 1983b), seedlings often average only 0.2 inch or less in basal diameter (Loftis, 1988). If the above shelterwood prescription were applied, relations among seedling survival, growth and dominance probabilities indicate that 1000 well-distributed seedlings/acre would result in 10–40 dominant and codominant red oaks/ acre 20 years after final overstorey removal across the site index range of 90–70 ft. Even though these estimates are hypothetical and remain to be verified by long-term field tests, they nevertheless illustrate the potentials and problems in maintaining red oak in regenerated stands in this and other regions where yellow-poplar is present. Success in maintaining the species largely depends on site quality, stand density and competition control. The documented successes and failures in applying the shelterwood method in mesic ecosystems in the eastern USA emphasize the importance of controlling competition, including interfering herb, shrub and tree layers (Johnson et al., 1989; Horsley, 1991; Crow, 1992; Jacobs and Wray, 1992; Schuler and Miller, 1995). Herbicides may provide the most efficient method of competition control, but opposition to their use has prompted evaluation of other methods. And soil scarification, especially on steep terrain, may not always be feasible or environmentally acceptable. The shelterwood method is sometimes effective in regenerating northern red oak in the driftless area of south-western Wisconsin (Johnson et al., 1989). Based on two case histories (red oak site index 65–70 ft), reduction in overstorey density to 70–80 ft2/acre 10  years before shelterwood removal resulted in 200–400 dominant and codominant red oaks/acre during the second decade after shelterwood removal. However, early results from a study on a similar site in south-eastern Minnesota suggested that the method may be more effective when reduction in overstorey

Even-aged Silvicultural Methods

density coincides with a good acorn crop and competition is reduced by soil scarification (Bundy et al., 1991). Application of the shelterwood method has produced encouraging results in mesic oak forests in southern Michigan (Hill and Dickmann, 1988), southern Wisconsin (Lorimer, 1989), north-eastern Alabama, as well as in hydric green tree reservoirs in south-eastern Missouri (Motsinger et al., 2010; also see Chapter 13, this volume). However, additional silvicultural intervention may be necessary to ensure successful recruitment. The minimum duration of the shelterwood period (i.e. the length of time between the shelterwood cut and shelterwood removal) is uncertain. Results from the driftless area suggest that 1 or 2 years may be sufficient if competition is controlled. Results from one case history in south-western Wisconsin even indicate that northern red oak can be successfully regenerated with little or no oak advance reproduction provided that the final harvest occurs during the dormant season following a good acorn crop (Johnson et al., 1989). Although those results challenge the axiom that advance reproduction is required, the necessary combination of a large mast crop, favourable site conditions and favourable weather is an infrequent occurrence. The safest approach is to obtain adequate oak reproduction before the seed source is removed in the final harvest. The potential for regenerating oaks using prescribed burning in combination with the shelterwood method has been demonstrated in principle (Nyland et al., 1983; Van Lear and Waldrop, 1988; Keyser et al., 1996; Brose and Van Lear, 1998a, b). Its primary value may be on highly productive sites where oak regeneration is most problematic. Burning is unlikely to benefit stands on dry sites where oak reproduction naturally accumulates. Wherever it is used, success will depend on matching the frequency, timing and intensity of burns with other stand characteristics. Based on a review of the literature, Van Lear and Waldrop (1988) concluded that frequent low-intensity back fires (i.e. fires burning against the wind) with low flame heights would be the most useful for building up oak reproduction under a shelterwood. Such burns do not damage the overstorey or site. In contrast, high-intensity head fires (i.e. fires burning with the wind) are likely to wound the boles and reduce timber value or cause excessive overstorey mortality if flames reach into tree crowns. Such fires also may cause damage to the site. Single low-intensity burns are not likely to effectively increase oak regeneration potential (Johnson, 1974; Nyland et  al., 1983; Merritt and Pope, 1991;

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­ ill-Wolf, 1991). A single burn may actually stimuW late the germination of yellow-poplar seed (Shearin et al., 1972), which often remains viable in the forest floor in large numbers for 4 years or longer (Clark and Boyce, 1964). In contrast, repeated prescribed burns under pine stands have been shown to increase the build-up of oak reproduction at the expense of the pine and fire-sensitive hardwoods such as yellow-poplar (Clatterbuck, 1998). When consistent with other management objectives, shelterwoods can be maintained until a two-aged stand or ‘irregular’ shelterwood develops (Beck, 1991; Smith et al., 1997; Brose et al., 1999). Although seldom used, the irregular shelterwood method is ­ potentially compatible with managing oak forests for continuous acorn production (Chapter 13, this volume) and other objectives such as maintaining a continuous overstorey canopy while retaining the essential characteristics of even-aged stands. Similarly, the group shelterwood method has been proposed and described with respect to its possible application to oaks, but has seldom been used (Stroempl and Secker, 1993). Irregular shelterwoods also may be compatible with managing oak forests for acorn production. Acorns are important to many species of wildlife because they are highly nutritious and available during the critical autumn and winter months when other wildlife food is scarce. The population and health of many birds and mammals consequently rise and fall with the cyclic production of acorns (Koenig, 1990; Healy, 1991; Pfannmuller, 1991). However, only a small proportion of oaks inherently produce good acorn crops. Sustaining high rates of acorn production therefore requires monitoring acorn crops over time to identify and retain good acorn producers (Johnson, 1993, 1994). In principle, the irregular shelterwood method can be used to retain good acorn producers until acorn production from the regenerated stand occurs or is close to occurring, and thereby sustaining acorn production.

c­ ontrol the development of competition, and thus do not favour the accumulation of advance oak reproduction in the understorey. In the few reported cases where the method has been applied to oak forests, the seed trees contributed little to regeneration (DeBell et al., 1968; Johnson and Krinard, 1983). Nevertheless, the method may be useful if sustaining acorn production on harvested areas is important. It can provide for substantial acorn production for wildlife if good seed producers are retained. However, identifying the seed producers requires long-term records on seed production, which few forest managers are likely to have (Chapter 13, this volume). The method nevertheless creates more structural diversity and visual appeal than a clearcut. When applied for this purpose, the seed tree method does not differ from clearcutting with reserves. Sharp (1958) observed that fewer than 30% of white oaks produced acorns and many of those were poor producers. If this is typical of most oak species, leaving ten good acorn producers/acre would retain 40% or more of the acorn-producing capacity of the original stand. This assumes that there are about 75 oaks in the overstorey at the end of the rotation. Moreover, under the open-grown conditions created by the seed tree method, the crowns of individual seed trees can potentially expand to their maximum area and branch density to maximize acorn production per tree. However, the crowns of some seed trees may degenerate from crown dieback because of their sudden exposure to full light (Smith et al., 1997). However, the snags that ultimately develop provide valuable habitat for cavity-nesting birds, and the standing dead wood provides habitat for organisms essential to maintaining biodiversity (Franklin et al., 1989; Hansen et al., 1991). Moreover, seed trees can be quickly converted to dead snags by girdling once it was determined they had little or no value for acorn production or other purposes.

The seed tree method

Artificial Regeneration Methods

The seed tree method is an even-aged method that usually leaves ten or fewer seed-producing trees on every acre (Smith et al., 1997). In its conventional application, the intent is to provide seed after most of the overstorey is removed. The method is generally not recommended for regenerating oaks because the seed trees provide too little reproduction too late, do not produce light levels that

Where there is concern about insufficient oak regeneration potential, artificial regeneration methods including seeding and planting can be used to supplement natural seedlings during the application of any of the even-aged methods (Chapter 10, this volume). Underplanting oaks in shelterwoods has been widely practised to enhance oak regeneration success.

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Chapter 8

Intermediate Cuttings Definitions and theory Intermediate cuttings include any treatment or method of tending designed to improve the growth, quality, vigour or composition of a stand. They include thinning, cleaning, release cutting and various types of improvement cutting including salvage and sanitation cuttings as discussed below. The objective of thinning is to concentrate growth on fewer trees per acre than would occur in unthinned stands (Chapter 6, this volume), and in the process to improve stand composition and tree quality. Several types of thinning methods have been described including: low thinning, crown thinning, selection thinning, mechanical thinning and free thinning, which are defined as follows (Smith et al., 1997): ●● Low thinning, often called ‘thinning from below’, removes trees in the lower crown and diameter classes. ●● Crown thinning removes trees from the middle and upper ranges of crown and diameter classes. The objective is to favour the development of the best trees in these same classes. ●● Selection thinning removes dominant trees to stimulate the growth of trees in the lower crown classes. The method can be used to eliminate poorly formed dominants and release less vigorous trees of better form. ●● Mechanical thinning is any method based primarily on the spacing between trees (e.g. row thinning in plantations), and is usually applied to young stands with relatively undifferentiated crown classes. ●● Free thinning is designed to free crop trees without regard to their position in the crown canopy. The method is applicable to stands with very irregular structure and composition. All of these thinning methods are potentially applicable to even-aged oak stands. However, low thinning is the most frequently used method and is the focus of the discussion on thinning presented below. Although low thinning in oak forests concentrates on removing trees in the lower crown and diameter classes, some trees in the higher classes also may be removed to obtain the desired stocking. Thus, the practical difference between low and high thinning is usually one of degree of removal of trees in low versus high crown classes. Existing stand

Even-aged Silvicultural Methods

structure and composition and other factors may dictate methods other than low thinning. Regardless of thinning method, trees of high value should largely occupy the growing space at the end of the rotation. This objective does not rule out incorporating other management objectives such as retaining some cull or other trees of low economic value that are of value for wildlife and other non-timber objectives. When thinning maintains stand densities that fully utilize growing space, there is theoretically no loss in total standing tree biomass or volume yield by the end of the rotation. In fact, yields of merchantable timber are likely to be increased by thinning because thinnings recover merchantable products that would otherwise be lost to natural mortality from selfthinning (Chapter 6, this volume). Not everybody agrees on how and when thinnings should be made in oak stands. However, silviculturists generally agree that stands originating primarily from seedlings and seedling sprouts should be treated differently from those originating primarily from stump sprouts. The latter require early intermediate cuttings if tree quality matters. The former may sometimes benefit from no thinning for 40 years or longer (Carvell, 1971). Conversely, oak stands are usually not thinned later than 60 years in the eastern USA. After that, growth responses are usually no better than in unthinned stands. However, it is sometimes desirable to salvage trees that have succumbed to insects, disease or windthrow late in the rotation. Under normal conditions, silviculture applied late in the rotation should be aimed at preparing for regeneration as discussed in ‘The shelterwood method’ section of this chapter. When thinnings are concentrated in the lower crown classes throughout the life of an oak stand, the final crop trees will be of large diameter and thus low stump sprouting capacity (Chapter 2, this volume, Fig. 2.25). Although many of the smalldiameter oaks removed in thinning will sprout, the moderate-to-high overstorey densities usually maintained in well-managed oak stands are not conducive to stump sprout survival to the end of the rotation (Gardiner and Helmig, 1997). Intensive management for sawlogs therefore can be expected to progressively reduce the importance of oak stump sprouts and thereby force a reliance on regeneration from oak seedlings and seedling sprouts. Lengthening the rotation will have a similar effect, because for a given diameter, older oaks

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have lower sprouting probabilities than younger oaks (Fig. 2.25). In addition to thinning, other types of intermediate cuttings are also applicable to oak forests. These include cleanings, which are applied to stands not past the sapling stage (including stand initiation and early stem exclusion stages, see Chapter 5, this volume, Fig. 5.3). The objective of a cleaning is to free the favoured trees from the less desirable trees of the same age class that overtop them or are likely to (Smith et al., 1997; Helms, 1998). Liberation cuttings are similar to cleanings, but involve the removal of older trees left from a previous stand or for other reasons were present before the new stand was established. Both types of cuttings, by definition, are applicable to stands not past the sapling stage. Improvement cuttings are defined as those made in stands past the sapling stage to improve their composition and quality by removing from the main canopy, trees of undesirable species, form and condition (Smith et al., 1997). They are often conducted preliminary to, or simultaneously with, regular thinnings. Improvement cuttings are applicable to many oak stands that have not previously been managed, which often include undesirable species or trees of poor quality. Improvement cuttings include salvage cuttings made to remove trees that are in danger of being killed or that have already been damaged or recently killed by injurious agents other than competition. Examples of their application to oak forests include cutting dead or dying trees affected by gypsy moth defoliation and oak decline. When such cuttings are made to pre-empt damage to high-risk trees, they are called presalvage cuttings. Sanitation cuttings remove trees that have been attacked by, or appear to be in danger of attack by, certain insects and diseases to prevent their spread to other trees (Smith et al., 1997). Salvage, presalvage, sanitation and other types of cuttings can be used to control several insect and disease problems in oak forests as discussed below. Application Thinning and cleaning Intermediate cutting focuses on maintaining an adequate density of dominant and codominant trees, and improving species composition and stand quality. Because oak stands usually comprise a wide range of tree diameters and crown classes,

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i­ntermediate cuttings that remove only the largest trees automatically promote the smaller ones to crop trees. However, such trees have grown so slowly and often are of such low vigour that they usually respond poorly to thinning. This lengthens the rotation and also may produce dysgenic effects (i.e. effects that are detrimental to the genetic qualities of the future stand). The objective of thinning is to minimize rotation length by growing trees as rapidly as is consistent with maintaining tree quality and full utilization of growing space. This is usually accomplished by restricting cutting to the lower half of the diameter range (Roach and Gingrich, 1968) and maintaining moderate stand densities (e.g. near B-level as defined by the stocking charts described in Chapter 6, this volume). However, trees substantially larger than the dominant and codominant trees in the main stand, which may represent an older age class of trees persisting from an earlier stand condition, are also candidates for removal. This is especially true if such trees are numerous enough to seriously hinder the development of the predominant age class. Trees to be retained thus should be selected on the basis of species, size, stem form, stand structure and apparent vigour, as evidenced by crown condition and spacing. Thinning can be applied to stands before they produce merchantable products. This is likely to be the case in stands that are not yet pole-size (i.e. sapling stand less than approximately 5 inches dbh; the size and dbh limit varies by market). Any treatments to stands during the sapling stage that are designed to free the favoured trees from less desirable trees of the same age class that overtop them or are likely to do so is called a cleaning (Helms, 1998). In oak forests of the eastern USA, the sapling period ranges from about 5 to 15 years, depending on site quality and species composition. In general, this period spans the late stand initiation stage to the early stem exclusion stage of stand development (Chapter 5, this volume, Figs 5.2 and 5.3). Because the cost of thinning during this period is usually not recovered in the sale of timber products, the term ‘precommercial’ thinning is often applied. The objective of a cleaning is not to remove all undesirable stems, but to select potential crop trees and to release only those that need it. Opportunities for precommercial thinning occur in the early stem exclusion stage of stand development when trees are sapling-size and tree crown positions are well established (Fig. 8.11A). Young high-density stands of desirable species composition facilitate selecting

Chapter 8

(A)

(B)

Fig. 8.11.  (A) An unthinned sapling-size oak stand in south-eastern Ohio (Central Hardwood Region). Stocking is at or near 100% and comprised of many oaks of good form. Such stands are good candidates for precommercial thinning. (B) A thinned sapling-size mixed upland oak stand in southeastern Ohio thinned to a prescribed density. Only the largest and best-formed crop trees were retained on this research plot. This is an area-wide thinning with very uniform spacing between trees. In practice, precommercial thinnings are usually not applied area-wide as illustrated here. Rather, thinning is restricted to a prescribed distance around selected crop trees that leaves intervening unthinned areas. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

crop trees at an optimum spacing from among the best individuals (Fig. 8.11B) (Perkey and Wilkins, 1993). A spacing of 15–20 ft between crop trees usually provides acceptable stocking by the time trees are large enough for a commercial thinning.

Even-aged Silvicultural Methods

Smaller trees are unlikely to grow fast enough to keep up with the rapidly developing main canopy. Because cleaning is expensive and costs are carried for long periods at compound interest, the application of the method is usually limited to the better sites. There nevertheless is much disagreement and uncertainty over the efficacy of precommercial thinning. For example, results from thinning West Virginia oak stands during the sapling stage (at 15  years) indicated there was little advantage to precommercial thinning (Carvell, 1971). The thinnings adjusted spacing, favoured the desired species, and removed poorly formed trees. Ten years later when plots were compared to plots receiving no thinning, the cleaned plots were only slightly better in composition. Even when significant improvements in growth, quality and species composition result, carrying the costs of cleaning at compound interest over most of the rotation often renders the practice economically unfeasible. Exceptions may occur where there are markets for small products. Cleanings applied to stands in the stand initiation stage of development (Chapter 5, this volume, Fig.  5.3A) may be even more problematic. During that period, crown classes are changing rapidly and unpredictably. This was observed in a West Virginia study (red oak site index 70 ft) where removing competitors within a 5 ft radius of 9-year-old crop trees did not significantly increase height or diameter growth or length of clear stem, nor did it prevent crown-class regression during the 5-year study period (Lamson and Smith, 1978). Similar results were observed in 7-year-old stands (oak site index 60 ft) (Trimble, 1974). When cleanings are applied to stands in the sapling stage of development, recommendations are to do so conservatively by creating canopy gaps no more than 5 ft wide on three or four sides of selected crop trees (Sampson et al., 1983). Despite these problems and uncertainties, cleanings can potentially increase yields. In the Central Hardwood Region, cubic-foot yields of oak stands can be increased by 50% or more when thinning is begun at age 10 (a cleaning) than when begun at age 60 (Gingrich, 1971). Although economic considerations may preclude cleanings, oak stands often can be profitably thinned as early as stand age 25 where there is a market for fuelwood or pulpwood (Carvell, 1971). Even if returns are slightly less than costs, thinning may be justified by the early acceleration of the growth of crop trees. Early thinning therefore should not be focused on early profits, but on increasing the growing space for the

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final crop trees. Not only will the growth rates of residual trees be increased, but early thinning also provides the opportunity to select trees of the best species and highest quality without the largely unrecoverable expense incurred in cleanings. The first thinning should remove undesirable species and poorly formed stump sprouts and other trees. Additional trees may need to be cut to improve spacing among dominant and codominant trees. Even when the first thinning is compelled by an economic return, the primary goal should be the long-term improvement of stand composition, structure and growth. A maxim of thinning is to never compromise the quality and growth potential of the stand. The first thinning is thus the first step in accelerating the growth of crop trees (Perkey and Wilkins, 1993, 2001). Periodic thinnings are thereafter needed to sustain high growth rates. However, thinning should not reduce stand density below full utilization of growing (e.g. as defined by regional stocking guides). Cutting to lower densities creates unused growing space and thus reduced yields, may encourage an undesirable understorey, and may reduce tree quality by encouraging epicormic branching on tree boles (Fig. 8.12). Even though maximum yields may occur when stocking is below B-level (Dale, 1972; Leak, 1981), the lower limit of thinning is usually set at B-level to ensure bole quality (Dale and Sonderman, 1984). Thinning below B-level risks increasing epicormic branching on the lower bole with consequent loss of tree value. Even light thinnings reduce competition for growing space thereby reducing the natural pruning of trees. However, the number of branches a tree retains is partly determined by genetics and partly by its growth rate coupled with competition and side shading. Accordingly, selecting crop trees should favour those with clean boles and rapid growth. Stand density also can potentially influence other bole characteristics such as forking and stem taper. Nevertheless, there was no evidence that thinning over a wide range of stocking levels from < 50 to > 75% (based on Fig. 6.9, Chapter 6, this volume) significantly affected stem taper of dominant and codominant oaks during a 12-year study period in the Central Hardwood Region (Hilt and Dale, 1979). A different strategy for applying intermediate cuttings is recommended for northern red oak stands in central New England. In this region, where red oak site index ranges from about 55 to 75 ft, red oak commonly occurs in association with red maple and birch (Oliver, 1978, 1980; Hibbs

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Fig. 8.12.  The lower bole of this black oak is covered with ‘epicormic branches’. They originated from dormant buds under the bark that ‘sprouted’ when the tree was suddenly exposed to high light intensity after heavy thinning. Epicormic branches that persist and grow reduce bole quality and tree value. Maintaining adequate lateral shading of boles by controlling stand density, and thus light, is essential to minimizing epicormic branching. Because the propensity for epicormic branching in oaks is inherited, crop trees should be selected from among those with few such branches at time of thinning. Oak stump sprouts also arise from dormant buds. However, those buds originate at or near the tree base, and they sprout after the tree is cut or topkilled. (Chapter 2, this volume, Fig. 2.23). (Photographs courtesy of USDA Forest Service, Northern Research Station.)

and Bentley, 1983). Although the maple and birch typically outnumber and outgrow the oaks during the first two decades of stand life, red oak ultimately emerges to form the dominant canopy because of its greater survival and sustained growth rates (Oliver, 1978; Hibbs, 1981). Only about twice the number of red oak crop trees required at the end of the rotation are needed at stand age 25 for the

Chapter 8

stand to be considered adequately stocked with red oak (Hibbs, 1981). During the first 45 years, high stand densities encourage rapid height growth and the development of long, branchless boles. Although early thinning increases diameter growth, merchantable height is reduced. The resulting value lost to reduced merchantable height is not compensated by gains in diameter growth (Hibbs and Bentley, 1984). First thinnings are therefore delayed to stand age 45. Stands are then thinned to B-level stocking (Sampson et al., 1983) every 10 years until shelterwood cuttings are made to obtain oak reproduction (Hibbs and Bentley, 1983). The recommended rotation, assuming current utilization technology, is about 95 years when diameters of individual trees reach financial maturity, which occurs at 21–25 inches dbh depending on site quality (Hibbs and Bentley, 1984). Thinnings and improvement cuts thus are not continued indefinitely. As trees increase in size and cuttings remove only a few trees per acre, relatively few trees in the residual stand will benefit from thinning. Late in the rotation, cutting only a few trees per acre may harvest periodic growth but increase the growing space of only a few residual trees. This largely defeats the intent of thinning. A  practical rule is to terminate thinnings for the purpose of stand density control by the time a stand reaches 75% of rotation age (Roach and Gingrich, 1968). For upland oaks in eastern USA, this corresponds to about 60–70 years on average sites, and fewer years on better sites. Guidelines for thinning even-aged oak stands are presented in Roach and Gingrich (1968). Although these guidelines are specifically designed for the Central Hardwood Region, their research-based development makes them broadly applicable in principle to many other kinds of oak forests. Included in the guidelines is a decision key for both thinning and regenerating Central Hardwood oak stands. The effects of thinning on the growth and yield of oak stands are further discussed in Chapter 15. Tending oak coppice (stump sprouts) In some species such as northern red and scarlet oaks, multiple stems often persist within clumps of stump sprouts for decades (Chapter 2, this volume, Fig. 2.27 and Fig. 8.13A and B). Northern red oak clumps often retain four or more live stems for 20 years and longer (Chapter 2, Fig. 2.26). As coppice stems grow larger, within-clump competition increases and reduces the diameter growth of

Even-aged Silvicultural Methods

(A)

(B)

(C)

(D)

Fig. 8.13.  (A) A 75-year-old northern red oak coppice stand in the driftless area of south-western Wisconsin (Central Hardwood Region); site index for northern red oak is 70 ft. The stand has never been thinned and is largely comprised of multiple-stemmed clumps. (B) A 20-year-old clump of northern red oak sprouts. All six living stems in this clump are seriously degraded by ‘sweep’ (stem curvature); the three stems on the right are fused at the base. (C) Cross section of the base of a 20-year-old northern red oak stump sprout. The two larger stems are fused together but still remain separated by bark; they envelop a small dead stem. (D) In this 20-year-old northern red oak clump, the stump on the left enveloped three sprouts that fused together and developed a common cambium. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

i­ndividual stems (Johnson and Rogers, 1984). One or more stems within each clump usually occupy and maintain a dominant or codominant crown position. In central Appalachian forests, an estimated 80–90% of northern red oak clumps contain at least one stem of potentially high future value (Lamson, 1976).

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Thinning within clumps increases the growth rate of the residual stems. The amount of increase depends on: (i) species; (ii) the initial diameters of the retained stems; (iii) clump age; (iv) site quality; and (v) clump density. The latter effect can be quantitatively expressed by the ratio of basal area of a given stem to total clump basal area (relative crop stem basal area or RBA) after thinning (Johnson and Rogers, 1980, 1984). Thus, it is not the number of stems left in a clump, per se, that determines the diameter growth of a crop stem, but other factors being equal, how much within-clump basal area competition the crop stem experiences. Consequently, thinning clumps to one stem (RBA = 1.0) results in maximum diameter growth of any given crop stem. Diameter growth of northern red oak sprouts can be substantially increased by early clump thinning (Johnson and Rogers, 1980). In unthinned clumps on good sites, a stem that is 1 inch dbh at age 5 will grow to about 6.5 inches dbh at age 25; the same stem in a clump thinned to one stem at age 5 will grow to about 10.8 inches dbh – or 1.7 times larger. Estimated 25th-year diameters indicate that early clump thinning to one stem is likely to produce economically mature stems 20 or more years earlier than trees of seedling or seedling sprout origin. For a given clump age, the largest stem within a clump usually produces the greatest growth over a

given time interval. However, the largest stem may not always be the most desirable stem when stem quality and the preferred low basal attachment to the stump are considered. Good diameter growth from smaller stems can be obtained when clumps are thinned early to one stem. For example, in 5-year-old clumps, stems with diameters one-third that of the largest stem will on average be only 6% smaller 20 years later. So if thinned early, stems substantially smaller than the largest stem can be selected as crop stems without sacrificing much growth. Conversely, stems that are considerably larger than the average dominant stem for a given age may respond little to clump thinning because their RBAs before thinning are already close to 1.0. The longer thinning is delayed, the greater the reduction in expected 25-year diameters. For example, for average-size stems on average sites, delaying thinning from age 5 to age 10 will reduce the 25-year diameter by about 12%; delaying thinning to age 15 or 20 will reduce 25-year diameter by 23% and 30%, respectively (Table 8.3). To sustain maximum growth of thinned sprout clumps, competitors surrounding the sprout clump should be removed about every 10 years. A simple guideline for determining an appropriate thinning radius around a crop tree is provided by the ‘D + 1’ rule (i.e. remove trees within D + 1 ft of the crop tree where D is the crop tree dbh measured in inches).

Table 8.3.  Effects of delaying thinning of northern red oak sprout clumps based on estimated 25th-year diameters. (Adapted from Johnson and Rogers, 1980, 1984.) Estimated 25 th-year dbh (in.)b when clumps are thinned to one stem at age: Site indexa 50

60

70

Stem dbh at age 5 years (in.)

5 years

10 years

15 years

20 years

Average dbh of unthinned clumps (in.)

0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0

8.3 8.5 8.8 9.1 9.4 9.7 10.0 10.3 10.6 10.8 11.2 11.6

7.1 7.6 8.1 8.8 7.9 8.5 9.1 9.9 8.7 9.4 10.2 10.9

6.1 6.8 7.7 8.4 6.6 7.5 8.5 9.5 7.1 8.2 9.4 10.6

5.2 6.3 7.2 8.2 5.4 6.8 8.0 9.2 5.7 7.2 8.7 10.3

4.4 5.8 6.9 8.0 4.4 6.1 7.6 8.9 4.7 6.5 8.3 10.0

a

In feet at an index age of 50 years; based on the site index curves of Gevorkiantz (1957). The values given assume that stand density is maintained at a level that provides the maximum amount of growing space a tree of a given diameter can utilize.

b

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This distance approximates the maximum tree area for upland oaks in the Central Hardwood Region (Gingrich, 1967) and therefore assures a crop tree receives approximately the maximum growing space it can use at the time of thinning. A more accurate spacing for a crop tree of any given dbh and desired residual stand density can be obtained by applying a method called ‘rule thinning’ (Rogers and Johnson, 1985). With rule thinning, the optimal thinning radius for each crop tree is precisely determined by its diameter and the residual stocking goal for the stand. Improvements in stem quality as well as increases in growth are generally needed to justify investments in precommercial clump thinning. Early clump thinning can eliminate or minimize many of the defects that later develop in oak coppice. The most common defects are: ●● ●● ●● ●● ●●

decay; seams or radial shakes; poor stem form caused by sweep; branching and forking in the lower bole; and weak attachment of sprout to the root system.

decay.  In oak coppice, the two primary entry points of decay-causing organisms are through heartwood connections with the parent stump or with dead companion stems (Fig. 8.13C). By selecting crop stems that originate at or below the ground line, heartwood connections between sprouts and stump can be minimized (Roth and Hepting, 1943, 1969). Decay entry through companion stems can be largely eliminated by thinning clumps to one stem before the largest stems exceed about 2 inches dbh (about 10 years old or younger in most eastern oak forests). In sprouts larger than this, the point of sprout origin is often obscured and the faster growing stems begin to fuse with and envelop slower growing stems. Fusion of oak stems in unthinned clumps eventually produce heartwood unions (Fig. 8.13D). Removing one stem from such a union consequently creates an entryway for heartwooddecay organisms into remaining stems (Roth and Sleeth, 1939). Stems forming V-shaped unions are apt to have heartwood unions, especially after fused stems reach 3 inches dbh; stems with U-shaped unions are less likely to have heartwood unions. Stems forming V-shaped unions therefore should not be selected as crop stems when clumps are thinned (Stroempl, 1983).

Even-aged Silvicultural Methods

Even when decay develops, it tends to be restricted, or compartmentalized, by a ‘barrier zone’ formed by the cambium after wounding (Shigo, 1979). Because of compartmentalization, the potential for spread of decayed wood in oak coppice is limited. Dead companion stems and basal stubs nevertheless predispose crop stems to other defects such as seams. A precaution applicable to oaks in the red oak group is to avoid clump thinning during spring and early summer in areas of known oak wilt activity. The stumps created by thinning can become entryways for oak wilt disease into interconnected crop stems (Kuntz and Drake, 1957; also see the section titled ‘Oak Wilt’ in Chapter 11, this volume). seams.  Seams, also known as radial shakes, are separations of wood tissue along the radial plane of living trees (Butin and Shigo, 1981). In stump sprouts, radial shakes are commonly associated with the occurrence of dead stems. Shakes originate from barrier zones that create planes of structural weakness along which radial shakes can be initiated. Stresses such as wind and sudden temperature drops may cause further radial separations and result in obvious external vertical seams or ‘frost cracks’. These usually occur in the lower 6 ft of the bole (Fig. 8.14). Radial shakes are serious defects because the separations often continue for the life of the tree – and they are only rarely compartmentalized. Consequently, radial shake is a serious problem if high-value sawlogs are the desired product. Early clump thinning to one or two stems (Fig.  8.15) can reduce the incidence of radial shake in oak stump sprouts. Where seams already occur, affected stems should be removed during clump thinning. Because wounds can initiate radial shake, care should be taken not to injure crop stems during clump thinning. Dead branch stubs are also initiation points for radial shake, so early pruning of crop stems may reduce the incidence of seams. sweep. 

The crowns of stems in high-density clumps have a tendency to lean away from the clump centre causing stem curvature or ‘sweep’ in the lower bole (Fig. 8.13B). Even moderate sweep is considered a serious defect in young hardwoods (Sonderman, 1979). Consequently, stems with sweep should be removed when thinning clumps. The earlier clumps are thinned, the less likely it is that sweep will become a serious problem.

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(A)

(B)

Fig. 8.14.  A seam or radial shake on the lower bole of a northern red oak. Such defects are common in oaks and seriously reduce tree quality because the cracks extend from the centre to the outside of the bole and are seldom overgrown (i.e. they usually ‘grow’ with the tree). Radial shake is common in oak stump sprouts but can be reduced by early clump thinning to one or two stems. (Photograph courtesy of USDA Forest Service, Northern Research Station.) branching and forking in the lower bole.  Branching and forking in the lower bole (i.e. below 17 ft) contributes to serious stem degrade in young hardwood (Sonderman, 1979). Because low stand density exacerbates both problems, maintaining adequate stand density throughout the life of the stand is critical for growing high quality sawtimber. However, stump sprouts by themselves seldom provide adequate stand stocking. Adequate stocking after regeneration cutting usually requires an abundance of advance reproduction of seedlings and seedling sprouts before final harvest. When total stand regeneration potential is adequate, stump sprouts often develop into stems of

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Fig. 8.15.  (A) An unthinned 5-year-old northern red oak sprout clump. The ten or more living stems completely envelop the base of the 95-year-old parent tree. (B) Thinning clumps early to one stem (shown here at age 5) maximizes diameter growth by eliminating within-clump competition. This practice also eliminates basal fusion between companion stems and reduces the incidence of decay, seams and sweep. Selected crop stems should be attached to the parent tree at or below the ground line. At this age, most stem quality problems associated with oak stump sprouts have not yet developed. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

Chapter 8

satisfactory quality because overall stand density promotes natural pruning and minimizes epicormic branching on boles (Fig. 8.12). Although clump density in unthinned clumps is often high enough to produce at least one stem of potentially high quality (Lamson, 1976), trees around the clump must be relied upon to control epicormic branching and thus crop stem quality after clump thinning. Clump thinning slows natural pruning to some extent (Lamson, 1983). Clear-bole length of thinned sapling-size sprouts of northern red oak 5 years after thinning averaged about 2 ft less than unthinned clumps. However, a combination of clump thinning and manual pruning can off-set losses in clear-bole length associated with thinning alone. Low stand density or excessive crop tree release also may result in epicormic branching of boles. However, species differ in their propensity to produce epicormic branches. Northern red oak and white oak produce large numbers of epicormic branches whereas red maple produces relatively few; American basswood is intermediate between the oaks and red maple (Trimble and Seegrist, 1973). Clump thinning to one or two stems did not increase epicormic branching of sapling-size northern red oak stump sprouts in West Virginia (Lamson, 1983). Epicormic branching also tends to be greater among intermediate and suppressed than among codominant and dominant trees (Trimble and Seegrist, 1973). Clumps that show little propensity for epicormic branching before thinning are the preferred candidates for clump thinning (Ward, 1966). Epicormic branching of boles may appear to be more damaging than it actually is for the following reasons: ●● Most sprouting occurs on the upper stem, but the greatest volume and value of the tree are usually in the lower log. ●● Intermediate and overtopped trees sprout the most, but the most valuable trees are dominant and codominant. ●● Many small epicormic branches are short lived and therefore do not cause significant degrade (Trimble and Seegrist, 1973). weak basal attachment. 

Despite care in selecting crop stems that originate low and thus appear to have sound basal attachments, some of those stems may eventually break off at the base. In the overall population of coppice stands, the most common cause of stem mortality (not to be confused with

Even-aged Silvicultural Methods

clump mortality) is stem breakage at the point where a stem is attached to the stump (Lamson, 1983). In part, this is a natural consequence of clump self-thinning. However, the unique characteristics of coppice-origin stems produce a self-thinning dynamic unlike that occurring between individual trees. For example, stems within sprout clumps often break because of constricted vascular connections caused by included stump bark, which can girdle the growing sprout (Wilson, 1968). Although the attachments may appear to be strong when viewed externally, the interior vascular connections may be small and weak. Although there is no practical way to non-destructively identify stems with this defect, loss of selected crop stems due to such breakage appears to be a minor problem in most oak coppice stands (Johnson and Rogers, 1980; Lamson, 1983). Before thinning oak sprout clumps, factors related to the timing of thinning, residual clump density, stand selection, crop stem selection and overall stand density should be considered according to the following general guidelines. timing of thinning and residual clump density.  Thin to one or two stems per clump as early as practical and preferably before age 10 (Fig. 8.15B). stand selection. 

Select stands that are on average or better sites that are well stocked; neither poor sites nor poorly stocked stands will yield the high value trees needed to justify the expenditure of precommercial thinning. Thin clumps before crop stems reach 3 inches dbh (about age 15 in many upland eastern forests) and preferably before they reach 2 inches dbh (about age 10). Beyond 3 inches dbh, options in crop stem selection are few and most of the potential growth gain from clump thinning is lost.

crop stem selection. 

In clumps with potential crop stems 3 inches dbh or less, select as the crop stem the largest single stem that is well attached to the stump at or below the ground line and that is not connected to another stem. For stems between 2 and 3 inches dbh, discriminate against those with sweep, crook and seams. For smaller stems, consideration of stem quality factors is probably neither necessary nor meaningful. If two crop stems are retained, the stems should be far enough apart so that during their expected life span they do not fuse together at a common base (Roth, 1956; Stroempl, 1983) (Fig. 8.13C and D).

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For stems larger than 3 inches dbh, the lower 17  ft should: (i) be free of sweep, crook, seams, forks and visible decay; (ii) be free of V-shaped connections with other stems; and (iii) show little evidence of epicormic branching. These factors being equal, select the stem with the fewest branches in the lower bole. thinning around crop stems. 

Growth and quality can be optimized by thinning around crop stems to provide adequate growing space but at the same time leaving enough shade to minimize epicormic branching and forking, and to promote the development of straight lower boles. Appropriate thinning radii can be derived from known growing space requirements for each species (Chapter 6, this volume). Subsequent thinnings (e.g. at 10-year intervals) around each crop stem will be required to maintain near-maximum growth throughout the rotation. Such crop-tree thinning also can be integrated with area-wide thinning to obtain a relatively uniform overall stand density (Rogers and Johnson, 1985). Despite the potential tree growth and quality advantages provided by clump thinning, economic returns might not justify the practice. For example, an economic analysis of a mixed oak stand in the Ozark Highlands (oak site index 63 ft) indicated that the projected response of sprout clumps to thinning to one stem at age 5 was not economically feasible when costs were carried to the end of a 60-year rotation (Dwyer et al., 1993).

Economic, Environmental and Social Considerations The clearcutting method Despite its simplicity and economic and technical efficacy, clearcutting has generally not received public acceptance (Bliss, 2000). The method also has received criticism for allegedly scientific reasons, especially with respect to issues related to forest fragmentation, endangered habitats, soil erosion and long-term decline in forest productivity (see ‘Effects of harvesting on site productivity’ in Chapter 4, this volume). In addition to being perceived as unattractive in appearance, clearcutting is widely believed to accelerate soil erosion and runoff, and to increase stream sedimentation. Probably no other silvicultural practice has received more public condemnation than clearcutting. This social reaction to clearcutting, in turn, has led to the

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widespread abandonment of clearcutting in favour of often-misapplied uneven-aged silvicultural methods. Ironically, clearcutting’s rise to prominence during the late 1960s and 1970s began with dissatisfaction with uneven-aged silviculture. Disappointment with single-tree selection, combined with early regeneration successes with clearcutting, ushered in the clearcutting era in the Central Hardwood Region in the 1960s. The practice was reinforced by the seminal publication Even-aged Silviculture for Upland Central Hardwoods by Roach and Gingrich (1968). This publication represented a synthesis of years of even-aged silvicultural research and experience. The shift from uneven-aged to evenaged silviculture thus was not borne of inexperience with alternative silvicultural systems, nor the absence of a scientific basis for the technical efficacy of evenaged silviculture. Silviculturists for several reasons enthusiastically accepted clearcutting. In the Central Hardwood Region, it met the ecological requirements of the commercially valuable, shade-intolerant species, including the oaks, white ash, black cherry and yellow-poplar. Clearcutting was also economically efficient. Logging, road building and administrative costs were minimized because large timber volumes could be harvested from relatively small areas. Other factors contributing to the acceptance of clearcutting among forest managers included its endorsement by wildlife biologists, the development of even-aged stocking guides and prescriptions for its application (Gingrich, 1967; Roach and Gingrich, 1968; Roach, 1977), the simplicity of creating balanced distributions of stand age classes, and other perceived advantages (McGee, 1987). Clearcutting thus became the most widely recommended and applied regeneration method in the region, albeit with mixed success in oak forests. In oak forests, it was most successful in the drier ecosystems such as the Ozark Highlands of Missouri. There, the method’s success coincided with the natural occurrence of large pre-established oak seedling sprouts (Sander, 1971; Sander and Clark, 1971; Sander et al., 1984; Johnson, 1993). The method was less successful in regenerating oaks in the more productive ecosystems of the Ohio Valley, Appalachians and elsewhere (Gammon et al., 1960; Johnson, 1976; Loftis, 1983b; Beck and Hooper, 1986). Even there, other commercially valuable species usually replaced the oaks. Clearcutting none the less often accelerated the succession of oak-dominated stands to stands with less oak or no oak (Abrams and Downs, 1990; Abrams and Nowacki, 1992).

Chapter 8

The rise of environmental activism in the 1970s focused public attention on real or perceived negative consequences of clearcutting, including diminished aesthetic value, biodiversity, old growth, certain wildlife values, and soil and water values. To counter these concerns, clearcutting was modified in various ways: (i) size of clearcuts was reduced; (ii) snags, cull trees and uncut islands of trees were retained to enhance wildlife and aesthetic values; (iii) the removal of non-commercial residual trees was deferred; (iv) cuts were shaped to fit more aesthetically into the landscape; and (v) uncut strips were left where clearcuts bordered roads, lakes, streams and other sensitive areas (USDA Forest Service, 1973, 1980; Evans and Conner, 1979; Smith et al., 1997) (see also Chapter 13, this volume). Despite these modifications, clearcutting continued to fall into public disfavour. The public demanded systems that focused less on producing commodities and more on preserving aesthetics, biodiversity and other intangible values (Perry and Maghembe, 1989; Salwasser, 1990; Gale and Cordray, 1991; Hansen et al., 1991; Kessler, 1991). Attitudes towards clearcutting nevertheless may be more complex than commonly realized. For example, in a survey of non-industrial private forest (NIPF) owners in the Mid-South, nearly half of the respondents considered clearcutting to be an acceptable practice on NIPF lands, whereas only 14% considered the practice acceptable on governmentowned lands (Bliss et al., 1997). With respect to its application on private lands, a significantly larger proportion of college-educated respondents (52%) considered it an acceptable practice than did less educated respondents (42%). Moreover, the responses of NIPF owners were similar to those of the general public (Bliss et al., 1994). Despite the use of clearcutting as a possible solution to many regeneration problems, the widespread adoption of clearcutting itself produced unanticipated social and political consequences. One outcome was the ‘Monongahela Decision’ of 1970, which imposed uneven-aged silviculture as the primary management tool on the Monongahela National Forest in West Virginia. The decision reinforced organized intervention by the critics of clearcutting, which resulted in a major reduction in its application on all the National Forests. But it also had a wider effect. As Hicks (1997) noted: ‘It imposed the will of the public over that of foresters, who had always been regarded as the experts in forest resource management.’ The solution to an innocent silvicultural problem thus spiralled into the more

Even-aged Silvicultural Methods

profound problem of determining who plays a dominant role in decisions about silvicultural practices on public forests. The clearcutting controversy has demonstrated that solutions to silvicultural problems are likely to transcend the scientific and technical approaches familiar to foresters. Foresters (especially those managing public lands) no longer have exclusive control of their domain. They now must engage in public dialogue on diverse, and often competing, forest uses. Effective dialogue requires recognizing and considering widely differing perspectives of forest values, some of which may be anathema to traditional forestry theory and practice. The emergence of this value-centred debate has precipitated a profound change in silvicultural practice: the replacement of the economically focused sustainable timber yield paradigm by the more ecologically centred sustainable forest paradigm. However, it may not be the abandonment of clearcutting that characterizes the new paradigm as much as it represents a search for a wider range of methods for meeting diverse social, economic and environmental demands that are unprecedented. The contemporary social complexity of silvicultural issues extends in part from an increasingly comprehensive human view of forests as a part of the ‘commons’, which includes not only trees but the air, water, soil, settings for human spiritual renewal, and other values that society demands from forests (Walter and Johnson, 2004). Accordingly, it might seem appropriate, especially in a democratic society, to form forestry policy around popular perceptions of what should comprise the forest commons. Although this strategy might assure the implementation of socially acceptable practices, it may not produce socially desirable outcomes. A forest based solely on a publicly agreed-upon visualization may be no more sustainable than the one the public earlier disagreed with. In developing and recommending silvicultural practices, forest managers therefore could benefit from a better understanding of how social views of the forested landscape evolve. A first step towards this goal would be to recognize that the personal idealization of nature, the human landscape experience, and the dislike of disruption and change are all parts of human culture. Only when silviculture is given its full definition of comprising more than scientific and technical solutions can a socially desirable and socially acceptable framework for its application be formulated. Perhaps the greatest challenge to silviculturists and forest

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­ anagers today is to define the line that divides these m two goals. If nothing else, the clearcutting controversy has brought this social, scientific and technical dilemma into focus. The shelterwood and seed tree methods The uniform shelterwood and seed tree methods suffer from much the same social drawbacks as clearcutting; that is, regenerated stands look much like clearcuts after the final shelterwood cut or, in the case of clearcuts with reserves or seed trees, few trees per acre remain. Perhaps as Murphy and others (1993) suggested, the shelterwood method suffers from a ‘guilt of association’ with other even-­aged methods. The method is none the less potentially flexible because shelterwoods can be retained, at least hypothetically, until a two-aged stand develops (Beck, 1991; Smith et al., 1997). In theory, a shelterwood can be removed in steps that are so gradual that eventually the regenerated stand developing beneath it grows up and becomes indistinguishable from the shelterwood before the latter is completely removed. The resulting ‘irregular shelterwood’ thus may have more aesthetic appeal than the regular shelterwood or clearcutting methods. The method is also potentially applicable to stands dedicated to acorn production and other biodiversity objectives as discussed above.

References Abrams, M.D. (1992) Fire and the development of oak forests. BioScience 42, 346–353. https://doi. org//10.2307/1311781 Abrams, M.D. (2005) Prescribing fire in eastern oak forests: is time running out? Northern Journal of ­ Applied Forestry 22, 190–196. https://doi.org//10.1093/ njaf/22.3.190 Abrams, M.D. and Downs, J.A. (1990) Successional replacement of old-growth white oak by mixed mesophytic hardwoods in southwestern Pennsylvania. Canadian Journal of Forest Research 20, 1864–1870. https://doi.org//10.1139/x90-250 Abrams, M.D. and Nowacki, G.J. (1992) Historical variation in fire, oak recruitment, and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119, 19–28. https://doi. org/­10.2307/2996916 Abrams, M.D. and Nowacki, G.J. (2008) Native Americans as active and passive promoters of mast and fruit trees in the eastern USA. The Holocene 18, 1123–1137. https://doi.org//10.1177/0959683608095581

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Allen, J.A., Keeland, B.D., Stanturf, J.A., Clewell, A.F. and Kennedy, H.E., Jr (2001) A guide to bottomland hardwood restoration. USDA Forest Service General Technical Report SRS-40 (revised 2004). USDA Forest Service, Southern Research Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/ treesearch/pubs/2813 (accessed 1 July 2018). Beck, D.E. (1991) The shelterwood method, a research perspective. In: USDA Forest Service Proceedings 1990 Genetics/Silviculture Workshop. USDA Forest Service, Washington, DC, pp. 252–258. Beck, D.E. and Hooper, R.M. (1986) Development of a southern Appalachian hardwood stand after clearcutting. Southern Journal of Applied Forestry 10, 168–172. https://doi.org//10.1093/sjaf/10.3.168 Bliss, J.C. (2000) Public perceptions of clearcutting. Journal of Forestry 98(12), 4–9. https://doi.org//10.1093/ jof/98.12.4 Bliss, J.C., Nepal, S.K., Brooks, R.T., Jr and Larsen, M.D. (1994) Forestry community or granfalloon? Journal of Forestry 92(9), 6–10. https://doi.org//10.1093/jof/92.9.6 Bliss, J.C., Nepal, S.K., Brooks, R.T., Jr and Larsen, M.D. (1997) In the mainstream: environmental attitudes of Mid-South forest owners. Southern Journal of Applied Forestry 21, 37–43. https://doi.org//10.1093/sjaf/21.1.37 Bowling, D.R. and Kellison, R.C. (1983) Bottomland hardwood stand development following clearcutting. Southern Journal of Applied Forestry 7, 110–116. https://doi.org//10.1093/sjaf/7.3.110 Braun, E.L. (1972) Deciduous Forests of Eastern North America. Hafner, New York. Broadfoot, W.M. and Williston, J.H. (1973) Flooding effects on southern forests. Journal of Forestry 71(9), 584–587. https://doi.org//10.1093/jof/71.9.584 Brose, P.H. and Van Lear, D.H. (1998a) Effects of seasonal prescribed fires on hardwood advance regeneration in shelterwood stands. USDA Forest Service General Technical Report SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 310–314. Available at: https://www.fs.usda. gov/treesearch/pubs/2481 (accessed 1 July 2018). Brose, P.H. and Van Lear, D.H. (1998b) Responses of hardwood advance regeneration to seasonal prescribed fires in oak-dominated shelterwood stands. Canadian Journal of Forest Research 28, 331–339. https://doi.org//10.1139/x97-218 Brose, P.H., Van Lear, D.H. and Keyser, P.D. (1999) A shelterwood-burn technique for regenerating productive upland oak sites in the Piedmont Region. Southern Journal of Applied Forestry 23, 158–163. Available at: https://www.fs.usda.gov/treesearch/pubs/2146 (accessed 1 July 2018). Brose, P.H., Gottschalk, K.W., Horsley, S.B., Knopp, P.D., Kochenderfer, J.N., McGuinness, B.J., Miller, G.W., Ristau, T.E., Stoleson, S.H. and Stout, S.L. (2008) Prescribing regeneration treatments for mixed-oak forests in the Mid-Atlantic region. USDA Forest

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Service General Technical Report NRS-33. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 100 pp. https://doi.org//10.2737/ NRS-GTR-33 Bundy, P.P., Alm, A.A. and Baughman, M.J. (1991) Red oak regeneration following scarification and harvesting: a case study. Northern Journal of Applied Forestry 8, 173–174. https://doi.org//10.1093/njaf/8.4.173 Butin, H. and Shigo, A.L. (1981) Radial shakes and ‘frost cracks’ in living oak trees. USDA Forest Service Research Paper NE-478. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs. usda.gov/treesearch/pubs/15026 (accessed 1 July 2018). Carpenter, S.B. (1976) Stand structure of a forest in the Cumberland Plateau of eastern Kentucky fifty years after logging and burning. Castanea 41, 325–336. Carvell, K.L. (1971) Silvicultural aspects of intermediate cuttings. In: USDA Forest Service Northeastern Forest Experiment Station Oak Symposium Proceedings. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 60–64. Available at: https://www.fs.usda.gov/treesearch/ pubs/14478 (accessed 1 July 2018). Clark, F.B. and Boyce, S.G. (1964) Yellow-poplar seed remains viable in the forest litter. Journal of Forestry 62, 564–567. https://doi.org//10.1093/jof/62.8.564 Clatterbuck, W.K. (1998) Use of prescribed fire to promote oak regeneration. USDA Forest Service General Technical Report SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 315–318. Available at: https://www.fs.usda.gov/ treesearch/pubs/2481 (accessed 1 July 2018). Clatterbuck, W.K. (2015) An evaluation of the hardwood regeneration model (regen) 16 years post-harvest of a regenerated stand in East Tennessee. USDA Forest Service General Technical Report SRS-203. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 352–357. Available at: https:// www.fs.usda.gov/treesearch/pubs/47635 (accessed 1 July 2018). Clatterbuck, W.K. and Hodges, J.D. (1988) Development of cherrybark oak and sweet gum in mixed, evenaged bottomland stands in central Mississippi, USA. Canadian Journal of Forest Research 18, 12–18. https://doi.org//10.1139/x88-003 Cline, A.C. and Lockard, C.R. (1925) Mixed white pine and hardwood. Harvard Forest Bulletin 8. Harvard University, Harvard Forest, Petersham, Massachusetts. Congdon, L.D. (1993) Natural regeneration of oak in northern lower Michigan. Msc. thesis, University of Missouri, Columbia, Missouri. Crow, T.R. (1992) Population dynamics and growth patterns for a cohort of northern red oak (Quercus rubra) seedlings. Oecologia 91, 192–200. https://doi. org//10.1007/BF00317783

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Dale, M.E. (1972) Growth and yield predictions for upland oak stands 10 years after initial thinning. USDA Forest Service Research Paper NE-241. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/23656 (accessed 1 July 2018). Dale, M.E. and Sonderman, D.L. (1984) Effect of thinning on growth and potential quality of young white oak crop trees. USDA Forest Service Research Paper NE-539. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/15090 (accessed 1 July 2018). DeBell, D.S., Langdon, O.G. and Stubbs, J. (1968) Reproducing mixed hardwoods by a seed-tree cutting in the Carolina Coastal Plain. Southern Lumberman 217, 121–123. Dey, D.C. (1991) A comprehensive Ozark regenerator. PhD dissertation, University of Missouri, Columbia, Missouri. Dey, D.C. (2014) Sustaining oak forests in eastern North America: regeneration and recruitment, the pillars of sustainability. Forest Science 60, 926–942. https:// doi.org//10.5849/forsci.13-114 Dey, D.C. and Parker, W.C. (1996) Regeneration of red oak (Quercus rubra L.) using shelterwood systems: ecophysiology, silviculture and management recommendations. Ontario Forestry Research Institutute Forest Research Information Paper 126. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs. usda.gov/treesearch/pubs/12423 (accessed 1 July 2018). Dey, D.C. and Parker, W.C. (1997) Overstory density affects field performance of underplanted red oak (Quercus rubra L.) in Ontario. Northern Journal of Applied Forestry 14, 120–125. Available at: https:// www.fs.usda.gov/treesearch/pubs/12408 (accessed 1 July 2018). Dey, D.C., Johnson, P.S. and Garrett, H.E. (1996a) Modeling the regeneration of oak stands in the Missouri Ozark Highlands. Canadian Journal of Forest Research 26, 573–583. https://doi.org//10.1139/ x26-066 Dey, D.C., Ter-Mikaelian, M., Johnson, P.S. and Shifley, S.R. (1996b) Users guide to acorn: a comprehensive Ozark regeneration simulator. USDA Forest Service General Technical Report NC-180. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10244 (accessed 1 July 2018). Dwyer, J.P., Dey, D.C. and Kurtz, W.B. (1993) Profitability of precommercially thinning oak stump sprouts. Northern Journal of Applied Forestry 10, 179–183. https://doi.org//10.1093/njaf/10.4.179 Evans, K.E. and Conner, R.N. (1979) Snag management. USDA Forest Service General Technical Report NC51. USDA Forest Service, North Central Forest

327

Experiment Station, St Paul, Minnesota, pp. 214–225. Available at: https://www.fs.usda.gov/treesearch/ pubs/10116 (accessed 1 July 2018). Fei, S., Gould, P.J., Steiner, K.C. and Finley, J.C. (2006) Aggregate height – a composite variable to predict early-stage mixed-oak stand development. Forest Ecology and Management 223, 336–341. https://doi. org//10.1016/j.foreco.2005.11.014 Fei, S., Steiner, K.C. and Finley, J.C. (2007) Stocking equations for regeneration in mixed oak stands. USDA General Technical Report SRS 101. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 55–59. Available at: https://www. fs.usda.gov/treesearch/pubs/27751 (accessed 1 July 2018). Franklin, J.F., Perry, D.A., Schowalter, T.D., Harmon, M.E., McKee, A. and Spier, T.A. (1989) Importance of ecological diversity in maintaining long-term site productivity. In: Perry, D.A., Meurisse, R., Thomas, B., Miller, R., Boyle, J. and Means, J. (eds) Maintaining the Long-term Productivity of Pacific Northwest Forest Ecosystems. Timber Press, Portland, Oregon, pp. 82–97. Gale, R.P. and Cordray, S.M. (1991) What should forests sustain? Eight answers. Journal of Forestry 89(5), 31–36. https://doi.org//10.1093/jof/89.5.31 Gammon, A.D., Rudolph, V.J. and Arend, J.L. (1960) Regeneration following clearcutting of oak during a seed year. Journal of Forestry 58, 711–715. https:// doi.org//10.1093/jof/58.9.711 Gardiner, E.S. and Helmig, L.M. (1997) Development of water oak stump sprouts under a partial overstory. New Forests14,55–62.https://doi.org//10.1023/A:1006502107495 Gevorkiantz, S.R. (1957) Site index curves for red oak in the Lake States. USDA Forest Service Lake States Forest Experiment Station Technical Note 485. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gevorkiantz, S.R. and Scholz, H.F. (1948) Timber yields and possible returns from the mixed-oak farmwoods of southwestern Wisconsin. USDA Forest Service Lake States Forest Experiment Station Publication 521. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org//10.1093/forestscience/13.1.38 Gingrich, S.F. (1971) Management of young and intermediate stands of upland hardwoods. USDA Forest Service Research Paper NE-195. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/23703 (accessed 1 July 2018). Golden, M.S. (1993) Establishment of regeneration in small patch clearcut openings in an Alabama River floodplain forest. USDA Forest Service General Technical Report SO-93. USDA Forest Service,

328

Southern Forest Experiment Station, New Orleans, Louisiana, pp. 161–167. Available at: https://doi. org/10.2737/SO-GTR-93 (accessed 1 July 2018). Golden, M.S. and Loewenstein, E.F. (1991) Regeneration of tree species 7 years after clearcutting in a river bottom in central Alabama. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 76–83. https:// doi.org//10.2737/SE-GTR-70 Gottschalk, K.W. (1994) Shade, leaf growth and crown development of Quercus rubra, Quercus velutina, Prunus serotina and Acer rubrum seedlings. Tree Physiology 14, 735–749. https://doi.org//10.1093/ treephys/14.7-8-9.735 Gresham, C.A. (1985a) Clearcutting is not enough for early establishment of desirable species in Santee River swamp. Southern Journal of Applied Forestry 9, 52–54. https://doi.org//10.1093/sjaf/9.1.52 Gresham, C.A. (1985b) Pine and hardwood regeneration alternatives for harvested bottomland hardwood stands. USDA Forest Service General Technical Report SO-54. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 87–92. Available at: https://doi.org/10.2737/SO-GTR-54 (accessed 1 July 2018). Groninger, J.W. and Long, M. (2008) Oak ecosystem management considerations for Central Hardwood stands arising from silvicultural clearcutting. Northern Journal of Applied Forestry 25, 173–179. https://doi. org//10.1093/njaf/25.4.173 Hannah, P.R. (1987) Regeneration methods for oaks. Northern Journal of Applied Forestry 4, 97–101. https://doi.org//10.1093/njaf/4.2.97 Hansen, A.J., Spies, T.A., Swanson, F.J. and Ohmann, J.L. (1991) Conserving biodiversity in managed forests. BioScience 41, 382–392. https://doi.org//10.2307/ 1311745 Hanson, P.J., Isebrands, J.G. and Dickson, R.E. (1987) Carbon budgets for Quercus rubra L. seedlings at selected stages of growth: influence of light. In: Proceedings of the 6th Central Hardwood Forest Conference. University of Tennessee, Knoxville, Tennessee, pp. 269–276. Harper, J.L. (1977) Population Biology of Plants. Academic Press, London. Healy, W.M. (1991) Trends in management of oak forests for wild turkeys. In: Proceedings of the Oak Resource in the Upper Midwest. University of Minnesota, St Paul, Minnesota, pp. 45–55. Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Hibbs, D.E. (1981) Development of the forest canopy – a key to silviculture. Northern Logger and Timber Processor (May), 62–78. Hibbs, D.E. and Bentley, W.R. (1983) A management guide for oak in New England. University of Connecticut

Chapter 8

Cooperative Extension Bulletin 83-12. Cooperative Extension Service, College of Agriculture and Natural Resources, The University of Connecticut, Storrs, Connecticut. Hibbs, D.E. and Bentley, W.R. (1984) A growth model for red oak in New England. Canadian Journal of Forest Research 14, 250–254. https://doi.org//10.1139/x84-048 Hicks, R.R., Jr (1997) A resource at the crossroads: a history of the central hardwoods. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 1–22. Available at: https://www.fs.usda.gov/treesearch/pubs/15603 (accessed 1 July 2018). Hill, J.P. and Dickmann, D.I. (1988) A comparison of three methods for naturally reproducing oak in southern Michigan. Northern Journal of Applied Forestry 5, 113–117. https://doi.org//10.1093/njaf/5.2.113 Hilt, D.E. (1985a) Species composition of young Central Hardwood stands that develop after clearcutting. In: Proceedings of the 5th Central Hardwood Forest Conference. University of Illinois, ChampaignUrbana, Illinois, pp. 11–14. Hilt, D.E. (1985b) Where has all my yellow-poplar gone? Northern Journal of Applied Forestry 2, 67–69. https://doi.org//10.1093/njaf/2.3.67a Hilt, D.E. and Dale, M.E. (1979) Stem form changes in upland oaks after thinning. USDA Forest Service Research Paper NE-433. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/14967 (accessed 1 July 2018). Horsley, S.B. (1982) Development of reproduction in Allegheny hardwood stands after herbicide-clearcuts and herbicide-shelterwood cuts. USDA Forest Service Research Note NE-308. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/14479 (accessed 1 July 2018). Horsley, S.B. (1991) Using Roundup and Oust to control interfering understories in Allegheny hardwood stands. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 281–290. Available at: https://www.fs.usda.gov/ treesearch/pubs/13505 (accessed 1 July 2018). Horsley, S.B. and Marquis, D.A. (1983) Interference by weeds and deer with Allegheny hardwood reproduction. Canadian Journal of Forest Research 13, 61–69. https://doi.org//10.1139/x83-009 Hosner, J.F. and Boyce, S.G. (1962) Tolerance to water saturated soil of various bottomland hardwoods. Forest Science 8, 180–186. https://doi.org//10.1093/ forestscience/8.2.180 Hosner, J.F. and Minckler, L.S. (1960) Hardwood reproduction in the river bottoms of southern Illinois. Forest Science 6, 67–77. https://doi.org//10.1093/forestscience/6.1.67

Even-aged Silvicultural Methods

Hosner, J.F. and Minckler, L.S. (1963) Bottomland hardwood forests of southern Illinois – regeneration and succession. Ecology 44, 29–41. https://doi.org//10.2307/1933178 Isebrands, J.G. and Crow, T.R. (1985) Techniques for rooting juvenile softwood cuttings of northern red oak. In: Proceedings of the 5th Central Hardwood Forest Conference. University of Illinois, ChampaignUrbana, Illinois, pp. 228–233. Jacobs, R.D. and Wray, R.D. (1992) Managing oak in the Driftless Area. Minnesota Extension Service Bulletin NR-BU-5900-S. Minnesota Extension Service, University of Minnesota Natural Resources, St Paul, Minnesota. Jenkins, M.A. and Parker, G.R. (1998) Composition and diversity of woody vegetation in silvicultural openings of southern Indiana forests. Forest Ecology and Management 109, 57–74. https://doi.org//10.1016/ S0378-1127(98)00256-4 Johnson, P.S. (1974) Survival and growth of northern red oak seedlings following a prescribed burn. USDA Forest Service Research Note NC-177. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11354 (accessed 1 July 2018). Johnson, P.S. (1975a) Growth and structural development of red oak sprout clumps. Forest Science 21, 413–418.https://doi.org//10.1093/forestscience/21.4.413 Johnson, P.S. (1976) Modal development of regeneration in clearcut red oak stands in the Driftless Area. In: Proceedings of the 1st Central Hardwood Forest Conference. University of Southern Illinois, Carbondale, Illinois, pp. 455–475. Johnson, P.S. (1992) Oak overstory/reproduction relations in two xeric ecosystems in Michigan. Forest Ecology and Management 48, 233–248. https://doi. org//10.1016/0378-1127(92)90147-2 Johnson, P.S. (1993) Perspectives on the ecology and silviculture of oak-dominated forests in the central and eastern states. USDA Forest Service General Technical Report NC-153. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10217 (accessed 1 July 2018). Johnson, P.S. (1994) How to manage oak forests for acorn production. USDA Forest Service Technical Brief TB-NC-1. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/ pubs/10998 (accessed 1 July 2018). Johnson, P.S. and Rogers, R. (1980) Predicting growth of individual stems within northern red oak sprout clumps. In: Proceedings of the 3rd Central Hardwood Forest Conference. University of Missouri, Columbia, Missouri, pp. 420–439. Johnson, P.S. and Rogers, R. (1984) Predicting 25thyear diameters of thinned stump sprouts of northern red oak. Journal of Forestry 82, 616–619. https://doi. org//10.1093/jof/82.10.616

329

Johnson, P.S. and Sander, I.L. (1987) Quantifying regeneration potentials of Quercus forests in the Missouri Ozarks. USDA Forest Service General Technical Report NC-120, Vol. 1. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 377–385. Available at: https://www.fs.usda.gov/ treesearch/pubs/10184 (accessed 1 July 2018). Johnson, P.S., Jacobs, R.D., Martin, A.J. and Godell, E.D. (1989) Regenerating northern red oak: three successful case histories. Northern Journal of Applied Forestry 6, 174–178. https://doi.org//10.1093/njaf/6.4.174 Johnson, R.L. (1975b) Natural regeneration and development of Nuttall oak and associated species. USDA Forest Service Research Paper SO-104. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www. fs.usda.gov/treesearch/pubs/43015 (accessed 1 July 2018). Johnson, R.L. and Deen, R.T. (1993) Prediction of oak regeneration in bottomland forests. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 146–155. Available at: https://doi.org/10.2737/SE-GTR-84 (accessed 1 July 2018). Johnson, R.L. and Krinard, R.M. (1983) Development of seven hardwood species in small forest openings, 22-year results. Southern Journal of Applied Forestry 7, 153–156. Available at: https://www.fs.usda.gov/ treesearch/pubs/43007 (accessed 1 July 2018). Johnson, W.C. and Webb, T. III (1989) The role of blue jays (Cyanocitta cristata L.) in the postglacial dispersal of fagaceous trees in eastern North America. Journal of Biogeography 16, 561–571. https://doi. org/10.2307/2845211 Kabrick, J.M., Zenner, E.K., Dey, D.C., Gwaze, D. and Jensen, R.G. (2008) Using ecological land types to examine landscape-scale oak regeneration dynamics. Forest Ecology and Management 255, 3051–3062. https://doi.org//10.1016/j.foreco.2007.09.068 Kabrick, J.M., Dey, D.C., Van Sambeek, J.W., Coggeshall, M.V. and Jacobs, D.F. (2012) Quantifying flooding effects on hardwood seedling survival and growth for bottomland restoration. New Forests 43, 695–710. https://doi.org//10.1007/s11056-012-9331-y Kabrick, J.M., Villwock, J.L., Dey, D.C., Keyser, T.L. and Larsen, D.R. (2014) Modeling and mapping oak advance reproduction density using soil and site variables. Forest Science 60, 1107–1117. https://doi. org//10.5849/forsci.13-006 Kessler, W.B. (1991) New perspectives for managing forested lands: keeping pace with society. Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 34–38. Keyser, P.D., Brose, P.H., Van Lear, D. and Burtner, K.M. (1996) Enhancing oak regeneration with fire in shelterwood stands: preliminary trials. In: Transactions of

330

the 61st North American Wildlife and Natural Resource Conference. Wildlife Management Institute, Washington, DC, pp. 215–219. Koenig, W.D. (1990) Oaks, acorns and the acorn woodpecker. Fremontia 18, 77–79. Korstian, C.F. (1927) Factors controlling germination and early survival in oaks. Yale University School of Forestry Bulletin 19. Yale University, New Haven, Connecticut. Kuntz, J.E. and Drake, C.R. (1957) Tree wounds and longdistance spread of oak wilt. Phytopathology 47, 22. Lamson, N.I. (1976) Appalachian hardwood stump sprouts are potential sawlog crop trees. USDA Forest Service Research Note NE-229. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/19267 (accessed 1 July 2018). Lamson, N.I. (1983) Precommercial thinning increases diameter growth of Appalachian hardwood stump sprouts. Southern Journal of Applied Forestry 7 (May), 93–97. https://doi.org//10.1093/sjaf/7.2.93 Lamson, N.I. and Smith, H.C. (1978) Response to croptree release: sugar maple, red oak, black cherry, and yellow-poplar saplings in a 9-year-old stand. USDA Forest Service Research Paper NE-394. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/14647 (accessed 1 July 2018). Leak, W.B. (1981) Do stocking guides in the eastern United States relate to stand growth? Journal of Forestry 79, 661–664. https://doi.org//10.1093/ jof/­79.10.661 Liming, F.G. and Johnston, J.P. (1944) Reproduction in oak–hickory forest stands of the Missouri Ozarks. Journal of Forestry 42, 175–180. https://doi.org//­ 10.1093/jof/42.3.175 Lockhart, B.R., Gardiner, E., Leininger, T. and Stanturf, J. (2008) A stand-development approach to oak afforestation in the Lower Mississippi Alluvial Valley. Southern Journal of Applied Forestry 32, 120–129. Available at: https://www.fs.usda.gov/treesearch/ pubs/30765 (accessed 1 July 2018). Loftis, D.L. (1983a) Regenerating southern Appalachian mixed hardwood stands with the shelterwood method. Southern Journal of Applied Forestry 7, 212–217. https://doi.org//10.1093/sjaf/7.4.212 Loftis, D.L. (1983b) Regenerating red oak on productive sites in the southern Appalachians: a research approach. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 144–150. Available at: https://doi.org/10.2737/ SE-GTR-24 (accessed 1 July 2018). Loftis, D.L. (1988) Regenerating oaks on high-quality sites, an update. Society of American Foresters Publication

Chapter 8

88-03. Society of American Foresters, Bethesda, Maryland, pp. 199–209. Loftis, D.L. (1989) Species composition of regeneration after clearcutting Southern Appalachian hardwoods. In: Miller, J.H. (ed.) Proceedings of the 5th biennial southern silvicultural research conference. USDA Forest Service General Technical Report SO-74. USDA Forest Service, Southern Experiment Station, New Orleans, Louisiana, pp. 253–257. Available at: https://www.fs.usda.gov/treesearch/pubs/33428 (accessed 1 July 2018). Loftis, D.L. (1990a) Predicting post-harvest performance of advance red oak reproduction in the southern Appalachians. Forest Science 36, 908–916. Available at: https://www.fs.usda.gov/treesearch/pubs/1148 (accessed 1 July 2018). Loftis, D.L. (1990b) A shelterwood method for regenerating red oak in the southern Appalachians. Forest Science 36, 917–929. Available at: https://www.fs.usda.gov/ treesearch/pubs/1146 (accessed 1 July 2018). Lorimer, C.G. (1989) The oak regeneration problem: new evidence on causes and possible solutions. University of Wisconsin Forestry Research Analyses 8. University of Wisconsin-Madison, Madison, Wisconsin. Margalef, R. (1963) On certain unifying principles in ecology. American Naturalist 97, 357–374. https://doi. org//10.1086/282286 Margalef, R. (1968) Perspectives in Ecological Theory. University of Chicago Press, Chicago, Illinois. Marquis, D.A. (1974) The impacts of deer browsing on Allegheny hardwood regeneration. USDA Forest Service Research Paper NE-308. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15415 (accessed 1 July 2018). Marquis, D.A. (1981) Survival, growth, and quality of residual trees following clearcutting in Allegheny hardwood forests. USDA Forest Service Research Paper NE-477. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/15025 (accessed 1 July 2018). Marquis, D.A., Grisez, T.J., Bjorkbom, J.C. and Roach, B.A. (1975) Interim guide to regeneration of Allegheny hardwoods. USDA Forest Service General Technical Report NE-19. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/3965 (accessed 1 July 2018). Marquis, D.A., Ernst, R.L. and Stout, S.L. (1992) Prescribing silvicultural treatments in hardwood stands of the Alleghenies (revised). USDA Forest Service General Technical Report NE-96. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/4132 (accessed 1 July 2018).

Even-aged Silvicultural Methods

Martin, J., McGill, D. and Phibbs, A. (1992) Red oak regeneration in southwest Wisconsin. University of Wisconsin Forestry Research Note 279. University of Wisconsin-Madison, Madison, Wisconsin. McGee, C.E. (1987) Clearcutting in upland hardwoods: panacea or anathema? Proceedings of the 6th Central Hardwood Forest Conference. University of Tennessee, Knoxville, Tennessee, pp. 21–29. McGee, C.E. and Hooper, R.M. (1970) Regeneration after clearcutting in the southern Appalachians. USDA Forest Service Research Paper SE-70. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www. fs.usda.gov/treesearch/pubs/42333 (accessed 1 July 2018). McKevlin, M.R. (1992) Guide to regeneration of bottomland hardwoods. USDA Forest Service General Technical Report SE-76. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda. gov/treesearch/pubs/1139 (accessed 1 July 2018). McKnight, J.S., Hook, D.D., Langdon, O.G. and Johnson, R.L. (1981) Flood tolerance and related characteristics of trees of the bottomland forests of the southern United States. In: Clark, J.R. and Benforado, J. (eds) WetlandsofBottomlandHardwoodForests:Proceedings of a Workshop on Bottomland Hardwood Forest Wetlands of the Southeastern United States. Elsevier Science, New York, pp. 29–69. https://doi.org//10.1016/ B978-0-444-42020-6.50010-X McNab, W.H. and Avers, P.E. (1994) Ecological subregions of the United States: section descriptions. USDA Forest Service Administrative Publication WO-WSA-5. USDA Forest Service, Washington, DC. McQuilkin, R.A. (1974) Site index prediction table for black, scarlet, and white oaks in southeastern Missouri. USDA Forest Service Research Paper NC-108. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10629 (accessed 1 July 2018). Merritt, C. and Pope, P.E. (1991) The effect of environmental factors, including wildfire and prescribed burning, on the regeneration of oaks in Indiana. Purdue University Department of Forestry and Natural Resources Agricultural Experiment Station Bulletin 612. Department of Forestry and Natural Resources Agricultural Experiment Station, Purdue University, West Lafayette, Indiana. Motsinger, J.R., Kabrick, J.M., Dey, D.C., Henderson, D.E. and Zenner, E.K. (2010) Effect of midstory and understory removal on the establishment and development of natural and artificial pin oak advance reproduction in bottomland forests. New Forests 39, 195–213. https://doi.org//10.1007/s11056-009-9164-5 Murphy, P.A., Shelton, M.G. and Graney, D.L. (1993) Group selection – problems and possibilities for the

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more shade-intolerant species. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 229–247. Available at: https:// www.fs.usda.gov/treesearch/pubs/22211 (accessed 1 July 2018). Nix, L.E. and Lafaye, A. (1993) Successful regeneration of quality oaks in a red river bottomland stand of South Carolina. USDA Forest Service General Technical Report SO-93. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 81–85. Available at: https://doi.org/10.2737/ SO-GTR-93 (accessed 1 July 2018). Nowacki, G.J., Abrams, M.D. and Lorimer, C.G. (1990) Composition, structure, and historical development of northern red oak stands along an edaphic gradient in north-central Wisconsin. Forest Science 36, 276–292. https://doi.org//10.1093/forestscience/36.2.276 Nyland, R.D., Abrahamson, L.P. and Adams, K.B. (1983) Use of prescribed fire for regenerating red and white oak in New York. In: Proceedings of the 1982 Society of American Foresters National Convention, Cincinnati, Ohio, 19–22 September. Society of American Foresters, Bethesda, Maryland, pp. 163–167. Oliver, C.D. (1978) The development of northern red oak in mixed stands in Central New England. Yale University School of Forestry and Environmental Science Bulletin 91. Yale University, New Haven, Connecticut. Oliver, C.D. (1980) Even-aged development of mixedspecies stands. Journal of Forestry 78, 201–203. https://doi.org//10.1093/jof/78.4.201 Oliver, C.D. and Larson, B.C. (1996) Forest Stand Dynamics. Wiley, New York. Olson, D.F., Jr (1959) Site index curves for upland oak in the southeast. USDA Forest Service Southeastern Forest Experiment Station Research Note 125. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https:// www.fs.usda.gov/treesearch/pubs/5130 (accessed 1 July 2018). Peet, R.K. (1992) Community structure and ecosystem function. In: Glenn-Lewin, D.C. and Peet, R.K. (eds) Plant Succession. Chapman & Hall, New York, pp. 103–151. Perkey, A.W. and Wilkins, B.L. (1993) Crop tree management in eastern hardwoods. USDA Forest Service Report NA-TP-19-93. USDA Forest Service, Northeastern Area State and Private Forestry, Newtown Square, Pennsylvania. Perkey, A.W. and Wilkins, B.L. (2001) Crop tree field guide: selecting and managing crop trees in the Central Appalachians. USDA Forest Service Report NA-TP-10-01. USDA Forest Service, Northeastern Area State and Private Forestry, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ naspf/sites/default/files/publications/crop_tree_field_ guide.pdf (accessed 1 July 2018).

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Perry, D.A. and Maghembe, J. (1989) Ecosystem concepts and current trends in forest management: time for reappraisal. Forest Ecology and Management 26, 123–140. https://doi.org//10.1016/0378-1127(89)90040-6 Pfannmuller, L.A. (1991) Significance of oaks and oak forest communities for nongame wildlife. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 56–64. Roach, B.A. (1977) A stocking guide for Allegheny hardwoods and its use in controlling intermediate cuttings. USDA Forest Service Research Paper NE-373. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/14498 (accessed 1 July 2018). Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA Forest Service Agriculture Handbook 355. USDA Forest Service, Washington, DC. Available at: https://www.fs. usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Rogers, R. and Johnson, P.S. (1985) Rule thinning: a field method for meeting stocking goals in oak stands. In: Proceedings of the 5th Central Hardwood Forest Conference. University of Illinois, Champaign-Urbana, Illinois, pp. 106–110. Roth, E.R. (1956) Decay following thinning of sprout oak clumps. Journal of Forestry 54, 26–30. https://doi. org//10.1093/jof/54.1.26 Roth, E.R. and Hepting, G.H. (1943) Origin and development of oak stump sprouts as affecting their likelihood to decay. Journal of Forestry 41, 27–36. https:// doi.org//10.1093/jof/41.1.27 Roth, E.R. and Hepting, G.H. (1969) Prediction of butt rot in newly regenerated sprout oak stands. Journal of Forestry 67, 756–760. https://doi.org//10.1093/ jof/67.10.756 Roth, E.R. and Sleeth, B. (1939) Butt rot in unburned sprout oak stands. USDA Technical Bulletin 684. United States Department of Agriculture (USDA), Washington, DC. Salwasser, H. (1990) Gaining perspective: forestry for the future. In: Proceedings of the 1990 Society of American Foresters National Convention, Washington, DC, 29 July–1 August. Society of American Foresters, Bethesda, Maryland, pp. 55–63. https://doi.org//10.1093/ jof/88.11.32 Sampson, T.L., Barrett, J.P. and Leak, W.B. (1983) A stocking chart for northern red oak in New England. University of New Hampshire Agriculture Experiment Station Research Paper 100. New Hampshire Agriculture Experiment Station, University of New Hampshire, Durham, New Hampshire. Sander, I.L. (1971) Height growth of new oak sprouts depends on size of advance reproduction. Journal of Forestry 69, 809–811. https://doi.org//10.1093/jof/69.11.809 Sander, I.L. (1977) Manager’s handbook for oaks in the North Central States. USDA Forest Service General

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Technical Report NC-37. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10102 (accessed 1 July 2018). Sander, I.L. (1979) Regenerating oaks. In: Proceedings of the National Silviculture Workshop. USDA Forest Service, Washington, DC, pp. 212–221. Sander, I.L. and Clark, F.B. (1971) Reproduction of Upland Hardwood Forests in the Central States. USDA Forest Service Agriculture Handbook 405. USDA Forest Service, Washington, DC. Available at: https://www.fs.usda.gov/treesearch/pubs/37460 (accessed 1 July 2018). Sander, I.L., Johnson, P.S. and Watt, R.F. (1976) A guide for evaluating the adequacy of oak advance reproduction. USDA Forest Service General Technical Report NC-23. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10088 (accessed 1 July 2018). Sander, I.L., Johnson, P.S. and Rogers, R. (1984) Evaluating oak advance reproduction in the Missouri Ozarks. USDA Forest Service Research Paper NC-251. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10038 (accessed 1 July 2018). Scholz, H.F. (1952) Age variability of northern red oak in the Upper Mississippi woodlands. Journal of Forestry 50, 518–521. https://doi.org//10.1093/jof/50.7.518 Scholz, H.F. (1955) Effect of scarification on the initial establishment of northern red oak reproduction. USDA Forest Service Lake States Forest Experiment Station Technical Note 425. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Schuler, T.M. and Miller, G.W. (1995) Shelterwood treatments fail to establish oak reproduction on mesic forest sites in West Virginia – 10-year results. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 375–387. Available at: https://www.fs.usda.gov/treesearch/ pubs/12805 (accessed 1 July 2018). Sharp, W.M. (1958) Evaluating mast yields in the oaks. Pennsylvania State University Agriculture Experiment Station Bulletin 635. Pennsylvania State University, College of Agriculture Experiment Station, University Park, Pennsylvania. Shearin, A.T., Bruner, M.H. and Goebel, N.B. (1972) Prescribed burning stimulates natural regeneration of yellow-poplar. Journal of Forestry 70, 482–484. https://doi.org//10.1093/jof/70.8.482 Shigo, A.L. (1979) Tree decay an expanded concept. USDA Forest Service Agriculture Information Bulletin 419. USDA Forest Service, Washington, DC. Available at: https://www.fs.usda.gov/treesearch/pubs/4425 (accessed 1 July 2018).

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Shugart, H.H. and West, D.C. (1977) Development of an Appalachian deciduous forest succession model and its application to assessment of the impact of the chestnut blight. Journal of Environmental Management 5, 161–179. Smalley, G.W. (1979) Classification and evaluation of forest sites on the southern Cumberland Plateau. USDA Forest Service General Technical Report SO-23. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www. fs.usda.gov/treesearch/pubs/2381 (accessed 1 July 2018). Smalley, G.W. (1982) Classification and evaluation of forest sites on the Mid-Cumberland Plateau. USDA Forest Service General Technical Report SO-38. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org//10.2737/ SO-GTR-38 Smalley, G.W. (1984) Classification and evaluation of forest sites in the Cumberland Mountains. USDA Forest Service General Technical Report SO-50. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org//10.2737/SO-GTR-50 Smalley, G.W. (1986) Classification and evaluation of forest sites on the Northern Cumberland Plateau. USDA Forest Service General Technical Report SO-60. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi. org//10.2737/SO-GTR-60 Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, P.M.S. (1997) The Practice of Silviculture: Applied Forest Ecology, 9th edn. Wiley, New York. Sonderman, D.L. (1979) Guide to the measurement of tree characteristics important to the quality classification system for young hardwood trees. USDA Forest Service General Technical Report NE-54. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4049 (accessed 1 July 2018). Sork, V.L. (1984) Examination of seed dispersal and survival in red oak, Quercus rubra (Fagaceae), using metal-tagged acorns. Ecology 65, 1020–1022. https:// doi.org//10.2307/1938075 Stanturf, J.A., Auchmoody, L.R. and Walters, R.S. (1997) Regeneration responses of oak-dominated stands to thinning and clearcutting in northwestern Pennsylvania. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 321–331. Available at: https://www.fs.usda.gov/treesearch/ pubs/15684 (accessed 1 July 2018). Streng, D.R., Glitzenstein, J.S. and Harcombe, P.A. (1989) Woody seedling dynamics in an east Texas floodplain forest. Ecological Monographs 59, 177–204. https://doi.org//10.2307/2937285 Stroempl, G. (1983) Thinning clumps of northern hardwood stump sprouts to produce high quality timber.

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Ontario Ministry of Natural Resources Forest Research Information Paper 104. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Stroempl, G. and Secker, P.W. (1993) Guide to the group shelterwood cutting method for regenerating northern red oak. Ontario Ministry of Natural Resources Forest Resource Information Paper 120. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Swaim, T.J., Dey, D.C., Saunders, M.R., Weigel, D.R., Thornton, C.D., Kabrick, J.M. and Jenkins, M.A. (2016) Predicting the height growth of oak species (Quercus) reproduction over a 23-year period following clearcutting. Forest Ecology and Management 364,101–112.https://doi.org//10.1016/j.foreco.2016.01.005 Teclaw, R.M. and Isebrands, J.G. (1993a) An artificial regeneration system for establishing northern red oak on dry-mesic sites in the Lake States, USA. Annales des Sciences Forestieres 50, 543–552. https://doi.org/10.1051/forest:19930603 Teclaw, R.M. and Isebrands, J.G. (1993b) Artificial regeneration of northern red oak in the Lake States with a light shelterwood: a departure from tradition. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 185–194. Available at: https://www.fs.usda.gov/treesearch/pubs/15315 (accessed 1 July 2018). Trimble, G.R., Jr (1974) Response to crop-tree release by 7-year-old stems of red maple stump sprouts and northern red oak advance reproduction. USDA Forest Service Research Paper NE-303. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/15410 (accessed 1 July 2018). Trimble, G.R., Jr and Seegrist, D.W. (1973) Epicormic branching on hardwood trees bordering forest openings. USDA Forest Service Research Paper NE-261. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/23633 (accessed 1 July 2018). Tryon, E.H. and Carvell, K.L. (1958) Regeneration under oak stands. West Virginia University Agricultural Experiment Station Bulletin 424T. West Virginia University Agricultural Experiment Station, Morgantown, West Virginia. USDA Forest Service (1973) National Forest Landscape Management: Vol. 1 – Concepts and Principles. USDA Agriculture Handbook 434. USDA Forest Service, Washington, DC. USDA Forest Service (1980) National Forest Landscape Management: Vol. 2 – Timber. USDA Forest Service Agriculture Handbook 559. USDA Forest Service, Washington, DC.

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Van Lear, D.H. and Waldrop, T.A. (1988) Effects of fire on natural regeneration in the Appalachian Mountains. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 56–70. Veblen, T.T. (1992) Regeneration dynamics. In: GlennLewin, D.B., Peet, R.K. and Veblen, T.T (eds) Plant Succession, Theory and Prediction. Chapman & Hall, London, pp. 152–187. Vickers, L.A., Fox, T.R., Loftis, D.L. and Boucugnani, D.A. (2011) Predicting forest regeneration in the Central Appalachians using the regen expert system. Journal of Sustainable Forestry 30, 790–822. https:// doi.org//10.1080/10549811.2011.577400 Waldrop, T.A., Buckner, E.R., Shugart, H.H., Jr and McGee, C.E. (1986) forcat: a single tree model of stand development following clearcutting on the Cumberland Plateau. Forest Science 32, 297–317. https://doi.org//10.1093/forestscience/32.2.297 Walter, W.D. and Johnson, P.S. (2004) Sustainable silviculture for Missouri forests. In: Toward Sustainability for Missouri Forests. USDA Forest Service General Technical Report NC-239. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 173–192. https://doi.org//10.2737/ NC-GTR-239 Ward, W.W. (1966) Epicormic branching of black and white oaks. Forest Science 12, 290–296. https://doi. org/10.1093/forestscience/12.3.290 Wendel, G.W. (1975) Stump sprout growth and quality of several Appalachian hardwood species after clearcutting. USDA Forest Service Research Paper NE-329. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/­ 15457 (accessed 1 July 2018). Will-Wolf, S. (1991) Role of fire in maintaining oaks in mesic oak maple forests. In: Proceedings of the Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 27–33. Wilson, B.F. (1968) Red maple stump sprout development the first year. Harvard Forest Paper 18. Harvard University, Harvard Forest, Petersham, Massachusetts. Wuenscher, J.E. and Kozlowski, T.T. (1971) Relationship of gas-exchange resistance to tree-seedling ecology. Ecology 52, 1016–1023. https://doi.org/10.2307/ 1933807 Zaczek, J.J., Harding, J. and Welfley, J. (1997) Impact of soil scarification on the composition of regeneration and species diversity in an oak shelterwood. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 341–348. Available at: https://www.fs.usda.gov/treesearch/pubs/15692 (accessed 1 July 2018).

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Uneven-aged Silvicultural Methods

Introduction The traditional objective of uneven-aged silviculture is to create and maintain uneven-aged stands for the sustained, even flow of forest products. A regulated uneven-aged forest is one where all individual stands or management units within it are unevenaged and, under continued management, collectively produce an even, sustainable yield of timber. Ideally, each individual stand is a self-contained sustained yield unit. By definition, an uneven-aged stand contains at least three age classes of trees that are closely intermingled on the same area (Smith et al., 1997). In reality, uneven-aged stands are usually comprised of even-aged groups of trees, but the groups are so small that visually it is difficult to tell where one group begins and another ends. An identifying characteristic of such stands is the vertical crown stratification of trees within a relatively small area (Fig. 9.1). Maintaining the uneven-aged state requires periodic recruitment of reproduction into the overstorey. This must occur at least three times over a time span corresponding to age of the oldest trees retained in the stand. Recruitment of reproduction must be sufficient to replace trees that die or that are periodically harvested and to maintain desired levels of stand density, yield and tree quality. Timber yields are obtained by periodically harvesting individual trees or small groups of trees within stands; the overstorey is never completely removed. In Europe, this practice is called Dauerwald, which in German refers to the permanence of the overstorey (Schabel and Palmer, 1999). Unlike even-aged silviculture, there is no rotation. Instead, forests are regulated through the periodic control of stand structure and density by timber harvesting. The number of years between harvests is termed the cutting cycle (Fig. 9.2). The periodic harvests reduce stand density to desired levels and manipulate stand size structure. They can also be utilized to modify species composition. In contrast

to even-aged silviculture where stand structure changes constantly over a rotation, the goal of uneven-aged silviculture is to create a desired stand size structure and maintain it over time. Periodic harvests typically remove 25% or less of standing volume and usually remove trees across a wide range of diameter and crown classes. One goal of uneven-aged silviculture is to continually improve the quality of the residual stand by removing the poor quality trees and retaining the high quality trees at each harvest. To attain non-timber objectives, den trees, snags and culls can be retained to satisfy wildlife, biodiversity and aesthetic objectives. In contrast to even-aged forests, which are regulated through area control, uneven-aged forests are regulated through control of stand structure. The traditional goal is to create a stand structure that is sustainable over time and that produces a relatively constant yield of timber. The method therefore is sometimes said to be based on volume control. However, it is only through the maintenance of a specified stand structure that effective control of volume is possible. The requisite stand structure is generally based on a diameter frequency distribution characterized by decreasing numbers of trees with increasing tree diameter. Bell-shaped, skewed bell-shaped or other irregular diameter distributions are not, as far as we know, sustainable naturally or silviculturally (Leak, 1996). In a fully regulated uneven-aged forest, the trees in each stand typically maintain a smooth, reverse J-shaped diameter frequency distribution. Two silvicultural methods are used in unevenaged management: (i) single-tree selection; and (ii) group selection.

The Single-tree Selection Method Principles of application The single-tree selection method is a complete silvicultural system and a regeneration method.

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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Fig. 9.1.  Uneven-aged stands are characterized by an intermingling of trees of at least three age classes.

The periodic harvest of individual trees is carried out so that the desired stand age and size structure, species composition and stocking are maintained. In Europe, the method is called Plenterwald meaning selection forest in German (Hagner, 1999). In principle, the maximum size of the canopy gaps resulting from harvesting single trees is limited to the crown areas of the largest trees cut. For oaks 16–20 inches dbh, the resulting canopy gaps range from about 1/100 to 1/30 acre, depending on tree dbh and stand density. In practice, however, larger canopy gaps are often created when small groups of dead and dying trees are salvaged. Sustaining stand size and age structure with the single-tree selection method depends on the recruitment (ingrowth) of reproduction within canopy gaps into the overstorey. Each pulse of ingrowth forms the heartbeat of the three-stage cycle of reduction in stand density, recruitment and stand growth (Fig. 9.3). The single-tree selection method differs from even-aged methods in the spatial scale and frequency of disturbance, and associated regeneration dynamics. An uneven-aged stand could be defined as one where, after full regulation is attained, the forestwide structure reoccurs at a ‘small’ scale (e.g. < 1 acre) at high probability (e.g. with > 91% frequency) in

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space and time. This probabilistic view of the unevenaged state recognizes the existence and acceptance of some departure from the ‘ideal’ or desired stand structure. This is an important qualification because it recognizes that the silviculturally idealized uneven-aged state need not occur at all times nor everywhere within a stand. In applying the single-tree selection method, five stand characteristics should be considered at each periodic harvest in each stand or management unit: (i) the distribution of tree ages; (ii) the distribution of tree diameters; (iii) stand stocking; (iv) reproduction; and (v) species composition. With the exception of age distribution, all of the above stand characteristics also are considered at various times during the life of a managed even-aged stand. In principle, the need to assess all of these factors at every harvest entry makes the application of the single-tree selection method more complex than any even-aged method. However, the application of the single-tree selection method is usually simplified by assuming the desired stand age distribution follows the creation and maintenance of the desired diameter distribution. Consequently, stand structure in single-tree selection silviculture is usually described quantitatively by tree diameter distributions

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Uneven-aged cutting cycle 1

2

4

5

6

100

0

0

20

40

60 Years

80

100

120

Even-aged rotation

(B) Relative stand volume

3

1

100

0

0

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100

3

200

4

300

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Years Fig. 9.2.  Change in relative stand volume for forests under uneven-aged and even-aged silviculture: (A) volume change over six 20-year cutting cycles in an uneven-aged stand; and (B) volume change over four 100-year rotations in an even-aged management. In this example, two intermediate cuttings are made within each rotation.

Pulse of reproduction recruitment into overstorey

Regeneration process Periodic reduction in stand density through harvesting

Increase in stand density through growth

Fig. 9.3.  The single-tree selection method is sustained through the three-stage cycle of periodic reduction in stand density, followed by a pulse of reproduction recruitment into the overstorey, and then a renewed increase in stand density that continues to the end of the cutting cycle. Regeneration is a ubiquitous and continuous process in the well-managed uneven-aged forest.

Uneven-aged Silvicultural Methods

rather than age distributions. Much attention therefore is given to defining the desired diameter distribution of an uneven-aged forest. Uneven-aged stands can assume innumerable diameter distributions – some of which are also characteristic of even-aged stands. In general, unevenaged diameter distributions are placed into two categories: balanced and unbalanced (or irregular) diameter distributions. Balanced diameter distributions form relatively smooth ‘depletion curves’ representing a continual decline in numbers of trees as diameter increases, whereas unbalanced distributions can assume any shape (Fig. 9.4). Uneven-aged stands with unbalanced diameter distributions are common to many oak forests, especially those repeatedly disturbed by undesigned timber harvesting or disturbances from fire and wind. In natural uneven-aged stands, balanced diameter distributions commonly occur in old growth and other relatively undisturbed forests, especially those dominated by shade-tolerant species. However, even-aged stands may form, at some point in their development, diameter distributions similar to uneven-aged stands. Thus, the shape of a diameter distribution, by itself, does not confirm the existence of the uneven-aged state (Lorimer and Krug, 1983). Despite the ambiguities of using diameter distributions to infer stand age structure, the classical application of the single-tree selection method is based on creating and maintaining balanced diameter distributions. Balanced diameter distributions provide the basis for systematically sustaining even yields of timber – and the uneven-aged state itself. There is no established theory on how to sustain unbalanced structures. But even if they were sustainable, unbalanced structures are unlikely to

Unbalanced Trees/acre

Relative stand volume

(A)

Balanced

Dbh Fig. 9.4.  Examples of balanced and unbalanced uneven-aged diameter distributions.

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produce even yields of timber over time. Stands with unbalanced structures may drift towards the even-aged state – especially stands comprised of relatively shade-intolerant species. From a silvicultural perspective, a stand with an unbalanced structure is like a ship adrift, that is its future structural state is indeterminate. Balanced diameter distributions form the basis for the traditional approach to uneven-aged silviculture. They are generally referred to as reverse J-shaped distributions and are characterized by decreasing numbers of trees in successively larger diameter classes. However, the specific shape of such distributions may vary and different names have been applied to certain departures from the classical reverse J-shape. For example, reverse J-shaped distributions with a somewhat flattened mid-section are sometimes called rotated sigmoid distributions (e.g. Goff and West, 1975; Lorimer and Frelich, 1984; Leak, 1996). Regardless of their specific quantitative properties, such distributions result from natural forest development that involves three simultaneous processes: (i) tree mortality; (ii) tree growth; and (iii) ingrowth (i.e. the growth of reproduction into the overstorey). With respect to mortality, a reverse J-shaped distribution represents a depletion process caused when a proportion of trees in each diameter class fails to survive and grow into the next larger class. In managed stands, development over time is altered by a fourth process: the periodic reduction in stand density through timber harvesting. Harvesting reduces the rate of natural mortality from self-thinning, increases the growth rate of individual trees in the residual stand and accelerates the ingrowth of reproduction into the overstorey. Although various types of diameter frequency distributions representing depletion curves have been used to characterize natural and managed uneven-aged forests (Goff and West, 1975; Lorimer and Frelich, 1984; Hansen and Nyland, 1987; Leak, 1996), the distribution most widely used in silviculture to describe a balanced diameter distribution is mathematically defined by the negative exponential function. The negative exponential diameter distribution The collective diameter distribution of a series of even-aged stands forms a negative exponential diameter distribution (Fig. 5.4, this volume). In this context, the negative exponential distribution

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could be equated to a forest-wide redistribution of trees in each age class to each acre of the forest. Each cohort or age class accordingly would occupy the same amount of space within each unevenaged stand as it did across an entire regulated even-aged forest. Thus, the diameter distribution characteristics of the regulated even-aged forest are preserved, but at a much smaller spatial scale. Because the actual spatial scale is unspecified in uneven-aged silviculture, control of stand structure is substituted for area control. The negative exponential distribution provides an objective basis for organizing an uneven-aged forest that otherwise might be a chaos of tiny and spatially unrecognizable even-aged units. Although a great variety of uneven-aged, unbalanced stand structures are possible, a balanced distribution of diameters is required for the forest as a whole if there is to be a reasonable assurance of sustaining the uneven-aged state. We know of no theory or system for perpetuating a balanced collection of unbalanced stands where diameter distributions depart from the reverse J-shape or similar distributions representing depletion curves. Where sustained yield is not required, it may be sufficient under some circumstances to maintain stands in loosely defined uneven-aged states and to obtain irregular yields whenever such yields are consistent with broader management objectives. For example, this strategy may be applicable to forests where some yield is desired but where an even flow of timber products is impractical or secondary in importance to maintaining non-timber values. But perpetuating even a single stand in an unbalanced and silviculturally sustainable size structure is problematic without a systematic procedure for doing so. For these reasons, we present the single-tree selection method based on the application of the negative exponential diameter distribution to the individual stand, the basic silvicultural unit. The theory of the selection method assumes that within each stand a negative exponential diameter distribution in some form is silviculturally stable and sustainable in perpetuity. This does not necessarily mean that the distribution would occur naturally and remain stable in the absence of silviculture, but it does imply that the distribution is sustainable under the imposed silvicultural system. Moreover, the common occurrence of the negative exponential diameter distribution in old-growth hardwood forests and its relation to the process of self-thinning (Chapter 6, this volume) provide an underlying

Chapter 9

ecological basis for its application in single-tree selection silviculture. The negative exponential frequency distribution forms a straight line when the logarithm of the number of trees per unit area is plotted against tree diameter. Numbers of trees decrease at a constant exponential rate as tree diameter increases. In its non-linear form, the relation is given by: Ni = ae bdi



[9.1]

where Ni is the number (frequency) of trees per acre within dbh class di, e is the base of the natural logarithm, and a and b are parameters that define stand density and slope shape, respectively. The parameter b is always negative and expresses the constant exponential decrease in N per unit change in diameter. Parameter b also can be used to derive the associated exponential depletion rate k from the relation: k = eb 

[9.2]

Thus, for a given value of b, k is the proportion of trees of a given diameter class that occur in the next larger diameter class. In general the proportion, p, of trees of given diameter, di, that occur at a diameter that is n units larger than di can be computed as: p = kn



[9.3]

If we assume that k is a rate of change that represents a steady state in a stand at average maximum density, p is the proportion of trees surviving from di to di + n. Self-thinning is defined as density-dependent mortality in stands at average maximum density (Chapter 6, this volume). The quantitative expression of self-thinning has largely evolved from observations in even-aged stands that are continually increasing in mean diameter. Although an unevenaged stand at average maximum density is not necessarily changing in mean diameter, it is undergoing self-thinning. In that context, self-thinning as a rate could be defined by k, the constant probability that a tree of a given diameter class survives to the next larger class. An analogous definition of self-thinning rate for stands that are changing in average diameter would be the variable probability that a randomly selected tree in a stand of a specified mean diameter survives to a mean stand diameter one diameter unit larger (from Chapter 6, Equation 6.2).1 Unlike the constant rate k, this probability is variable in relation to mean stand

Uneven-aged Silvicultural Methods

diameter. Both definitions of self-thinning apply only to stands that are at average maximum density. Silviculturists usually re-express the rate b as the ratio of the number of trees in one dbh class to the number in the next larger class. This ratio, or quo-1 tient (q), is given by ( e b ) or k -1 for diameter classes of unit width. The resulting values have traditionally been referred to simply as q values (or q factors) by silviculturists.2 As discussed later, the value of q in field applications is affected by the width of the diameter classes used to describe the diameter distribution, and quotients for any diameter class of width w are given by qw. For an observed diameter distribution, q can be derived by dividing the number of trees in one diameter class by those in the next larger class and repeating the process for the range of observed diameters; q then can be estimated as the average of all quotients. However, q can be more efficiently and accurately derived using readily available computer regression programs. These programs also usually generate goodness-of-fit statistics that are helpful in assessing how well the negative exponential function fits an observed or hypothetical diameter distribution. They also can be used to fit other mathematical functions that may better characterize stand structure (Leak, 1996). High values of q produce rapidly descending curves representing relatively large numbers of small trees and small numbers of large trees. Small values of q produce slowly descending curves representing relatively few small trees and relatively large numbers of large trees. In silvicultural applications in North American hardwood forests, commonly recommended values of q range from 1.1 to 1.4 based on 1 inch dbh classes (1.2–2.0 for 2 inch classes) (Trimble, 1970; Leak, 1978, 1987; Smith, 1980; Smith and Lamson, 1982; Leak et al., 1987; Law and Lorimer, 1989). Tree diameters in old-growth hardwood stands (including some dominated by oaks) often follow the negative exponential distribution (Meyer and Stevenson, 1943; Lorimer, 1980; Richards et  al., 1995; Shifley et al., 1995). Old-growth stands therefore may provide silviculturally valuable information on the diameter distribution possibilities for uneven-aged forests at or near average maximum density. Distributions may vary from extremely smooth (balanced) to extremely irregular (unbalanced), and from steep to gently sloping. Even within the same type of old-growth forest, the slope shape of the diameter distribution (and thus q) may

339

340

distribution to evaluate how it conforms to a desired uneven-aged distribution. One method of doing this is by visual inspection of graphs and by curve fitting. Stands that do not initially show some predisposition towards a reverse J-shaped diameter distribution are often poor candidates for the single-­ tree selection system. Curve fitting based on stand inventory therefore can be used to prioritize stands for placement under uneven-aged silviculture or to evaluate how they have responded to earlier silvicultural attempts to bring them into conformity with target diameter distributions, or ‘guiding curves’. As the term implies, the purpose of the guiding curve is to guide timber harvesting. An appropriate curve would be one that is compatible with the natural dynamics of the forest as well as with management objectives and constraints. In principal, periodic harvesting removes only the number of trees in each diameter class that are in excess of the guiding curve (Fig. 9.6). Selecting a guiding curve requires not only choosing a slope shape (q), but also a residual stand density (associated with a in Equation 9.1), and the diameter range across which the residual stand density is calculated. In choosing these values, both ecological and economic realities should be considered. A prerequisite to selecting a guiding curve is an understanding of how to specify a curve that meets management goals for stand structure and density. Curve specification thus differs from fitting curves to existing tree data or selecting a guiding curve. 160

q 1.4

300 240

120 Trees/acre

vary appreciably depending on stand history, disturbance events, species composition, rates of succession and other factors (Meyer et  al., 1952; Lorimer, 1980). Early studies of undisturbed, old-growth stands have shown that the parameter estimates a and b (Equation 9.1) derived from the diameter distribution of those stands are positively correlated (Meyer and Stevenson, 1943; Meyer et al., 1952). This correlation is a consequence of the natural development of such stands and the upper limits of stand density as discussed in Chapter 6 (this volume). Because parameters a and b define stocking and slope shape, respectively, and because oldgrowth stands are usually at or near average maximum density, any increase in the number of trees in one section of the diameter distribution must be accompanied by a decrease in the number of trees in another section. Such changes in the shape of the diameter distribution may result from slow, successional changes in species composition and associated changes in survival and diameter growth rates. They also may be related to sudden gap-scale disturbances that affect both stand composition and structure. Whatever the cause, the shape of the diameter distribution must self-adjust over time for stands to maintain themselves near average maximum density. For any fixed stand density, the relation can be illustrated by comparing a series of negative exponential distributions and varying q (Fig. 9.5). As q (slope steepness) increases or decreases, a also must increase or decrease to maintain a constant stand density (Fig. 9.5 inset). The structure of undisturbed old-growth stands can provide valuable information on the natural dynamics and thus the shape characteristics (b or q) of uneven-aged diameter distributions for a given forest type. However, old-growth forests usually have stand densities at or near the maximum attainable for the site. In the case of oak forests, undisturbed oldgrowth stands also are relatively rare. Old-growth stands therefore are likely to be of limited value as silvicultural models for uneven-aged stand structure because, as discussed later in this chapter, successful application of the single-tree selection method in oak forests requires maintaining stands at relatively low densities. At stand densities below average maximum levels, it is hypothetically possible to silviculturally create, if not sustain, many different combinations of slope shape (q) and stand density (a). Uneven-aged management requires measurement and monitoring of the shape of a stand’s diameter

a

80

1.3

180 120 60 0 1.10 1.15 1.20 1.25 1.30 1.35 1.40 q

40 1.2 1.1 0

2

6

10 Dbh (in.)

14

18

Fig. 9.5.  The negative exponential diameter distributions for four values of q (for 1 inch dbh classes) and a fixed stand density (80 ft2/acre of basal area). Inset: the relation between a (from Equation 9.1) and q for the same distributions.

Chapter 9

Moser (1976) presented a more direct and accurate method for deriving a guiding curve. His method produces an exact curve for any prespecified q and stand density. The method also accommodates expressing stand density as basal area, stocking per cent based on the tree-area ratio (Chisman and Schumacher, 1940; Gingrich, 1967) or crown competition factor (Krajicek et  al., 1961). The two latter measures of stand density are given by:

60 Trees/acre

50 40 30 20 10 0

2

4

6

8 10 12 14 16 18 20 Dbh (in.)

Fig. 9.6.  The diameter distribution of an uneven-aged oak stand at 86% stocking (bars) and the guiding curve representing a q of 1.3 (for 1 inch dbh classes) and 60% stocking over the dbh range of 2–18 inches. The number of trees in each dbh class above and to the right of the guiding curve would be periodically harvested to bring the stand back to the guiding curve. Stocking is based on Gingrich (1967).

Specifying the distribution Specifying a guiding curve refers to defining a diameter frequency distribution that meets predefined goals for stand structure and stand density over the range of tree diameters to be considered. The current diameter distribution of a given stand may or may not be similar to the guiding curve specified for that stand by the silviculturist. A traditional approach to specifying the guiding curve has been first to select the largest dbh class in the residual stand (i.e. the largest tree that will remain after a harvest). This diameter depends on species, site quality, management objectives and other factors. To derive a trial guiding curve, the number of trees in the largest dbh class can be arbitrarily set to one/acre. The number of trees in each of the smaller diameter classes then can be calculated by multiplying the next larger class by the selected q. From this, the basal area in each dbh class and total basal area or stocking can be calculated. The outcome provides a trial stand structure with a shape specified by the value of q. However, the resulting stand density may be larger or smaller than desired. The trial curve can be adjusted upwards or downwards based on the ratio between its current basal area (or stocking) and the desired basal area (or stocking) until the appropriate curve is obtained (Marquis, 1978).

Uneven-aged Silvicultural Methods

Density = c1 åNi + c2 ådi Ni + c3 ådi2 Ni [9.4] i

i

i

where i represents the ith diameter class, the summation is over all diameter classes, di and Ni are as defined in Equation 9.1, and the coefficients c1, c2 and c3 are derived by species or species groups using appropriate methods (Chisman and Schumacher, 1940; Krajicek et al., 1961; Ernst and Knapp, 1985; Stout and Nyland, 1986; Stout and Larson, 1988; also see Chapter 6, this volume). Coefficients applicable to several oak forest types are presented in Table 9.1; the single coefficient for basal area, c3, is independent of species. An alternative expression of Equation 9.4 can be derived by substituting the right-hand side of Equation 9.1 for Ni in Equation 9.4 so that: Density = c1 åae bdi + c2 ådi ae bdi + c3 ådi2 ae bdi [9.5] i i i Then, solving for a (the density parameter): a=

Density + c2di + c3di2 ) e bdi [9.6] 1

å (c i

Because b = ln ( q ) , Equation 9.6 can be restated as: a=

Density é ln q ù é di ê úù ê å ( c1 + c2di + c3di2 ) e ë w û ú [9.7] êë i úû

where w is diameter class width and the b associated with ln q is always negative (Moser, 1976). Equation 9.7 therefore can be used to calculate the stand density parameter a for specified values of stand density and of q for given diameter ranges. Moreover, it can be applied to various measures of stand density for several oak and other forest types (Table 6.2, this volume, and Table 9.1). A more convenient method of specifying a guiding curve that does not require calculating the parameter a is given by the following equation:

341

Table 9.1.  Coefficients for Equation 9.4 related to measures of stand density for several oak forest types. (From Krajicek et al., 1961; Gingrich, 1967; Krajicek, 1967; McGill et al., 1991; Stout, 1991.)a Coefficient Measure of stand density

Region/forest type

Basal area Stocking per cent Stocking per cent Stocking per cent Stocking per cent Crown competition factor Crown competition factor

All Central Hardwood/oak–hickoryb Wisconsin/northern red oakc Wisconsin/northern red oakd Allegheny Plateau/mixed oak–northern hardwoodse Central and southern bottomlands/pin oakf Central Hardwood/oak–hickoryg

c1

c2

c3

0 –0.00507 0.01360 0.02476 –0.0068718 0.1480 0.01750

0 0.01698 0.00930 0.04182 0.016787 0.04982 0.02050

0.005454 0.00317 0.00320 0.00267 0.0019797 0.004193 0.00600

a

See Chapter 6 (this volume) for a discussion of measures of stand density. From Gingrich (1967). Based on data from stands that were predominantly oak (white, scarlet, northern red and chestnut oaks); includes hickories and associated species in Central Hardwood forests. c From McGill et al. (1991). Based on data from stands that were predominantly northern red oak. Includes only contributions to stocking from ‘main canopy’ trees (intermediate, codominant and dominant crown classes). d From McGill et al. (1991). Based on data from stands that were predominantly northern red oak. Includes contributions to stocking from all trees including trees other than northern red oak. e From Stout (1991). Based on data from north-western Pennsylvania, a transition zone between the Allegheny Hardwood Region and the Northern Hardwood Region. Associated stands are defined as those with at least 25% of basal area in oak and at least 25% in northern hardwood species, and at least 65% of basal area is comprised of those species plus the common associates of each. f From Krajicek (1967). (See Chapter 6, this volume, for a definition of crown competition factor.) g From Krajicek et al. (1961). b

N MAX =

RSD

[9.8]

( DMAX - Di ) ù é  ú ê åAi × q w êë i úû

where NMAX is the number of trees to be retained in the largest diameter class, RSD is the selected residual stand density (expressed as basal area per unit area, stocking per cent or crown competition factor), i represents the ith dbh class, the summation is over all diameter classes, Ai is the basal area or tree area (expressed in the same units as RSD) of the tree of dbh Di, q is the selected stand structure represented by the guiding curve, DMAX is the maximum tree dbh represented by the guiding curve, Di is the midpoint dbh of the ith diameter class, and w is dbh class width; (D w− D ) is always a whole number and corresponds to (i–1) of Brender’s formula.3 After calculating NMAX (the number of trees in the largest diameter class), the number of trees in the other diameter classes can be calculated by starting with the largest diameter class and sequentially multiplying q times the number of trees in the diameter class to obtain the number in the next smaller diameter classes. Equation 9.8 is convenient for specifying a guiding curve using a computer spreadsheet program. MAX

342

i

Using Equations 9.1 and 9.8, families of curves can be generated to illustrate graphically how varying stand density and q jointly affect diameter distributions (Fig. 9.7A). The resulting curves facilitate comparison of alternative stand structures. The method also can be extended to describe graphically how the allocation of growing space changes among diameter classes as q and stand density change (Fig. 9.7B). For high values of q, a relatively large proportion of growing space is occupied by small trees. In contrast, low values of q produce less skewed diameter distributions, and large trees comprise proportionately more growing space for a given stand density. For high values of q, reducing stand density reduces the variability among diameter classes relative to the amount of growing space occupied by any one diameter class. The stocking distribution curves thus provide a more detailed view of how growing space is allocated than do frequency distribution curves. Stocking distribution curves (Fig. 9.7B) may be especially useful for assessing the allocation of growing space among trees in the larger diameter classes, which are often not apparent from frequency distribution curves (Fig. 9.7A). Although sawtimber-size trees are relatively few in number, minor differences in their numbers per acre may represent large differences in

Chapter 9

Trees/acre/dbh class

(A)

150 120

Stocking/dbh class (%)

80 60

90 40 60 100 80 60 30 40 0

(B)

width, doubling the diameter class width requires squaring the value of q to maintain the same slope in the diameter distribution curve (Fig. 9.8). In general, increasing the diameter class width by any multiple, m, requires recomputing q as qm in order to obtain comparable guiding curves. In applications, diameter class width used to inventory a stand and to compute q values for the current stand conditions and for guiding curves need to be considered. Equations 9.7 and 9.8 explicitly account for diameter class width.

Stocking (%) 180 100

2

Correlation between tree age and diameter 4

6

8

10 12 14 16 18 20

12 Stocking (%) 10 8 6

100 80

60 40 100 2 80 60 40 0 2 4

4

6

q = 1.2

8 10 12 14 16 18 20 Dbh class (in.) q = 1.4

Fig. 9.7.  Distribution of numbers of trees and stocking by dbh classes for two q values and four stocking levels. (A) Families of curves for negative exponential diameter distributions for 1 inch dbh classes. Maximum tree dbh is set at 20 inches for q = 1.2, and 16 inches for q = 1.4. (B) Related distribution of stocking. Stocking per cent (relative stand density) is based on Gingrich’s (1967) minimum tree area equation for oak–hickory forests in the Central Hardwood Region.

periodic yield and economic value. The curves also can be used to examine graphically the effect of varying maximum tree diameter and/or varying the range of diameters across which stocking is defined. As noted earlier in this section, the value of q (i.e. the ratio of the number of trees in one diameter class to the next larger diameter class) depends upon the width of the diameter classes used to describe the diameter distribution. For a given value of q, a given stand density and a given diameter class

Uneven-aged Silvicultural Methods

It is commonly assumed that stands with balanced negative exponential diameter distributions represent correspondingly balanced age distributions. However, the age distribution of a stand can only be conclusively determined by directly observing tree ages (Lorimer and Krug, 1983). Even though it is usually impractical to obtain the information necessary to describe accurately a stand’s age distribution, it is instructive to examine the possible relations between tree age and diameter in unevenaged stands. One way to observe the relation between age and diameter distributions is to compare graphically their frequency distributions. The relative similarity or dissimilarity in the shape of the graphs reflects the degree of agreement between tree age and diameter. However, such comparisons may be misleading unless trees down to the very smallest diameters are included. If they are not, the ages of trees below the minimum observed diameter are not accounted for. The result is an incomplete and biased age frequency distribution (Loewenstein et  al., 2000). Because of low to moderate correlation between tree age and diameter, the ages of the excluded trees may range from very young to relatively old. Discrepancies between the graphically apparent and actual age distributions of all trees may be especially great in uneven-aged stands because of the preponderance of small-diameter trees. Alternatively, the relation between tree age and diameter can be assessed by calculating the correlation coefficient between observed ages and diameters. Stands can be categorized based on the magnitude of the coefficient: (i) high correlation coefficients (close to 1) associated with balanced all-aged stands (category 1 stands); (ii) mid-range correlation coefficients associated with irregular uneven-aged stands (category 2 stands); and (iii) low correlation

343

90 q = 1.44 (2 in. dbh classes)

80 Trees/acre

70 60 50 40 30 20 10

q = 1.2 (1 in. dbh classes) 2

(B)

6

10 14 Dbh (in.)

18

100

Trees/acre

q = 1.44 (2 in. dbh classes) 10 q = 1.2 (1 in. dbh classes)

1

2

6

10

14

18

Dbh (in.) Fig. 9.8.  (A) Equivalent dbh frequency distributions for two diameter-class widths: 1 inch and 2 inches (w in Equation 9.8). The relation is illustrated for a q of 1.2 (for 1 inch dbh classes) and the corresponding value of q = 1.44 (or 1.22) for 2 inch classes. (B) When the slopes of the two distributions are linearized by logarithmic transformation of the vertical axis (as shown), the curves are parallel, that is they have a common slope (parameter b in Equation 9.1). However, this is only true if the dbh ranges of the two distributions share the same diameter class midpoints for the smallest and largest dbhs considered. The greater number of trees per acre in each 2 inch dbh class elevates the curve above that for 1 inch classes (i.e. the trees per acre in two 1 inch classes must be combined to equate to a 2 inch dbh class). There are also 7% more trees per acre represented by the curve for 2 inch dbh classes. This difference results from the wider dbh interval for the smallest and largest dbh classes for the 2 inch curve (as shown).

344

coefficients (close to 0) associated with even-aged stands (category 3 stands). The correlation between tree age and diameter in an even-aged stand should be very small because trees have the same age regardless of dbh. Nevertheless, reverse J-shaped diameter distributions are not uncommon in evenaged stands. They are the rule in very young evenaged stands (Fig. 5.4, this volume), and also may occur after timber harvests in even-aged stands that leave numerous small-diameter trees and remove most large-diameter trees. Diameter distributions appearing to be uneven-aged therefore may be even-aged. Category 1 stands represent the theoretically ideal state under uneven-aged management where the observed frequency distributions of both diameter and age would form a relatively smooth, exponentially declining curve. Sustaining such distributions requires the periodic recruitment of trees (reproduction) into the smallest overstorey size class. This recruitment and that of existing overstorey trees into successively larger diameter classes must occur at approximately the same rate across the entire range of diameters in order to maintain the negative exponential curve shape. To realize this objective, the selected guiding curve must be consistent with a forest’s growth and regeneration dynamics. Thus, the guiding curve is usually based on what is known about the dynamics of the forest to be managed. An ideal guiding curve would be one that remains relatively stable from one cutting cycle to the next (Fig. 9.9). The selected guiding curve accordingly should maintain its shape and also sustain the desired

Trees/acre

(A) 100

En

do

Beg

inn

ing

fc

of c

utt

ing

utti

ng

cy

cyc

cle

le

q 1.7 1.7

Dbh Fig. 9.9.  The theory and application of guiding curves assume that the shape of the selected diameter distribution, and thus q, remains relatively constant during the cutting cycle.

Chapter 9

residual stand density, species composition, volume growth, tree quality and other stand attributes deemed important. After selecting a guiding curve, the diameter distribution can be created and maintained by periodically reducing stand density through timber harvesting, typically at intervals of 10–20 years. Maintaining the distribution requires cutting trees across a wide range of diameter classes, not just the largest. Failure to maintain a stable diameter distribution over time can result from inadequate ingrowth of reproduction of desired species into the overstorey, failing to harvest trees in smaller diameter classes, changes in species composition, irregular mortality and other factors. Changes in diameter growth rates may be associated with changes in species composition that are often the result of succession and difficult to control silviculturally. Setting a high minimum diameter for harvested trees also will result in poor control over both the shape of the dbh distribution and the stand density. The portion of the diameter distribution below the minimum harvest dbh tends to develop towards its natural ecological equilibrium, which does not necessarily preserve the desired overall shape of the distribution. Any of these problems can produce stands that fall into category 2. Category 2 stands arise from factors that fall into two general groups: ●● variation (dispersion) of diameters within a single age class (cohort); and ●● spatial and temporal variation related to site factors and stand characteristics. Variation in diameters within a cohort is related to competition within and among cohorts and will occur even in the absence of other sources of environmental variation. This dispersion results from genetic variation in growth rate and other factors such as susceptibility to insects and disease, and imperfect spacing of trees. Imperfect spacing, which is partially related to clumped spatial distributions of reproduction, eventually results in unequal growth rates among trees (Rogers, 1983). Small trees within a single young age cohort have relatively limited diameter dispersion. But this dispersion increases with time as the mean diameter of the cohort increases. The magnitude of dispersion is evident from stand tables for unmanaged evenaged oak stands at average maximum density in the Central Hardwood Region (e.g. Fig. 5.4, this volume).

Uneven-aged Silvicultural Methods

Within 80-year-old cohorts on average sites, diameters span a 12–14 inch range (Schnur, 1937). Although the dynamics of an even-aged stand are not the same as those of an uneven-aged stand, even-aged stand tables illustrate the principle of diameter dispersion within age cohorts that occurs even with uniform stand conditions and limited disturbance. This dispersion tends to be compounded in uneven-aged stands due to competition within and among age cohorts and by mixtures of species with different growth rates. Consequently, in a naturally developing uneven-aged stand, a high correlation among age and diameter would be unlikely if for no other reason than dispersion of tree diameters within age cohorts. Periodic harvesting tends to reduce within-cohort diameter dispersion if cutting were concentrated on trees of low vigour and slow growth across the entire diameter range. This practice compresses the left tail of each cohort’s diameter distribution and thus reduces the diameter range of each age cohort. This tends to increase the correlation between tree diameter and age within the residual stand. When trees are harvested across only a portion of the diameter range, we might expect the correlation coefficient between age and dbh to fall somewhere between low and high extremes, depending on how much of the diameter range is unregulated. For example, in a forest where only trees 11 inches dbh and larger are cut, stand structure below 11 inches dbh is uncontrolled and free to develop naturally. In one such uneven-aged oak forest in the Ozark Highlands, tree diameter explained 43% of the variation (r = 0.65) in the ages of oaks 2 inches dbh and larger after two cutting cycles (Fig. 9.10A). The associated diameter distribution nevertheless is relatively well balanced despite the irregular age distribution (Fig. 9.10B). Relatively balanced diameter distributions therefore are possible within very irregular age distributions. Irregularity in the timing and intensity of harvesting also may reduce the correlation between tree age and diameter by increasing heterogeneity in stand stocking. Because diameter growth is very sensitive to stand density, anything that increases spatial heterogeneity in stand density is likely to decrease the correlation between tree age and dbh. Other factors that increase spatial heterogeneity in stand density include irregular mortality, natural disturbances, cutting cycles that fluctuate with market conditions and inconsistent application of

345

100 80 60 40 20 0

(B)

8

12 16 Dbh (in.)

20

140 120

Number of trees

4

100 80 60 40

Number of trees

Tree age (years)

(A)

140 120 100 80 60 40 20 0

Sustainability and stability

0 20 40 60 80 100 120 140 Tree age class (years)

20 0

2 4 6 8 10 12 14 16 18 20 22 Dbh (in.)

Fig. 9.10.  Age and diameter distributions of 578 randomly selected oaks in a 640 acre uneven-aged oak forest in the Ozark Highlands of Missouri. (A) The bivariate distribution of ages and diameters of trees 100 years old or less (98% of the sample population). Dbh accounts for 43% of the variation in tree age; the correlation coefficient is 0.65. (B) The observed frequency distribution of trees in 1 inch dbh classes (bars) and the negative exponential function fitted to the observed frequencies (curved line); q = 1.32 for 1 inch dbh classes. Inset: the age frequency distribution of the same population. The age distribution is incomplete and biased because observations are truncated at the 2 inch dbh class (see text for related discussion). This forest has been managed for 40 years by the singletree selection method and has been through two cutting cycles. The predominant species are white, scarlet, black, northern red and chinkapin oaks. (Adapted from Loewenstein, 1996.)

cutting guidelines and prescriptions. Low correlations between age and dbh occur in even-aged stands that are in silvicultural transition to unevenaged structure, but have progressed through only one or two cutting cycles. Normal variation in the oak regeneration process (Chapter 3, this volume)

346

also may influence the correlation between age and dbh in uneven-aged stands. Most applications of uneven-aged silviculture are likely to begin with stands in various irregular uneven-aged states with a range of diameter distributions ranging from poorly balanced to well balanced. Theoretically, an ecologically appropriate and consistently applied guiding curve should gradually increase the correlation between tree age and dbh. However, it is unlikely that the correlation would ever approach unity for the reasons cited, and because monitoring tree ages and cutting to maintain an actual age distribution is impractical.

Silviculturally, the term sustainability usually connotes a forest or stand attribute that can be maintained to perpetuity. This includes the traditional concept of sustained yield, which implies that timber yield is sustainable indefinitely through a combination of controlled periodic harvesting and other silvicultural practices. The sustainability concept also can be applied to other forest attributes such as wildlife habitat, water yield, biodiversity and aesthetic values. Regardless of context, sustainability implies permanence and continuity. It is nevertheless difficult to verify scientifically that a given forest attribute such as a particular species composition and size structure is indeed sustainable. This follows for two reasons. First, the concept of sustainability infers a centuries-long time period that lies beyond managerial or scientific experience. Secondly, ecosystems are continually changing and thus are not intrinsically stable. Silviculture nevertheless implies some degree of control over natural processes and therefore predictability regarding when and where certain forest conditions will occur. Silviculture is used to direct and sometimes ‘suspend’ succession within a narrow range of ecological states that satisfy human objectives. Specifically, uneven-aged silviculture attempts to suspend stands and forests at a relatively constant species composition, age structure and size structure. This makes uneven-aged silviculture more complex than evenaged silviculture. The term silvicultural stability is used here to refer to an ecological state or narrow range of states that can be maintained through silviculture. An ideal uneven-aged silvicultural system would create an uneven-aged state that is both economically viable and ecologically sustainable. And once

Chapter 9

the desired state has been created, it must be maintained by periodically harvesting trees in excess of requirements for an ecologically appropriate guiding curve. The sustainability of the resulting tree diameter distribution will depend on both ecological and economic factors. To be ecologically sustainable, both the stand density and the shape of diameter distribution must be consistent with site factors and the biological characteristics of the tree species present, including growth and survival rates, shade tolerance and regeneration dynamics. An appropriate stand size structure also must be consistent with sustaining the selected residual density, which in turn must provide periodic timber harvests that satisfy economic and operational requirements. Silvicultural application of the negative exponential distribution assumes that only a fraction of the trees in any one diameter class grow into the next larger class and that this fraction is constant across all diameter classes. Sustainability of stand structure requires a sustained flow of reproduction into the overstorey. Timber yields are realized through the periodic harvest of the trees in each dbh class that are excess to the silviculturally prescribed guiding curve (Fig. 9.6), and these harvests are necessary to maintain the desired diameter distribution. Trees therefore must be harvested across a wide range of tree diameters, not just the largest diameters. Concentrating each harvest on only the largest diameter classes is unlikely to sustain the necessary uneven-aged stand structure or stocking. This practice, sometimes called ‘selective cutting’, results in poor control of stand density, stand structure and tree quality, and is unlikely to be sustainable. Despite the similarity between the terms ‘selective cutting’ and ‘selection cutting’ (or ‘singletree selection’), the two practices have little in common silviculturally. Too often this difference is not recognized or understood. Maintenance of the desired stand structure and composition may not be assured even when trees are harvested across the entire range of diameters in accordance with a guiding curve. Doing so assumes that the resulting tree survival and growth will, through successive harvests, conform to the artificially imposed q. But conformity requires that q remain constant across the entire range of diameters as stand density rebounds after each harvest. However, there is no a priori reason to assume that this will happen. Survival rates among trees of different diameter classes may change as a function of

Uneven-aged Silvicultural Methods

stand density. Thus, if thinning to a specified q and a residual stand density results in higher rates of survival among small-diameter trees than largediameter trees, the resulting q value over the low end of the diameter range eventually would assume a lower value than that of trees in the upper end of the diameter range. Maintaining the desired q also requires a rate of ingrowth of trees into the overstorey that may not conform to that assumed by the selected q. Such non-conformities may be an indication that uneven-aged silviculture and the single-tree selection method are: (i) inappropriate; or (ii) appropriate but the selected guiding curve is inconsistent with stand dynamics. From outward appearances, the single-tree selection method may seem to represent the most ‘natural’ of the silvicultural systems. The naturalness of the method nevertheless may be deceptive on two counts. First, the success of the method depends on a silviculturally controlled rate of recruitment of natural reproduction into the overstorey that must continually balance periodic removals. Sustained regeneration of suitable species is therefore essential to the method’s success. Secondly, the method requires relatively intensive control of stand structure and density. Sustaining the system consequently depends on perpetuating the three-stage cycle of periodic reduction of stand density, recruitment of reproduction into the overstorey and stand growth (Fig. 9.3). For all but the most shade-tolerant species, greater silvicultural control is required to apply and sustain effectively the single-tree selection method than any other silvicultural method. This is the major problem in applying the method to the relatively shade-intolerant oaks with their erratic seed production cycles, seedling establishment and other regeneration uncertainties. Despite these problems, there is evidence that the method is suited to some North American oak forests. Applicability to oak forests Based on experience in the oak-dominated forests of the Central Hardwood Region, Gingrich (1967: p. 47) was pessimistic about successfully applying the single-tree selection method to oak forests: Unlike northern hardwood stands, upland hardwood stands (except for short periods after heavy cutting) rarely have high frequencies in the small diameters because such stands lack the tolerant species needed to maintain the inverse, J-shaped distribution. Indeed, there is no evidence

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that the common species comprising upland hardwood stands can maintain the inverse, J-shaped distribution naturally, and formal research attempts to maintain it by cutting have failed.

Roach (1968: p. 12) expressed similar pessimism: Selection cutting is a good forestry theory, although it is still only a theory and for timber production has not yet been either proved or disproved. I dislike to use it in the mixed oaks and upland hardwoods for three reasons: The expense required for its proper application is exorbitant ... We do not get satisfactory reproduction of preferred species under it. And when reproduction is slow or uncertain, selection cutting is very difficult to regulate for sustained yield, and the chances for error are numerous and serious.

If we were to heed the advice of these experienced forest scientists, we would not undertake the uneven-aged management of oak forests, or perhaps do so only apprehensively. However, the oaks cover a broad geographic area and include many different forest types, each with different ecological attributes. Possibilities and limitations Roach and Gingrich raise two ecologically and silviculturally important issues related to the unevenaged silviculture of oaks: (i) sustaining the regeneration process; and (ii) creating and sustaining a reverse J-shaped (negative exponential) diameter distribution. Both problems are central to sustaining uneven-aged stands. Maintaining a negative exponential diameter distribution requires the periodic establishment and development of oak reproduction. Although it may be possible to create the requisite diameter distribution in the absence of adequate regeneration, it cannot be sustained unless there is periodic recruitment of reproduction into the overstorey. Sustained, periodic recruitment of oak reproduction into the overstorey determines the success or failure of the single-tree selection method in oak forests. The applicability of the method to a given stand or forest accordingly depends on the intrinsic regeneration characteristics of a forest, especially its capacity to accumulate, or build up, oak reproduction over successive acorn crops (Chapter 3, this volume). Such accumulation is favoured on the drier sites where oaks tend to persist. In contrast, oaks are less likely to regenerate successfully on the more mesic sites because the

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requisite accumulation of oak reproduction is less likely to occur there. On those sites, maintaining oaks is difficult regardless of regeneration method. Additional problems are created by the selection forest’s multi-layered canopy and the perpetual shade it casts on developing oak reproduction. Even in stands that are intrinsic accumulators of oak reproduction, the recruitment dynamics of oak reproduction into the overstorey are poorly understood. Generally the movement of oak reproduction into the overstorey will be greatest shortly after harvest and then decline as stand density and crown closure increase. Decline in recruitment also may occur as the population of large reproduction ready for recruitment into the overstorey is temporarily exhausted by its own ingrowth into the overstorey. Significant recruitment of oaks into the overstorey therefore may occur only during a fraction of a cutting cycle in any given stand (Fig. 9.11). This fraction may not necessarily represent consecutive years, depending on weather and other environmental or biotic factors. It is consequently unlikely that a reverse J-shaped dbh distribution will consistently extend down into the reproduction size classes. Oak reproduction density and size in a selection forest varies with overstorey density (Fig. 9.12), which in a given stand is correlated with time since last harvest. Moreover, oak reproduction is characterized by relatively unpredictable shoot growth, dieback, resprouting and mortality – which may be only partially controlled by stand density (Liming and Johnston, 1944; Johnson, 1979; Crow, 1992; Lorimer et  al., 1994; Dey and Parker, 1996; Dey et  al., 1996). Consequently, the distribution of reproduction is often best expressed probabilistically. Diameter distributions of unmanaged even-aged oak stands typically form a bell-shaped or normal distribution by the time they reach a mean diameter of about 8 inches (Chapter 5, this volume). However, in oak-dominated ecosystems that are successional to shade-tolerant non-oaks, the tolerant species typically develop a subcanopy with a reverse J-shaped diameter distribution. The oak component, which continually shrinks in importance as shade-tolerant species capture the site, maintains its bell-shaped diameter distribution until it virtually disappears as a component of the stand. This pattern of successional displacement frequently occurs in oak forests that are successional to shade-tolerant species such as red and sugar maples and American beech or to aggressive

Chapter 9

Fig. 9.11.  Oak reproduction growing beneath an uneven-aged forest canopy on a site that favours the accumulation of oak seedlings and seedling sprouts over successive acorn crops. Growth of reproduction into the overstorey depends on the creation of canopy gaps and the presence of seedling sprouts with large root systems and the capacity for rapid shoot growth. Even when canopy conditions are favourable, significant recruitment may occur only during a fraction of a cutting cycle, depending on the availability of ‘recruitment ready’ reproduction. 0.7 0.6 Oak reproduction (genets/acre)

0.5 P

0.4

≥ 100

0.3

≥ 200

0.2 0.1 0.0

≥ 400 0

20 40 60 80 100 Overstory basal area (ft2/acre)

120

Fig. 9.12.  The estimated probability of occurrence (P) of oak reproduction densities of at least 100, 200 or 400 genets/acre that are at least 4.5 ft tall and up to 1.6 inches dbh in relation to overstorey density in an uneven-aged oak forest in the Ozark Highlands of Missouri. The oak reproduction is predominantly white, black and scarlet oaks. Based on a logistic regression and reproduction densities observed at a scale of 1/50acre. (From Larsen et al., 1997.)

shade-intolerant species such as yellow-poplar (Lorimer, 1980, 1981, 1983, 1984; McGee, 1984; Nowacki et al., 1990; Cho and Boerner, 1991). The diameter distribution of the overall stand may be

Uneven-aged Silvicultural Methods

reverse J-shaped, but the oaks themselves retain a normal diameter distribution that may be obscured at a scale that depicts the overall distribution (Fig. 5.11, this volume). The oak’s inability to maintain a negative exponential diameter distribution because of its shade intolerance and related regeneration failures ultimately seals its successional fate in forests that are recalcitrant accumulators of oak reproduction (Fig. 3.18, this volume). Moreover, reduction of stand density by itself is unlikely to resolve the problem of inadequate oak regeneration in stands undergoing successional displacement. Reduction in stand density may actually hasten the rate of displacement (Abrams and Nowacki, 1992; Jenkins and Parker, 1998). Exceptions may occur where wildfires, prescribed burning or other disturbances recurrently reduce stand density and simultaneously facilitate the build up of oak reproduction. Prescriptions that reproduce these effects may be feasible under evenaged silviculture, where they can be effectively applied once or twice towards the end of the rotation (see Chapter 8, this volume). But to effectively sustain the requisite build up of oak reproduction and its recruitment into the overstorey of an uneven-­ aged stand, these techniques may need to be applied during each cutting cycle. The economic practicality of such intensive practices and attendant risks are

349

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(A)

32

Trees/acre

28 24 20 16 12 8 4 0 (B) 80 70 Trees/acre

questionable. Practical limitations therefore would appear to outweigh possibilities for effectively applying the single-tree selection method to forests that are recalcitrant accumulators of oak reproduction and have strong successional patterns that favour non-oaks. Much of the experience in applying single-tree selection nevertheless has been obtained in such oak forests. The collective experience has produced a history of documented failures in applying the single-tree selection method to oaks and other relatively intolerant hardwoods (Gingrich, 1967; Roach and Gingrich, 1967; Trimble, 1970, 1973; Schlesinger, 1976; Smith, 1980; Della-Bianca and Beck, 1985; Smith and Miller, 1987). In turn, those results have often been generalized as a silvicultural principle that single-tree selection is prone to failure in all oak forests (e.g. Sander, 1977; Hibbs and Bentley, 1983; Marquis and Johnson, 1989; Marquis et al., 1992). Applying the single-tree selection method to oak forests that intrinsically accumulate oak reproduction represents other possibilities. Because these forests are generally the more xeric and less productive ecosystems, we might question the economics of applying this relatively costly silvicultural method to them. From a strictly ecological perspective, it nevertheless would appear to be more feasible to apply the method there than to stands that are recalcitrant accumulators for two related reasons: (i) regeneration dynamics; and (ii) diameter distributions. Whereas the regeneration dynamics of xeric oak forests have been discussed earlier (Chapter 3, this volume), the significance of diameter distributions requires explanation. Relatively undisturbed xeric oak stands of the eastern USA are often comprised of numerous small-diameter white oaks. This condition is exemplified by, but is not unique to, the white oak–black oak forests growing on deep sands in northern Lower Michigan. These forests, which range in site index from about 50 to 60, often form unbalanced uneven-aged stands (Johnson, 1992). Where there has been little or no previous management, the diameter distribution of white oak approaches a negative exponential distribution. In contrast, black oak (often mixed with northern pin oak) typically forms a bell-shaped diameter distribution (Fig. 9.13A). These inherently different diameter distributions reflect species’ differences in shade tolerance, survival rates and recruitment rates into the overstorey. The composite size structure of a typical stand approaches, but does not attain, a balanced

2

4

6

8

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20

Stocking (%) (q = 1.3) 80

60

60

50

50

40 30 20 10 0 2

4

6

8

10

Dbh class (in.) Black oak

White oak

Fig. 9.13.  Representative size structure of white oak–black oak stands in northern Lower Michigan. (A) The distribution of diameters by species for a stand at 81% stocking (33% white, 48% black oak, with mean diameters of 5.1 and 8.0 inches, respectively). (B) The composite diameter distribution (bars) and guiding curves for three levels of stocking (based on Gingrich, 1967) over the dbh range of 2–20 inches. (Adapted from Johnson, 1992.)

negative exponential distribution. Diameter distributions are often deficient in the 2–3 inch diameter classes (Fig. 9.13B). Similar diameter distributions have been reported for relatively undisturbed oak stands in the Ozark Highlands of Missouri (Shifley et al., 1995). How then might the single-tree selection method be applied to these oak forests? To answer, assume that Fig. 9.13B characterizes the current size structure of one such forest. Note that there is a deficiency of trees in the two smallest dbh classes. A plausible explanation for this deficiency might be that the smaller oaks are dying from suppression at

Chapter 9

a relatively high rate and that there is insufficient recruitment of reproduction into the overstorey at the prevailing overstorey density (81% stocking). If that were the case, we should be able to increase the survival and recruitment rate through appropriate periodic reductions in stand density. Numbers of small oaks accordingly would eventually move upwards towards a balanced negative exponential distribution such as that represented by the 50% or 60% stocking curves illustrated in Fig. 9.13B. This is the case in similar oak forests of the Ozark Highlands of Missouri. There, sustaining the requisite recruitment of oak reproduction requires reducing total stand density to about 50% stocking at the beginning of each 15–20 year cutting cycle

(Larsen et  al., 1997, 1999; Loewenstein et  al., 2000). Although this is 5–10% below the minimum stocking usually recommended for oak forests in the Central Hardwood Region (Roach and Gingrich, 1968; Sander, 1977), stand densities rebound to about 75–80% stocking by the end of a 20-year cutting cycle on average sites. Such densities, combined with a guiding curve based on a q of 1.2–1.3 (for 1 inch dbh classes), appear to be consistent with the natural regeneration and stand dynamics of these forests (Larsen et al., 1999). A 155,000-acre forest in the Ozark Highlands dominated by white, black and scarlet oaks has been successfully managed for more than 40 years using the single-tree selection system (Fig. 9.14).

Fig. 9.14.  An uneven-aged oak stand in the Ozark Highlands of Missouri. By maintaining moderately low stand densities and using the single-tree selection method, an uneven-aged forest comprising a reverse J-shaped diameter frequency distribution representing all age classes of oaks can be sustained. Unlike the oak forests of many other ecoregions, these diameter distributions are silviculturally sustainable because of the oak regeneration dynamics of the region’s forests. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

Uneven-aged Silvicultural Methods

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reproduction into the overstorey and increases the mortality rate of small trees as suppression by larger trees intensifies. This trend gradually flattens the diameter distribution, which reduces q. The result is an oscillation between steepening and flattening of the diameter distribution about the 10  inch harvest threshold diameter, which is perpetuated by periodic partial cutting above the threshold. The threshold diameter effectively acts as fulcrum about which the shape of the diameter distribution below the threshold diameter oscillates (Fig. 9.15). The portion of the stand 10 inches in diameter, while largely controlled silviculturally, is also responding to natural stand dynamics. Trees smaller than 10 inches dbh, while largely controlled by natural stand dynamics, are also influenced by the periodic reduction on stocking of larger trees. White oak plays a key role in this process because of its predominance in the small diameter classes (including reproduction), shade tolerance and persistence as a canopy dominant. The application of the single-tree selection method in the Ozark Highlands thus produces a

In this forest, only trees ≥ 10 inches dbh are harvested. Analyses of continuous forest inventory records have shown that, after two cutting cycles, diameter distributions above the minimum cutting threshold have maintained a balanced negative exponential diameter distribution that reoccurs at a small (< 1 acre) scale (Loewenstein, 1996; Loewenstein et al., 2000). However, the shape of the distribution below the threshold fluctuates significantly (Wang, 1997). This fluctuation may be caused by the interdependency of stand densities above and below the threshold diameter, which in turn is perpetuated by periodic cutting (Wang, 1997). Accordingly, a partial cut of trees ≥ 10 inches dbh instantaneously steepens the diameter distribution curve, which increases q. Further curve steepening may result from reduced mortality of small trees and increased recruitment of reproduction into the overstorey associated with the reduction in the density of trees above the 10 inch threshold diameter. The steepening trend eventually reverses itself as stand density above the threshold gradually rebounds. This rebounding, in turn, slows the recruitment rate of

70 50 Merchantable trees

Trees/acre

30 q

20

=

1.2

8

q

=

1.3

T Fl

5

uc

10 8

tu

at

ion

in

q

q Gu

6

q

Unmerchantable trees 4 2

4

6

8

10

idi

=

=

ng

1.3

1.3

cu

rv e

12

Dbh (in.) Fig. 9.15.  Fluctuation in q for trees smaller than 10 inches dbh during a single cutting cycle in an uneven-aged oak forest in the Ozark Highlands. (Adapted from Wang, 1997.) Diameter distributions are shown in logarithmic scale to emphasize the magnitude of fluctuation. Trees below the 10 inch threshold dbh (T) are unmerchantable and are not harvested. In this ecosystem the threshold diameter appears to act as a fulcrum about which the shape of the diameter distribution to the left of T oscillates. The resultant ‘self-limiting instability’ of q below T is perpetuated by periodic partial cutting to a guiding curve above T (see text).

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Chapter 9

self-limiting instability of the diameter distribution characterized by an oscillation in q below the minimum cutting diameter. This fluctuation in slope shape tends to return q to a relatively stable value once stand densities return to about 70% stocking (Wang, 1997). Self-limiting instability of the diameter distribution therefore appears to be a characteristic of the forest’s response to the silvicultural system. Regardless of the explanation, there is substantial evidence that the single-tree selection method can be used to sustain uneven-aged oak stands in the Ozark Highlands. General considerations Apart from the preceding example, the long-term sustainability of the single-tree selection method in oak forests that are intrinsic accumulators of oak reproduction remains to be verified in practice. Nevertheless, factors consistent with the method’s application to those forests include a natural regeneration process that sustains an adequate oak regeneration potential and a naturally occurring diameter distribution that often approaches a negative exponential distribution. Although it is often assumed that oak stands are typically even-aged, they can occur as uneven-aged populations at the relatively small spatial scales (e.g. 1 acre) required in selection silviculture (Loewenstein, 1996; Loewenstein et al., 2000). Both regeneration and stand structure therefore are potentially adaptable to creating and sustaining negative exponential diameter distributions in these ecosystems. Such distributions are most likely to occur when stands are managed at moderately low residual densities and low q values. Logical candidates for uneven-aged silviculture accordingly include oak stands with a pre-existing wide range of diameters and ages that occur on sites that are intrinsic accumulators of oak reproduction. In selecting a guiding curve for application to oak forests that are potentially compatible with the single-tree selection method, two factors are of paramount importance: (i) the steepness of the curve slope as expressed by q, which determines the proportion of small to large diameter trees; and (ii) the height (intercept) of the guiding curve, which determines stand density. An appropriate guiding curve therefore would be one that defines a moderately low overall stand density (e.g. 60% stocking) and q values within the range of 1.1–1.3 for 1 inch dbh classes. This combination would appear to be consistent with the light requirements for oak

Uneven-aged Silvicultural Methods

reproduction and the natural dynamics of these ecosystems. However, similar oak forests in other regions may respond differently. Guiding curves for values of q that exceed 1.3 (based on 1 inch dbh classes) are unlikely to be compatible with the natural dynamics of oak stands that intrinsically accumulate reproduction because natural regeneration in those stands will be unlikely to produce enough oak reproduction or recruit it to the overstorey. The success of the method will partly depend on a satisfactory growth response of trees in inferior crown classes to periodic reductions of stand density. At least two studies have shown that the response of overtopped white oaks to release is highly variable and in part depends on tree vigour as evidenced by crown and stem form, diameter and previous growth rate (Schlesinger, 1978; McGee and Bivens, 1984). McGee and Bivens (1984) concluded that ‘overtopped white oak trees are not good candidates for crop trees. Even if high-quality overtopped oaks are present, their performance following release will at best be variable and their potential to produce high-quality products questionable.’ These conclusions are consistent with the oak’s weak apical dominance (Zimmermann and Brown, 1971; Oliver and Larson, 1996) and its associated inability to regain apical dominance after prolonged suppression. This physiological characteristic, by itself, would seem to work against successful application of the singletree selection method. Oaks in inferior crown classes also have a propensity to produce epicormic branches following release. Both characteristics are potentially problematic in sustaining tree quality, and thus in providing a sound economic basis for the method (Trimble and Seegrist, 1973; McGee and Bivens, 1984). Nevertheless, it could be argued that many of these negative effects might be avoided or minimized by managing oak stands at relatively low densities and concentrating periodic removals on trees of low vigour within each diameter class. The long-term outcome of such practices nevertheless remains undetermined. Application of the single-tree selection method to a large forest property that covers a wide range of sites from xeric to mesic also may not be uniformly sustainable because of associated variation in oak regeneration potential. This may require mixing even-aged with uneven-aged silvicultural methods to accommodate site-related variation in oak regeneration potential.

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Field implementation The guidelines outlined below assume the objective is to sustain oaks as an important stand component using the single-tree selection method. Where the method is deemed inappropriate and oaks are the desired species, we recommend other silvicultural methods such as shelterwood (Chapter 8, this volume) or group selection methods discussed later in this chapter. In some cases, the silviculturist may choose to apply the single-tree selection method and to accept implicitly the eventual successional displacement of an existing oak component by other species. However, the following guidelines assume that the objective is to perpetuate oakdominated stands in ecosystems showing some propensity to sustain oaks in a negative exponential or similar diameter distribution. prelimary inventory.  A preliminary stand inventory is requisite to: (i) deciding whether the singletree selection method is ecologically appropriate; (ii) describing the current diameter distribution of the stand; and (iii) selecting an appropriate guiding diameter distribution curve. The overstorey of each stand should be inventoried to provide a quantitative description of its size structure, density and species composition. Inventories should record diameters by species and include trees down to the 2 inch dbh class. Including small-diameter trees is important in assessing the sustainability of oaks with the single-tree selection method. Diameter frequency distributions should then be prepared for the stand as a whole and for individual species or species groups. For stands or species groups that approximate a negative exponential distribution, a preliminary estimate of q can be obtained by determining the proportion of basal area in the large (e.g. sawtimber) size classes (Fig. 9.16). Figure 9.16 or similarly derived curves together with point sample estimates of basal area (Kershaw et  al., 2016) can facilitate a quick approximation of q. Advance reproduction also should be inventoried. If a regional regeneration guide appropriate to the stand is available (see Chapter 8, this volume), it may be necessary to follow its recommended inventory procedure to obtain an adequate evaluation of the quantity of advance reproduction. Even though such guides are usually designed for evenaged applications, directly or indirectly they may provide useful information on the sustainability of

354

1.5 1.4 q 1.3 1.2 1.1

15 25 35 45 55 65 75 Stand basal area in sawtimber (%)

Fig. 9.16.  Relation between q (for 1 inch dbh classes) and percentage of stand basal area in sawtimber (trees ≥ 11 inches dbh) for diameter distributions that conform exactly to the negative exponential distribution. The closer the observed distribution is to the negative exponential, the more accurate is the estimate of q derived from the curve. The curve shown is based on trees 2 inches dbh and larger. The relation facilitates quick preliminary estimates of q from point samples of basal area in stands with negative exponential or similar diameter distributions. Values of q for other dbh class-widths in inches (w) are given by qw.

oaks. Advance reproduction inventories should include an assessment of the number of trees in the 1 inch dbh class or other large reproduction in comparison to the number of trees specified for the 2 inch dbh class by the guiding curve. One indication of adequate regeneration potential would occur when the product of q (from the guiding curve) and the density of trees in the 2 inch dbh class approximate the density of reproduction in the 1 inch dbh class. interpreting the overstorey inventory. 

For a given species, a diameter frequency distribution that forms a bell-shaped curve indicates that there has not been sustained recruitment of that species into the overstorey. The relative abundance of oaks in the smaller diameter classes thus provides one indicator of the potential sustainability of oaks under the single-tree selection method. This is particularly true if white oak, one of the more shadetolerant oaks in eastern forests, is a major stand component. If its diameter distribution is more-or-less bell-shaped and lacking in small trees, it is unlikely that the oaks will be sustainable as an uneven-aged population. The presence of large numbers of shade-tolerant non-oaks, especially maples, that

Chapter 9

form reverse J-shaped diameter distributions spanning a range of diameters from small to large is good evidence that the successional displacement of oaks is already underway. Wherever oaks share canopy dominance with shade-tolerant competitors such as sugar maple or fast-growing intolerant competitors such as yellow-poplar, the single-tree selection method is unlikely to sustain the oaks. However, poor representation of overstorey maples in the smaller diameter classes, even when maple reproduction is present, may indicate a limited potential for maple to displace the species that currently dominate the site. Although site index is not always a good indicator of oak regeneration potential, in general it is difficult to regenerate oaks in uneven-aged stands where oak site index exceeds 65 (Weitzman and Trimble, 1957; Trimble, 1973). Local or regional ecological classification systems sometimes provide additional information on the relative persistence of oaks within defined ecosystem types (see Chapter 1, this volume). Generalized interpretations of diameter distributions as discussed above often may be complicated by variation in stand density, disturbance history, site quality, species composition and other factors (Lorimer and Krug, 1983). Variation in any of these factors may produce conditions that are misleading with respect to the sustainability of oaks. Even in ecosystems that are intrinsic accumulators of oak reproduction, time since last disturbance may affect diameter distributions. We nevertheless propose that wherever numerous small-diameter oaks are growing together with large-diameter oaks at moderate to high relative stand densities, it is likely that oaks can sustain a negative exponential diameter distribution. A stand’s current age and size structure are often valuable indicators of the relative difficulty or ease of silviculturally creating and sustaining a balanced uneven-aged structure. interpreting the advance reproduction inventory. 

An inventory of the advance reproduction of a stand can provide insight into its replacement potential, and how that potential varies spatially within the stand. Local or regional regeneration guides such as those described in Chapter 8 (this volume) may be helpful. Local ecological classification and site evaluation guides also may be helpful in assessing a stand’s regeneration and successional potential (e.g. Gysel and Arend, 1953; Smalley, 1978, 1979, 1982, 1984, 1986, 1988; Allen, 1987, 1990; Hix, 1988; Kotar et al., 1988; Jones, 1991;

Uneven-aged Silvicultural Methods

Cleland et al., 1993; Bakken and Cook, 1998; Van Kley et al., undated). The presence of several hundred large (e.g. ≥ 2 ft tall) oak seedling sprouts/acre, which commonly occurs in the drier oak–hickory forests in much of the eastern USA, might be a good indicator that a stand is an intrinsic accumulator of oak reproduction and thus potentially sustainable under singletree selection. An abundance of small oak seedlings, by itself, does not indicate that a stand is necessarily an intrinsic accumulator of oak reproduction (Chapter 3, this volume). In some oak stands, abundant maple reproduction may be a good indicator of the maple’s potential to successionally displace oaks. However, this is not always true, especially on xeric or xero-mesic sites. In mesic and hydric sites the presence of maple or other shade-tolerant reproduction and aggressive shade-intolerant species is usually a clear indication of the successional potential of other species to displace the oaks. Distinguishing which process prevails may be evident from observing nearby stands on similar sites. Where a local or regional ecological classification system is available, the prevailing process may be apparent from observing stands within the same ecological class. Moreover, the ecological classification system itself may specify the successional status and thus relative persistence of oaks in a defined ecological unit. Old-growth stands also can provide useful benchmarks for assessing the successional status of oaks on similar sites. The presence of non-oak reproduction with site- or life-history-restricted growth potentials such as red and sugar maples, blackgum, sassafras, sourwood and flowering dogwood may not, by itself, restrict oak’s regeneration potential. Their potential to do so is often specified by or can be inferred from local or regional ecological site classification systems. Personal silvicultural experience in the same or similar ecosystems sometimes may provide the best basis for decisions. Often there is no absolute assurance of the desired outcome, only reasonable and informed judgements. If the current structure and composition of the overstorey and reproduction is judged on the best available criteria to be suitable for single-tree selection, the next step is to define an appropriate guiding diameter distribution curve. selecting a guiding diameter distribution curve.  The guiding curve applied to regulate a stand’s structure should be consistent with its ecological

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conditions and stand dynamics as discussed earlier. It also should include diameters down to about 2 inches dbh. Managing stands at relatively low q values (1.1–1.3 for 1 inch dbh classes) and periodically reducing residual stand densities to about 50 ft2 of basal area/acre is generally consistent with oak regeneration dynamics. For a given stand density, low q values allocate proportionately more growing space to the larger and more valuable crop trees than do higher q values (Fig. 9.7B). However, in mesophytic and hydric stands it may be difficult to maintain low q values. There, reproduction of shade-tolerant or aggressive shade-intolerant non-oaks is likely to capture much of the growing space made available after each reduction in stand density. This, in turn, may steepen the diameter distribution curve and increase q. Low residual stand density also is likely to exacerbate this effect. Given those conditions, the best course may be to abandon pretensions of sustaining oaks with the single-tree selection method. Other techniques such as the shelterwood (Loftis, 1990) or group selection (Murphy et al., 1993; Miller et al., 1995) methods may be better options. Selecting a guiding curve also requires selecting the diameter of the largest tree to be retained after a harvest cut. The value selected should be based on site quality, species longevity, local timber markets and financial maturity of trees. Other factors being equal, trees should be grown to larger diameters on good sites than on poor sites. However, choices are sometimes limited by species characteristics. For example, scarlet oak is vulnerable to dieback and other causes of early mortality, regardless of site quality (Starkey and Oak, 1989; Oak et al., 1996). The minimum diameter for the guiding curve also must be specified because the diameter range and q must be set to obtain a guiding curve that conforms to both the desired q and the residual stand density (e.g. by application of Equation 9.8). A guiding curve also must specify the diameter class width (w in Equation 9.8) to which the curve is applicable. Appropriately applied, Equation 9.8 can transform implementation of the single-tree selection method from an applied art form to a more objective science. It also can be coupled with mathematical growth models, linear programming, goal programming and other management tools to optimize cutting cycle length, cutting schedules for converting irregular stands to the target structure, species mixes and stocking for value growth

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(Adams and Ek, 1974; Adams, 1976; Hann and Bare, 1979; Buongiorno et al., 1995). However, in their application to uneven-aged oak forests, these management techniques should be constrained by ecological realities. There are few reasons to believe there is great flexibility in using the single-tree selection method to manage oaks. Where the objective is to sustain oaks, the method is constrained by the need to: ●● limit the method’s application to ecosystems that are intrinsic accumulators of oak reproduction; ●● maintain relatively low residual stand densities by reducing total stand density to about 50 ft2 of basal area/acre as frequently as practical (e.g. 15–20 year cutting cycles); and ●● use a guiding curve with a q value of 1.2–1.3 based on 1 inch dbh classes (Larsen et al., 1999). marking guidelines.  Graphical representation of the guiding curve (e.g. Figs 9.5 and 9.6) is unlikely to provide a practical field guide for marking trees to be harvested. It is usually more efficient to mark trees based on the desired residual basal area of a few broad size classes (e.g. saplings, poles and two sawtimber classes). In the field, a running tally of basal area can be efficiently obtained by size classes using a prism or other point sampling device. Marking to the correct residual density per acre requires knowing for each size class the basal area that conforms to the guiding curve as well as the actual basal area present. After the guiding curve has been specified, the corresponding residual stand basal area can be calculated for each diameter class of interest. Of the total stand basal area represented by the guiding curve, the proportion to be retained in each size class will depend on q and the range of diameters considered. For any guiding curve based on the negative exponential distribution and a given q, the percentage of total stand basal area to be retained in a given dbh class will be the same, regardless of the selected residual density (Table 9.2). Combined with stand inventories, these percentages can be used to specify the actual residual and harvest basal areas per acre for any dbh class. To ensure that the goals of the guiding curve are met, frequent checks on residual stand basal area should be made during the course of timber marking. Trees should be marked for removal over the full range of merchantable size classes and concentrated in size classes with surplus trees. Although the general silvicultural rule is to remove trees

Chapter 9

Table 9.2.  The percentage of total stand basal area by tree size classes for selected q values.a Stand basal area (%) according to q value of Tree size class (dbh in inches)

1.1

1.2

1.3

1.4

1.5

1.7

Saplings (≤ 5) Poles (6–10) Small sawtimber (11–16) Large sawtimber (17+)

5 21 42 32

11 30 39 20

19 38 32 11

28 42 24 6

37 43 17 3

54 37 8 1

a

Percentages are applicable to the dbh range of 2–20 inches and all stand densities; q values are for 1 inch dbh classes.

across the entire diameter range that are in excess of the guiding curve (Fig. 9.6), this may not always be necessary. The need to do so may vary among ecosystems and the stand dynamic unique to each. A good rule none the less is to remove trees at least across the range of diameters from 10 inches and larger, and within this range to leave the number of trees prescribed by an appropriate guiding curve. Removal of some undesirable pole-size trees will improve the survival of higher value residual trees and also concentrate growth on those stems (Smith and Miller, 1987). Within a dbh class, removals should be concentrated on trees of low vigour as evidenced by poor crown and stem form. Spacing and species composition should also be considered. The general rule is ‘cut the worst and leave the best’. In previously unmanaged stands with irregular diameter distributions, it may be necessary to work gradually towards the desired diameter distribution over several cutting cycles. To maintain adequate overall stocking, it is often necessary to compensate for an inadequate number of trees in one dbh class by retaining stocking in excess of the guiding curve in another class. To minimize epicormic branching, it is generally recommended that no more than onethird of total stand basal area be removed in one cut (Sonderman, 1985; Law and Lorimer, 1989). length of cutting cycle.  The cutting cycle is the interval between harvests. The length of a cycle will depend on stand growth rate and value, and local wood utilization and logging norms. The time between harvests should allow for accumulation of sufficient volume for an economically operable harvest. In hardwood forests of the eastern USA, economically practical cutting cycles range from about 10 to 20 years. On average upland oak sites in the Central Hardwood Region, stand stocking per cent based on Gingrich’s (1967) stocking equation increases at approximately 1.3%/year on average sites. On those sites, stands periodically reduced to

Uneven-aged Silvicultural Methods

50% stocking can be expected to increase to about 70% in 15 years. Due to the oak’s relative intolerance of shade, it is desirable to use short cutting cycles and minimize the length of time stands are at high basal areas and high stocking percentages (e.g. 80% stocking). A shorter cutting cycle also provides better opportunities to salvage mortality and losses in bole quality as a result of logging damage (Smith and Miller, 1987). Consequently, cutting cycles should be as short as is economically feasible. method variants. 

Variants of the single-tree selection method largely centre around various schemes for harvesting to specified diameter limits (e.g. Smith, 1980; Smith and Miller, 1987; Miller and Smith, 1993). Although such harvesting is not true single-tree selection, the results sometimes approximate cutting to a specified stand structure. As commonly applied, diameter-limit cutting usually refers to cutting all trees of sawlog size (often 11 inches dbh and larger) at each harvest. Such a method is not likely to be sustainable. However, diameterlimit harvests can be set to higher limits so that a proportion of the sawlog component is always retained. But whether diameter limits are set high or low, there is relatively little control over the density, structure, composition and quality of the residual stand. A variant of the diameter-limit approach, called ‘flexible diameter limit cutting’ has been tested in mixed mesophytic forests containing oaks in the central Appalachians (Miller and Smith, 1993). The method produced results similar to those expected using single-tree selection. Large trees with the potential to increase in value are retained whereas other merchantable trees are cut to improve stand quality. Residual stand density guidelines are used to adjust diameter limits before trees are marked to prevent overcutting and thus sustain yields. Accordingly, merchantable trees are harvested that are of low quality, high-risk undesirable species or

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are not earning an acceptable rate of return. Because rates of return decline for large sawtimber trees, harvesting this largest component of the desirable growing stock is flexibly focused on trees 18 inches dbh and larger. The minimum cutting diameter for those trees varies by species, site quality, the desired rate of return and potential for future improvement in log grade. Each species has its own diameter limit based on potential growth and rate of return. Harvest of other merchantable trees provides for continual improvement in overall stand quality. In effect, all size classes within the range of merchantable diameters are managed on the basis of potential rate of return. Using this method, residual stand structure is left somewhat to chance. After 20 years, the method nevertheless produced a stand structure similar to that expected using the single-tree selection method. monitoring.  Periodic inventories of stands or entire forests are necessary for monitoring the effectiveness of the single-tree selection method in meeting objectives for stand size structure, density and species composition. If objectives are not being met, adjustments can be made to the guiding curve or other silvicultural strategies. Determining the feasibility of implementing and continuing the application of the single-tree selection method depends on continually gathering and interpreting information on stand or forest conditions. Such information is essential to making informed silvicultural decisions. It is useful to develop decisionmaking guidelines applicable to defined ecoregions, and stand and site conditions. An example is provided by guidelines for selecting and managing upland oak stands for single-tree selection in the Ozark Highlands of Missouri (Table 9.3). The guidelines, based on pre-existing stand structure, density and composition, include recommendations for applying the method where appropriate, and refer the user to information on alternative methods where its application is not appropriate. Such guidelines can be detailed or generalized, tailored to meet various management objectives, and modified as experience and knowledge in their application accumulate.

Converting stands from even-aged to uneven-aged A common difficulty in applying uneven-aged silviculture occurs when the process of creating an

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uneven-aged stand begins with an even-aged or other ‘irregular’ age structure (Schütz, 2001, 2002). In even-aged stands, the conversion process requires a time-series of cuttings whose details vary with the initial age of the stand (i.e. its age at the time the first conversion cut is made). The success of conversion depends on whether the overstorey, or some persistent component of it, can survive the conversion period. This in turn depends on stand age and species composition. The longevity of trees present together with stand age provides an estimate of the time available for completing the conversion before the remaining overstorey must be removed or before age-dependent mortality becomes pervasive. Unpredictable and excessive mortality can become the bane of the conversion process because it threatens the control of stand structure, which provides the basis for the conversion. Conversion strategies have been described for the oak forests of the Ozark Highlands of Missouri (Loewenstein and Guldin, 2004; Loewenstein, 2005). Those forests (Figs 1.6 and 1.15, this volume) are amenable to uneven-aged silviculture largely because of their oak regeneration dynamics as discussed earlier in this chapter and elsewhere (Loewenstein, 1996; Larsen et  al., 1997, 1999; Wang, 1997). The conversion process is based on partitioning the utilization of growing space, that is stocking (Gingrich, 1967), among different tree crown strata. The goal is attained by a series of cuttings designed to create three crown strata comprising three stocking levels in the ratio of 3:2:1 from upper to lower strata. Although this ratio approximates a q value of 1.7 (for 2 inch dbh classes), obtaining a specific q value is not a silvicultural goal during the conversion period. Instead, each tree age group is treated as a separate even-aged unit. Then proper tree spacing combined with selecting for species and stem form determine tree removals. The 3:2:1 ratio thus assigns the largest proportion of growing space to the larger trees, which in turn determines stocking in the smaller size classes. At each cutting entry, stocking in most cases is reduced to 60%, which is deemed sufficient to sustain the recruitment of trees into the overstorey (Larsen et al., 1997, 1999). The conversion process can be specified by a cutting timetable for adjusting stocking to obtain the required three-tiered canopy (Table 9.4). The timetable details vary with the stage of stand development at the time of the first cut (designated as year

Chapter 9

Uneven-aged Silvicultural Methods

Table 9.3.  Silvicultural decision table and guidelines for applying uneven-aged silviculture to upland oak stands in the Ozark Highlands based on existing stand structure, density and composition.a Stand density and composition Adequate

Inadequate

a

Stand structure Adequate

Inadequate

Stand condition I: Stand structure, density and composition are all adequate for applying the single-tree selection method. Oaks form a relatively balanced reverse J-shaped dbh distribution for trees 2 inches dbh and larger. Basal area of oaks ≥ 2 inches dbh is ≥ 45 ft2/acre.b Subcanopy oaks (2–4 inch dbh classes) number at least 125/acre.

Stand condition II: Stand density and composition are adequate; stand structure is marginally inadequate. Oaks form a relatively balanced reverse J-shaped dbh distribution for trees ≥ 6 inches dbh, but not in the smaller dbh classes. Subcanopy oaks (2–4 inches dbh) number > 70 but < 125/acre. Basal area of oaks ≥ 2 inches dbh is ≥ 45 ft2/acre.b Recommended action: Same as Stand condition I, except mark the oak component of the merchantable stand to a q of 1.5.c Gradually increase q to 1.7c in succeeding harvest cuts as stocking of the oak subcanopy increases.

Recommended action: Mark the oak component of the merchantable stand to a guiding curve of 1.7c based on a total oak density of 45 ft2/acreb (oaks ≥ 2 inches dbh). If current total stand basal area is ≥ 85 ft2/acre, reduce total stand density to 60 ft2/acre in the first harvest cut. Across all merchantable diameters, cut the worst trees and leave the best (after considering biodiversity goals). Stand condition III: Overall stand structure (oaks + non-oaks) is adequate, but composition and density is inadequate. Stands form a reverse J-shaped dbh distribution including oaks ≥ 6 inches dbh. However, the subcanopy is dominated by non-oak hardwoods. An oak subcanopy (2–4 inches dbh) is nearly or completely absent and/or total oak basal area (oaks ≥ 2 inches dbh) is < 45 ft2/acre.b Recommended action: (i) Apply the group selection method to create conditions favourable to regenerating oaks and developing the desired uneven-aged stand structure and composition. Evaluate the various group selection options (see Table 9.7); (ii) Alternatively, apply even-aged silviculture (clearcut or shelterwood methods).

Stand condition IV: Stand structure and density are inadequate. Total oak basal area (oaks ≥ 2 inches dbh) is < 45 ft2/acre.b Stands are often characterized by bell-shaped dbh distributions of black and/or scarlet oaks averaging ≥ 8 inches dbh. Stocking of subcanopy oaks is inadequate (i.e. oaks 2–4 inches dbh number < 70/acre). Recommended action: Same as Stand condition III.

The table assumes uneven-aged silviculture is the preferred option and that a wide range of tree sizes from sapling to sawtimber classes are present. On average, commercially valuable non-oaks will comprise an additional 5 ft2 of basal area. If non-oak basal area is < 3 ft2/acre or absent, increase the minimum required oak stocking to 50 ft2 of basal area/acre. c Based on 2 inch dbh classes. b

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Table 9.4.  Timetables for converting even-aged to uneven-aged stands for three different starting conditions (immature, mature and over-mature stands). The row values show stocking per cent by tree age (expressed as an ‘age group’) for a given year before (+) or after (–) the initial conversion cut (C). (From Loewenstein and Guldin, 2004.) Stocking goals are attained by creating three-tiered uneven-aged stands with a residual stocking ratio of 3:2:1 representing upper, mid- and lower storeys, respectively. (a) Immature stands (e.g. even-aged stands in the stem exclusion or early understorey reinitiation stages of development; see Chapter 5, this volume): Stocking is at 100% 15 and 30 years before the first conversion cut (C). The first cut leaves the stand at 30% stocking. However, a second age class comprising another 30% stocking is quickly recruited from stump sprouts originating from the first conversion cut. This cut (C) is comprised of relatively large numbers of smalldiameter trees with high sprouting probabilities (Fig. 2.25, this volume). At C+15 the stand is thinned to 60% stocking distributed across two age groups. All or some of the cuts at C, C+15 and C+30 may entail precommercial thinning, depending on stand age at C; in some cases the C+15 cut may not be necessary (see text). (b) Mature stands: Stocking is reduced to 66% 15 years before (C–15) the first conversion cut (C). When that cut is made, the upper storey is reduced to 50% stocking and the mid-storey to 10%. Fifteen years later (at C+15) the upper storey is reduced to 40% and the mid-storey to 20%; then 15 years later (C+30) the upper storey is reduced to 30%, mid-storey to 20%, and lower storey to 10%. (c) Over-mature stands: The goal is similar to mature stands, but the stocking of older trees at any given year is higher. Note that stocking, in all three scenarios from time C on, is always 60% immediately after a cut.

Tree age group Year

1

2

3

C–30 C–15 C C+15 C+30 C+45 C+60 C+75 C+90 C+105 C+120 C+135 C+150

100 100 30 30 30 30 0 0 0 0 0 0 0

0 0 30 30 20 20 30 30 0 0 0 0 0

0 0 0 0 10 10 20 20 30 30 0 0 0

C–30 C–15 C C+15 C+30 C+45 C+60 C+75 C+90 C+105 C+120 C+135 C+150

100 66 50 40 30 30 0 0 0 0 0 0 0

0 0 10 20 20 20 30 30 0 0 0 0 0

0 0 0 0 10 10 20 20 30 30 0 0 0

C–30 C–15 C C+15 C+30

100 66 40 30 0

0 0 20 30 40

0 0 0 0 20

4 Stocking (%) (a) Immature stands 0 0 0 0 0 0 10 10 20 20 30 30 0 (b) Mature stands 0 0 0 0 0 0 10 10 20 20 30 30 0 (c) Over-mature stands 0 0 0 0 0

5

6

7

0 0 0 0 0 0 0 0 10 10 20 20 30

0 0 0 0 0 0 0 0 0 0 10 10 20

0 0 0 0 0 0 0 0 0 0 0 0 10

0 0 0 0 0 0 0 0 10 10 20 20 30

0 0 0 0 0 0 0 0 0 0 10 10 20

0 0 0 0 0 0 0 0 0 0 0 0 10

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0 Continued

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Chapter 9

Table 9.4.  Continued. Tree age group Year C+45 C+60 C+75 C+90 C+105 C+120 C+135 C+150

1 0 0 0 0 0 0 0 0

2

3

4

5

6

7

40 30 30 0 0 0 0 0

20 20 20 30 30 0 0 0

0 10 10 20 20 30 30 0

0 0 0 10 10 20 20 30

0 0 0 0 0 10 10 20

0 0 0 0 0 0 0 10

C in Table 9.4). In theory, the method can be applied to even-aged stands in virtually any stage of development. However, the method may be problematic in very young stands (e.g. during the stem exclusion stage of development, Fig. 5.2, this volume). There, second (C+15) and/or third (C+30) conversion cuts (Table 9.4) may be precommercial and therefore not cost-effective. In some cases, the C+15 cut (scenario (a) in Table 9.4) may be unnecessary. For those stands, the early cutting interval should be based more on how rapidly stocking increases rather than on a rigid time schedule. The method assumes that successful conversion depends on periodically reducing stocking to B-level (approximately 60%) based on Gingrich’s (1967) stocking chart. This level is deemed sufficient to sustain adequate overstorey stocking (and thus an economically viable periodic yield) and to also sustain the conditions necessary for recruitment of oak reproduction into the overstorey (Fig. 9.3). The method is more fully discussed elsewhere (Loewenstein and Guldin, 2004; Loewenstein, 2005).

The Group Selection Method The group selection method is a modification of the single-tree selection method where openings larger than the size of the largest individual trees are made in the forest canopy. Typical opening sizes range from 0.2 to 0.5 acre. The group selection method is a regeneration method usually aimed at obtaining reproduction of shade-intolerant and mid-tolerant species. The method reduces the negative visual impacts of harvesting trees over large areas associated with most even-aged methods. It nevertheless has much in common with the clearcutting and shelterwood methods, but at a smaller spatial scale.

Uneven-aged Silvicultural Methods

Like those methods, consistent success in regenerating oaks depends on obtaining adequate advance reproduction before group openings are created. In effect, the method is a melding of even-aged and uneven-aged systems (Fig. 9.17). In practice, some group openings are unavoidably created in the normal application of the single-tree selection method as a result of irregular mortality, associated salvage cutting and the occurrence of other unplanned irregularities in the forest canopy. Compared to the single-tree selection method the distinguishing feature of the group selection method is the planned creation of larger canopy openings. Despite little documented experience with the method, it has generated considerable interest among hardwood silviculturists. Whereas some have rejected the method (Nelson et  al., 1973; Roach, 1974) others have been more optimistic about its application (Minckler and Woerheide, 1965; Clark and Watt, 1971; Rudolph and Lemmien, 1976; Leak and Filip, 1977; Heald and Haight, 1979; Marquis and Johnson, 1989; Minckler, 1989; Jacobs and Wray, 1992; Murphy et  al., 1993; Miller et  al., 1995). Rejection has centred largely on perceived difficulties in yield regulation (Roach, 1974) rather than on regeneration or other silvicultural limitations. The method is potentially applicable to oak forests because it can provide the light necessary for oak regeneration (Fig. 9.18). Opening size controls light within the opening, and much attention has been given to defining a minimum opening size suitable for regenerating oaks. Minima for regenerating oaks in the Central Hardwood Region have been defined based on a computer simulation model of the solar radiation received on the forest floor (Fischer, 1979, 1981). The model expresses solar radiation as a percentage of that received in the open, which varies with surrounding tree

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Fig. 9.17.  Group openings can provide adequate light for the development of oak and other shade-intolerant species that often fail to regenerate under single-tree selection. Because of shading and root competition from the adjacent stand, reproduction grows most rapidly near the centre of the opening.

height, slope aspect and slope gradient. The estimated opening sizes assume that the minimum light requirement for oaks is one-third of full light. This value is based on the observed minimum light intensity for maximum rates of photosynthesis of northern red oak seedlings (Phares, 1971). Because light in small openings is directly related to the height of the surrounding trees, it is sometimes convenient to express minimum opening size in terms of those heights. Estimates of minimum opening sizes needed to support oak reproduction range from one to two tree heights, depending on aspect and slope (Table 9.5). Minimum opening size for Central Hardwood forests accordingly would range from about 0.1 to 0.6 acre for tree heights from 60 to 90 ft, respectively, across a wide

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range of aspects and slopes (Table 9.6). In bottomland oak forests in the South, where trees can attain heights of 125 ft or more, minimum opening size may exceed 1.6 acres. The amount of light initially received in an opening decreases with time because of crown expansion of the residual stand around the opening. Small openings therefore close more rapidly than large openings (Table 3.3, this volume). Consequently, crown closure rates probably set the lower limits of practical opening size for oaks to about two tree heights based on the one-third full-light criterion. Group openings of two tree heights also are consistent with earlier subjective assessments of minimum opening size (Trimble and Tryon, 1966; Miller et al., 1995; Smith et al., 1997; Jacobs et al., 2006).

Chapter 9

Fig. 9.18.  The interior of a one-eighth acre group opening (approximately one tree height in diameter) in an oak stand in south-eastern Ohio 5 years after cutting. Despite the small size of the opening, there is abundant tree reproduction including oaks. The shading effect of the adjacent stand is evident from the short trees near the opening edge (right foreground). (Photograph courtesy of USDA Forest Service, North Central Research Station.) Table 9.5.  Effects of selected aspects, slope gradients and opening sizes on percentage of direct solar radiation received on the forest floor in square openings. (From Fischer, 1981.)

Aspect (azimuth) (°)

Slope gradient (°)

Direct solar radiation (%)ab according to opening size (in number of tree heights) 0.5

1.0

2.0

4.0

0

30

 1

15

34

57

0 0 180 180

15  0 15 30

 4  5  5 12

19 24 30 37

47 56 66 66

74 86 89 87

a

Based on determining whether a given point on the forest floor within a forest opening is in the shade or sun. When a point is in the sun, the instantaneous direct beam solar radiation at that time is calculated and assumed for a given time interval. Direct solar radiation is then summed for the total time period considered and expressed as a percentage of that received on an open horizontal surface of equivalent area. Calculations are based on solar radiation received on clear, sunny days during the growing season (12 April–30 August) at latitude 40° 14’ 00“. b Bold values approximate or exceed minimum light requirements for maximum photosynthesis of northern red oak.

Uneven-aged Silvicultural Methods

Although these calculated values provide an objective basis for defining minimum opening size, light near the forest floor (i.e. at ‘reproduction height’) may be controlled as much or more by low vegetation as by canopy density (Lorimer et  al., 1994). Moreover, herbaceous and shrub species together with competing non-oak tree reproduction may respond more rapidly to canopy openings than the existing oak reproduction. Like the clearcutting method, adequate oak advance reproduction is essential to the successful regeneration of group openings. Oak reproduction therefore must be present in adequate numbers, size and spatial distribution (Sander et  al., 1984; Dey, 1991). Only then are oaks likely to be consistently competitive with other established vegetation after the opening is created. Although there is evidence that oak reproduction established after openings are made can contribute to future stand stocking in some situations (Johnson et  al., 1989; Jacobs and Wray, 1992; Golden, 1995; Loewenstein and Golden, 1995), it would be prudent to obtain the requisite advance reproduction before cutting.

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Table 9.6.  Area of circular openings in acres in relation to perimeter tree height. Opening size (number of tree heights) 1 2 3 4

Mean height of dominant and codominant perimeter trees (ft) 60

70

80

90

100

110

120

130

0.07 0.26 0.58 1.04

0.09 0.35 0.80 1.41

0.12 0.46 1.04 1.85

0.05 0.58 1.31 2.34

0.18 0.72 1.62 2.89

0.22 0.87 1.96 3.49

0.26 1.04 2.34 4.15

0.31 1.22 2.74 4.88

Whereas the lower limits of opening size can be objectively defined, it is less clear what the upper limits should be. Ultimately, the mosaic of group openings determines the minimum spatial scale at which the uneven-aged state will occur. Because uneven-aged stands include at least three age classes, a complete uneven-aged unit must be at least as large as the average area that will include at least three age classes of trees in the future. For example, three contiguous 1/3-acre openings form a 1-acre unevenaged unit, whereas three 1/2-acre openings form a 1.5-acre unit. As opening size increases, the canopy mosaic becomes increasingly ‘coarse grained’. At some upper size, the method will fail to provide the visual appearance of a continuous, all-aged forest canopy. Openings of 1/2 acre or less generally provide visual effects similar to that of the single-tree selection method (Marquis and Johnson, 1989). Visual impacts nevertheless are only one of many effects determined by opening size. Silviculturists have traditionally focused on the effects of opening size on reproduction and related edge effects of trees bordering the opening. Small openings create a large ratio of perimeter to opening area. For example, ten 1/3-acre circular openings (3.3 acres) create 3.2 times more perimeter (edge) than a single 3.3-acre circular clearcut. Oaks bordering openings are prone to developing epicormic branches when exposed to the high light intensities occurring along edges, which in turn reduces their bole quality and value (Trimble and Seegrist, 1973). At the same time, shade and competition from border trees suppresses reproduction near edges. Other possible edge effects include variation in species composition within openings that is related to seed dispersal distances, browsing of reproduction (Marquis, 1974; Marquis et  al., 1976; Collins, 2003), damage to reproduction by felling of surrounding trees, damage to the surrounding forest by logging within groups and associated pathogenic effects such as oak decline (Starkey et  al., 1989; Oak et  al., 1996), and effects on breeding

364

birds (Thompson et  al., 1995, 1996; Annand and Thompson, 1997; Thompson and Dessecker, 1997). Collectively, such effects are likely to be expressed at different scales and across different ‘influence zones’ (Fig. 9.19). There is therefore not a single edge effect, but many such effects. Factors other than regeneration and yield regulation thus may determine opening size or even the feasibility or appropriateness of the group selection method. Despite the attention given to opening size, other factors may be as or more important. For example, in bottomland oak–mixed hardwood stands in the South, no optimum gap size could be defined for oak regeneration. Instead, the importance of gap size is often overridden by other factors such as the density of the surrounding forest canopy, acorn abundance and predation, competition within gaps, browsing by animals, flood duration and microtopography (Collins and Battaglia, 2002, 2008; Collins, 2003). Similarly, in upland oak forests, oak regeneration in openings depends on factors besides opening size. These include ground cover composition and density, distance from gap centre, microsite soil moisture, size of oak reproduction at time of gap formation, animal browsing, changes in gap composition with time, topographic position of gaps, and patterns of vegetation change unique to canopy openings that vary in time and space (Clinton and Boring, 1994; Berg, 2004; Chiang et al., 2005; Heitzman and Grell, 2006; Heitzman and Stephens, 2006; Jacobs et al., 2006). Artificial regeneration of oaks in group openings offers one possible solution to the dilemma of the many factors that confound the natural regeneration process. Preliminary trials with northern red oak in southern Indiana produced promising results (Jacobs et al., 2006; see also Chapter 10, this volume, ‘Enrichment planting’). The problem of oak regeneration in group openings is fundamentally no different from that in even-aged forests as discussed in Chapter 8 (‘Natural Regeneration Methods’). As in regenerating even-aged

Chapter 9

1 2 3 4 5 6 Fig. 9.19.  Potential influence zones associated with edge effects related to: 1. Epicormic branching of trees in the residual stand; 2. Growth of tree reproduction; 3. Browsing of reproduction by deer; 4. Postharvest dispersal of acorns by rodents; 5. Damage to tree reproduction by felling surrounding trees, postharvest dispersal of acorns by blue jays; 6. Impact of timber harvesting in opening on oak decline in surrounding forest, nest predation and brood parasitism of birds. (Adapted from Bradshaw, 1992.)

oak forests, there are two requirements for success in using the method: (i) obtaining adequate oak advance reproduction before gaps are created; and (ii) having a predictive regeneration model for estimating the likelihood of regeneration success based on the reproduction present at the time group openings are created (see Chapter 8, this volume, ‘Regeneration models’). Then, meeting the first requirement (adequacy) can be defined by the second (the predictive model). To be useful, predictive regeneration models need to be developed for specific regions or ecological units (e.g. Figs 1.8 and 1.13, this volume). As of this writing, such models have yet to be developed for application to the group selection method. Assuming the method meets management objectives, it can be applied in various ways. One method is to follow procedures for the single-tree selection method with the added proviso of creating openings in each stand at each entry (Law and Lorimer, 1989). No attempt is made to keep track of individual groups. It is assumed that groups eventually become spatially indistinguishable or ‘lost’ in the overall forest matrix. This expectation

Uneven-aged Silvicultural Methods

is consistent with the objective of maintaining a balanced stand structure at a relatively small spatial scale. The strategy requires marking the stand to obtain a balanced diameter distribution in all but the group openings. Roach (1974) asserted that maintaining this balance under group selection will be difficult at best. He argued that it becomes increasingly difficult with successive cutting cycles to fit additional group openings into a stand. Unless the number and size of group openings are relatively small, a visibly apparent group-opening matrix may persist and eventually characterize the stand. According to Roach, this would eventually create practical problems in marking stands. As trees originating in groups become more prominent with each successive cutting cycle, the marking guides initially used between groups to maintain a reverse-J diameter distribution would over time increasingly shift to accommodate marking to some other criterion within the even-aged groups. Roach’s concern thus implies that groups remain identifiable, at least through several cutting cycles. The resulting confusion, he argued, would not be conducive to the

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practical considerations of marking stands to maintain a balanced stand structure. Roach further pointed out that there would not necessarily be an equal area of group-opening age classes by the time a stand would otherwise have attained full regulation. This, in turn, would further exacerbate attaining a balanced stand structure and thus result in poor yield regulation. The extent to which these problems materialize may depend on the number and size of group openings created at each harvest. Where it is deemed important to maintain a large representation of shade-intolerant and mid-tolerant species using uneven-aged silviculture, the number and/or size of openings may need to be maximal. If only a minor representation of these species is acceptable, then the number of group openings created at each harvest can be minimal and thus less problematic in the stand-wide maintenance of a relatively homogeneous uneven-aged state. Both objectives assume the group selection method is effective in sustaining the desired species composition. Results of applying the method for 38 years in a northern hardwood forest in New Hampshire resulted in a balanced reverse-J diameter distribution after three cutting cycles (Leak and Filip, 1977). Intolerant and mid-tolerant species comprised 25–33% of trees from 4 to 12 inches dbh. The method has been used in the central Appalachians and Ohio valley where it effectively maintained a higher proportion of intolerant and mid-tolerant species than single-tree selection (Minckler and Woerheide, 1965; Schlesinger, 1976; Smith, 1980; Miller et al., 1995; Weigel and Parker, 1995). However, oaks were poorly represented in group openings except in the drier ecosystems. Alternatively, the group selection method can be applied using area regulation within individual stands or management units. As in even-aged silviculture, a rotation length must be established. In this method, group openings are created at each harvest entry according to a fixed schedule set by the cutting cycle, stand area and the conversion period. Accordingly, the total area in group openings and their number must be calculated for each stand at each harvest entry. Area in openings (AO) can be calculated by: AO = CC ( SA/R) [9.9] where CC = cutting cycle, SA = stand area and R = rotation length (or conversion period) (Murphy et  al., 1993). Number of openings per stand per harvest entry (N) is thus:

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N = AO /OS [9.10] where OS = opening size. The method thus mimics the clearcutting method but at a much smaller spatial scale. A fraction of each stand is regenerated by completely removing the overstorey in groups over the number of harvest entries equal to the conversion period divided by the cutting cycle. At the end of the conversion period, the stand will be a fully regulated uneven-aged stand comprised of N even-aged groups. Whether or not the groups are visually distinguishable at that time will depend on group opening size and age. Larger openings can be expected to maintain their identity longer than smaller openings, and for a given opening size, older groups will be more difficult to identify than younger groups. This method may be especially appropriate in previously high-graded stands comprised of overstoreys with little or no acceptable growing stock. Each stand is systematically renewed by removing the overstorey over a given number of harvests equal to the rotation age divided by the cutting cycle. The method also may overcome some of the problems of regulation control as discussed above. How to treat areas between the most recently created openings at each harvest entry nevertheless remains problematic. There are at least three options: 1. Treat each group as an even-aged mini-stand and thin from below as recommended in even-aged silviculture. 2. Treat each group as part of an uneven-aged stand and thin to a specified stand structure as previously described. 3. Apply ‘free thinning’ to each group whereby cutting favours desired trees of good form regardless of crown position. No attempt is made to create and maintain a pre-defined stand structure. Option 1 implies that the identity of individual groups can be maintained. The method therefore may be most applicable to relatively large group openings (e.g. > 0.5 acre). However, maintaining group identity may not be essential in the oldest age classes (e.g. > 60 years). At the end of the conversion period, fitting new groups into old groups might be accomplished by considering a combination of factors including species composition, presence or absence of adequate advance reproduction and the biological or economic maturity of the overstorey. The approach may have application in Chapter 9

small privately owned forests (e.g. < 100 acres) where the owner-manager is intimately acquainted with each silvicultural unit. The approach also may be compatible with planting all or some of the group openings to oaks or other desirable species following the guidelines for shelterwood planting as discussed in Chapter 10, this volume. Option 2 implies that each stand will form, with the possible exception of the more recently created openings, a relatively homogeneous uneven-aged unit. This option may be appropriate where the intent is to shift to single-tree selection at the end of the conversion period, and the primary reason for creating groups is to renew a decadent or otherwise undesirable stand. This strategy assumes that maintaining a desirable species mix is feasible using single-tree selection. In applying the method, creating small group openings (e.g. < 0.5 acre) may facilitate the transition from group selection to single-tree selection. Option 3 implies that an uncertain future stand structure is acceptable. Uncertainty results from abandoning the goals of creating and maintaining either a balanced stand-wide collection of evenaged groups (option 1), or a balanced, sustainable and relatively homogeneous uneven-aged stand structure represented by option 2. The result is likely to produce an unbalanced, uneven-aged structure whose future state is at best questionable and at worst unpredictable. The method nevertheless may have application where sustaining predefined stand structure, species composition and associated values (including regulated yield over time) are not important. Examples may include riparian zones, forest management primarily for water quality (e.g. municipal watersheds), areas around campgrounds, and other recreational and scenic areas that lie outside yield-regulation zones. This option is likewise applicable to the single-tree selection method. In either case, the principle of ‘cut the worst and leave the best’ should be followed. Given that the goal of a sustainable, predefined stand structure is set aside, potential problems in applying the group selection method may largely disappear (Roach, 1974). The trade-off is the loss of assurance of sustaining a defined structural state or the oaks themselves. The options described above also could incorporate retention of one or more uncut (reserve) trees in openings where openings are relatively large (e.g. > 0.5 acre). There are thus many possible variants of the group selection method (Table 9.7). Additional

Uneven-aged Silvicultural Methods

variants of the method also can be designed to meet specific objectives. Although the success of these methods in oak forests remains to be verified, their potential value nevertheless is suggested by theoretical considerations, trials in other forest types and preliminary results in oak forests (Minckler and Woerheide, 1965; Clark and Watt, 1971; Rudolph and Lemmien, 1976; Schlesinger, 1976; Leak and Filip, 1977; Fischer and Merritt, 1978; Heald and Haight, 1979; Smith, 1980; Hill and Dickmann, 1988; Toliver and Jackson, 1988; Guldin and Parks, 1989; Law and Lorimer, 1989; Jacobs and Wray, 1992; Murphy et  al., 1993; Golden, 1995; Miller et  al., 1995; Weigel and Parker, 1995).

Economic, Environmental and Social Considerations Selection silviculture is the least economically efficient of all the silvicultural systems. This largely results from the relatively small amount of timber removed per acre per harvest. Timber marking, administrative and road construction costs per unit of volume removed per harvest are also high. Although group selection may reduce unit costs somewhat, costs nevertheless remain relatively high in comparison with even-aged methods (Shaffer et  al., 1993). Moreover, logging damage to the residual stand sometimes may reduce the value of future crop trees (Dwyer et  al., 2000; Dwyer and Jensen, 2004). Oak mortality related to oak decline also may be accelerated by selection silviculture, especially where species in the red oak group predominate (Starkey and Oak, 1989). In the group selection method, reduced value of oak logs is associated with high ratios of edge to opening area and resulting epicormic branching and associated bole degrade (Trimble and Smith, 1970; Trimble and Seegrist, 1973; Smith, 1980). Although earlier experiences in applying singletree selection to oak forests were largely negative from a technical perspective (Gingrich, 1967; Roach, 1968; Sander, 1978), its apparently successful application to some types of oak forests indicates that it should no longer be universally dismissed as an option for the oaks (Guldin and Parks, 1989; Loewenstein et al., 1995; Loewenstein, 1996; Wang, 1997; Larsen et al., 1999). Where the method is ecologically appropriate, there appear to be few limitations to applying uneven-aged silviculture in one or more of its variants. Some of the

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Table 9.7.  Some variants of the group selection method and their application. Type

Option

Description of method Objectives and method of implementing

Primary objectives: To provide light for regeneration of shademid-tolerant and -intolerant species while maintaining a relatively continuous and homogeneous canopy cover; to sustain adequate representation of smallest dbh classes. Method: Small group openings (≤ 0.5 acres) comprising 10% or less of stand area are created at each harvest cut. Requires stand-wide control of stand structure that ultimately produces a balanced reverse J-shaped dbh distribution. To regenerate oaks, requires advance reproduction of oaks in group openings. 2 Groups with reserves Primary objectives and methods: Same as above except some trees within groups are not cut to attain goals other than regeneration. Area All options Group selection with Primary objectives: To provide light for regeneration of shade-midcontrol listed area control tolerant and -intolerant species. The selected variant of the method below (see below) depends on other objectives. Method: Area control with or without stand structure control. Area in openings (AO) is calculated by: AO = CC(SA/R), where CC = cutting cycle, SA = stand area and R = rotation length (‘conversion period’). Number of openings per stand per harvest entry (N) is N = AO/OS where OS is opening size. 1 Groups maintained Primary objectives: To maintain group identity to preserve area as identifiable regulation within stands. May also be compatible with pre-harvest even-aged units underplanting in group openings. Method: Create large groups and thinned from (> 0.5 acres). Thin each group from below as in even-aged below silviculture and maintain group identity through most or all of the rotation. May be compatible with planting oaks as a supplement to natural reproduction as in the shelterwood-underplanting method (Chapter 10, this volume). 2 Groups not Primary objectives: To maintain a relatively homogeneous canopy maintained as cover by creating small groups and/or to facilitate the transition to identifiable units; single-tree selection after the stand has been regulated by area balanced unevencontrol. Applicable to stands with little or no acceptable stocking. aged structure Method: Create small groups (≤ 0.5 acres). Maintain a stand-wide maintained standreverse J-shaped dbh distribution. wide 3 Groups not Primary objectives: To maintain a relatively continuous canopy cover maintained as that does not necessarily form a reverse J-shaped dbh distribution. identifiable units; Applicable to small private ownerships, recreation areas, municipal free thinning watersheds and other areas where sustained yield of timber applied stand-wide products is not required. Method: Create small group openings (≤ 0.5 acres). Apply stand-wide free thinning to favour trees of good form regardless of crown position. No attempt is made to create and maintain a pre-defined stand structure. 4 Groups with reserves Primary objectives: Any of those listed for the last three options with the added provision of retaining some trees in group openings to attain goals other than regeneration. Structure 1 control

Group selection with stand structure control

economic disadvantages may even be offset by the relatively frequent economic returns provided, which may be an important management incentive for some forest owners. However, the caveats that apply may, by themselves, be of sufficient importance to preclude the method’s application to a

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specific stand or forest. Risks in applying unevenaged silviculture to oak forests nevertheless are partially mitigated by the maintenance of a more publicly acceptable forest condition than that obtained under even-aged silviculture (Hull and Buhyoff, 1986; Gobster, 1994; Herrick and Rudis,

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1994). Where uneven-aged silviculture is ecologically appropriate, it offers an opportunity to simulate late-successional forest dynamics, the maintenance of certain types of wildlife habitats and aesthetic values that are generally under-represented and decreasing in occurrence across the landscape as a whole (Guldin, 1996).

Notes 1

 There is also a mathematical connection between Equations 6.2 and 9.1 (Zeide, 1984) and thus the two definitions. 2  The expression q is sometimes referred to as de Liocourt’s q, after its 19th century originator, the French forester de Liocourt (de Liocourt, 1898; Meyer et al., 1952). 3  Equation 9.8 is a generalization of Brender’s (1973) procedure for calculating the number of trees in each diameter class, starting with the largest diameter class and proceeding to the smallest as described in Moser (1976). The procedure assumes that the inverse J-shaped distribution can be represented by a geometric series involving m, number of trees in the largest diameter class and q, the ratio of the series, and is given by: ni = m ⋅ q( i −1) where i = 1 to k diameter classes and where i = 1 is the largest diameter class, i = 2 is the next largest, etc. The number of trees in the largest class thus would be m and the next largest would be m·q, the next largest would be m·q2 and so forth; m is determined by dividing the basal area of the tree associated with the midpoint diameter of the largest class into the target basal area of the stand. As presented in Moser (1976) Brender’s formula (when RSD is expressed as basal area) is: m=

specified basal area n

0.005454∑Di2 q( i −1) i

D1 is diameter class midpoint of the largest class and Dn is the midpoint diameter of the smallest diameter class. For any i, 0.005454Di2 is the basal area of the tree having the midpoint diameter of the i th class.

References Abrams, M.D. and Nowacki, G.J. (1992) Historical variation in fire, oak recruitment, and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119, 19–28. https://doi. org/10.2307/2996916 Adams, D.M. (1976) A note on the interdependence of stand structure and best stocking in a selection ­forest. Forest Science 22, 180–184. https://doi. org/10.1093/forestscience/22.2.180

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Adams, D.M. and Ek, A.R. (1974) Optimizing the management of uneven-aged forest stands. Canadian Journal of Forest Research 4, 274–287. https://doi. org/10.1139/x74-041 Allen, B.H. (1987) Ecological type classification for California: the Forest Service approach. USDA Forest Service General Technical Report PSW-98. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-98 Allen, B.H. (1990) Classification of oak woodlands. Fremontia 18(3), 22–25. Annand, E.M. and Thompson, F.R. III (1997) Forest bird response to regeneration practices in central hardwood forests. Journal of Wildlife Management 61, 159–171. https://doi.org/10.2307/3802425 Bakken, P.N. and Cook, J.E. (1998) Regeneration potential of six habitat types common to north-central Wisconsin. Northern Journal of Applied Forestry 15, 116–123. https://doi.org/10.1093/njaf/15.3.116 Berg, E. (2004) Survivorship and growth of oak regeneration in wind-created gaps. USDA General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 143– 149. Available at: https://www.fs.usda.gov/treesearch/ pubs/6523 (accessed 20 September 2018). Bradshaw, F.J. (1992) Quantifying edge effect and patch size for multiple-use silviculture – a discussion paper. Forest Ecology and Management 48, 249–264. https://doi.org/10.1016/0378-1127(92)90148-3 Brender, E.V. (1973) Silviculture of loblolly pine in the Georgia Piedmont. Georgia Forest Research Council Report 33. Georgia Forest Research Council, Macon, Georgia. Buongiorno, J., Peyron, J., Houllier, F. and Bruciamacchie, M. (1995) Growth and management of mixed-species, uneven-aged forests in the French Jura: implications for economic returns and tree diversity. Forest Science 41, 397–429. https://doi.org/10.1093/forest science/41.3.397 Chiang, J.M., Arthur, M.A. and Blankenship, B.A. (2005) The effect of prescribed fire on gap fraction in an oak forest understory on the Cumberland Plateau. The Journal of the Torrey Botanical Society 132, 432–441. https://doi.org/10.3159/1095-5674(2005) 132[432:TEOPFO]2.0.CO;2 Chisman, H.H. and Schumacher, F.X. (1940) On the tree-area ratio and certain of its applications. Journal of Forestry 38, 311–317. https://doi.org/10.1093/jof/ 38.4.311 Cho, D.-S. and Boerner, R.E.J. (1991) Canopy disturbance patterns and regeneration of Quercus species in two Ohio old-growth forests. Vegetatio 93, 9–18. https://doi.org/10.1007/BF00044920 Clark, F.B. and Watt, R.F. (1971) Silvicultural methods for regenerating oaks. In: Proceedings of the Oak Symposium. USDA Forest Service, Northeastern

369

Forest Experiment Station, Newtown Square, Pennsylvania, pp. 37–43. Available at: https://www. fs.usda.gov/treesearch/pubs/14473 (accessed 20 September 2018). Cleland, D.T., Hart, J.B., Host, G.E., Pregitzer, K.S. and Ramm, C.W. (1993) Ecological Classification and Inventory System of the Huron–Manistee National Forests. USDA Forest Service, Washington, DC. Clinton, B.D. and Boring, L.R. (1994) Regeneration patterns in canopy gaps of mixed-oak forests of the southern Appalachians: influences of topographic position and evergreen understory. American Midland Naturalist 132, 308–319. https://doi.org/10.2307/ 2426587 Collins, B. (2003) Ground layer competition and herbivory effects on cherrybark oak (Quercus pagoda Raf.) regeneration in experimental canopy gaps. The Journal of the Torrey Botanical Society 130, 147–157. https://doi.org/10.2307/3557550 Collins, B. and Battaglia, L. (2002) Microenvironmental heterogeneity and Quercus michauxii regeneration in experimental gaps. Forest Ecology and Management 155, 279–290. https://doi.org/10.1016/S0378-1127 (01)00565-5 Collins, B. and Battaglia, L.L. (2008) Oak regeneration in southeastern bottomland hardwood forest. Forest Ecology and Management 255, 3026–3034. https:// doi.org/10.1016/j.foreco.2007.09.023 Crow, T.R. (1992) Population dynamics and growth patterns for a cohort of northern red oak (Quercus rubra) seedlings. Oecologia 91, 192–200. https://doi. org/10.1007/BF00317783 de Liocourt, F. (1898) De l’amenagement des Sapinieres. Bulletin de la Societé forestière de Franche – Comte et du Territorie de Belfort 4, 396–409, 645–647. Della-Bianca, L. and Beck, D.E. (1985) Selection management in southern Appalachian hardwoods. Southern Journal of Applied Forestry 9, 191–196. Available at: https://www.fs.usda.gov/treesearch/ pubs/1145 (accessed 20 September 2018). Dey, D.C. (1991) A comprehensive Ozark regenerator. PhD dissertation, University of Missouri, Columbia, Missouri. Dey, D.C. and Parker, W.C. (1996) Regeneration of red oak (Quercus rubra L.) using shelterwood systems: ecophysiology, silviculture and management recommendations. Ontario Forest Research Institute Forest Research Information Paper 126. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs.usda.gov/treesearch/pubs/12423 (accessed 20 September 2018). Dey, D.C., Johnson, P.S. and Garrett, H.E. (1996) Modeling the regeneration of oak stands in the Missouri Ozark Highlands. Canadian Journal of Forest Research 26, 573–583. https://doi.org/10.1139/ x26-066

370

Dwyer, J. and Jensen, R. (2004) Harvest impacts in uneven-aged and even-aged Missouri Ozark forests. Northern Journal of Applied Forestry 21, 187–193. Available at: https://www.fs.usda.gov/treesearch/ pubs/18975 (accessed 20 September 2018). Dwyer, J.P., Dey, D.C., Walter, W.D. and Jensen, R.G. (2000) Logging impact in uneven-aged stands of the Missouri Ozark Forest Ecosystem Project. In: Proceedings of the 1999 Society of American Foresters National Convention. Society of American Foresters, Bethesda, Maryland, pp. 210–222. Available at: https://www.fs.usda.gov/treesearch/pubs/ 12210 (accessed 20 September 2018). Ernst, R.L. and Knapp, W.H. (1985) Forest stand density and stocking: concepts, terms, and the use of stocking guides. USDA Forest Service General Technical Report WO-44. USDA Forest Service, Washington, DC. Fischer, B.C. (1979) Managing light in the selection method. In: Proceedings of 1979 J.S. Wright Forestry Conference. Purdue University, West Lafayette, Indiana, pp. 43–53. Fischer, B.C. (1981) Designing forest openings for the group selection method. USDA Forest Service General Technical Report SO-34. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 274–277. Available at: https:// www.srs.fs.usda.gov/pubs/gtr/uncaptured/gtr_so034. pdf (accessed 20 September 2018). Fischer, B.C. and Merritt, C. (1978) shados: a computer model to simulate light energy distribution in small forest openings. In: Proceedings of the Central Hardwood Forestry Conference II. Purdue University, West Lafayette, Indiana, pp. 302–319. Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Gobster, P.H. (1994) The aesthetic experience of sustainable forest ecosystems. USDA Forest Service General Technical Report RM-247. Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado, pp. 246–255. Available at: https://www. fs.fed.us/rm/pubs_rm/rm_gtr247/rm_gtr247_ 246_255.pdf (accessed 20 September 2018). Goff, F.G. and West, D. (1975) Canopy–understory interaction effects on forest population structure. Forest Science 21, 98–108. https://doi.org/10.1093/ forestscience/21.2.98 Golden, M.S. (1995) Establishment of oak regeneration in group selection openings in a river flood-plain forest. USDA Forest Service General Technical Report SRS-1. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 413–418. https://doi.org/10.2737/SRS-GTR-1 Guldin, J.M. (1996) The role of uneven-aged silviculture in the context of ecosystem management. Western

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Journal of Applied Forestry 11, 4–12. https://doi. org/10.1093/wjaf/11.1.4 Guldin, J.M. and Parks, T. (1989) Development of cherrybark oak in an uneven-aged stand in West Tennessee. USDA Forest Service General Technical Report SO-74. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 327–331. Available at: https://www.srs.fs.usda. gov/pubs/1749 (accessed 20 September 2018). Gysel, L.W. and Arend, J.L. (1953) Oak sites in southern Michigan: their classification and evaluation. Michigan State College of Agriculture Experiment Station Technical Bulletin 236. Michigan State College of Agriculture Experiment Station, Michigan State University, East Lansing, Michigan. Hagner, S. (1999) Forest management in temperate and boreal forests: current practices and the scope for implementing sustainable forest management. Forestry Sector Outlook Studies FAO/FPIRS/03. United Nations Food and Agriculture Organization (FAO), Rome, p. 48. Hann, D.W. and Bare, B.B. (1979) Uneven-aged forest management: state of the art (or science?). USDA Forest Service General Technical Report INT-50. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah. Hansen, G.D. and Nyland, R.D. (1987) Effects of diameter distribution on the growth of simulated uneven-aged sugar maple stands. Canadian Journal of Forest Research 17, 1–8. https://doi.org/10.1139/x87-001 Heald, R.C. and Haight, R. (1979) A new approach to uneven-aged silviculture and management of mixed conifer–oak forests. California Agriculture May, 20–22. Heitzman, E. and Grell, A. (2006) Planting oaks in group selection openings on upland sites: two case studies from Arkansas. Southern Journal of Applied Forestry 30, 117–122. https://doi.org/10.1093/ sjaf/30.3.117 Heitzman, E. and Stephens, J. (2006) Using group selection to regenerate oaks in Northern Arkansas. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 547–550. Available at: https://www.fs.usda.gov/treesearch/ pubs/23460 (accessed 20 September 2018). Herrick, T.A. and Rudis, V.A. (1994) Scenery in the Quachita National Forest. USDA Forest Service General Technical Report SO-112. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 212–222. Available at: https:// www.srs.fs.usda.gov/pubs/gtr/gtr_so112.pdf (accessed 20 September 2018). Hibbs, D.E. and Bentley, W.R. (1983) A management guide for oak in New England. University of Connecticut Cooperative Extension Bulletin 83–12. Cooperative Extension Service, College of Agriculture

Uneven-aged Silvicultural Methods

and Natural Resources, The University of Connecticut, Storrs, Connecticut. Hill, J.P. and Dickmann, D.I. (1988) A comparison of three methods for naturally reproducing oak in southern Michigan. Northern Journal of Applied Forestry 5, 113–117. https://doi.org/10.1093/njaf/5.2.113 Hix, D.M. (1988) Multifactor classification and analysis of upland hardwood forest ecosystems of the Kickapoo River watershed, southwestern Wisconsin. Canadian Journal of Forest Research 18, 1405–1415. https:// doi.org/10.1139/x88-219 Hull, R.B. IV and Buhyoff, G.J. (1986) The scenic beauty temporal distribution method: an attempt to make scenic beauty assessments compatible with forest planning efforts. Forest Science 32, 271–286. https:// doi.org/10.1093/forestscience/32.2.271 Jacobs, D., Rathfon, R., Davis, A. and Carlson, D. (2006) Stocktype and harvest gap size influence northern red oak regeneration success. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 247–250. Available at: https://www. fs.usda.gov/treesearch/pubs/23386 (accessed 20 September 2018). Jacobs, R.D. and Wray, R.D. (1992) Managing oak in the Driftless Area. Minnesota Extension Service Bulletin NR-BU-5900-S. Minnesota Extension Service, University of Minnesota Natural Resources, St Paul, Minnesota. Jenkins, M.A. and Parker, G.R. (1998) Composition and diversity of woody vegetation in silvicultural openings of southern Indiana forests. Forest Ecology and Management 109, 57–74. https://doi.org/10.1016/ S0378-1127(98)00256-4 Johnson, P.S. (1992) Oak overstory/reproduction relations in two xeric ecosystems in Michigan. Forest Ecology and Management 48, 233–248. https://doi. org/10.1016/0378-1127(92)90147-2 Johnson, P.S., Jacobs, R.D., Martin, A.J. and Godel, E.D. (1989) Regenerating northern red oak: three successful case histories. Northern Journal of Applied Forestry 6, 174–178. https://doi.org/10.1093/ njaf/6.4.174 Johnson, R.L. (1979) Adequate oak regeneration – a problem without a solution? In: Proceedings of the 7th Annual Hardwood Symposium of the Hardwood Research Council. Hardwood Research Council, Memphis, Tennessee, pp. 59–65. Available at: https:// www.fs.usda.gov/treesearch/pubs/43002 (accessed 20 September 2018). Jones, S.M. (1991) Landscape ecosystem classification for South Carolina. USDA Forest Service General Technical Report SE-68. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 59–68. Available at: https://www.fs.usda.gov/treesearch/pubs/936 (accessed 20 September 2018).

371

Kershaw, J.A., Jr, Ducey, M.J., Beers, T.W. and Husch, B. (2016) Forest Mensuration, 5th edn. Wiley, Chichester, UK. Available at: https://onlinelibrary.wiley.com/ doi/book/10.1002/9781118902028 (accessed 20 September 2018). Kotar, J., Kovach, J.A. and Locey, C.T. (1988) Field Guide to Forest Habitat Types of Northern Wisconsin. University of Wisconsin, Madison. Krajicek, J.E. (1967) Maximum use of minimum acres. In: Proceedings of the 9th Southern Forest Tree Improvement Conference. Southern Forest Tree Improvement Conference, Knoxville, Tennessee, pp. 35–37. Krajicek, J.E., Brinkman, K.A. and Gingrich, S.F. (1961) Crown competition – a measure of density. Forest Science 7, 35–42. https://doi.org/10.1093/forestscience/ 7.1.35 Larsen, D.R., Metzger, M.A. and Johnson, P.S. (1997) Oak regeneration and overstory density in the Missouri Ozarks. Canadian Journal of Forest Research 27, 869–875. https://doi.org/10.1139/x97-010 Larsen, D.R., Loewenstein, E.F. and Johnson, P.S. (1999) Sustaining recruitment of oak reproduction in unevenaged stands in the Ozark Highlands. USDA Forest Service General Technical Report NC-203. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https:// www.fs.usda.gov/treesearch/pubs/10267 (accessed 20 September 2018). Law, J.R. and Lorimer, C.G. (1989) Managing unevenaged stands. USDA Forest Service Central Hardwood Note 6.08. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/ pubs/11652 (accessed 20 September 2018). Leak, W.B. (1978) Stand structure. In: Uneven-aged Silviculture and Management in the United States. USDA Forest Service Timber Management Research, Washington, DC, pp. 104–114. Leak, W.B. (1987) Characteristics of five climax stands in New Hampshire. USDA Forest Service Research Note NE-336. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 21598 (accessed 20 September 2018). Leak, W.B. (1996) Long-term structural change in uneven-aged northern hardwoods. Forest Science 42, 160–165. Available at: https://www.fs.usda.gov/ treesearch/pubs/53560 (accessed 20 September 2018). Leak, W.B. and Filip, S.M. (1977) Thirty-eight years of group selection in New England northern hardwoods. Journal of Forestry 75(10), 641–643. https://doi. org/10.1093/jof/75.10.641 Leak, W.B., Solomon, D.S. and DeBald, P.S. (1987) Silvicultural guide for northern hardwood types in the Northeast (revised). USDA Forest Service Research

372

Paper NE-603. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Penn­ sylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/4421 (accessed 20 September 2018). Liming, F.G. and Johnston, J.P. (1944) Reproduction in oak–hickory forest stands of the Missouri Ozarks. Journal of Forestry 42, 175–180. https://doi.org/ 10.1093/jof/42.3.175 Loewenstein, E.F. (1996) An analysis of the size- and age-structure of a managed uneven-aged oak forest. PhD dissertation, University of Missouri, Columbia, Missouri. Loewenstein, E. (2005) Conversion of uniform broadleaved stands to an uneven-aged structure. Forest Ecology and Management 215, 103–112. https://doi. org/10.1016/j.foreco.2005.05.007 Loewenstein, E.F. and Golden, M.S. (1995) Establishment of water oak is not dependent on advance reproduction. USDA Forest Service General Technical Report SRS-1. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 443–446. https://doi.org/10.2737/SRS-GTR-1 Loewenstein, E.F. and Guldin, J. (2004) Conversion of successionally stable even-aged oak stands to an uneven-aged structure. USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 264–268. Available at: https://www.fs.usda.gov/ treesearch/pubs/6561 (accessed 20 September 2018). Loewenstein, E.F., Garrett, H.E., Johnson, P.S. and Dwyer, J.P. (1995) Changes in a Missouri Ozark oak– hickory forest during 40 years of uneven-aged management. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 159–164. Available at: https://www. fs.usda.gov/treesearch/pubs/12750 (accessed 20 September 2018). Loewenstein, E.F., Johnson, P.S. and Garrett, H.E. (2000) Age and diameter structure of a managed unevenaged oak forest. Canadian Journal of Forest Research 30, 1060–1070. https://doi.org/10.1139/x00-036 Loftis, D.L. (1990) A shelterwood method for regenerating red oak in the southern Appalachians. Forest Science 36, 917–929. https://doi.org/10.1093/ forestscience/36.4.917 Lorimer, C.G. (1980) Age structure and disturbance history of a southern Appalachian virgin forest. Ecology 61, 1169–1184. https://doi.org/10.2307/1936836 Lorimer, C.G. (1981) Survival and growth of understory trees in oak forests of the Hudson Highlands, New York. Canadian Journal of Forest Research 11, 689–695. https://doi.org/10.1139/x81-095 Lorimer, C.G. (1983) Eighty-year development of northern red oak after partial cutting in a mixed- species Wisconsin forest. Forest Science 29, 371–383. https://doi.org/10.1093/forestscience/29.2.371

Chapter 9

Lorimer, C.G. (1984) Development of the red maple understory in northeastern oak forests. Forest Science 30, 13–22. https://doi.org/10.1093/forestscience/30.1.3 Lorimer, C.G. and Frelich, L.E. (1984) A simulation of equilibrium diameter distributions of sugar maple (Acer saccharum). Bulletin of the Torrey Botanical Club 111, 193–199. https://doi.org/10.2307/2996019 Lorimer, C.G. and Krug, A.G. (1983) Diameter distributions in even-aged stands of shade-tolerant and midtolerant tree species. American Midland Naturalist 109, 331–345. https://doi.org/10.2307/2425414 Lorimer, C.G., Chapman, J.W. and Lambert, W.D. (1994) Tall understorey vegetation as a factor in the poor development of oak seedlings beneath mature stands. Journal of Ecology 82, 227–237. https://doi. org/10.2307/2261291 Marquis, D.A. (1974) The impact of deer browsing on Allegheny hardwood regeneration. USDA Forest Service Research Paper NE-308. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15415 (accessed 20 September 2018). Marquis, D.A. (1978) Application of uneven-aged silviculture and management on public and private lands. In: Uneven-aged Silviculture and Management in the United States. USDA Forest Service Timber Management Research, Washington, DC, pp. 25–61. Marquis, D.A. and Johnson, R.L. (1989) Silviculture of eastern hardwoods. USDA Forest Service General Technical Report WO-55. USDA Forest Service, Washington, DC, pp. 9–15. Marquis, D.A., Eckert, P.L. and Roach, B.A. (1976) Acorn weevils, rodents, and deer all contribute to oak-regeneration difficulties in Pennsylvania. USDA Forest Service Research Paper NE-356. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/14481 (accessed 20 September 2018). Marquis, D.A., Ernst, R.L. and Stout, S.L. (1992) Prescribing silvicultural treatments in hardwood stands of the Alleghenies (revised). USDA Forest Service General Technical Report NE-96. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4132 (accessed 20 September 2018). McGee, C.E. (1984) Heavy mortality and succession in a virgin mixed mesophytic forest. USDA Forest Service Research Paper SO-209. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. McGee, C.E. and Bivens, D.L. (1984) A billion overtopped white oak – assets or liabilities? Southern Journal of Applied Forestry 8, 216–220. https://doi. org/10.1093/sjaf/8.4.216

Uneven-aged Silvicultural Methods

McGill, D., Martin, J., Rogers, R. and Johnson, P.S. (1991) New stocking charts for northern red oak. University of Wisconsin Forestry Research Notes 277. University of Wisconsin-Madison, Madison, Wisconsin. Meyer, H.A. and Stevenson, D.D. (1943) The structure and growth of virgin beech–birch–maple–hemlock forests in northern Pennsylvania. Journal of Agricultural Research 67, 465–484. Meyer, H.A., Recknagel, A.B. and Stevenson, D.D. (1952) Forest Management. Ronald Press, New York. Miller, G.W. and Smith, H.C. (1993) A practical alternative to single-tree selection. Northern Journal of Applied Forestry 10, 32–38. Available at: https:// www.fs.usda.gov/treesearch/pubs/14279 (accessed 20 September 2018). Miller, G.W., Schuler, T.M. and Smith, H.C. (1995) Method for applying group selection in central Appalachian hardwoods. USDA Forest Service Research Paper NE-696. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-696 Minckler, L.S. (1989) Intensive group selection silviculture in central hardwoods. USDA Forest Service General Technical Report NC-132. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 35–39. Available at: https://www. fs.usda.gov/treesearch/pubs/19621 (accessed 20 September 2018). Minckler, L.S. and Woerheide, J.D. (1965) Reproduction of hardwoods 10 years after cutting as affected by site and opening size. Journal of Forestry 63, 103–107. https://doi.org/10.1093/jof/63.2.103 Moser, J.W., Jr (1976) Specification of density for the inverse J-shaped diameter distribution. Forest Science 22, 177–180. https://doi.org/10.1093/ forestscience/22.2.177 Murphy, P.A., Shelton, M.G. and Graney, D.L. (1993) Group selection – problems and possibilities for the more shade-intolerant species. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 229–247. Available at: https:// www.fs.usda.gov/treesearch/pubs/22211 (accessed 20 September 2018). Nelson, R.E., Young, R.A. and Gilmore, A.R. (1973) Twenty-two years of management of upland hardwoods in southern Illinois. University of Illinois Agricultural Experiment Station Forestry Research Report 73-3. University of Illinois Agricultural Experiment Station, University of Illinois at UrbanaChampaign, Urbana-Champaign, Illinois. Nowacki, G.J., Abrams, M.D. and Lorimer, C.G. (1990) Composition, structure, and historical development of northern red oak stands along an edaphic gradient in north-central Wisconsin. Forest Science 36, 276–292. https://doi.org/10.1093/forestscience/ 36.2.276

373

Oak, S., Tainter, F., Williams, J. and Starkey, D. (1996) Oak decline risk rating for the southeastern United States. Annales des Sciences Forestieres 53, 721– 730. https://doi.org/10.1051/forest:19960248 Oliver, C.D. and Larson, B.C. (1996) Forest Stand Dynamics. Wiley, New York. Phares, R.E. (1971) Fertilization tests with potted red oak seedlings. USDA Forest Service Research Note NC-114. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11292 (accessed 20 September 2018). Richards, R.H., Shifley, S.R., Rebertus, A.J. and Chaplin, S.J. (1995) Characteristics and dynamics of an upland Missouri old-growth forest. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 11–22. Available at: https://www.fs.usda.gov/treesearch/ pubs/4300 (accessed 20 September 2018). Roach, B.A. (1968) Is clear cutting good or bad? Keep Tennessee Green Journal 8, 4–5, 12–14. Roach, B.A. (1974) Selection cutting and group selection. State University of New York Applied Forestry Research Institute (AFRI) Miscellaneous Report 5. Applied Forestry Research Institute, State University of New York, Syracuse, New York. Roach, B.A. and Gingrich, S.F. (1967) Upland hardwoods can be grown efficiently. Pulpwood Annual 1967, 85–87. Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA Forest Service Agriculture Handbook 355. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 20 September 2018). Rogers, R. (1983) Spatial pattern and growth in a Missouri oak–hickory stand. PhD dissertation, University of Missouri, Columbia, Missouri. Rudolph, V.J. and Lemmien, W.A. (1976) Silvicultural cuttings in an oak–hickory stand in Michigan: 21-year results. In: Proceedings of the Central Hardwood Forestry Conference I. University of Southern Illinois, Carbondale, Illinois, pp. 431–453. Sander, I.L. (1977) Manager’s handbook for oaks in the North Central States. USDA Forest Service General Technical Report NC-37. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10102 (accessed 20 September 2018). Sander, I.L. (1978) Silvicultural systems for the oak–hickory forest type. In: North America’s Forests: Gateway to Opportunity. Society of America Forestry and Canadian Institute of Forestry, Bethesda, Maryland, pp. 344–348. Sander, I.L., Johnson, P.S. and Rogers, R. (1984) Evaluating oak advance reproduction in the Missouri Ozarks. USDA Forest Service Research Paper

374

NC-251. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10038 (accessed 20 September 2018). Schabel, H.G. and Palmer, S.L. (1999) The Dauerwald: its role in the restoration of natural forest. Journal of Forestry 97, 20–25. https://doi.org/10.1093/jof/97.11.20 Schlesinger, R.C. (1976) Sixteen years of selection silviculture in upland hardwood stands. USDA Forest Service Research Paper NC-125. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.nrs.fs.fed.us/pubs/ rp/rp_nc125.pdf (accessed 20 September 2018). Schlesinger, R.C. (1978) Increased growth of released white oak poles continues through two decades. Journal of Forestry 76(11), 726–727. https://doi. org/10.1093/jof/76.11.726 Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. https://naldc.nal.usda. gov/download/CAT86200555/PDF Schütz, J. (2001) Opportunities and strategies of transforming regular forests to irregular forests. Forest Ecology and Management 151, 87–94. https://doi. org/10.1016/S0378-1127(00)00699-X Schütz, J. (2002) Silvicultural tools to develop irregular and diverse forest structures. Forestry 75, 329. https:// doi.org/10.1093/forestry/75.4.329 Shaffer, R.M., Brummel, K.R., Reisinger, T.W. and Stokes, B.J. (1993) Impact of group selection silviculture on timber harvesting productivity and cost in Appalachian hardwood timber stands. Northern Journal of Applied Forestry 10, 170–174. https://doi. org/10.1093/njaf/10.4.170 Shifley, S.R., Roovers, L.M. and Brookshire, B.L. (1995) Structural and compositional differences between old-growth and mature second-growth forests in the Missouri Ozarks. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 23–36. Available at: https:// www.fs.usda.gov/treesearch/pubs/4301 (accessed 20 September 2018). Smalley, G.W. (1978) Classification and evaluation of forest sites for the Interior Highlands. In: Proceedings of Central Hardwood Forest Conference II. Purdue University, West Lafayette, Indiana, 257 pp. Smalley, G.W. (1979) Classification and evaluation of forest sites on the southern Cumberland Plateau. USDA Forest Service General Technical Report SO-23. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Available at: https://www.fs.usda.gov/treesearch/ pubs/2381 (accessed 20 September 2018). Smalley, G.W. (1982) Classification and evaluation of forest sites on the Mid-Cumberland Plateau. USDA

Chapter 9

Forest Service General Technical Report SO-38. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/10.2737/ SO-GTR-38 Smalley, G.W. (1984) Classification and evaluation of forest sites in the Cumberland Mountains. USDA Forest Service General Technical Report SO-50. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/10.2737/SO-GTR-50 Smalley, G.W. (1986) Classification and evaluation of forest sites on the Northern Cumberland Plateau. USDA Forest Service General Technical Report SO-60. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/ 10.2737/SO-GTR-60 Smalley, G.W. (1988) Soil-site relations of upland oaks in northern Alabama. USDA Forest Service Research Note SO-64. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, M.S. (1997) The Practice of Silviculture: Applied Forest Ecology, 9th edn. Wiley, New York. Smith, H.C. (1980) An evaluation of four uneven-age cutting practices in central Appalachian hardwoods. Southern Journal of Applied Forestry 4, 193–200. https://doi.org/10.1093/sjaf/4.4.193 Smith, H.C. and Lamson, N.I. (1982) Number of residual trees: a guide for selection cutting. USDA Forest Service General Technical Report NE-80. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4093 (accessed 20 September 2018). Smith, H.C. and Miller, G.W. (1987) Managing Appalachian hardwood stands using four regeneration practices – 34-year results. Northern Journal of Applied Forestry 4, 180–185. https://doi.org/10.1093/ njaf/4.4.180 Sonderman, D.L. (1985) Stand density – a factor affecting stem quality of young hardwoods. USDA Forest Service Research Paper NE-561. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/21735 (accessed 20 September 2018). Starkey, D.A. and Oak, S.W. (1989) Silvicultural implications of factors associated with oak decline in southern upland hardwoods. USDA Forest Service General Technical Report SO-74. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 579–585. Available at: https://www.srs. fs.usda.gov/pubs/1749 (accessed 20 September 2018). Starkey, D.A., Oak, S.W., Ryan, G.W., Taintner, F.H., Redmond, C. and Brown, H.D. (1989) Evaluation of oak decline areas in the South. USDA Forest Service Southern Region Protection Report R8-PR-17. USDA Forest Service, Washington, DC.

Uneven-aged Silvicultural Methods

Stout, S.L. (1991) Stand density, stand structure, and species composition in transition oak stands of northwestern Pennsylvania. USDA Forest Service Research Paper NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 194–206. Available at: https://www.fs.usda.gov/treesearch/pubs/13499 (accessed 20 September 2018). Stout, S.L. and Larson, B.C. (1988) Relative stand density: why do we need to know? USDA Forest Service General Technical Report INT-243. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, pp. 73–79. Stout, S.L. and Nyland, R.D. (1986) Role of species composition in relative density measurement in Allegheny hardwoods. Canadian Journal of Forest Research 16, 574–579. https://doi.org/10.1139/ x86-099 Thompson, F.R. III and Dessecker, D.R. (1997) Management of early-successional communities in Central Hardwood Forests. USDA Forest Service Research Paper NC-195. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10259 (accessed 20 September 2018). Thompson, F.R. III, Probst, J.R. and Raphael, M.G. (1995) Impacts of silviculture: overview and management recommendations. In: Martin, T.E. and Finch, D.M. (eds) Ecology and Management of Neotropical Migratory Birds. Oxford University Press, New York, pp. 201–219. Thompson, F.R. III, Robinson, S.K., Whitehead, D.R. and Brawn, J.D. (1996) Management of central hardwood landscapes for the conservation of migratory birds. USDA Forest Service General Technical Report NC-187. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 117–143. Available at: https://www.nrs.fs.fed.us/ pubs/gtr/other/gtr-nc187/index.html (accessed 20 September 2018). Toliver, J.R. and Jackson, B.D. (1988) Recommended silvicultural practices in southern wetland forests. USDA Forest Service General Technical Report SE-50. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 72–77. Available at: https://www.srs.fs.usda.gov/pubs/ gtr/gtr_se050.pdf (accessed 20 September 2018). Trimble, G.R., Jr (1970) Twenty years of intensive uneven-aged management: effect on growth, yields, and species composition in two hardwood stands in West Virginia. USDA Forest Service Research Paper NE-154. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 23764 (accessed 20 September 2018). Trimble, G.R., Jr (1973) The regeneration of Central Appalachian hardwoods with emphasis on the effects

375

of site quality and harvesting practice. USDA Forest Service Research Paper NE-282. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15387 (accessed 20 September 2018). Trimble, G.R., Jr and Seegrist, D.W. (1973) Epicormic branching on hardwood trees bordering forest openings. USDA Forest Service Research Paper NE-261. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/23633 (accessed 20 September 2018). Trimble, G.R., Jr and Smith, H.C. (1970) Sprouting of dormant buds on border trees. USDA Forest Service Research Paper NE-179. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www. fs.usda.gov/treesearch/pubs/23788 (accessed 20 September 2018). Trimble, G.R., Jr and Tryon, E.H. (1966) Crown encroachment into openings cut in Appalachian hardwood stands. Journal of Forestry 64, 104–108. https://doi.org/10.1093/jof/64.2.104 Van Kley, J.E., Parker, G.R., Franzmeier, D.P. and Randolph, J.C. (undated) Field Guide: Ecological

376

Classification of the Hoosier National Forest and Surrounding Areas of Indiana. USDA Forest Service, Hoosier National Forest, Bedford, Indiana. Wang, Z. (1997) Stability and predictability of diameter distributions in a managed uneven-aged oak forest. PhD dissertation, University of Missouri, Columbia, Missouri. Weigel, D.R. and Parker, G.R. (1995) Tree regeneration following group selection harvesting in southern Indiana. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 316–325. Available at: https://www. fs.usda.gov/treesearch/pubs/12773 (accessed 20 September 2018). Weitzman, S. and Trimble, G.R., Jr (1957) Some natural factors that govern the management of oak. USDA Forest Service Northeastern Forest Experiment Station Paper 88. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/13623 (accessed 20 September 2018). Zeide, B. (1984) Exponential diameter distribution: interpretation of coefficients. Forest Science 30, 907–912. https://doi.org/10.1093/forestscience/30.4.907 Zimmermann, M.H. and Brown, C.L. (1971) Trees, Structure and Function. Springer-Verlag, New York.

Chapter 9

10

Artificial Regeneration

Introduction Oak seeding and planting can be applied to meet a range of objectives. When applied to open areas largely devoid of trees, these practices are termed afforestation. In contrast, the term reforestation usually refers to the replacement of one crop of trees with another by either natural or artificial regeneration methods (Helms, 1998). Both terms represent the replacement of one vegetation type or tree crop by another. A related but more complex method of reforestation, called enrichment planting, involves planting trees among existing forest vegetation (Helms, 1998). Here the objective is to improve forest composition and stocking or to increase forest biodiversity by planting trees to supplement the natural regeneration potential of an existing stand. Enrichment planting of oak is usually done in conjunction with clearcutting and shelterwood methods, but in theory it can be applied with any even-­aged silviculture method (see Chapter 8, this volume,) and the group selection method (see Chapter 9, this volume). Historically, afforestation in the USA focused on the rapid establishment of trees for timber production on sites where trees no longer occur, or for protection where they have never occurred (e.g. prairie-region shelterbelts). With respect to the oaks, afforestation is today applied to a broader range of objectives including: ●● restoring oaks on lands earlier converted to ­agriculture; ●● reducing the severity and duration of flooding in floodplains; ●● restoring or maintaining biodiversity – not only of oaks but also the array of organisms including birds and mammals that depend on oaks for food and shelter (see ‘Managing Oak Forests for Wildlife’, Chapter 13, this volume);

●● improving wildlife habitat by increasing tree cover and mast production, decreasing forest fragmentation, and providing travel corridors; and ●● creating specialized habitat such as green tree reservoirs for waterfowl (see ‘Acorn production in green tree reservoirs’, Chapter 13, this volume). Where forest vegetation already exists, enrichment plantings can be used to: ●● restore degraded woodlands and savannahs; ●● replace an ageing oak cohort that has declined in acorn production (Tecklin et al., 2002; McCreary and Tecklin, 2005); and ●● increase the stocking of oaks to meet timber production or other objectives. A common goal of oak artificial regeneration is to establish an acceptable number of trees at least possible cost. Attaining acceptable survival and growth of planted acorns or oak seedlings is only part of the process. By the time of crown closure (about 10–15 years after planting), planting success is defined by the competitive position of planted oaks relative to other on-site vegetation. Thus, prescriptions for artificial oak regeneration, like natural regeneration, require making decisions at the time of establishment that can flow into future stand development (Lockhart et al., 2008). Related considerations include the following (Allen et  al., 2001; Dey et al., 2008): ●● objectives of seeding/planting and measure(s) of success; ●● site evaluation and species selection; ●● site preparation; ●● competition control; ●● control of herbivores; ●● management of soil nutrients; and ●● protection. Inattention to any of these factors can potentially negate an otherwise well-designed regeneration

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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plan (Dey et al., 2008). However, thoughtful decisions in one component (e.g. site preparation) may facilitate success in another (e.g. competition control). Although much is known about the factors requiring consideration, there is no low-cost, universally effective prescription for planting success. These uncertainties require monitoring regeneration investments in order to identify the need for possible intervention. The principles underpinning successful natural regeneration of oaks also apply to artificial regeneration.

Site Evaluation and Species Selection Oaks can be established on many kinds of sites by seeding or planting. Suitable sites range from dry uplands to frequently flooded bottomlands. Because the site requirements of oaks differ greatly among species, species selection and site compatibility should be of first consideration in artificial regeneration. It has long been recognized that the oaks segregate by species along a moisture gradient (e.g. Fig. 1.4, this volume). This natural ecophysiological order also occurs across climatic zones from regional to local to microsite scales (Chapter 1, this volume). Selecting species for compatibility with a planting site thus should reflect this reality. For discussion, the artificial regeneration of oaks can be subdivided into uplands and lowlands, although the distinction is sometimes arbitrary. Uplands Oaks occur across a wide range of upland sites. The species occurring there range from those with high economic value and growth rate to scrub and chaparral species with little commercial value. Our definition of ‘value’, however, has in recent decades shifted to accommodate much more than economic value. It now encompasses a wide range of ecosystem services (see Chapter 13, this volume). Moreover, with impending climate change, uplands that are now moist (mesic) may within a few decades become warmer and subject to more variable precipitation (see Chapter 14, this volume). Proper site selection for artificial regeneration in uplands is grounded in an understanding of the natural distribution of species and their relation to climate, soil, physiography and associated vegetation (Chapters 1 and 4, this volume). Climate change aside, the upland oaks predictably respond to relatively fixed features of site including: (i) soil

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physical characteristics (notably soil texture, depth and impermeable layers); and (ii) physiography (slope steepness, aspect or exposure, and position). Inseparable from physical site effects are biotic factors, notably those associated with the non-oak woody and herbaceous vegetation that co-occurs and competes with oaks on a given site. Other factors being equal, competition with other plants is often the primary determinant of the success or failure of both natural and artificial oak regeneration. Forces originating outside the planting site (i.e. exogenous factors including fire, wind, extreme weather, insects and disease) add additional uncertainty to artificial regeneration outcomes. These and other considerations are discussed below under specific methods of artificial regeneration. Bottomlands Compared with uplands, bottomlands are fraught with frequent and unpredictable events originating from beyond the planting site (exogenous factors). Their ecology is complicated by often frequent and unpredictable flooding and resultant interactions with other physical and biotic factors. On periodically flooded sites, changes in elevation of less than 1 ft can create ecological differences that determine where the regeneration of a given oak species is suitable (Fig. 1.17, this volume) (Hodges and Switzer, 1979; Wharton et al., 1982). Moreover, such micro-elevational relations can be changed by a single flood that alters the spatial distribution of microsites, only to again change with the next flood. Different oak species react differently to subtle changes in bottomland site conditions. For example, overcup oak, water oak, willow oak, and Nuttall oak are relatively tolerant of flooding and can survive soil saturation and inundation for several months during the growing season (Wharton et al., 1982; Clatterbuck, 2005). Those species naturally occur on the higher microsites within floodplains and depressions on terraces. Other bottomland oaks, including bur, swamp white, swamp chestnut, cherrybark and Shumard, are less flood tolerant; they occur on higher elevations on floodplains or terraces and can tolerate saturated soils or flooding for only a month or less during the growing season. Pin oak is limited to poorly drained soils that are saturated only during the dormant season. Bur oak is even more limited by flooding and can tolerate it only on better drained soils within moist bottoms

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such as hummocky microsites and terraces. Some bottomland soils with high pH (≥ 8.0) are suitable for bur and Shumard oaks, sycamore and green ash. In contrast, cherrybark, Nuttall, pin and water oaks are sensitive to high pH and become chlorotic under these conditions, in turn leading to poor growth and high mortality. Guidelines for selecting oak species for planting on bottomland sites are presented by Allen et al. (2001) and Lockhart et al. (2008). Selecting oak species suitable for a given bottomland site is often complicated by interactions of micro-topography, presence of levees, and the frequency and cumulative effects of flooding (Hodges and Switzer, 1979; Wharton et al., 1982; Allen et  al., 2001; Lockhart et  al., 2008). Small differences in bottomland microtopography, by themselves, may differentiate suitable from unsuitable conditions. But such differences are sometimes eliminated by land levelling where intensive agriculture has been practiced (Stanturf et  al., 2009). Although wetlands and other highly productive sites can provide conditions for rapid tree growth, they are also places where intensive weed control is likely to be required during the early life of a stand. Soil pH is important because it affects nutrient cycling, availability, toxicity and other factors controlling plant growth. Sites with pH values below 4.5 or above 7.5 are usually problematic (Allen et  al., 2001). Alluvial soils may have high pH (> 7.5), whereas upland soils derived from sandstone and that are highly weathered may be acidic; either extreme in pH may determine the oak species suitable for planting. Floodplain soils under previous cultivation can be excessively high in phosphorus from decades of fertilization, yet low in organic matter and nitrogen, which in turn can cause serious reductions in tree growth (Stanturf et al., 2009). Highly compacted soils, common in upper soil layers of agricultural lands that have been cultivated for decades, are limiting for many tree species due to restricted root growth. In turn, compaction can exacerbate both seasonal drought and soil saturation. Soils that are shallow or of high rock content, excessively well-drained are prone to prolonged summer drought. However, moderate compaction, especially on sandy soils, may increase the survival and growth of planted oak seedlings (Fleming et al., 2006). Mine spoils also present unusual tree planting problems, which are not addressed in this book. Where the seeding or planting objective is

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habitat restoration, existing native forests on nearby similar sites often are useful guides to species selection. Ecological classification systems (see Chapter 1, this volume) also provide information for matching species to site. In the Lower Mississippi Alluvial Valley (LMAV), growing high quality timber may be a primary or secondary objective of planting bottomlands. There, methods have been developed for identifying appropriate species mixes for meeting timber production objectives (Lockhart et al., 2008). These guidelines consider the growth patterns of oaks and other species and species’ interactions over the life of a stand. They consider 24 non-oak species for their relative value in assisting the production of high quality oak timber in mixed-species plantings. Non-oaks are ranked according to a combination of tree characteristics including: (i) tree form; (ii) height growth pattern; (iii) branching pattern; (iv) twig and branch sturdiness; and (v) shoot growth characteristics. Some species are planted to serve as ‘trainers’ to improve the form and early height growth of oaks while also shading oak boles to accelerate selfpruning (i.e. branch shedding) (Lockhart et  al., 2008). As a mixed-species stand matures, non-oaks are relegated to subordinate canopy layers by faster growing oaks.

Artificial Regeneration Methods There are two general methods of artificial regeneration: (i) direct seeding; and (ii) planting. Both can be applied to afforestation or reforestation objectives (see ‘Introduction’). Afforestation using oaks occurs on a relatively small acreage compared with that of native oak forests. Given enough time and money, afforestation can be used to establish oaks on virtually any area. However, a welldesigned afforestation plan considers the numbers and kinds of trees required to attain a given objective at acceptable cost over a given time. Oak afforestation in bottomlands presents special problems related to flooding. However, many of these sites are highly productive, capable of producing high-value oak timber, and thus are often preferred as afforestation investments. For meeting that goal, there are published guides for the afforestation of bottomlands with oaks (e.g. Allen et  al., 2001; Dey et al., 2004; Lockhart et al., 2008). Afforestation can involve multiple species simultaneously, including planting non-oaks for meeting multiple objectives. Species mixtures increase tree

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species diversity, and this can improve chances of forest recovery after flooding, drought, herbivory or other stresses that impact the afforested area. Adding fast-growing species such as eastern cottonwood or sweetgum to a bottomland oak planting also can accelerate development of the vertical woody structure and habitat necessary for certain wildlife species (Twedt and Portwood, 1997; Twedt et al., 2002; Hamel, 2003). Moreover, species mixtures can be specifically designed so that as a stand matures, the oaks (considered desirable for timber and mast production) also can benefit from compatible ‘trainer’ species (e.g. sweetgum). ‘Trainers’ are so-named because they accelerate the height growth of the oaks through lateral root and crown competition, and also hasten oak branch shedding (self-pruning), which in turn increases the quality and value of the oak boles (Lockhart et al., 2008). The growth rate and habit of thoughtfully selected non-oak trainers also reduces the risk of other vegetation overtopping and suppressing planted oaks. In addition, including fast-growing species such as eastern cottonwood in bottomland oak plantings can accelerate the development of vertical woody structure that supports greater diversity and abundance of high-priority forest bird species (Twedt and Portwood, 1997; Twedt et  al., 2002; Hamel, 2003). Management systems for multispecies plantings have been designed to facilitate the establishment and development of high quality oaks, provide early woody structure for wildlife, and also early economic returns (e.g. Dey et  al., 2010). Multispecies plantings on bottomland fields may be necessary on large fields where adjacent forest edges are further than 300–500 ft away. There, natural regeneration is often inadequate to meet stocking and diversity goals (Allen, 1990, 1997; Twedt, 2004; Stanturf et al., 2009). As with any silvicultural system, afforestation involves trade-offs between costs and the certainty of outcome. For example, if the goal is to obtain 50 competitively successful and well-distributed oaks/ acre at age 20, options might include: ●● discing the site and seeding 3000 acorns/acre with no follow-up; ●● discing the site, planting 400 nursery-run bareroot seedlings/acre, and discing for 3 years of weed control; ●● planting 200 exceptionally large bareroot seedlings, fertilizing and using herbicide for 3 years of weed control; or

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●● planting 55 large, 3-gallon, container-grown oaks/ acre with a compatible ground cover, weed barrier, slow-release tree fertilizer and no follow-up. Afforestation costs obviously increase as we proceed down the list. But which method represents the least-cost path to regeneration success? A site-­ specific quantitative answer to such comparisons is generally not available. Moreover, practical issues such as availability of acorns, planting stock, labour, or time may restrict the number of options. The important factors and relative trade-offs among the factors leading to success or failure, nevertheless, are well understood. These factors are the focus of the sections below on direct seeding and planting oak seedlings. Additional information is available from other sources (e.g. Dey and Buchanan, 1995; Allen et  al., 2001; Dey et  al., 2008, 2010, 2012; Lockhart et al., 2008; Löf et al., 2012). Direct seeding Although direct seeding of acorns has been used to successfully establish oaks on a wide range of sites, the method has been most widely reported for afforestation of former agricultural lands in the LMAV. Uncertainties in success of direct seeding acorns arise from many factors (Stanturf et  al., 2009), but direct seeding has the following advantages compared with planting seedlings (Allen et al., 2001; Dey et al., 2008; Stanturf et al., 2009): ●● relatively low cost – on southern bottomlands, about half to one-third the cost of planting; ●● flexibility in the timing of field operations; ●● root systems that develop on site, naturally and unconstrained, without transplant injury or distortion; ●● acorns naturally germinate when conditions are favourable (they can remain dormant during drought or flooding); ●● rapid sowing with tractor-mounted planters or simple manual tools; and ●● demonstrated success when properly implemented on suitable sites. Disadvantages of direct seeding include (Allen et al., 2001; Dey et al., 2008; Stanturf et al., 2009): ●● high vulnerability to seed predation by rodents (see ‘Acorn Predation and Dispersal’, Chapter 2, this volume); ●● often low or unpredictable acorn germination rates and survival under prevailing field conditions

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●● ●● ●●

●●

●●

(see ‘Oak Seedling Establishment’, Chapter 2, this volume); slow growth compared with planted seedlings, both initially and in the long term; less control of density or spatial arrangement of seedlings compared with planting seedlings; limited acorn supply or unavailability in many years; white oak acorns cannot be stored for more than 3 months (see Chapters 2 and 13, this volume, for more information on acorn production, storage and germination); difficult to simultaneously sow oaks in species mixtures that include light-seeded species such as sweetgum or ash; and the combination of irregular spacing of germinated acorns, slow initial seedling growth and abundant herbaceous competition create difficulties in evaluating tree establishment success for several years after planting and also in implementing weed control.

Failures in direct seeding of acorns are not uncommon. Nevertheless, Allen et al. (2001) noted that failures are likely to be the result of one or more of the following potentially controllable factors noted by Toumey and Korstian (1942): (i) seed quality; (ii) species selection; (iii) competing vegetation; (iv) soil conditions; (v) seed predators; (vi) seeding rate; (vii) seeding depth; and (viii) time of seeding. Acorn selection and handling Species should be selected to match site conditions (see ‘Site Evaluation and Species Selection’ above). Acorns should come from multiple parent trees, preferably those known to have desirable traits (e.g. growth, form, acorn production), and to have developed under climatic, hydrologic and physiographic conditions comparable to the planting site. Mixed-species plantings are often desirable to counter uncertainties about how any one species might perform on a given site. When direct-seeded trees encounter harsh weather or other unfavourable conditions, species mixtures increase the chances that at least one species succeeds. Before seeding, acorns should be properly stored; guidelines for storage are summarized in the later section ‘Nursery stock quality’ and elsewhere (e.g. USDA Forest Service, 2008). Maximum storage duration for species in the white oak group is approximately 3 months; little or no storage is

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preferable. Although species in the red oak group can tolerate several years of cold storage, cold stratification (before or after planting) is required for germination of most species. If possible, acorns should be tested for viability (percentage germination) before planting so that seeding rates can be adjusted accordingly (Allen et al., 2001; Dey et al., 2008). The need for site preparation should be evaluated before seeding, as discussed in section ‘Plantation establishment’ later in this chapter. Seeding methods Acorns can be seeded from late autumn through to late spring. Autumn seeding reduces the need for acorn storage and typically results in a higher acorn germination rate than spring seeding. Autumn seeding also assures that acorns can germinate as soon as conditions are favourable in the spring, which may be earlier than spring seeding operations allow. However, autumn seeding increases the length of time that acorns are exposed to rodents and other acorn predators (Allen et al., 2001). Appropriate seeding rates require anticipating losses to germination failure, predation and seedling mortality. Recommended rates range from about four to ten times the number of trees wanted 10 years after seeding. Germination rates of 35% for red oak species are typical in field plantings, and seeding rates of 1000–1500 acorns/acre in some cases have produced 300–450 seedlings/acre and 150–375 free-to-grow oaks at stand age 10 (Dey et  al., 2008). Seeding densities of 700–1000 acorns/acre are typical on favourable sites in the LMAV but are preferably increased to 1200–1500/ acre where soil conditions are adverse, or where acorn predation or competing vegetation are severe. It may even be possible to defeat these problems by increasing seeding densities to 2000 or more/acre. However, at some point, increasing seeding density defeats the cost advantage of direct seeding versus planting (Allen et al., 2001; Dey et al., 2008). Acorns can be seeded by hand or – where conditions permit – by machine using modified agricultural planters or equipment designed specifically for large-seeded trees. Some machines plant a single row of acorns per pass, others plant multiple rows. The recommended distance between planted rows is typically 8–15 ft. Seeding by hand is an option on any site. Typically a single worker can seed 6000–12,000 acorns/day depending on the site conditions, planting tool used

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and physical stamina. Simple planting tools have been designed to increase production rates for manually seeding acorns (Allen et al., 2001). Recommended seeding depths range from 2 to 6 inches. Placing acorns 2 to 4 inches into the soil results in better germination and seedling survival than seeding at greater depths. However, seeding acorns greater than 4 inches may be beneficial on sites subject to freezing, drought or high rodent depredation. Broadcast (surface) seeding is usually unsuccessful and the few successes reported have been on recently disced sites (Allen et al., 2001). Protecting acorns Acorn predation by small mammals is a common cause of direct seeding failures on sites ranging from humid bottomlands to dry Mediterranean climates (Madsen and Löf, 2005; Dey et al., 2008; Leiva and Vera, 2015). Predator damage can be managed in several ways. Seeded areas more than 2 acres are preferred, as large openings increase the distance small mammals must travel from nearby cover to find acorns; this exposes them to avian predators, especially where there is sparse ground cover, thus reducing seed predation pressure on acorns. Long, linear openings, almost regardless of size, minimize the distance from forest edge to centre of opening, and thereby increase the risk of seed predation. Although autumn planting increases the length of time that acorns are available to predators, predation during autumn may be relatively low if planting occurs in years of acorn abundance. Small mammal populations are typically lower in spring than in the preceding autumn, but their food sources are also typically lower in spring. The direction and magnitude of these potentially offsetting effects of seeding date on acorn predation thus are uncertain (Allen et al., 2001; Dey et al., 2008). Poisoned rodent baits, repellents and chemical acorn coatings have not yet proven to be effective deterrents to acorn predation. Although diversionary foods used to attract rodents have reduced seed predation in western conifers, they have not been tested in the direct seeding of oaks (Dey et  al., 2008). Discing, burning or herbicide treatments before seeding reduces vegetation cover on the seeded site and thereby increases exposure of small mammals to predators. Leaving an unseeded buffer of 200 ft along the edges of seeded sites also has been used to reduce acorn predation (Clatterbuck, 1997). Various shelters have been designed to

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physically protect acorns from small predators (e.g. Shirley, 1937; Stoeckler and Scholz, 1956; Stanturf et  al., 2004; Castro et  al., 2015). Although effective, they are expensive to install and maintain, especially on bottomland sites prone to flooding. Nevertheless, they may be necessary in areas of high rodent populations. Planting seedlings Nursery stock quality Whether planting on forested or non-forested sites, using high quality seedlings is essential to planting success. Assuring seedling quality begins with collecting acorns from stands or trees likely to be adapted to the planting site. From a practical perspective, this usually means collecting from areas within the same geographic area, climatic zone, and from sites with characteristics similar to the planting site. Potential gains in field performance from selected seed sources have been widely reported (LaFarge and Lewis, 1987; Buchschacher et  al., 1991; Rink and Coggeshall, 1995; Kormanik et al., 1997; Schlarbaum et  al., 1997a, b). However, the availability of site-matched oak nursery stock is limited in the USA. Acorn size is positively correlated with initial seedling size and survival under field conditions (Korstian, 1927; McComb, 1934; Auchmoody et  al., 1994; Miao, 1995; Kormanik et  al., 1998). However, when acorns of varying size are grown under ideal conditions such as in a nursery, acorn size has no statistically significant effect on seedling growth (Grossman et al., 2003). In general, there is only limited evidence that seed size is an inherited trait (Rice et  al., 1993; Kormanik et  al., 1998). Nevertheless, where possible, acorns should be collected from trees or stands known to produce seedlings suited to an intended planting site. After acorns are collected, their viability and germination potential can be maintained by placing them in water immediately after collection to hydrate for 16–24 h; those that float can be discarded. The method assumes that the ‘floaters’ largely represent weevilled or desiccated acorns and that the ‘sinkers’ largely represent sound acorns. However, in some years, many sound acorns may lose enough water through natural drying before they are collected to cause them to float. The percentage of sound acorns can be determined by cutting open a sample of ‘sinkers’ and ‘floaters’ to

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determine the proportion of weevilled or otherwise damaged acorns in each subpopulation. The population of floaters then can be discarded or saved depending on their internal appearance. In either case, hydration of acorns for 16–24 h assures full imbibition of water without harming the acorn. Acorns can be sown in the nursery bed immediately after collecting or placed in cold storage if sowing is to be delayed. Species in the white oak group preferably should be sown shortly after collection in the autumn; they cannot be successfully stored for more than 4–6 months (over winter) (Bonner and Vozzo, 1987). However, some species in the red oak group have been successfully stored for up to 3 years. For either species group, the best storage environment is one that: ●● maintains acorn moisture content above 30% for species in the red oak group, and 45–60% for those in the white oak group; ●● maintains temperatures near but above freezing (34–36°F or 1–2°C); and ●● allows some gas exchange with the atmosphere (Bonner, 1973; Bonner and Vozzo, 1987). Storing acorns in polyethylene bags of 4–10 mm thickness usually allows the necessary air exchange with that outside the bag, yet provides an effective barrier to moisture loss. However, thinner polyethylene (e.g. 1.75 mm) or cloth bags may be better for white oak acorns because of their greater air requirements (Rink and Williams, 1984). A common practice is to store acorns in cans or drums with a polyethylene liner bag. To maintain adequate aeration, the container tops should not be completely closed. Additional aeration of acorns during storage often occurs when weevil larvae emerge from acorns and eat through the polyethylene bags. However, the larvae-forming weevil eggs are deposited in the acorn before and not during storage. Weevil infestation therefore cannot increase during storage. Although there are methods for killing larvae (Olson, 1974), the risks from doing so outweigh the advantages. One method, which requires immersing acorns in hot water (120°F, 49°C) for 40  min, may kill acorns. Another, and even less desirable method, is to fumigate the acorns with methyl bromide, carbon disulfide or thiamine bisulfate. These treatments can also harm acorns if not done properly. The general recommendation is to avoid all chemical treatments (Bonner and Vozzo, 1987). Controlling seedling density in the nursery bed is also important to assuring seedling quality. This

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can be accomplished by sowing mature acorns of known germination capacity and high moisture content. High seedbed densities (e.g. > six seedlings/ft2) will result in small seedlings with reduced growth capacity. Obtaining a desirable density therefore requires adjusting sowing rates based on the percentage of acorns expected to germinate. After sowing, the seedbed should be mulched to prevent acorn desiccation and to protect acorns from freezing. In addition, it is usually necessary to protect seedbeds from bird and mammal predation until germination is complete and leaves are about one-half fully expanded. If acorns are inadvertently over-sown, seedlings can be removed (‘rogueing’) to obtain the desired bed density. Rogueing should be done early in the first growing season. Nursery practices have been developed for growing large bareroot oak seedlings that have well-developed root systems and numerous first-order lateral roots (Plate 8) (Kormanik et  al., 2004). The process requires sowing seed at a low nursery bed density followed by a specified fertilization and irrigation schedule; roots are then undercut at a depth of 8  inches. After seedlings are lifted, seedlings are graded to select those of a size sufficient to assure planting success. Seedlings can be lifted any time after seedlings are fully dormant in the autumn. Cold storage at 33–38°F (0.5–3°C) and proper packaging of seedlings from time of lifting to time of planting is essential for maintaining physiological quality of seedlings (Webb and von Althen, 1980). Seedlings can be planted in the autumn, winter or spring provided they are dormant. Seedling size and root morphology at the time of planting are important determinants of the physiological quality of seedlings and thus planted tree performance (Kormanik, 1989; Schultz and Thompson, 1991; Thompson and Schultz, 1995; Dey and Parker, 1997b; Ponder, 2000; Ward et al., 2000; Kormanik et al., 2002; Spetich et al., 2002; Jacobs et  al., 2005; Wilson and Jacobs, 2006; Jacobs et  al., 2012; Clark et  al., 2015). Based on studies in the Eastern Hardwood Region, oak seedlings with relatively large basal diameters (e.g. > 3/8 inches at 1 inch above the root collar) perform better than smaller seedlings. Planting seedlings that have been grown for 1 year in the nursery then lifted and transplanted back to the nursery (called 1+1 transplants) have been shown to outperform 1 or 2-year old seedlings that are not transplanted (Johnson, 1984). Similar but more cost-effective

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advantages can be obtained by undercutting seedlings in the nursery bed the first or second year (Schultz and Thompson, 1990; Buchschacher et al., 1991; Weigel and Johnson, 1998a, b). Seedlings can be undercut by drawing a tractor-mounted blade or other root-cutting device at a prescribed soil depth – usually 6–8 inches below the soil surface (Fig. 10.1A). This practice severs the taproot and stimulates the development of lateral roots (Fig. 10.1B) and thus the number of new roots initiated after field planting (Johnson, 1988). The combined effect of undercutting and low seedbed density can greatly increase the number of large lateral roots that are essential to post-planting root regeneration and successful field performance (Table 10.1). However, to obtain the full advantage of undercutting, the blade used to lift seedlings should be set below the undercutting depth. Undercutting is not be confused with root pruning, which is done after lifting to reduce roots to a common length (Fig. 10.1B) to accommodate their proper placement in a planting hole of the same depth. Removing seedling tops 6–8 inches above the root collar just before planting further improves planted tree performance when trees are planted under shelterwoods. This practice, known as shoot clipping, may not benefit seedlings planted in the open (including new clearcuts), but there is no evidence that doing so reduces seedling performance (Johnson, 1984, 1988, 1989). Shoot clipping increases the ratio of root mass to shoot mass, and this benefits seedling survival and growth on sites with low to moderate levels of competing vegetation. On bottomland sites which typically have intense competing vegetation, oak seedlings grown in the nursery to at least 18 inches tall and preferably 24–36 inches tall are recommended to keep crowns of planted oaks above the competition (Allen et al., 2001). Such seedlings should also have large root systems to balance the large shoots. In some situations large, container-grown oak seedlings with dense, fibrous root systems can be used in place of bareroot nursery stock. One type of containerized seedling involves a proprietary nursery production technique called the root production method (RPM®) (Plate 8E). The method employs a series of steps that start with selecting and planting large acorns, and from them selecting only the fastest growing seedlings. As they grow, selected seedlings are transplanted into successively larger containers with the smallest seedlings culled at each step. Seedlings receive intensive fertilization

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(A)

(B)

Fig. 10.1.  (A) Undercutting northern red oak seedlings in the nursery with a tractor-drawn blade in early summer of their second year. (B) Undercutting stimulates lateral root development (0 = not undercut, 1 = once undercut, 2 = twice undercut). These seedlings are also root pruned to a common length (10 inches) to facilitate field planting. Because undercutting retards overall seedling growth, a given nursery bed will yield fewer undercut seedlings of a given diameter than one without undercutting (Schultz and Thompson, 1990). A favourable time to undercut is when seedlings are in the resting (lag) phase between first and second flushes of shoot growth. The date of occurrence of this stage may occur anytime from late spring to midsummer, depending on nursery latitude, weather, nursery management practices and other factors. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

and the containers have open bottoms that cause the seedling roots to ‘air prune’ as they extend beyond the bottom of the container (Dey et  al., 2004). This process results in an extremely fibrous, dense root comprised of many lateral roots from which new root growth can be initiated after planting. The result is a large 1- or 2-year-old seedling in a 3- or 5-gallon container. These containerized seedlings have nine times the root volume and seven times the root dry weight of similarly aged bareroot nursery stock. Stem heights may average

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Table 10.1.  Response of northern red oak seedlings to variation in nursery bed density and undercutting treatments. (From Schultz and Thompson (1990); based on data from the Illinois State Forest Nursery.)

Bed density (undercutting treatment)a 3/ft2 (undercut) 3/ft2 (not undercut) 6/ft2 (undercut) 6/ft2 (not undercut) 12/ft2 (undercut) 12/ft2 (not undercut) Statistical significanced

Mean number of lateral roots > 0.04 in. (1 mm) diameter

Mean seedling height (in.)b

Mean seedling diameter (in.)b

Normal 1st order

Wound rootsc

Total

15.3 20.9 14.9 19.1 15.2 18.1 D, U, DU

0.32 0.39 0.29 0.33 0.27 0.30 D, U, DU

13.4 10.8 10.6 8.8 8.7 6.6 D, U

6.1 – 5.8 – 6.2 –

19.5 10.8 16.4 8.8 14.9 6.6

a

Nursery beds were reduced to the prescribed seedling densities in the spring before the second growing season. Seedlings were undercut during the second growing season at a depth of 6 inches when taproots at that depth were 0.25–0.5 inches diameter. b Measured after two growing seasons in the nursery; diameters were measured 0.5 inches above the root collar. c Wound roots are roots > 0.04 inches (1 mm) in diameter that develop at or near the undercutting wound. d Variables statistically significant at α = 0.01: D = bed density; U = undercutting treatments; DU = interaction between D and U.

about 6 ft, depending on species and growing conditions in any given year (Dey et al., 2008). Although limited in supply and expensive compared with typical bareroot nursery stock, the large roots and tops of RPM® stock result in high survival and rapid growth when trees are out-planted. Their large initial size and well-developed root systems help the crowns of RPM trees stay ahead of competing vegetation and deer herbivory after planting. When the method is coupled with compatible ground covers, weed control, and fertilization concentrated around individual planted trees, the method results in a high rate of successful plantation establishment. Consequently, establishing 50 successful oaks/acre may be possible with 55 planted trees and little or no additional treatment following initial site preparation and planting. Swamp white oak planted with RPM® stock to disced, open fields yielded long-term growth advantages averaging 1.5 times more growth in height, 2 times more in dbh and 3–4 times more in green biomass than planted bareroot seedlings (Van Sambeek et al., 2016). RPM® pin oak also showed greater survival, height growth and diameter growth than bareroot seedlings when underplanted in natural mixed oak bottomland forests where the midstorey had been removed (Motsinger et al., 2010). An added benefit of RPM® is that planted trees may produce acorns as early as 2–4 years after planting when species with precocious acorn production (e.g. swamp white oak) are planted (Grossman et al., 2003; Dey et  al., 2004). Despite the high cost per tree, RPM® stock provides a viable option for the

Artificial Regeneration

rapid establishment of relatively few oaks per acre with a high probability of success. Northern red oaks grown in large containers (e.g. 3–5 gallons) with the RPM® method showed sustained advantages over bareroot seedlings after 5 years when trees were planted in various-sized forest openings in southern Indiana (Morrissey et al., 2010). To date, large container stock primarily has been used in operational afforestation projects. Seedlings grown in smaller containers (< 30 in3 or 500 cc) and used in enrichment plantings have largely been restricted to research plantings and have produced mixed results (e.g. Johnson et al., 1986). Plantation establishment Oaks can be established as pure or mixed plantings on old fields or other open areas largely devoid of forest vegetation. On these sites, mechanical planters and site preparation equipment often can be used (Allen et  al., 2001; Löf et  al., 2012, 2016). Subsequent mechanical weeding and row-thinning methods such as those commonly applied to pine plantations also can be used. In the USA, more is known about oak plantation establishment in southern bottomlands than elsewhere. This is partially due to the ready availability of bottomland agricultural fields that were abandoned because of frequent flooding, downturns in agricultural markets, and the growing recognition of the benefits associated with the restoration of bottomland forests. The original forests covering

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these bottomlands included oaks mixed with other species and these areas rank among the most productive forests in the temperate region. Oak site index on southern bottomlands ranges from 80 to 100 ft or more at an index age of 50 years. Their high productivity and their importance to landscape biodiversity therefore may sometimes justify relatively high investments in site preparation, planting and weeding when restoring bottomland hardwood forests. Types of oak planting stock were discussed in the previous section. The remainder of this section addresses other factors that can affect the successful establishment of oak plantations. Many of these factors are also relevant to the direct seeding of acorns as discussed earlier in this chapter. site preparation. 

Site preparation before planting or seeding oaks is usually done to reduce competing vegetation or to create soil conditions more favourable for the early growth and survival of tree seedlings (Kabrick et al., 2005; Löf et al., 2012, 2016). Methods include: (i) prescribed burning; (ii) mechanical practices (i.e. discing, scarification, trenching, ripping or mounding); and (iii) the application of herbicides and soil amendments used singly, in a planned sequence, or in combination to accomplish multiple objectives as needed. Common site preparation methods have been summarized elsewhere (e.g. Allen et al., 2001; Löf et al., 2012, 2016). However, extensive site preparation is not always necessary, and the range of feasible practices may be dictated by site conditions. Former agricultural fields provide options for discing, tilling or herbicide application with mechanized equipment that may be difficult or impossible elsewhere. The most common reason for site preparation is to control competing vegetation. Discing, mowing, burning, use of herbicides and planting a compatible cover or nurse crop can be used to reduce unwanted vegetation that may compete with planted trees (Allen et al., 2001; Van Sambeek and Garrett, 2004; Steele et al., 2008). On former agricultural fields, planting an agricultural crop the growing season before tree planting can reduce weed competition and provide residual stubble to further create favourable conditions for planting (Allen et al., 2001). Methods used for weed control during site preparation should be compatible with methods used for subsequent weed control after planting. For example, if periodic discing will be used for weed control after tree planting, site

386

preparation should create suitable surface conditions for doing so. Discing is often used for site preparation on abandoned agricultural fields. It reduces soil compaction, destroys much of the existing competing vegetation, and incorporates the latter as additional organic matter into soil surface layers, which are often low in organic carbon. Although mowing removes the above-ground portion of competing vegetation, the roots of mowed vegetation may still compete with planted trees. Thus, mowing may not be an effective weed control where moisture and nutrients are limiting (Dey et al., 2004). Herbicides can effectively kill competing vegetation during site preparation. In many situations herbicide applications can be limited to rows or spots where trees will be planted. Properly selected herbicides may prove especially effective when used on woody plants, invasive species and noxious weeds. Compatible ground covers planted before tree planting can serve as living mulches that reduce competition from undesirable vegetation (Plate 9A, C and D) (Van Sambeek and Garrett, 2004; Steele et  al., 2008). Leguminous ground covers also can improve soil fertility by fixing nitrogen. In some situations legume forages used as ground cover can provide supplemental hay crops while trees are small. Woody nurse crops can be used to promote hardwood establishment and growth by reducing competing vegetation and browsing damage (Van Sambeek and Garrett, 2004; Jensen et  al., 2012; Löf et al., 2014). Prescriptions that emulate natural succession in floodplains by underplanting oaks in a young stand of pioneer species such as eastern cottonwood have shown promise for establishing oaks in the LMAV (Plate 10) (Gardiner et al., 2001, 2008; Stanturf et  al., 2009). The eastern cottonwood quickly forms a closed canopy within 2 years at which time oaks can be underplanted. The cottonwood canopy provides sufficient light (e.g. ≥ 30% of full sunlight) to support oaks planted in the understorey, yet casts shade sufficient to control competing herbaceous vegetation. Once established, the planted oak saplings are released from suppression when the cottonwood is harvested, usually about 10 years after the oaks are planted. Prescribed burning can be used to reduce competing vegetation during site preparation. Fire can effectively eliminate dense herbaceous and woody cover and also any accumulation of down wood. Used in late spring, prescribed burning is also

Chapter 10

effective in reducing the cover of pasture grasses (Allen et al., 2001). Damage from herbivores (or seed predators in the case of acorn seeding) can be prevented or minimized by reducing cover for small mammals, which in turn increases their exposure to natural predators. Damage from herbivores includes girdling of seedling stems and predation of seeded acorns. In old fields in the lower Missouri River Valley, cottontail rabbit density was reduced by planting redtop grass as a cover crop (Dey et al., 2004; Dugger et  al., 2004). The redtop provided poor cover for herbivores and thereby increased their predation risk. This practice also reduced the food source for rodents and rabbits in the winter when damage is heaviest. In contrast, the coarse woody forbs that ordinarily would dominate in winter are taller and more complex, thereby providing better thermal protection to hiding to animals. Winter cover can also be removed by mowing in the autumn. planting methods.  Seedlings can be planted manually or with a tractor-pulled planting machine. Manual planting can be done anywhere with inexpensive hand tools. In contrast, mechanical planters require a relatively clean site. Due to cost and availability, their use is usually limited to large planting areas. In some cases, they can be used to plant trees in crop stubble without additional site preparation. For planting on bottomlands on clean sites, a crew of about ten manual planters would be needed to match the daily output of a crew of two or three people using a mechanical planter (Allen et  al., 2001). The outcome of manual planting largely depends on the experience of the planting crew. Therefore, crew training and follow-up inspection during planting is recommended whenever manual planting is used. The spacing between planted trees also should be pre-planned and monitored during manual planting. Competition control after planting may not be required for oaks established on clean sites or where suitable ground covers are used. Fastgrowing oaks planted on site index 80–90 ft can be expected to attain 15–20 ft in height in 10–15 years under normal conditions (Kennedy, 1993). Oaks thus may be able to outgrow their competition. Because vines sometimes can overwhelm planted trees on bottomland sites, effective weed control after planting is usually recommended. Discing, mowing and herbicide application are often used to control competing vegetation. The

Artificial Regeneration

relative merits of each method are similar to those described earlier for site preparation. However, seedlings need to be protected from damage when these methods are applied. Sowing acorns or planting seedlings in rows makes competition control easier. Because young oaks sprout prolifically from dormant buds near the root collar, prescribed burning in some situations can be used to control weeds after planting. However, such burns should be delayed until planted oaks are at least 3 years old and have root collars at least 0.25 inches in diameter. If trees are correctly planted, the root collars should be about 1 inch below the soil surface. This will shield buds at the root collar from lethal temperatures during burning. Although top-kill from burning of many planted oaks is likely, dormant buds at the root collar usually sprout to form vigorous new shoots (Brose and Van Lear, 2004). Summer fires are more damaging to competing vegetation than dormant season fires. Burning thus can be delayed until competition is approximately as tall as the planted oaks. A surface fire at that time will top-kill many woody plants less than 4 inches dbh, including planted oaks. However, because the oaks have a greater capacity to sprout than many other woody plants, a series of fires will favour the oaks over most of their woody competitors (Dey et  al., 2008). But when oaks sprout, they usually produce multiple-stemmed tops. This can eventually reduce subsequent oak height and diameter growth if multiple-stemmed oaks are not reduced to a single stem either through self-thinning or later silvicultural treatment (see section ‘Stump Sprouts and Related Growth Forms’ in Chapter 2, and section ‘Tending oak coppice (stump sprouts)’ in Chapter 8, this volume). Prescribed burning thus is less precise than cultivation or herbicide application in its effect on both planted trees and competitors. The use and effectiveness of prescribed burning is also subject to vagaries in weather and other uncontrollable ­factors. Nevertheless, it is relatively inexpensive to implement, and can be effective on planted areas that are poorly suited for other methods of weed control due to large plantation size or topography roughness. In addition to cover crops that function as living mulches, competing vegetation around ­ planted seedlings can be controlled with organic mulches, plastic mulches or fabric weed barriers. Although these options provide good weed control in proximity to planted seedlings, they do not eliminate competition from tall vegetation or

387

vines rooted beyond their coverage. Moreover, these barriers may provide protective cover for rodents that can damage the bark or roots of planted trees. Unless the barriers are supplemented with additional area-wide weed control, their use may be counterproductive. Damage by rodents, rabbits, squirrels and other small mammals includes stem girdling and root damage. Oak seedlings can survive repeated browsing, and they frequently sprout when girdled by small herbivores. However, the resulting loss of height growth may prevent them from attaining dominance and reaching the overstorey. Reducing vegetative cover benefits planted or seeded trees and increases the exposure of damaging animals to avian and other predators. Elevated roosts are sometimes constructed around plantings to encourage small mammal predators. In some areas, deer browsing can seriously damage planted oaks (see ‘Deer’, Chapter 11, this volume). For example in the Allegheny Plateau, deer browsing significantly impedes the natural regeneration of hardwood forests when deer populations exceed about 15 deer/square mile (Brose et  al., 2008). When alternative deer foods are abundant, populations may reach about 30 deer/square mile before comparable damage to forest regeneration occurs. Two possible solutions to this over-browsing problem are to: (i) fence to exclude deer from planting sites; or (ii) encage individual trees to a height of about 5 ft. Both are expensive, require maintenance and eventually require removal. In some cases it may be possible to overwhelm browsing effects by planting exceptionally large areas and/or by using cultural practices that accelerate tree height growth so that tree crowns are soon beyond the reach of deer (Marquis and Brenneman, 1981; Marquis et al., 1992). Although removing herbaceous vegetation between planted trees (e.g. via clean tilling or herbicide) can reduce small mammal habitat and their activity in the area, it also may increase damage by deer. In weed-free fields, the planted oaks provide the only food for deer. Highly fertilized nursery stock is especially nutritious and attractive to deer and other herbivores, including insects. Well-chosen cover crops can provide an alternative browse for deer and also reduce hiding cover for small mammals. Fertilization of trees usually increases their growth, other factors being equal. Ideally, soil should be tested to identify possible nutrient deficiencies and potential soil pH problems (see site

388

preparation guidelines above). Although specific fertilization guidelines for oaks are unavailable, potential nutrient deficiency problems can be resolved by applying a balanced, slow-release fertilizer around planted seedlings. In most cases, areawide fertilization is not recommended because it is likely to be cost-ineffective and may be counterproductive by increasing growth of competitors. In contrast, applying fertilizers to individual trees is relatively easy and inexpensive, especially if done during hand planting. But if fertilizers are applied without additional weed control, they also can increase vegetative cover for damaging mammals around planted trees. protection using tree shelters. 

Protection of planted trees in a broad sense includes controlling weeds and herbivory as described above. It may also require intervention to avoid or limit problems with insects, disease, livestock, wildfire or flooding (McCreary and Tecklin, 2005). Tree shelters are a planted-tree protection system. Moreover, they can be used in both artificial and natural regeneration to protect individual seed spots or planted trees, and also to protect individual stems of natural reproduction. Tree shelters are transparent or translucent plastic tubes that protect individual trees from animal damage and also create a greenhouse effect around each tree (Potter, 1991). They are commercially available in a range of materials, durability, sizes (diameter and height) and colours (Windell, 1991). Sizes typically range from 3 to 6 inches in diameter and 4–5 ft in height. Five-foot shelters are recommended where deer browsing is severe or snow is deep (Kays, 1996; Schuler and Miller, 1996). Commonly available colours include white, green, tan and brown. Some are circular in cross section whereas others are square or rectangular. Trees planted in shelters usually grow faster than non-sheltered trees. Accelerated growth may result from increased air temperature and CO2, and reduced wind inside the shelter (Potter, 1988, 1991; Mayhead and Jones, 1991; Windell, 1991; Minter et  al., 1992). Although ambient air temperatures inside shelters may exceed 100°F (38°C), leaf temperatures on actively transpiring trees may be 20–30°F (−7 to −1°C) lower (Potter, 1988). However, inside and outside temperatures of shelters with perforated walls may not differ (Minter et al., 1992). Several studies have demonstrated the effectiveness of shelters in increasing the survival and height

Chapter 10

growth of oak seedlings. The period of accelerated growth is most dramatic while seedlings are still in the tree shelter, and slows after they emerge. Emergence may take 2 years or longer depending on tree shelter height and other factors. In southern England, sheltered sessile oak seedlings grew five times faster in height than did unsheltered seedlings over 4- and 5-year periods (Potter, 1988, 1991). In the USA, reported height growth of northern red oak in tree shelters ranged from about 30% to 230% greater than that of non-sheltered seedlings (cf. Lantagne, 1991; Teclaw and Isebrands, 1991; Zastrow and Marty, 1991; Minter et al., 1992; Bardon and Countryman, 1993; Smith, 1993; Walters, 1993; Gillespie et  al., 1996; Ponder, 1997, 2003). Variation in growth responses among studies may be related to differences in competition, site quality, climate and weather, shelter design and colour, overhead shade, seedling quality, length of period observed, herbivory and other factors. Although tree shelters have not always been shown to accelerate growth of planted oaks (Lantagne, 1996; Teclaw and Zasada, 1996; Lantagne and Miller, 1997), they have increased oak seedling survival (Potter, 1988, 1991; Lantagne, 1991, 1996; Zastrow and Marty, 1991; Bardon and Countryman, 1993; Smith, 1993). Because of the relatively high cost of tree shelters and additional costs for staking materials, installation and maintenance, it may be difficult to justify their use unless the potential for animal or other types of damage are severe, future tree value is high, or other factors merit the investment. Shelters nevertheless can reduce the cost of applying herbicides because the shelter itself protects the seedling from herbicide damage. Shelters protect against damage from mechanical equipment such as mowers and string trimmers, and shelters facilitate finding planted trees for subsequent inspection and cultural treatments. Tree shelters can be used in conventional plantation establishment in old fields and other treeless areas, as well as in enrichment plantings in conjunction with clearcuts and shelterwood harvests (see the next section ‘Enrichment planting’). When tree shelters are used beneath a shelterwood overstorey, tree shelters that block little light are recommended (Potter, 1991; Schuler and Miller, 1996). Other factors being equal, shelterwood density affects the growth of seedlings within tree shelters. In a Wisconsin study, the height growth of northern red oak seedlings in tree shelters decreased with

Artificial Regeneration

increasing shelterwood density (Teclaw and Isebrands, 1991). But even under high-density shelterwoods (100% crown cover), seedlings in tree shelters grew faster than unsheltered seedlings. Where late spring frosts are frequent and severe (such as in the Northern Hardwood Region), tree shelters used in combination with shelterwoods may be necessary to obtain any growth advantage from the tree shelter. Tree shelters did not benefit northern red oak seedlings planted in clearcuts in northern Wisconsin because of dieback caused by late spring frosts, but did benefit seedlings under shelterwoods with 50% or 75% crown cover (Teclaw and Zasada, 1996). However, tree shelters had a detrimental effect on the height growth of northern red oak planted under shelterwoods in a northern Michigan study (Lantagne and Miller, 1997). Tree shelters also have potential application in: (i) sheltering natural reproduction (Potter, 1991; Kittredge et  al., 1992); (ii) direct seeding with acorns (Smith, 1993; Walters, 1993; Bailey et  al., 1996; Schuler and Miller, 1996); (iii) rehabilitating partially failed plantings (Potter, 1991; Gillespie et  al., 1996); and (iv) re-vegetating strip-mined lands (Farley et al., 1996). Seedlings grown in tree shelters develop a nearly columnar stem with little stem taper (Potter, 1988). Although rapid height growth helps keep the sheltered tree above competitors and out of reach of browsing animals, stems are usually so weak that the tree shelter is needed for physical support. While this acceleration in stem growth occurs at the expense of root growth, the delay in root growth is more than compensated by the third year after planting (Ponder, 1996). It nevertheless may take 5 years or longer for the tree to develop sufficient diameter to stand by itself (Potter, 1988, 1991). Tree shelters therefore should be sufficiently durable to last until trees can emerge from the shelter. After trees emerge from the top of the tree shelter, height growth slows and more growth is allocated to stem diameter (Schuler and Miller, 1996; Strobl and Wagner, 1996; Windell and Haywood, 1996). Many of the commercially available tree shelters are made of polypropylene, which is not durable in sunlight unless an ultraviolet inhibitor is added (Potter, 1988; Windell and Haywood, 1996). The top rim of a shelter also must be smooth to prevent abrasion and damage to the thin stems. In bottomlands, tree shelters may be dislodged by flooding (Dey et al., 2008). Further information on planting

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northern red oak using tree shelters, including direct seeding and shelterwood plantings, has been presented by others (Schuler and Miller, 1996), and for using them to restore oaks to California rangelands (McCreary, 2001; McCreary and George, 2005; McCreary and Tecklin, 2005). Although promising in principle, much remains to be learned about tree shelters and their application to oak regeneration. Enrichment planting Enrichment planting is defined as planting to improve the proportion of desirable species or to increase biodiversity by establishing young trees among existing forest growth (Helms, 1998). The method potentially can be combined with any natural regeneration method. In oak forests, experience in enrichment planting largely has been limited to clearcutting and shelterwood methods. Opportunities for planting oaks via enrichment plantings vary among ecosystems. In upland oak stands in the eastern USA, best opportunities for the major oak species occur within the oak site index range of 60–75 ft (index age 50). There, site quality is sufficient to meet the growth requirements of most oak species, yet competition from other vegetation is not too severe. Outside this site index range, it may be difficult to justify planting oaks due to the poor likelihood of success or the high cost. Enrichment plantings are most effectively used in conjunction with existing natural regeneration guides. Guidelines for evaluating the natural regeneration potential of oak stands are available for several regions (see ‘Regeneration models’ in Chapter 8, this volume). Planting costs accordingly can be minimized by planting only the number of trees needed to attain a future target stocking goal (Johnson and Rogers, 1985; Johnson et al., 1986). However, this strategy requires information on the expected performance of the planted oaks. The outcome will depend on site quality, overstorey and understorey composition and structure of the present stand, planting stock size, animal browsing, frequency and intensity of management actions, and other factors. enrichment planting in clearcuts.  Oaks can be successfully planted in clearcuts despite the numerous reported failures of such plantings (e.g. Hilt, 1977; Loftis, 1979; McGee and Loftis, 1986). One reason for planting failures in clearcuts is the

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delayed onset of root growth in bareroot seedlings (Johnson et al., 1984). The use of container-grown seedlings and tree shelters (as discussed above) combined with competition control offers improved performance provided the additional costs can be justified. Because of the problems in planting oaks in clearcuts, enrichment plantings under shelterwoods are preferred, as discussed below. enrichment planting under shelterwoods.  Like natural oak reproduction, planted oaks also can benefit from a shelterwood (see ‘The shelterwood method’, Chapter 8, this volume). The period under a shelterwood allows seedlings time to establish and grow to larger size before they compete with the surge of competition that develops after complete overstorey removal. For bareroot nursery stock, this recovery period is critical because the physiological disruptions to seedlings from lifting, handling and planting delay the initiation of root and shoot growth the first year after planting (Johnson et  al., 1984; Johnson, 1988; Struve and Joly, 1992). Shelterwoods of appropriate density thus provide planted oaks with sufficient light while allowing time for them to re-establish and expand their root systems before final shelterwood removal and exposure to full light (Dey and Parker, 1996, 1997a). Although the ecophysiology of many oak species remains poorly understood, much is known for some of the widely distributed commercial species such as white oak and northern red oak. In general, oak seedlings growing in a shaded understorey increase in survival and growth provided that light is sufficient. Nevertheless, there are important differences among the oaks in their response to light (Gottschalk, 1994; Paquette et  al., 2007; Rebbeck et al., 2011). For example, white oak is one of the more shade-tolerant oaks in eastern North America. Although it expresses little increase in shoot growth with increasing light from 6 to 25% of full sunlight, it nevertheless increases in root mass by allocating proportionately more carbohydrates to roots than shoots. White oak thus is likely to be morphologically and physiologically ready to respond opportunistically to sudden increases in light because of its relatively high root:shoot ratio – which in turn facilitates rapid shoot growth and competitive capacity. The root:shoot ratio of white oak seedlings is typically double that of northern red and chestnut oaks under similar understorey conditions (Dillaway et al., 2007; Rebbeck et al., 2011).

Chapter 10

Artificial Regeneration

100 Canopy closure or PAR (%)

Different oak species have different responses in root growth to increasing light levels in the understorey. As light in the forest understorey increases from 4 to 89% of full sunlight, the root growth of oak species across a range of shade tolerances varied, but there was little root growth in any oak species where light was below 15% of full sunlight (Brose, 2008). White oak and chestnut oak root growth was maximum in the midrange of availability, but for northern red oak and black oak maximum root growth occurred at highest light levels (Brose, 2008). Others have found that the growth of northern red oak in heavily shaded understories improves as light is increased, but especially when it is greater than 20% of full sunlight (Gottschalk, 1985, 1987, 1994). Similarly, Parker and Dey (2008) reported that net photosynthesis and leaf conductance to water vapour increased by two to three times in planted 2-01 and natural northern red oak seedlings compared with that of their major competitor, sugar maple, when light increased from 1 to 49% of full sunlight after a final shelterwood removal. They also found that both planted and natural seedlings were similar in their response to light. Similarly, Paquette et al. (2007) found that the shoots of planted seedlings of northern red oak that were containerized grew faster as light increased from near 0 (zero) to 43% of full sunlight. Thus it is generally recommended for understorey oaks that light levels be above 20% of full sunlight. A range of 30–50% provides for good growth without overly stimulating competing vegetation (Dey et  al., 2008, 2012). Although an increase in available light does promote the growth of competing vegetation, it does not necessarily limit oak regeneration success. Understanding the relation between stand structure, density, stocking and understorey light is essential to effectively applying the shelterwood method to enhance oak regeneration (see Blizzard et al., 2013, for example) (Fig. 10.2). A practical problem in integrating enrichment planting with the shelterwood method is quantitatively expressing and accurately predicting the expected outcome. Unlike plantings designed to create a monotype (i.e. a single-species stand), planting oaks under shelterwoods takes advantage of a stand’s natural oak regeneration potential and uses enrichment planting as a stand-specific supplement when needed. The number of planted trees (and thus planting costs) required to obtain a given future stocking thereby can be minimized. However,

80 60 40 20 0 0

20

40

60

80

100

Stocking (%) PAR = 22.4 + 71.3 exp (–0.034 stocking %) Canopy closure = 41.8 log10 (1.2 + stocking %)

Fig. 10.2.  Relation between stocking, canopy closure and photosynthetically active radiation (PAR) in stands thinned from below in the Missouri Ozarks. (Adapted from Blizzard et al., 2013.)

enrichment planting among naturally established trees and vegetation complicates predicting the overall regeneration outcome. Uncertainties are largely the consequence of unknowns related to the competitive struggle between planted trees and competitors in a relatively heterogeneous physical and biotic environment. The two major determinants of planting success, planted tree survival and growth, have traditionally been treated as separate and independent responses. In establishing monotypes by planting, first-year or second-year survival may be a good indicator of planting success – especially when intensive preplanting site preparation eliminates competitors. In  a shelterwood, however, it is more useful to define a planted tree’s competitive capacity, i.e. its expected capacity to survive and grow at a rate sufficient to attain and maintain dominance among its competitors over a specified period. Accordingly, it makes little difference whether a planted tree dies or grows so slowly that it becomes overtopped by competitors. In either case, the seedling has failed silviculturally. A suitable quantitative expression of competitive capacity accordingly would consider survival and growth simultaneously. Ideally, this quantification also would account for the effects of nursery stock quality and planting environment.

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The latter includes site quality, anticipated changes in competition, and planned modifications of the environment before and after planting such as weeding, control of shelterwood density, and number of years planted trees remain under the shelterwood. Dominance probabilities (see ‘Regeneration models’ in Chapter 8, this volume) provide a useful and convenient quantitative expression of competitive capacity. In a shelterwood context, dominance probability is the probability that an individual planted tree attains dominance or codominance a given number of years after planting or after shelterwood removal. These probabilities are likely to change through time as a result of differences in the survival and growth of planted trees and their competitors (Dey et al., 2009). In principle, dominance probabilities can increase, decrease, remain constant or reverse their direction through time and with management. They therefore provide a potentially flexible quantitative expression of a planted tree’s competitive capacity. Dominance probabilities can be calculated as observed averages for a specific type of seedling in a given planting environment and number of years after planting. Alternatively, they can be estimated statistically (e.g. Weigel and Johnson, 1998a, b, 2000; Spetich et  al., 2002; Weigel and Peng, 2002). The silvicultural value of dominance probabilities (P) lies not so much in the probabilities themselves, but in their reciprocals (i.e. 1/P). The reciprocals define how many seedlings are needed to obtain one competitively successful tree at a future point in time. These reciprocals are sometimes called planting factors (Johnson, 1984; Johnson et  al., 1986; Weigel and Johnson, 1998a, b). The shelterwood method provides flexibility in controlling stand density (and therefore overhead light), in timing of shelterwood removal, and consequently in controlling understorey competition. Four related questions in planting oaks under shelterwoods are: ●● What level of overhead shading (or shelterwood density) is appropriate? ●● Is control of understorey vegetation necessary? ●● How long should the shelterwood be retained? ●● After final overstorey removal, is further silvicultural intervention necessary to assure satisfactory survival and growth of planted trees? Oak reproduction in a mature, fully-stocked forest understorey, whether planted or natural, is

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usually unable to grow or survive long because light is often < 5% of full sunlight, especially on mesic sites (Gardiner and Yeiser, 2006; Parker and Dey, 2008; Lhotka and Loewenstein, 2009; Parrott et  al., 2012; Craig et  al., 2014). Removal of a shade-­tolerant mid-storey may increase light in the understorey to 10–20% of full sunlight, which is usually adequate for the survival and growth of oak advance reproduction (Lorimer et  al., 1994; Motsinger et al., 2010; Parrott et al., 2012; Craig et  al., 2014). But greater reductions in overstorey density are usually necessary to provide the light sufficient for satisfactory growth of oak reproduction (Fig. 10.3). Residual stand densities near 50–60% stocking or 70–80% crown cover often provide the requisite conditions for oak growth in the understorey (Gottschalk and Marquis, 1982; Pubanz and Lorimer, 1992; Dey and Parker, 1996, 1997a). This provides about 20 to 30% of full sunlight at the forest floor (Sander, 1979; Parker and Dey, 2008; Schweitzer and Dey, 2011; Blizzard et  al., 2013). Further reducing overstorey density often stimulates the growth of understorey competitors at the expense of planted trees. Controlling low shade from shrubs, small trees, stump sprouts, and herbaceous vegetation may be as important as controlling high shade (Horsley, 1991; Pubanz and Lorimer, 1992; Teclaw and Isebrands, 1993a, b). Control of understorey vegetation provides the dual advantages of increasing light during the shelterwood period as well as reducing competition after shelterwood removal provided that the method results in the death of competitors and not merely top-kill followed by sprouting. The length of time planted trees should remain under a shelterwood therefore partly depends on the growth of the desired reproduction to competitive sizes, how quickly the understorey competition develops, and whether additional competition control is economically justifiable or otherwise needed. The effect of shelterwood density, understorey density, and various seedling factors is demonstrated by the performance of northern red oaks planted in the Boston Mountains of northern Arkansas (Central Hardwood Region). Based on dominance probabilities 11 years after enrichment planting beneath a shelterwood overstorey and 8 years after complete shelterwood removal, northern red oaks planted under low to moderate shelterwood densities (40% and 60% stocking) outperformed trees planted under higher shelterwood densities (80% stocking) (Fig. 10.3) (see also

Chapter 10

Site index 18 m (59 ft)

0.8

W2

0.6

W1

0.4

W0

0.2 0.0 10 20 9 16 m) ea 8 r (m fte 12 e 7 p i l a (ye r pl 8 gc 6 ars ant dlin 4 ing ) see l a i Init

Tim

11th-year dominance probability

ability Dominance prob

0.8 W2 W2

0.6

W1 W1 W0

0.4

W0 0.2

0.0 4

12 8 16 20 Initial seedling caliper (mm)

Site index 24 m (79 ft)

0.8 W2

Dominance pr

0.6

W1

0.4

W0

0.2 0.0

Tim

10

9

20 16 ) ea 8 mm ft 12 7 er ( p i l (ye er pl a 8 c 6 ars ant ling 4 ing ) eed s l ia Init

11th-year dominance probability

obability

0.8 W2 0.6

W2 W1

0.4

W1 W0

0.2

WO

0.0

4

8

12

16

20

Initial seedling caliper (mm) D4060

D80

Fig. 10.3.  Estimated dominance probabilities for 2-year-old northern red oak seedlings planted under shelterwoods in the Boston Mountains of northern Arkansas (Central Hardwood Region). The estimates are based on logistic regression analysis and are shown for two values of red oak site index in relation to shelterwood density (expressed as stocking per cent, D), 11 years after planting (three-dimensional graphs), initial seedling caliper (stem diameter 2 cm above the root collar) and weed control (W) treatments: W0 = no weed control. W1 = one herbicide application (the winter before spring planting) + cutting stems of all tree reproduction ≥ 30 cm (1 ft) tall to 3.8 cm (1.5 inches) dbh 30 cm above the ground the winter before overstorey removal. W2 = two herbicide applications before final shelterwood removal. The shelterwoods were removed 3 years after planting. Site index was estimated from Graney and Bower (1971). The three-dimensional graphs show probabilities for shelterwoods at 40% and 60% stocking (D4060), which did not differ significantly (α = 0.001); the 80% shelterwood density (D80) differed significantly from the average of D40 and 60. The two-dimensional graphs show 11th-year probabilities. All estimates shown are for seedlings with shoots clipped 20 cm (8 inches) above the root collar at time of planting; estimates for unclipped seedlings are significantly smaller other factors being equal. All predictors are significant at α = 0.001. The model is based on 4128 planted trees distributed across five study sites. (From authors’ analysis of USDA Forest Service data.)

Artificial Regeneration

393

Spetich et al., 2002, 2004). For a given initial seedling size and type, dominance probabilities increased with time and with increasing intensity of weed control. Probabilities decreased with increasing site quality (as expressed by site index). The latter effect is related to the greater abundance and rapid growth of competitors on the better sites. There, the dominant competitors are blackgum, red maple, black cherry, flowering dogwood, and other shade-tolerant species. For a given planting environment, seedling characteristics that influenced planting success included initial seedling size (expressed as basal stem diameter measured 1 inch above the root collar), and whether or not the tops of planted seedling were cut off (clipped) 8 inches above the root collar before planting. Dominance probabilities increased with increasing initial caliper (Fig. 10.3), and other factors being equal, probabilities were greater for clipped than unclipped seedlings. The effect of the timing of overstorey removal is demonstrated by a 19-year study on planting northern red oak in the Ozark Highlands of Missouri (Fig. 10.4). In this region, opportunities for planting red oak under shelterwoods often occur on northand east-facing slopes, where site index for black oak typically ranges from 60 to 75 ft. Although site quality there is favourable for northern red oak, the natural regeneration potential for northern red oak is often low. The number of years planted trees remain under a shelterwood significantly affects dominance probabilities. In the Missouri study, highest dominance probabilities occurred when shelterwoods were retained for 10 years. Shelterwoods retained for 3 or 6 years were second best, while those retained 0 years (i.e. trees planted in clearcuts) were least successful. Dominance probabilities also increased with increasing initial seedling caliper (i.e. basal stem diameter). For a given initial caliper, 2-year-old transplants outperformed 2-year-old seedlings and top-clipped trees performed better than unclipped trees (Fig. 10.5A). Where oaks occur with yellow-poplar, planting oaks under shelterwoods represents a special problem. Yellow-poplar outgrows most co-occurring oaks, is long-lived, and regenerates aggressively from seed and sprouts after complete or moderately heavy overstorey removal (Beck and Della-Bianca, 1981; Beck, 1991). The capacity of oaks to successfully regenerate naturally in competition with yellow-poplar is therefore severely limited. As a consequence, oaks are declining in abundance

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where the two species co-occur. Attempts to regenerate mixed oak and yellow-poplar stands by planting have produced a long history of planting failures, especially where site quality is high (e.g. red oak site index ≥ 75 ft) (Olson and Hooper, 1972; Russell, 1973; McGee and Loftis, 1986). In mixed oak and yellow-poplar stands in West Virginia (oak site index 60–70 ft) that were planted to northern red oak after clearcutting, 30–50% of planted seedlings were considered competitively successful after 5 years (Wendel, 1980). Northern red oaks planted under mixed oak/yellow-poplar shelterwoods in southern Indiana (black oak site index 75 ft) responded similarly (Weigel and Johnson, 2000). In the latter experiment, the shelterwood was thinned to 60% stocking and retained for 3 years. Five years after shelterwood removal (8 years after planting), dominance probabilities of planted oaks ranged from < 0.10 to > 0.55, depending on initial seedling caliper, shoot clipping and undercutting treatments (Fig. 10.6). However, these probabilities declined rapidly during the next 5 years as a result of suppression from overtopping yellow-poplar. Ten years after shelterwood removal, dominance probabilities for all classes and initial sizes of seedlings declined to < 0.10. Although dominance probabilities indicated that pre-planting and postharvest herbicide treatments temporarily created a favourable environment for 35% or more of some types and sizes of planted oaks, yellow-poplar dominated the planting sites 10 years after final harvest (Fig. 10.5B). On these and similar sites, successful oak planting requires controlling competitors, especially yellowpoplar, no later than the end of the early release interval and planting nursery stock with the requisite growth potential (Fig. 10.6). One approach to increasing light to promote oak advance reproduction without encouraging the development of yellow-poplar and other fast-growing shade-intolerant species is to remove the mid-storey canopy while maintaining an intact main overstorey (Loftis, 1990). Removing the mid-storey canopy on mesic sites may increase light levels to 15–20% of full sunlight to benefit survival and growth of both planted and natural oaks without exacerbating the competition problem (Lorimer et al., 1994; Lhotka and Loewenstein, 2009; Parrott et al., 2012; Craig et al., 2014). However, this approach also improves the growth and survival of shade-tolerant competitors such as red maple, which must be controlled where oak advance reproduction is preferred

Chapter 10

Time under shelterwood (years) 10 10 3, 6 3, 6

1+1 Transplants

Dominance probability

0.8 0.6

0 0

0.4 0.2 0.0

4

6

8 10 12 14 Initial shoot caliper (mm)

16

1+0 Seedlings

Dominance probability

0.8 10 10 3, 6 3, 6

0.6 0.4

0 0

0.2 0.0

4

6

8 10 12 14 Initial shoot caliper (mm) Clipped

16

Unclipped

Fig. 10.4.  Estimated dominance probabilities for planted northern red oak seedlings and transplants 16 years after shelterwood removal and 19 years after planting in the Ozark Highlands of Missouri (Central Hardwood Region); black oak site index is 60–70 ft. A dominance probability is here defined as the likelihood that a planted tree will attain dominance or codominance 16 years after shelterwood removal. Probabilities are statistically adjusted to a common number of years after shelterwood removal (16) based on a ‘time effect’ variable. Actual observed time-sinceoverstorey-removal ranged from 9 years (10-year treatment) to 19 years (0-year or clearcut treatment). Probabilities are based on logistic regression analysis and are shown in relation to the number of years planted trees remained under the shelterwood (0, 3, 6 and 10), initial shoot caliper and shoot clipping treatments. Probabilities for 3- and 6-year shelterwood periods did not significantly differ (α = 0.01). Probabilities for 10-year and 3- and 6-year shelterwood treatments differed significantly from the clearcut treatment. Clipped seedlings performed significantly better than unclipped seedlings. (From authors’ analysis of USDA Forest Service data.)

(Parrott et al., 2012; Craig et al., 2014). The midstorey can be reduced by the application of herbicides, mechanical cutting or prescribed burning. The use of herbicide can eliminate sprouting of treated stems. When they are cut or top-killed, sprouting rates are high for most species in the size range common to the mid-storey (Dey and Hartman, 2005;

Artificial Regeneration

Hutchinson et al., 2005; Parrott et al., 2012; Craig et al., 2014). Although maintaining a high overstorey density will retard sprout growth in the short term, the sprouts eventually will be released along with the oak in subsequent overstorey harvests. Frequent prescribed fires over 10 years can reduce the density of certain species of competing

395

(A)

(B)

Fig. 10.5.  (A) A planted northern red oak in the Ozark Highlands of Missouri (Central Hardwood Region) that is codominant 13 years after shelterwood removal. The tree remained under the shelterwood (at 60% stocking) for 3 years. This tree was a 1-year-old seedling with a clipped top and a caliper of 11 mm (approx. 3/8 inch) at time of planting. Based on estimated dominance probabilities (Fig. 10.4), about 42% of such seedlings would be expected to attain dominance or codominance 16 years after shelterwood removal. (B) A planted northern red oak (held in hand) in an intermediate crown class 10 years after shelterwood removal in a mixed oak–yellow-poplar stand in southern Indiana (Central Hardwood Region). Ten years after shelterwood removal, 90% of planted trees had died or were overtopped by yellow-poplars such as the two large trees in the foreground and the one in the background (Weigel and Johnson, 2000). (Photographs courtesy of USDA Forest Service, Northern Research Station.)

vegetation (Dey and Hartman, 2005; Fan et  al., 2012; Brose et al., 2013). Burning can impact the seed bank to cause direct seed mortality, but also can stimulate the germination of some species – which in many cases will be short lived (Schuler et  al., 2010). Burning thus can be used in conjunction with the shelterwood method to increase the natural regeneration potential of planted oaks by decreasing numbers of more fire-sensitive yellow-poplar, maples and other competitors (Nyland et  al., 1983; Keyser et  al., 1996; Brose and Van Lear, 1998a, b; Clatterbuck, 1998; Brose et al., 2013). Several regionally specific prescriptions for planting oaks under shelterwoods have been developed based on dominance probabilities (e.g.

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Johnson et al., 1986; Johnson, 1992b; Weigel and Johnson, 1998a, b, 2000; Spetich et  al., 2004). Collectively, these prescriptions emphasize the importance of site quality and initial seedling characteristics as discussed above. A prescription for planting northern red oak and white oak under shelterwoods in the Ozark Highlands of Missouri (Weigel and Johnson, 1998a, b) calls for: ●● planting on sites with a black oak site index of 60 ft or greater; ●● reducing overstorey density to 60% stocking based on Gingrich’s (1967) stocking chart (Fig. 6.9, this volume) by thinning from below (i.e. concentrating removals on subcanopy trees down to 2 inches dbh (Fig. 10.7A);

Chapter 10

Time after planting (years) 7

0.7

0.6

Dominance probability

0.5

10

11

12

13

9

10

1.0 Suppression interval

0.75 1.0 0.5

0.3

0.75

0.2

0.5 0.25

0.0

9

Initial seedling caliper (in.)

0.4

0.1

8

0.25 Early release interval 4

5

6

7

8

Time after shelterwood removal (years) U1C1

U0C0

Fig. 10.6.  Estimated dominance probabilities for 2-year-old northern red oak seedlings planted under mixed oak and yellow-poplar shelterwoods in southern Indiana (Central Hardwood Region); the site index for black oak is 75 ft. Shelterwoods were retained for 3 years. Probabilities are shown in relation to years after shelterwood removal and initial seedling caliper. Dominance probabilities are shown for the nominally ‘best’ and ‘worst’ undercutting (U) and shoot clipping (C) treatments. (U0C0: not undercut, not top-clipped; U1C1: undercut during the first growing season in the nursery, shoots clipped 8 inches above the root collar before planting). A herbicide was applied to the planting sites once before planting and once after planting using a directed spray that targeted individual woody stems 0.5–2 inches dbh. (From Weigel and Johnson, 2000.)

●● treating woody plants between 1/2 and 2 inches dbh on the planting site with an effective herbicide before planting; ●● planting top-clipped seedlings that average at least 1/4 inch in caliper measured 1 inch above the root collar (Fig. 10.7B); and ●● removing the shelterwood after three growing seasons. For either species, best results are obtained when shelterwood understories are planted in the spring with seedlings that are at least 1/4 inch in caliper and top-clipped. Based on the planting factors (the reciprocals of dominance probabilities) derived for this region, it would be necessary to plant 220 red oak seedlings averaging 1/4 inch in caliper to

Artificial Regeneration

obtain 100 competitively successful trees 10 years after shelterwood removal; 180 undercut white oak seedlings of the same size would meet the same goal 8 years after shelterwood removal (Table 10.2). Although numbers of planted trees required to meet a given future stocking goal decrease with increasing initial seedling caliper (Table 10.2), the larger seedling and planting costs may not justify the expense (Weigel and Johnson, 1998a, b). In contrast, planting red oaks in yellowpoplar stands will require seedlings ≥ 1/2 inch caliper to be competitively successful even during the early release interval (i.e. the first 5 years after shelterwood removal) (Fig. 10.6). Herbicide applications should target individual woody stems between 1/2 and 2 inches dbh. This

397

(A)

(B)

Fig. 10.7.  (A) Planting oaks under a shelterwood thinned to 60% stocking in the Ozark Highlands of Missouri (Central Hardwood Region). (B) A 2-year-old top-clipped northern red oak seedling prepared for planting. The top has been clipped 8 inches above the root collar. This seedling was undercut in the nursery the first year. The taproot and lateral roots were pruned to a common length of 8 inches below the root collar to facilitate planting. After planting, most new roots develop from large-diameter (≥ 2mm) first-order laterals and at or near the point of root pruning. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

approach minimizes the amount of herbicide applied while effectively reducing the primary understorey competitors of planted trees. When herbicides are applied to individual stems as a directed spray, the desirable natural reproduction already present can be preserved. Alternatively, it may be feasible in some situations to avoid the use of herbicides by adjusting planting factors and initial seedling caliper upwards to compensate for expected losses to increased competition (Johnson, 1992a). Alternative methods of weed control such as prescribed burning also may provide effective weed control (Brose and Van Lear, 1998a, b; Weigel and Johnson, 2000; Brose et  al., 2013). Other

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factors being equal, attaining a given stocking goal requires more intensive weed control on good sites than on poor sites (Fig. 10.3). In the Ozark Highlands, shelterwoods can be retained for at least 10 years without adversely affecting understorey oak survival or response to overstorey release (Fig. 10.4). However, this relatively long shelterwood period may not be advisable in regions where understorey competitors quickly redevelop after treatment. Shelterwood densities of 40–60% stocking provide the requisite light conditions to favour oaks when coupled with weed control (Fig. 10.3). A similar prescription for planting northern red oak under shelterwoods also has been developed for the oak forests of Arkansas’ Boston Mountains (Fig. 1.6, this volume) (Spetich et  al., 2004). This region is more mesophytic and floristically richer than the relatively xerophytic Ozark Highlands of Missouri. Competition control in the Boston Mountains therefore is more problematic and expensive than in the Ozark Highlands, but less so than in the Ohio Valley and elsewhere where yellow-poplar is a primary and often indomitable competitor of oaks during the regeneration period (Weigel and Johnson, 2000; Dey et  al., 2009). Recommendations for underplanting shelterwoods in the Boston Mountains are as follows (from Spetich et al., 2004): ●● Select upland sites with a site index range of 60–79 ft (base age 50) for red oaks. ●● Create a shelterwood by reducing overstorey density to 40–60% stocking by thinning from below (i.e. by removing mostly trees in intermediate and suppressed crown classes down to 1.5 inches dbh). ●● Treat the stumps of removed trees with an effective herbicide. ●● Before planting, cut all understorey woody vegetation 1 ft and taller and 1.5 inch dbh or less; then apply an effective herbicide to the cut surfaces. ●● Plant 2-year-old seedlings with tops clipped 6–8 inches above the root collar and that average at least 0.5 inches in diameter measured 0.8 inches above the root collar. Obtain seedlings from a local seed source. ●● Apply a second herbicide treatment to release understorey oaks before the shelterwood is removed. ●● Remove the shelterwood three growing seasons after planting. Treat the stumps of removed trees with an effective herbicide.

Chapter 10

Table 10.2.  Number of planted oaks in the Ozark Highlands of Missouri needed to obtain one dominant or codominant tree 8 or 10 years after shelterwood removal (planting factor) in relation to initial seedling caliper and nursery undercutting treatment. (From Weigel and Johnson, 1998a, b.) Planting factor White oakb (8 years after shelterwood removal) Initial seedling caliper (in.)a 1/4 3/8 1/2 5/8 3/4 7/8 1

Northern red oakc (10 years after shelterwood removal)

Not undercut

Undercut second year in nursery

Not undercut or undercut first year in nursery

3.2 2.4 2.0 1.8 1.6 1.5 1.4

1.8 1.5 1.4 1.3 1.2 1.2 1.2

2.2 2.0 1.9 1.8 1.8 1.8 1.8

a

Measured 1 inch above the root collar. Based on estimated dominance probabilities 8 years after shelterwood removal for 3-year-old seedlings with tops clipped 8 inches above the root collar after lifting and roots pruned to a common length of 10 inches before planting. c Based on estimated dominance probabilities 10 years after shelterwood removal for 2-year-old seedlings with tops clipped 8 inches above the root collar after lifting and roots pruned to a common length of 8 inches before planting (Fig. 10.7B). b

The prescription can be used with a predictive regeneration model called oakus. The model estimates the number of seedlings that must be planted to attain a given stocking goal. The stocking goal must be expressed as the number of competitively successful planted trees wanted (per acre) 11 years after planting (8 years after shelterwood removal). oakus is a user-interactive program available online at http://www.ncrs.fs.fed.us/oakus/. The user also is required to provide the following information: ●● site index of the planting site; ●● stocking per cent of the shelterwood; ●● the average caliper of the planting stock (2-0 seedlings1) and whether or not they have been top-clipped; ●● competition control treatments (none, one or two herbicide applications); and ●● the stocking goal (i.e. the number of competitively successful trees per acre wanted at year 11 after planting). This prescription and similar methods discussed above capitalize on a stand’s natural regeneration potential and thereby minimizes planting costs while maintaining diverse mixtures of tree species. Future advances in seedling quality through genetic selection of seedlings with rapid growth and roots with numerous large lateral roots may make shelterwood plantings and other oak planting

Artificial Regeneration

strategies even more dependable and cost-effective practices (Kormanik et al., 1997). The combined costs of weed control and planting under shelterwoods are relatively high for all of the prescriptions requiring such treatments (Weigel and Johnson, 1998a, b; Spetich et al., 2009). Nevertheless, investment analysis of Iowa and Missouri forests indicates that the method may be a viable economic alternative to allowing stands to naturally succeed to species of lower economic value (Countryman and Miller, 1989). Prescribed burning (see Chapter 7, this volume) after final shelterwood removal is an economical way of managing competing vegetation and increasing oak dominance during early regeneration.

Note 1

  This notation indicates a seedling’s age and how it was grown in the nursery. The first number is the number of years the seedling was grown in its original nursery bed before being lifted. The second number (if greater than zero) is the number of additional years the seedling resided in a nursery transplant bed.

References Allen, J.A. (1990) Establishment of bottomland oak plantations on the Yazoo National Wildlife Refuge Complex. Southern Journal of Applied Forestry 14, 206–210. https://doi.org/10.1093/sjaf/14.4.206

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Allen, J.A. (1997) Reforestation of bottomland hardwoods and the issue of woody species diversity. RestorationEcology5,125–134.https://doi.org/10.1046/j.1526100X.1997.09715.x Allen, J.A., Keeland, B.D., Stanturf, J.A., Clewell, A.F. and Kennedy, H.E., Jr (2001) A guide to bottomland hardwood restoration. USDA Forest Service General Technical Report SRS-40. USDA Forest Service, Southern Research Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/ pubs/2813 (accessed 20 September 2018). Auchmoody, L.R., Smith, H.C. and Walters, R.S. (1994) Planting northern red oak acorns: is size and planting depth important? USDA Forest Service, Northeastern Forest Experiment Station Research Paper NE-693. USDA Forest Service, Northeastern Forest Experiment Station, Radnor, Pennsylvania, 5 pp. https://doi.org/ 10.2737/NE-RP-693 Bailey, J.K., Zaczek, J.J. and Steiner, K.C. (1996) A comparison of four tree shelter systems using planted seedlings and direct seeded acorns of northern red oak at three sites in Pennsylvania. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, 64 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/ 4385 (accessed 20 September 2018). Bardon, R.E. and Countryman, D.W. (1993) Survival and growth for the first-growing season of northern red oak (Quercus rubra L.) seedlings underplanted in mixed upland hardwood stands in south central Iowa. USDA Forest Service General Technical Report NC161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 195–209. Available at: https://www.fs.usda.gov/treesearch/pubs/ 15316 (accessed 20 September 2018). Beck, D.E. (1991) The shelterwood method, a research perspective. USDA Forest Service Proceedings 1990 Genetics/Silviculture Workshop. USDA Forest Service, Washington, DC, pp. 252–258. Beck, D.E. and Della-Bianca, L. (1981) Yellow-Poplar: Characteristics and Management. USDA Forest Service Agriculture Handbook 583. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/288 (accessed 20 September 2018). Blizzard, E.M., Kabrick, J.M., Dey, D.C., Larsen, D.R., Pallardy, S.G. and Gwaze, G.P. (2013) Light, canopy closure, and overstory retention in upland Ozark forests. USDA Forest Service General Technical Report SRS 175. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 73–79. Available at: https://www.fs.usda.gov/treesearch/pubs/ 43478 (accessed 20 September 2018). Bonner, F.T. (1973) Storing red oak acorns. Tree Planters’ Notes 24(3), 12–13. Available at: https://www.fs.usda.gov/ treesearch/pubs/42830 (accessed 20 September 2018).

400

Bonner, F.T. and Vozzo, J.A. (1987) Seed biology and technology of Quercus. USDA Forest Service General Technical Report SO-66. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana. https://doi.org/10.2737/SO-GTR-66 Brose, P.H. (2008) Root development of acorn-origin oak seedlings in shelterwood stands on the Appalachian Plateau of northern Pennsylvania: 4-year results. Forest Ecology and Management 255, 3374–3381. https://doi.org/10.1016/j.foreco.2008.02.021 Brose, P.H. and Van Lear, D.H. (1998a) Effects of seasonal prescribed fires on hardwood advance regeneration in oak-dominated shelterwood stands. USDA Forest Service General Technical Report SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 310–314. https://doi. org/10.2737/SRS-GTR-20 Brose, P.H. and Van Lear, D.H. (1998b) Responses of hardwood advance regeneration to seasonal prescribed fires in oak-dominated shelterwood stands. Canadian Journal of Forest Research 28, 331–339. https://doi.org/10.1139/x97-218 Brose, P. and Van Lear, D. (2004) Survival of hardwood regeneration during prescribed fires: The importance of root development and root collar location. USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 123–127. Available at: https://www.fs.usda.gov/treesearch/pubs/6509 (accessed 20 September 2018). Brose, P.H., Dey, D.C., Phillips, R.J. and Waldrop, T.A. (2013) A meta-analysis of the fire-oak hypothesis: Does prescribed burning promote oak reproduction in eastern North America? Forest Science 59(3), 322–334. https://doi.org/10.5849/forsci.12-039 Buchschacher, G.L., Tomlinson, P.T., Johnson, P.S. and Isebrands, J.G. (1991) Effects of seed source and cultural practices on emergence and seedling quality of northern red oak nursery stock. USDA Forest Service General Technical Report SE-70, Vol. 1. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 126–130. https://doi. org/10.2737/SE-GTR-70 Castro, J., Leverkus, A.B. and Fuster, F. (2015) A new device to foster oak forest restoration via seed sowing. New Forests 46, 919–929. https://doi.org/10.1007/ s11056-015-9478-4 Clark, S.L., Schlarbaum, S.E. and Schweitzer, C.J. (2015) Effects of visual grading on northern red oak (Quercus rubra L.) seedlings planted in two shelterwood stands on the Cumberland Plateau of Tennessee, USA. Forests 6, 3779–3798. https://doi.org/10.3390/f6103779 Clatterbuck, W.K. (1997) Direct-seeding acorns for bottomland oak reforestation: guidelines for success. In: Proceedings of the Society of American Foresters Convention. Society of American Foresters, Bethesda, Maryland, pp. 397–398.

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Clatterbuck, W.K. (1998) Use of prescribed fire to promote oak regeneration. USDA Forest Service General Technical Report SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 315–318. https://doi.org/10.2737/SRS-GTR-20 Clatterbuck, W.K. (2005) Shade and flood tolerance of trees. The University of Tennessee Extension SP656. University of Tennessee, Knoxville, Tennessee, 6 pp. Countryman, D.W. and Miller, H.R. (1989) Investment analysis of upland oak stands with sugar maple understories: management for oak vs. conversion to sugar maple in Iowa and Missouri. Northern Journal of Applied Forestry 6, 165–169. https://doi.org/10.1093/ njaf/6.4.165 Craig, J.M., Lhotka, J.M. and Stringer, J.W. (2014) Evaluating initial responses of natural and underplanted oak reproduction and a shade-tolerant competitor to midstory removal. Forest Science 60(6), 1164–1171. https://doi.org/10.5849/forsci.13-602 Dey, D.C. and Buchanan, M. (1995) Red oak (Quercus rubra, L.) acorn collection, nursery culture and direct seeding: a literature review. Forest Research Information Paper No. 122. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs.usda. gov/treesearch/pubs/12354 (accessed 20 September 2018). Dey, D.C. and Hartman, G. (2005) Returning fire to Ozark Highland forest ecosystems: effects on advance regeneration. Forest Ecology and Management 217, 37–53. https://doi.org/10.1016/j.foreco.2005.05.002 Dey, D.C. and Parker, W.C. (1996) Regeneration of red oak (Quercus rubra L.) using shelterwood systems: ecophysiology, silviculture and management recommendations. Ontario Forestry Research Institutute Forest Research Information Paper 126. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste Marie, Ontario. Available at: https://www.fs.usda.gov/treesearch/pubs/12423 (accessed 20 September 2018). Dey, D.C. and Parker, W.C. (1997a) Overstory density affects field performance of underplanted red oak (Quercus rubra L.) in Ontario. Northern Journal of Applied Forestry 14, 120–125. Available at: https:// www.fs.usda.gov/treesearch/pubs/12408 (accessed 20 September 2018). Dey, D.C. and Parker, W.C. (1997b) Morphological indicators of stock quality and field performance of red oak (Quercus rubra L.) seedlings underplanted in a central Ontario shelterwood. New Forests 14, 145– 156. https://doi.org/10.1023/A:1006577201244 Dey, D.C., Lovelace, W., Kabrick, J.M. and Gold, M.A. (2004) Production and early field performance of RPM® seedlings in Missouri floodplains. USDA Forest Service General Technical Report NC-243. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 59–65. Available at:

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https://www.fs.usda.gov/treesearch/pubs/14707 (accessed 20 September 2018). Dey, D.C., Jacobs, D., McNabb, K., Miller, G.W., Baldwin, V. and Foster, G. (2008) Artificial regeneration of major oak (Quercus) species in the eastern United States – a review of the literature. Forest Science 54, 77–106. Available at: https://www.fs.usda.gov/treesearch/ pubs/14223 (accessed 20 September 2018). Dey, D.C., Spetich, M.A., Weigel, D.R., Johnson, P.S., Graney, D.L. and Kabrick, J.M. (2009) A suggested approach for design of oak (Quercus L.) regeneration research considering regional differences. New Forests 37, 123–135. https://doi.org/10.1007/s11056008-9113-8 Dey, D.C., Gardiner, E.S., Kabrick, J.M., Stanturf, J.A. and Jacobs, D.F. (2010) Innovations in afforestation of agricultural bottomlands to restore native forests in the eastern USA. Scandinavian Journal of Forest Research 25(1), 31–42. https://doi.org/10.1080/0282 7581.2010.485822 Dey, D.C., Gardiner, E.S., Schweitzer, C.J., Kabrick, J.M. and Jacobs, D.F. (2012) Underplanting to sustain future stocking of oak (Quercus) in temperate deciduous forests. New Forests 43, 955–978. https:// doi.org/10.1007/s11056-012-9330-z Dillaway, D.N., Stringer, J.W. and Rieske, L.K. (2007) Light availability influences root carbohydrates, and potentially vigor, in white oak advance regeneration. Forest Ecology and Management 250, 227–233. https://doi.org/10.1016/j.foreco.2007.05.019 Dugger, S., Dey, D.C. and Millspaugh, J.J. (2004) Vegetation cover affects mammal herbivory on planted oaks and success of reforesting Missouri River bottomland fields. USDA Forest Service, Southern Research Station, General Technical Report SRS-71. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 3–6. Available at: https:// www.fs.usda.gov/treesearch/pubs/6593 (accessed 20 September 2018). Fan, Z., Ma, Z., Dey, D.C. and Roberts, S.D. (2012) Response of advance reproduction of oaks and associated species to repeated prescribed fires in upland oak–hickory forests, Missouri. Forest Ecology and Management 266, 160–169. https://doi. org/10.1016/j.foreco.2011.08.034 Farley, M.E., Perry, P.S. and Woyar, P.R. (1996) Valley coal tree shelter field trial. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 60–63. Available at: https://www.fs.usda.gov/treesearch/pubs/4385 (accessed 20 September 2018). Fleming, R.L., Powers, R.F., Foster, N.W., Kranabetter, J.M., Scott, A.D., Ponder, F., Berch, S., Jr, Chapman, W.K., Kabzems, R.D., Ludovici, K.H., Morris, D.M., Page-Dumroese, D.S., Sanborn, P.T., Sanchez, F.G., Stone, D.M. and Tiarks, A.E. (2006) Effects of organic

401

matter removal, soil compaction, and vegetation control on 5-year seedling performance: a regional comparison of long-term soil productivity sites. Canadian Journal of Forest Research 36, 529–550. https://doi.org/10.1139/x05-271 Gardiner, E.S. and Yeiser, J.L. (2006) Underplanting cherrybark oak (Quercus pagoda Raf.) seedlings on a bottomland site in the southern United States. New Forests 32, 105–119. https://doi.org/10.1007/s11056005-4168-2 Gardiner, E.S., Schweitzer, C.J. and Stanturf, J.A. (2001) Photosynthesis of Nuttall oak (Quercus nuttallii Palm.) seedlings interplanted beneath an eastern cottonwood (Populus deltoides Bart. ex Marsh.) nurse crop. Forest Ecology and Management 149, 283–294. https://doi.org/10.1016/S0378-1127(00)00562-4 Gardiner, E.S., Stanturf, J.A., Leininger, T.D., Hamel, P.B., Dorris, L.C., Portwood, C.J. and Shepard, J.P. (2008) Establishing a research demonstration area initiated by managers: the Sharkey Restoration Research and Demonstration Site. Journal of Forestry 106, 363–369. Available at: https://www.fs.usda.gov/treesearch/pubs/ 33717 (accessed 20 September 2018). Gillespie, R., Rathfon, R. and Myers, R.K. (1996) Rehabilitating a young northern red oak planting with tree shelters. Northern Journal of Applied Forestry 13, 24–29. https://doi.org/10.1093/njaf/13.1.24 Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Gottschalk, K.W. (1985) Effects of shading on growth and development of northern red oak, black oak, black cherry, and red maple seedlings. I. Height, diameter, and root/shoot ratio. In: Proceedings of the 5th Central Hardwood Forest Conference, Urbana, Illinois, pp. 189–195. Available at: https://www.fs.usda.gov/treesearch/pubs/47682 (accessed 20 September 2018). Gottschalk, K.W. (1987) Effects of shading on growth and development of northern red oak, black oak, black cherry, and red maple seedlings. II. Biomass partitioning and prediction. In: Proceedings of the 6th Central Hardwood Forest Conference, Knoxville, Tennessee, pp. 99–110. Available at: https://www. fs.usda.gov/treesearch/pubs/47683 (accessed 20 September 2018). Gottschalk, K.W. (1994) Shade, leaf growth and crown development of Quercus rubra, Quercus velutina, Prunus serotina and Acer rubrum seedlings. Tree Physiology 14, 735–749. https://doi.org/10.1093/ treephys/14.7-8-9.735 Gottschalk, K.W. and Marquis, D.A. (1982) Survival and growth of planted red oak and white ash as affected by residual overstory density, stock size, and deer browsing. In: Proceedings of the 4th Central Hardwood Forest Conference. University of Kentucky, Lexington, Kentucky, pp. 125–140.

402

Graney, D.L. and Bower, D.R. (1971) Site index curves for red and white oaks in the Boston Mountains of Arkansas. USDA Forest Service Research Note SO-121. USDA Forest Service, Southern Research Station, New Orleans, Louisiana. Grossman, B.C., Gold, M.A. and Dey, D.C. (2003) Restoration of hard mast species for wildlife in Missouri using precocious flowering oak in the Missouri River floodplain, USA. Agroforestry Systems 59, 3–10. https://doi.org/10.1023/A:1026124717097 Hamel, P.B. (2003) Winter bird community differences among methods of bottomland hardwood forest restoration: Results after seven growing seasons. Forestry 76, 189–197. https://doi.org/10.1093/forestry/76.2.189 Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Hilt, D.E. (1977) Introduction of black walnut and northern red oak seedlings in an upland hardwood forest in southeastern Ohio. USDA Forest Service Research Note NE-241. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/19255 (accessed 20 September 2018). Hodges, J.D. and Switzer, G.L. (1979) Some aspects of the ecology of southern bottomland hardwoods. In: Gateway to Opportunity: Proceedings of the Society of American Foresters Convention. Society of American Foresters, Bethesda, Maryland, pp. 360–365. Horsley, S.B. (1991) Using Roundup and Oust to control interfering understories in Allegheny hardwood stands. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 281–290. Available at: https://www. fs.usda.gov/treesearch/pubs/13505 (accessed 20 September 2018). Hutchinson, T.F., Sutherland, E.K. and Yaussy, D.A. (2005) Effects of repeated prescribed fires on the structure, composition, and regeneration of mixed-oak forests in Ohio. Forest Ecology and Management 218, 210–228. https://doi.org/10.1016/j.foreco.2005. 07.011 Jacobs, D.F., Salifu, K.F. and Seifert, J.R. (2005) Relative contribution of initial root and shoot morphology in predicting field performance of hardwood seedlings. New Forests 30, 295–311. https://doi.org/10.1007/ s11056-005-5419-y Jacobs, D.F., Goodman, R.C., Gardiner, E.S., Salifu, K.F., Overton, R.P. and Hernandez, G. (2012) Nursery stock quality as an indicator of bottomland hardwood forest restoration success in the Lower Mississippi River Alluvial Valley. Scandinavian Journal of Forest Research 27(3), 255–269. https:// doi.org/10.1080/02827581.2011.628948 Jensen, A.M., Götmark, F. and Löf, M. (2012) Shrubs protect oak seedlings against ungulate browsing in

Chapter 10

t­emperate broadleaved forests of conservation interests: a field experiment. Forest Ecology and Management 266, 187–193. https://doi.org/10.1016/j. foreco.2011.11.022 Johnson, P.S. (1984) Responses of planted northern red oak to three overstory treatments. Canadian Journal of Forest Research 14, 536–542. https://doi. org/10.1139/x84-099 Johnson, P.S. (1988) Undercutting alters root morphology and improves field performance of northern red oak. In: Proceedings of the 10th North American Forest Biology Workshop. University of British Columbia, Vancouver, British Columbia, pp. 316–323. Johnson, P.S. (1989) Growing hardwood nursery stock for planting on forest sites with special reference to northern red oak. In: Proceedings 1989 Northeastern Area Nurserymens’ Conference. Illinois Division of Forest Resources, Mason State Nursery, Topeka, Illinois, pp. 46–62. Johnson, P.S. (1992a) Oak overstory/reproduction relations in two xeric ecosystems in Michigan. Forest Ecology and Management 48, 233–248. https://doi. org/10.1016/0378-1127(92)90147-2 Johnson, P.S. (1992b) Underplanting northern red oak in Missouri without herbicides. USDA Forest Service General Technical Report NC-152. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10216 (accessed 20 September 2018). Johnson, P.S. and Rogers, R. (1985) A method for estimating the contribution of planted hardwoods to future stocking. Forest Science 31, 883–891. https:// doi.org/10.1093/forestscience/31.4.883 Johnson, P.S., Novinger, S.L. and Mares, W.G. (1984) Root, shoot, and leaf area growth potentials of northern red oak planting stock. Forest Science 30, 1017–1026. https://doi.org/10.1093/forestscience/ 30.4.1017 Johnson, P.S., Dale, C.D., Davidson, K.R. and Law, J.R. (1986) Planting northern red oak in the Missouri Ozarks: a prescription. Northern Journal of Applied Forestry 3, 66–68. https://doi.org/10.1093/njaf/3.2.66 Kabrick, J.M., Dey, D.C., Van Sambeek, J.W., Wallendorf, M. and Gold, M.A. (2005) Soil properties and growth of swamp white oak and pin oak on bedded soils in the lower Missouri River floodplain. Forest Ecology and Management 204, 315–327. https://doi.org/10.1016/j.foreco.2004.09.014 Kays, J.S. (1996) Deer protection for small forest plantations: comparing costs of tree shelters, electric fencing and repellents. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 5–12. Available at: https:// www.fs.usda.gov/treesearch/pubs/4385 (accessed 20 September 2018).

Artificial Regeneration

Kennedy, H.E., Jr (1993) Artificial regeneration of bottomland oaks. USDA Forest Service General Technical Report SRS-84. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 241–249. Available at: https://www.srs.fs.usda. gov/pubs/gtr/gtr_se084.pdf (accessed 20 September 2018). Keyser, P.D., Brose, P.H., Van Lear, D. and Burtner, K.M. (1996) Enhancing oak regeneration with fire in shelterwood stands: preliminary trials. In: Transactions of the 61st North American Wildlife and Natural Resource Conference. Wildlife Management Institute, Washington, DC, pp. 215–219. Kittredge, D.B., Kelty, M.J. and Ashton, M.S. (1992) The use of tree shelters with northern red oak natural regeneration in southern New England. Northern Journal of Applied Forestry 9, 141–145. https://doi. org/10.1093/njaf/9.4.141 Kormanik, P.P. (1989) Importance of first-order lateral roots in the early development of forest tree seedlings. In: Proceedings of Interrelationships between Microorganisms and Plants in Soil. Institute of Microbiology, Czechoslovak Academy of Science, Prague, Czechoslovakia, pp. 157–169. https://doi. org/10.1016/S0166-2481(08)70210-1 Kormanik, P.P., Sung, S.S., Kormanik, T.L., Zarnoch, S.J. and Schlarbaum, S.E. (1997) Heritability of firstorder-lateral roots in five Quercus species: effect on 1-0 seedling quality evaluation. In: Steiner, K.C. (ed.) Proceedings Diversity and Adaptation in Oak Species. Pennsylvania State University, University Park, Pennsylvania, pp. 194–200. Available at: https:// www.fs.usda.gov/treesearch/pubs/1102 (accessed 20 September 2018). Kormanik, P.P., Sung, S.S., Kormanik, T.L., Schlarbaum, S.E. and Zarnock, S.J. (1998) Effect of acorn size on development of northern red oak 1-0 seedlings. Canadian Journal of Forest Research 28, 1805–1813. https://doi.org/10.1139/x98-152 Kormanik, P.P., Sung, S.S., Kass, D. and Zarnock, S.J. (2002) Effect of seedling size and first-order lateral roots on early development of northern red oak on mesic sites: eleventh years results. USDA Forest Service, Southern Research Station General Technical Report SRS-48. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 332– 337. Available at: https://www.fs.usda.gov/treesearch/pubs/3139 (accessed 20 September 2018). Kormanik, P.P., Sung, S.J.S. and Kormanik, T.L. (2004) Growing, selecting and establishing 1+0 Quercus rubra and Q. alba seedlings for rapid growth and early acorn production on forested lands in the southeastern United States. International Oaks 15, 119– 125. Available at: https://www.fs.usda.gov/treesearch/ pubs/43231 (accessed 20 September 2018). Korstian, C.F. (1927) Factors controlling germination and early survival in oaks. Yale University School of

403

Forestry Bulletin 19. Yale University, New Haven, Connecticut. LaFarge, T. and Lewis, R.A. (1987) Phenotypic selection effective in a northern red oak seedling seed orchard. In: Proceedings of the 19th Southern Forest Tree Improvement Conference. Texas Agricultural Experiment Station, College Station, Texas, pp. 200–207. Lantagne, D.O. (1991) Tree shelters increase heights of planted northern red oaks. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 291–298. Available at: https://www.fs.usda.gov/treesearch/pubs/ 13506 (accessed 20 September 2018). Lantagne, D.O. (1996) Effects of tree shelters on planted red oak in Michigan. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 24–28. Available at: https:// www.fs.usda.gov/treesearch/pubs/4385 (accessed 20 September 2018). Lantagne, D.O. and Miller, R. (1997) Tree shelters fail to enhance height growth of northern red oak in the Upper Peninsula. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 351. Available at: https://www.fs.usda. gov/treesearch/pubs/15695 (accessed 20 September 2018). Leiva, M.J. and Vera, M. (2015) Effect of artificial shelters of dead branches on acorn survival and dispersal in shrub-lacking Mediterranean dehesas. New Forests 46,965–978.https://doi.org/10.1007/s11056-015-9486-4 Lhotka, J.M. and Loewenstein, E.F. (2009) Effect of midstory removal on understory light availability and the 2-year response of underplanted cherrybark oak seedlings. Southern Journal of Applied Forestry 33(4), 171–177. https://doi.org/10.1093/sjaf/33.4.171 Lockhart, B.R., Gardiner, E., Leininger, T. and Stanturf, J. (2008) A stand-development approach to oak afforestation in the Lower Mississippi Alluvial Valley. Southern Journal of Applied Forestry 32, 120–129. Available at: https://www.fs.usda.gov/treesearch/ pubs/30765 (accessed 20 September 2018). Löf, M., Dey, D.C., Navarro, R.M. and Jacobs, D.F. (2012) Mechanical site preparation for forest restoration. New Forests 43, 825–848. https://doi. org/10.1007/s11056-012-9332-x Löf, M., Bolte, A., Jacobs, D.F. and Jensen, A.M. (2014) Nurse trees as a forest restoration tool for mixed plantations: effects on competing vegetation and performance of target tree species. Restoration Ecology 22(6), 758–765. https://doi.org/10.1111/rec.12136 Löf, M., Ersson, B.T., Hjältén, J., Nordfjell, T., Oliet, J.A. and Willoughby, I. (2016) Site preparation techniques for forest restoration. In: Stanturf, J.A. (ed.) Restoration

404

of Boreal and Temperate Forests, 2nd edn. CRC Press, Boca Raton, Florida, pp. 85–102. Loftis, D.L. (1979) Northern red oak performs poorly in North Carolina planting. USDA Forest Service Research Note SE-277. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Loftis, D.L. (1990) A shelterwood method for regenerating red oak in the southern Appalachians. Forest Science 36, 917–929. Available at: https://www.fs.usda.gov/ treesearch/pubs/1146 (accessed 20 September 2018). Lorimer, C.G., Chapman, J.W. and Lambert, W.D. (1994) Tall understory vegetation as a factor in the poor development of oak seedlings beneath mature stands. Journal of Ecology 82(2), 227–237. https:// doi.org/10.2307/2261291 Madsen, P. and Löf, M. (2005) Reforestation in southern Scandinavia using direct seeding of oak (Quercus robur L.). Forestry 78(1), 55–64. https://doi.org/ 10.1093/forestry/cpi005 Marquis, D.A. and Brenneman, R. (1981) The impact of deer on forest vegetation in Pennsylvania. USDA Forest Service General Technical Report NE-65. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4073 (accessed 20 September 2018). Marquis, D.A., Ernst, R.L. and Stout, S.L. (1992) Prescribing silvicultural treatments in hardwood stands of the Alleghenies (revised). USDA Forest Service General Technical Report NE-96. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4132 (accessed 20 September 2018). Mayhead, G.J. and Jones, D. (1991) Carbon dioxide concentrations within tree shelters. Quarterly Journal of Forestry 75, 228–232. McComb, A.L. (1934) The relation between acorn weight and the development of one year chestnut oak stands. Journal of Forestry 32, 479–484. https://doi. org/10.1093/jof/32.4.479 McCreary, D.D. (2001) Regenerating rangeland oaks in California. University of California Agriculture and Natural Resources Publication 21601. University of California Agriculture and Natural Resources, Berkeley, California. McCreary, D.D. and George, M.R. (2005) Managed grazing and seedling shelters enhance oak regeneration on rangelands. California Agriculture 59(4), 217– 222. https://doi.org/10.3733/ca.v059n04p217 McCreary, D.D. and Tecklin, J. (2005) Restoring native California oaks on grazed rangelands. USDA Forest Service General Technical Report RMRS-P-35. USDA Forest Service, Rocky Mountain Research Station, Ft Collins, Colorado, pp. 109–112. Available

Chapter 10

at: https://www.fs.usda.gov/treesearch/pubs/20899 (accessed 20 September 2018). McGee, C.E. and Loftis, D.L. (1986) Planted oaks perform poorly in North Carolina and Tennessee. Northern Journal of Applied Forestry 3, 114–116. https://doi.org/10.1093/njaf/3.3.114 Miao, S. (1995) Acorn mass and seedling growth in Quercus rubra in response to elevated CO2. Journal of Vegetation Science 6 (5), 697–700. https://doi. org/10.2307/3236440 Minter, W.F., Myers, R.K. and Fischer, B.C. (1992) Effects of tree shelters on northern red oak seedlings planted in harvested forest openings. Northern Journal of Applied Forestry 9, 58–63. https://doi.org/10.1093/ njaf/9.2.58 Morrissey, R.C., Jacobs, D.F., Davis, A.S. and Rathfon, R.A. (2010) Survival and competitiveness of Quercus rubra regeneration associated with planting stocktype and harvest opening intensity. New Forests 40, 273–287. https://doi.org/10.1007/s11056010-9199-7 Motsinger, J.R., Kabrick, J.M., Dey, D.C., Henderson, D.E. and Zenner, E.K. (2010) Effect of midstory and understory removal on the establishment and development of natural and artificial pin oak advance reproduction in bottomland forests. New Forests 39, 195–213. https://doi.org/10.1007/s11056-009-9164-5 Nyland, R.D., Abrahamson, L.P. and Adams, K.B. (1983) Use of prescribed fire for regenerating red and white oak in New York. In: Proceedings of the 1982 Society of American Foresters National Convention, Cincinnati, Ohio, 19–22 September. Society of American Foresters, Bethesda, Maryland, pp. 163–167. Olson, D.F., Jr (1974) Quercus L. Oak. USDA Forest Service Agriculture Handbook 450. USDA Forest Service, Washington, DC, pp. 88. Available at: https:// archive.org/details/seedsofwoodyplan00fore (accessed 20 September 2018). Olson, D.F., Jr and Hooper, R.M. (1972) Northern red oak plantings survive well in southern Appalachians. Tree Planters’ Notes 23(1), 16–18. Available at: https://rngr. net/publications/tpn/23-1 (accessed 20 September 2018). Paquette, A., Bouchard, A. and Cogliastro, A. (2007) Morphological plasticity in seedlings of three deciduous species under shelterwood under-planting management does not correspond to shade tolerance ranks. Forest Ecology and Management 241, 278–287. https://doi.org/10.1016/j.foreco.2007.01.004 Parker, W.C. and Dey, D.C. (2008) Influence of overstory density on ecophysiology of red oak (Quercus rubra) and sugar maple (Acer saccharum) seedlings in central Ontario shelterwoods. Tree Physiology 28, 797–804. https://doi.org/10.1093/treephys/28.5.797 Parrott, D.L., Lhotka, J.M., Stringer, J.W. and Dillaway, D.N. (2012) Seven-year effects of midstory removal on natural and underplanted oak reproduction.

Artificial Regeneration

Northern Journal of Applied Forestry 29(4), 182–190. https://doi.org/10.5849/njaf.12-001 Ponder, F., Jr (1996) Tree shelter effects on stem and root biomass of planted hardwoods. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 19–23. Available at: https://www.fs.usda.gov/treesearch/pubs/4385 (accessed 20 September 2018). Ponder, F., Jr (1997) Survival and growth of hardwood seedlings following preplanting-root treatments and treeshelters. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 332–340. Available at: https://www.fs.usda.gov/ treesearch/pubs/15685 (accessed 20 September 2018). Ponder, F., Jr (2000) Survival and growth of planted hardwoods in harvested openings with first-order lateral root differences, root-dipping, and tree shelters. Northern Journal of Applied Forestry 17, 45–50. https://doi.org/10.1093/njaf/17.2.45 Ponder, F., Jr (2003) Ten-year results of tree shelters on survival and growth of planted hardwoods. Northern Journal of Applied Forestry 20, 104–108. https://doi. org/10.1093/njaf/20.3.104 Potter, M.J. (1988) Treeshelters improve survival and increase early growth rates. Journal of Forestry 86(8), 39–41. https://doi.org/10.1093/jof/86.8.39 Potter, M.J. (1991) Treeshelters. Forestry Commission Handbook 7. Her Majesty’s Stationery Office (HMSO), London. Pubanz, D.M. and Lorimer, C.G. (1992) Oak regeneration experiments in southwestern Wisconsin: two-year results. University of Wisconsin-Madison College of Agriculture and Life Sciences Publication R3552. University of Wisconsin-Madison, Madison, Wisconsin. Rebbeck, J., Gottschalk, K. and Scherzer, A. (2011) Do chestnut, northern red, and white oak germinant seedlings respond similarly to light treatments? Growth and biomass. Canadian Journal of Forest Research 41, 2219–2230. https://doi.org/10.1139/x11-124 Rice, K.J., Gordon, D.R., Hardison, J.L. and Welker, J.M. (1993) Phenotypic variation in seedlings of a ‘keystone’ tree species (Quercus douglasii): the interactive effects of acorn source and competitive environment. Oecologia 96, 537–547. https://doi.org/10.1007/ BF00320511 Rink, G. and Coggeshall, M.V. (1995) Potential height gain from selection in a five-year-old white oak progeny test. Southern Journal of Applied Forestry 19, 10–13. https://doi.org/10.1093/sjaf/19.1.10 Rink, G. and Williams, R.D. (1984) Storage technique affects white oak acorn viability. Tree Planters’ Notes 35(1), 3–5. Available at: https://rngr.net/publications/ tpn/35-1 (accessed 20 September 2018). Russell, T.E. (1973) Survival and growth of bar-slit planted northern red oak studied in Tennessee. Tree Planters’

405

Notes 24(3), 6–9. Available at: https://rngr.net/ publications/tpn/24-3 (accessed 20 September 2018). Sander, I.L. (1979) Regenerating oaks with the shelterwood system. In: Proceedings of the 1979 J.S. Wright Forest Conference. Purdue University, West Lafayette, Indiana, pp. 54–60. Available at: https://www.fs.usda. gov/treesearch/pubs/45320 (accessed 20 September 2018). Schlarbaum, S.E., Kormanik, P.P., Tibbs, T. and Barber, L.R. (1997a) Oak seedlings: quality improved available now – genetically improved available soon. In: Proceedings of the 25th Annual Hardwood Symposium. National Hardwood Lumber Association, Memphis, Tennessee, pp. 123–129. Available at: https://www. fs.usda.gov/treesearch/pubs/525 (accessed 20 September 2018). Schlarbaum, S.E., Barber, L.R., Cecich, R.A., Cox, R.A., Grant, J.F., Kormanik, P.P., LaFarge, T., Lambdin, P.L., Lay, S.A., Post, L.S., Proffitt, C.K., Remaley, M.A., Saxton, A.M., Stringer, J.W. and Tibbs, T. (1997b) Research and development activities in a northern red oak (Quercus rubra L.) seedling seed orchard. In: Proceedings of Diversity and Adaptation in Oak Species. Pennsylvania State University, University Park, Pennsylvania, pp. 185–192. Schuler, T.M. and Miller, G.W. (1996) Guidelines for using tree shelters to regenerate northern red oak. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 37–45. Available at: https://www.fs.usda.gov/treesearch/ pubs/4385 (accessed 20 September 2018). Schuler, T.M., Thomas Van-Gundy, M., Adams, M.B. and Ford, W.M. (2010) Seed bank response to prescribed fire in the Central Appalachians. USDA Forest Service Research Paper NRS 9. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 9 pp. https://doi.org/10.2737/NRS-RP-9 Schultz, R.C. and Thompson, J.R. (1990) Nursery practices that improve hardwood seedling root morphology. Tree Planter’s Notes 41(3), 21–32. Available at: https://rngr.net/publications/tpn/41-3 (accessed 20 September 2018). Schultz, R.C. and Thompson, J.R. (1991) The quality of oak seedlings needed for successful artificial regeneration in the central states. In: Proceedings The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 180–186. Schweitzer, C.J. and Dey, D.C. (2011) Forest structure, composition, and tree diversity response to a gradient of regeneration harvests in the mid-Cumberland Plateau escarpment region. Forest Ecology and Management 262, 1729–1741. https://doi.org/10.1016/ j.foreco.2011.07.020 Shirley, H.L. (1937) Direct seeding in the Lake States. Journal of Forestry 35(4), 379–387. https://doi. org/10.1093/jof/35.4.379

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Smith, H.C. (1993) Development of red oak seedlings using plastic shelters on hardwood sites in West Virginia. USDA Forest Service Research Paper NE-672. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-672 Spetich, M.A., Dey, D.C., Johnson, P.S. and Graney, D.L. (2002) Competitive compacity of Quercus rubra L. planted in Arkansas Boston Mountains. Forest Science 48(3), 504–517. Available at: https://www.fs. usda.gov/treesearch/pubs/12401 (accessed 20 September 2018). Spetich, M.A., Dey, D.C., Johnson, P.S. and Graney, D.L. (2004) Success of underplanting northern red oaks. USDA Forest Service Southern Research Station, General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 206–211. Available at: https://www.fs.usda. gov/treesearch/pubs/6543 (accessed 20 September 2018). Spetich, M.A., Dey, D. and Johnson, P. (2009) Shelterwood-planted northern red oaks: integrated costs and options. Southern Journal of Applied Forestry 33(4), 182–186. Available at: https://www.fs. usda.gov/treesearch/pubs/33905 (accessed 20 September 2018). Stanturf, J.A., Conner, W.H., Gardiner, E.S. and Schweitzer, C.J. (2004) Recognizing and overcoming difficult site conditions for afforestation of bottomland hardwoods. Ecological Restoration 22, 183–193. https://doi.org/10.3368/er.22.3.183 Stanturf, J.A., Gardiner, E.S., Shepard, J.P., Schweitzer, C.J., Portwood, C.J. and Dorris, L.C., Jr (2009) Restoration of bottomland hardwood forests across a treatment intensity gradient. Forest Ecology and Management257,1803–1814.https://doi.org/10.1016/j. foreco.2009.01.052 Steele, K.L., Kabrick, J.M., Jensen, R.G., Wallendorf, M.J. and Dey, D.C. (2008) Analysis of riparian afforestation methods in the Missouri Ozarks. USDA Forest Service General Technical Report NRS-P-24. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 80–90. Available at: https://www.fs.usda.gov/treesearch/pubs/13915 (accessed 20 September 2018). Stoeckler, J.H. and Scholz, H.F. (1956) A cylindrical screen for protecting direct seeding of forest tree species. Journal of Forestry 54, 183–184. https://doi. org/10.1093/jof/54.3.183 Strobl, S. and Wagner, R.G. (1996) Early results with translucent tree shelters in southern Ontario. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 13–23. Available at: https://www.fs.usda.gov/ treesearch/pubs/4385 (accessed 20 September 2018).

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Struve, D.K. and Joly, R.J. (1992) Transplanted red oak seedlings mediate transplant shock by reducing leaf surface area and altering carbon allocation. Canadian Journal of Forest Research 22, 1441–1448. https:// doi.org/10.1139/x92-194 Tecklin, J., Connor, M. and McCreary, D.D. (2002) Rehabilitation of an oak planting project on cleared rangeland using treeshelters and grazing: a ten-year saga. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, p. 839. Available at: https://www. fs.usda.gov/treesearch/pubs/26186 (accessed 20 September 2018). Teclaw, R.M. and Isebrands, J.G. (1991) Artificial regeneration of northern red oak in the Lake States. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 187–197. Teclaw, R.M. and Isebrands, J.G. (1993a) An artificial regeneration system for establishing northern red oak on dry-mesic sites in the Lake States, USA. Annales des Sciences Forestieres 50, 543–552. https://doi.org/10.1051/forest:19930603 Teclaw, R.M. and Isebrands, J.G. (1993b) Artificial regeneration of northern red oak in the Lake States. USDA Forest Service General Technical Report NC-161. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 185–194. Available at: https://www.fs.usda.gov/treesearch/ pubs/15315 (accessed 20 September 2018). Teclaw, R. and Zasada, J. (1996) Effects of two types of tree shelters on artifical regeneration of northern red oak in northern Wisconsin. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, p. 68. Available at: https://www.fs.usda.gov/treesearch/pubs/4385 (accessed 20 September 2018). Thompson, J.R. and Schultz, R.C. (1995) Root system morphology of Quercus rubra L. planting stock and 3-year field performance in Iowa. New Forests 9, 225–236. https://doi.org/10.1007/BF00035489 Toumey, J.W. and Korstian, C.F. (1942) Seeding and Planting in the Practice of Forestry. Wiley, New York. Twedt, D.J. (2004) Stand development on reforested ­bottomlands in the Mississippi Alluvial Valley. Plant Ecology 172, 251–263. https://doi.org/10.1023/B: VEGE.0000026344.29613.4a Twedt, D.J. and Portwood, J. (1997) Bottomland hardwood reforestation for neotropical migratory birds: are we missing the forest for the trees? Wildlife Society Bulletin 25, 647–652. Available at: https://www. jstor.org/stable/3783514 (accessed 20 September 2018). Twedt, D.J., Wilson, R.R., Henne-Kerr, J.L. and Hamilton, R.B. (2002) Avian response to bottomland hardwood

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reforestation: the first 10 years. Restoration Ecology 10,645–655.https://doi.org/10.1046/j.1526-100X.2002. 01045.x USDA Forest Service (2008) Woody Plant Seed Manual. USDA Forest Service Agriculture Handbook 727. Available at: https://www.fs.fed.us/rm/pubs_series/ wo/wo_ah727.pdf (accessed 14 March 2017). Van Sambeek, J.W. and Garrett, H.E. (2004) Ground cover management in walnut and other hardwood plantings. USDA Forest Service, North Central Research Station, General Technical Report NC-243. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 85–100. Available at: https://www.fs.usda.gov/treesearch/pubs/14714 (accessed 20 September 2018). Van Sambeek, J.W., Godsey, L.D., Walter, W.D., Garrett, H.E. and Dwyer, J.P. (2016) Field performance of Quercus bicolor established as repeatedly air-rootpruned container and bareroot planting stock. Open Journal of Forestry 6, 163–176. http://dx.doi. org/10.4236/ojf.2016.63014 Walters, R.S. (1993) Protecting red oak seedlings with tree shelters in northwestern Pennsylvania. USDA Forest Service Research Paper NE-679. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi. org/10.2737/NE-RP-679 Ward, J.S., Gent, M.P.N. and Stephens, G.R. (2000) Effects of planting stock quality and browse protectiontype on height growth of northern red oak and eastern white pine. Forest Ecology and Management 127, 205–216. https://doi.org/10.1016/S0378-1127(99)00132-2 Webb, D.P. and von Althen, F.W. (1980) Storage of hardwood planting stock: effects of various storage regimes and packaging methods on root growth and physiological quality. New Zealand Journal of Forest Science 10, 83–96. Weigel, D.R. and Johnson, P.S. (1998a) Planting white oak in the Ozark Highlands: a shelterwood prescription. USDA Forest Service Technical Brief TB-NC-5. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www. fs.usda.gov/treesearch/pubs/11002 (accessed 20 September 2018). Weigel, D.R. and Johnson, P.S. (1998b) Planting northern red oak in the Ozark Highlands: a shelterwood prescription. USDA Forest Service Technical Brief TB-NC-6. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11003 (accessed 20 September 2018). Weigel, D.R. and Johnson, P.S. (2000) Planting red oak under oak/yellow-poplar shelterwoods: a provisional prescription. USDA Forest Service General Technical Report NC-210. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. https://doi.org/10.2737/NC-GTR-210

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Weigel, D.R. and Peng, C.-Y.J. (2002) Predicting stump sprouting and competitive success of five oak species in southern Indiana. Canadian Journal of Forest Research 32, 703–712. https://doi.org/10.1139/ x02-042 Wendel, G.W. (1980) Growth and survival of planted northern red oak seedlings in West Virginia. Southern Journal of Applied Forestry 4, 49–54. https://doi. org/10.1093/sjaf/4.1.49 Wharton, C.H., Kitchens, W.M., Pendleton, E.C. and Sipe, T.W. (1982) Ecology of bottomland hardwood swamps of the Southeast: a community profile. US Fish and Wildlife Service, Biological Services Program FWS/OBS-81/37. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Wilson, B.C. and Jacobs, D.F. (2006) Quality assessment of temperate zone deciduous hardwood s­eedlings.

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New Forests 31, 417–433. https://doi.org/10.1007/ s11056-005-0878-8 Windell, K. (1991) Tree shelters for seedling protection. USDA Forest Service Technical and Development Program Publication 9124-2834-MTDC. USDA Forest Service Technology and Development Program, Washington, DC. Windell, K. and Haywood, J.D. (1996) Intermediate results of a treeshelter durability study. USDA Forest Service General Technical Report NE-221. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 46–56. Available at: https://www.fs.usda.gov/treesearch/pubs/ 4385 (accessed 20 September 2018). Zastrow, D.E. and Marty, T.L. (1991) Tree shelter experiences. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 198–205.

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11

Managing Forest Health

Introduction Oak forests and woodlands are sometimes threatened by pests and diseases leading to the death of individual oaks or to widespread mortality in oak stands and forests. The colonization of oak stands by invasive plants may reduce oak regeneration success or otherwise be undesirable from a plant diversity and animal habitat perspective. Among those posing the most serious and widespread threats to oak forests are the gypsy moth, oak decline, oak wilt, rapid white oak mortality, sudden oak death and herbivory by deer.

Gypsy Moth Throughout much of the oak range in the eastern USA, the gypsy moth is a devastating forest pest that defoliates and damages millions of acres of forest each year. Damage caused by gypsy moth and programmes to monitor or slow down its spread costs tens of millions of dollars annually (Mayo et  al., 2003; USDA Forest Service, 2018). The insect is native to Europe and Asia and was accidentally introduced near Boston, Massachusetts in the late 1860s. It has since expanded its range south-westerly and westerly and now covers parts or all of 17 states (Fig. 11.1A). Defoliation is caused by the larval (caterpillar) stage of the gypsy moth (Fig. 11.2A). Defoliation has many consequences including: (i) tree mortality; (ii) loss of tree growth; (iii) undesirable changes in the species composition of forests; (iv) decreased aesthetic quality of forests; (v) reduced water quality; and (vi) the nuisance of numerous caterpillars around homes in affected areas (Campbell and Sloan, 1977; Twery, 1991; USDA Forest Service, 2018). Each new generation of the gypsy moth begins during the summer when the females lay clusters of eggs, usually on tree trunks, where they remain through the winter (Leonard, 1981). The eggs hatch

in the spring to form the first larval stage or instar. The newly hatched larvae are about 3 mm long, light in weight and have long hairs that greatly increase their surface area. The larvae leave the vicinity of the eggs and move upwards into tree crowns to feed. As they move, they spin silken threads to which they remain attached. The young larvae hang from these threads, which break when caught by the wind. The suspended larvae are then blown aloft and disperse to other trees. The larvae feed on the foliage of trees and other vegetation during the night and rest in bark crevices and other protected places during the day. Exceptions occur during population peaks, when feeding occurs all day. The larvae go through five to six moults, which occur at about weekly intervals. As the larvae grow, their consumption of foliage increases exponentially. During the last instar they eat more than during all the other stages combined. By the end of the larval stage of development, a single larva has consumed about 11 ft2 (1 m2) of foliage, has increased in length to about 50–90 mm, and in weight by more than 1000-fold (Leonard, 1981). The end of the 8-week larval stage marks the beginning of the pupal stage. The pupae live in a sparse silken cocoon on tree trunks for about 2 weeks and then emerge in mid-summer, usually in July, as adult moths (Fig. 11.2B). Mating and egg laying occurs during the approximately 1-week lifespan of the adult. The male moth locates the female with a pair of feathery antennae, which detects the sex pheromone emitted by the female. Although the female is unable to fly, wind dispersal during the first instar together with inadvertent dispersal of various life stages via human vehicles are effective mechanisms of range expansion (Leonard, 1981). When insect densities attain high levels, susceptible trees are often completely defoliated. Several successive years of defoliation coupled with other associated biological and physical stress factors often result in tree mortality. Although less than

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

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(A)

(C) WASHINGTON

MINNESOTA XXX MAINE WISCONSIN MICHIGAN

OREGON

NEW YORK

IDAHO

IOWA PENNSYLVANIA ILLINOIS

OHIO INDIANA WEST VIRGINIA

MISSOURI KENTUCKY

VIRGINIA

TENNESSEE

NORTH CAROLINA

ARKANSAS MISSISSIPPI ALABAMA

GEORGIA SOUTH CAROLINA

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NEVADA

MONTANA

MINNESOTA MAINE

SOUTH DAKOTA IDAHO

WISCONSIN MICHIGAN

WYOMING NEBRASKA

COLORADO

KANSAS

NEW YORK

IOWA ILLINOIS

UTAH

UTAH

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INDIANA

OHIO

PENNSYLVANIA

CALIFORNIA

MISSOURI VIRGINIA TENNESSEE

OKLAHOMA ARIZONA

NORTH CAROLINA

ARKANSAS

NEW MEXICO

ARIZONA ALABAMA GEORGIA TEXAS

LOUISIANA

FLORIDA

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Fig. 11.1.  (A) Distribution of the gypsy moth in the USA, 2016. The insect is also present in portions of eastern Canada. (B) Distribution of oak wilt, 2016. (C) Distribution of sudden oak death, 2016. Shading indicates occurrence at the county level (dark shading) and state level (light shading). (Maps courtesy of USDA Forest Service, Northern Research Station and Forest Health Protection. Alien Forest Pest Explorer – species map. Database last updated 28 July 2016 (USDA Forest Service, no date).)

(A)

(B)

Fig. 11.2.  (A) Male gypsy moth larvae (last instar) feeding on an oak leaf. (B) Adult female gypsy moths. The adults live for about a week during which they mate and lay their eggs on tree bark and other objects. Compared with the white- or cream-coloured, flightless females, the male moths are mottled brown with black wing markings, have a slimmer abdomen and can fly. (Photographs courtesy of USDA Forest Service.)

20% of defoliated trees usually die from an infestation, localized mortality can be extremely high. Despite suppression and eradication programmes by federal and state agencies, which have been effective in slowing the spread of the gypsy moth, the insect continues to expand its range. Not only do immediate economic losses result from gypsy moth defoliations, but often there are associated long-term changes in forest composition away from the oaks (a preferred host), and towards less valuable species. Such changes in species composition reduce acorn production, exacerbate the oak regeneration problem, negatively affect animal

Managing Forest Health

populations that depend on acorns, and thus reduce forest biodiversity. Although the larvae are capable of feeding on the foliage of hundreds of North American plant species (Liebhold et al., 1995), the most common hosts within the insect’s current range are oaks and aspens (Gansner and Herrick, 1985). Tree species that are readily eaten by gypsy moth larvae during all larval stages are categorized as susceptible, whereas species eaten when preferred foliage is not available or only by some larval stages are categorized as resistant. Susceptible (preferred) hosts include the oaks and many associated hardwoods (Table 11.1). Immune species are defined as those that are rarely eaten. Shrubs and herbaceous plants are also eaten by the gypsy moth, and they also vary in susceptibility. Because oaks rank among the preferred hosts of the gypsy moth, stands with a high proportion of oaks are susceptible to heavy defoliation (Gottschalk, 1991). However, not all oaks are equally susceptible. For example, chestnut, black and scarlet oaks are more susceptible than northern red oak (Gansner and Herrick, 1985). Guidelines for evaluating the susceptibility of forests to defoliation are based on the percentage basal area of oaks and other susceptible hosts (Gottschalk, 1993). In all tree species, heavy defoliation may cause trees to refoliate during the same year, which in turn depletes carbohydrate reserves and reduces tree vigour. Weakened trees are then vulnerable to attack by secondary organisms such as Armillaria root disease and the twolined chestnut borer (Gansner and Herrick, 1985; Gottschalk et  al., 1989). Trees at highest risk are those in suppressed (overtopped) and intermediate crown classes and within a given crown class, trees with poor crowns (Herrick and Gansner, 1987). The most significant economic effect of the gypsy moth on oaks is not mortality, but reduced growth, yield and wood quality (Naidoo and Lechowicz, 2001). Gypsy moth outbreaks occur at 7- to 10-year intervals and last 1–2 years (Gottschalk et al., 1989). It may take 3 years for stand growth to recover from a single defoliation (Twery, 1987). For two defoliations per decade, loss of volume increment has been estimated at about 10%, exclusive of mortality. For two defoliations in 5 years, estimated growth losses are about 19% (Twery, 1987). However, such losses may be offset by subsequent gains in growth of the survivors resulting from the thinning effect. To minimize loss in value

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Table 11.1.  Gypsy moth susceptibility ratings for some common overstorey and understorey species of the eastern USA. (Adapted from Gottschalk, 1993; Liebhold et al., 1995.)a Species Rating

Overstorey

Understorey

Susceptible: species readily eaten by gypsy moth larvae during all larval stages Resistant: species eaten when preferred foliage is not available and/or only by some larval stages Immune: species rarely eaten

American basswood, bigtooth aspen, gray birch, mountain ash, oak, paper birch, pear, quaking aspen, river birch, sweetgum, tamarack, willow

American hazel, eastern hop, hornbeam, hawthorn, speckled alder, witch-hazel

American beech, American chestnut, American elm, black cherry, black walnut, boxelder, butternut, eastern cottonwood, eastern hemlock, hickory, Norway maple, pine, red maple, sassafras, spruce, sugar maple, sweet birch, sweet cherry American holly, American sycamore, ash, bald cypress, balsam fir, blackgum, black locust, cucumber tree, eastern redcedar, Fraser fir, hackberry, honeylocust, horse chestnut, Kentucky coffee tree, northern catalpa, Ohio buckeye, pin cherry, red mulberry, silver maple, slippery elm, yellow buckeye, yellow-poplar

American hornbeam, beaked hazel, blackhaw, chokecherry, flowering dogwood, pawpaw, serviceberry, sourwood Alternate-leaf dogwood, American elder, common juniper, common persimmon, grey dogwood, mountain maple, nannyberry, possumhaw, redbud, red-osier dogwood, roundleaf dogwood, spicebush, striped maple

a

The list includes commonly occurring species in each category and is not exhaustive for any category.

of sawtimber-size oaks killed by defoliation, trees should be salvaged within the first year after death (Garges et al., 1984). Silviculture represents one aspect of managing the gypsy moth problem. Silviculture can be used to reduce the susceptibility (defoliation potential) and vulnerability (tree mortality) of forests to the insect. Silvicultural guidelines, including decision-making charts and prescriptions are available to aid forest managers in coping with the problem (Gottschalk, 1993). The following summarizes some of these prescriptions. However, the concerned reader is urged to review the guide itself, together with other current information on the gypsy moth.1 Three categories of defoliation imminence are considered in the application of silvicultural guidelines: ●● defoliation not imminent within 1–3 years; ●● defoliation imminent within 1–3 years or now occurring; and ●● defoliation has occurred. The first category usually applies to stands located outside generally affected areas, whereas the last two categories usually apply to stands within affected areas (Gottschalk, 1993). Forest pest management specialists can be consulted on the location and size of the area currently infested, expected trends in insect populations, and the time

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when the infestation is likely to occur in a given stand or locale. After gypsy moth populations have been established in an area, inventories of egg masses per acre are used to determine potential population levels. Egg masses are evident in infested areas for 7–8 months, which allows time for forest managers to plan and apply silvicultural treatments before outbreaks occur. After the imminence of defoliation is ascertained, a stand inventory and analysis is required to determine stand susceptibility to defoliation. In this context, susceptibility is defined as the probability of defoliation by gypsy moth given that the insect is present (Gottschalk, 1993). Stand susceptibility can be rated based on the factors that affect defoliation potential. These include the percentage of stand basal area in oaks, the percentage in highly preferred oaks (e.g. black and chestnut oaks), and the average stand dbh and crown condition (Fig. 11.3). Stands with a large proportion of highly preferred hosts for gypsy moth, especially when the crowns of those species are large and in good condition, are attractive to feeding insects. Potential defoliation in such stands is therefore high (Fig. 11.3). Other factors also influence stand susceptibility. Susceptible sites are associated with places where tree growth is slow such as shallow or sandy soils, dry ridge tops, and wherever drought stress is frequent. Some stages of larval development find refuge

Chapter 11

Defoliation potential

Percentage of BA in chestnut and black oaks

≥ 70%

< 70%

Percentage of BA in good crown condition

≥ 60%

40%

< 60%

31%

Percentage of BA in good crown condition

≥ 30%

29%

< 30%

22%

≥ 40%

32%

10–39%

23%

≥ 7 in.

24%

< 7 in.

15%

20–49%

18%

< 20%

9%

≥ 50%

Percentage of BA in chestnut and black oaks

Percentage of BA in oaks

≥ 40%

< 40%

Percentage of BA in good crown condition

Mean stand dbh (in.)

< 50%

Defoliation level Heavy

≥ 30%

Moderate 20–29%

Light

< 20%

Fig. 11.3.  Estimated gypsy moth defoliation potential based on a central Pennsylvania model. Defoliation predictors are the percentage of stand basal area (BA) in oaks and the percentage in black and chestnut oaks, percentage of BA in good crown condition, and average stand dbh. (Adapted from Herrick and Gansner, 1986.)

in specific tree features such as bark flaps, large dead branches, bole wounds and holes, dead sprout stubs, deep bark fissures and other protected sites (Bess et  al., 1947; Campbell et  al., 1975a, b; Gottschalk, 1993). Removing these structural features can reduce stand susceptibility. However, recommendations are to concentrate removals of such refuges that are high in trees before reducing those low in trees or on the ground where they may provide predator habitat (Gottschalk, 1993). Susceptible sites also have fewer small mammal predators such as mice and shrews and are less desirable habitats for those animals than the less susceptible sites (Yahner and Smith, 1991). Environmental stresses that can increase susceptibility include damage associated with frost, other

Managing Forest Health

insects and logging. Bands of susceptible forests often evolve around housing developments, recreational areas, roads, rights-of-way and other areas at the interface of forests and human activity. The associated disturbances can create gypsy moth refuges and stresses in trees, thereby increasing their susceptibility to defoliation (Campbell et al., 1976; McManus and Houston, 1979; Gottschalk, 1993). Crown condition (Table 11.2) is an important determinant of stand susceptibility and is a better indicator of a tree’s capacity to recover from defoliation than is crown class (or tree size) (Gottschalk and MacFarlane, 1993). Crown condition is therefore a determinant of stand vulnerability, which is defined as the probability of occurrence of stand damage given that defoliation has occurred

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1972; Valentine and Campbell, 1975) and the Pocono Mountains of Pennsylvania (Herrick, 1982; Gansner and Herrick, 1984). These models differ in the predictors used to estimate mortality. For example, the New England model uses species, crown condition, crown class, defoliation intensity and defoliation frequency to estimate the probability of mortality. The Pocono model is based on crown condition, species and slope aspect as determinants of vulnerability (Fig. 11.4). Stand-level mortality models have been developed for New England (Valentine and Campbell, 1975), the Pocono Mountains, New Jersey (Kegg, 1974) and the central Appalachian Ridge and Valley area (Crow and Hicks, 1990). Both individual tree and

(Gottschalk, 1993). The criterion for ‘damage’ is usually based on tree mortality. Other factors being equal, defoliated trees in subordinate crown classes are more likely to die than those in dominant or codominant classes. However, trees in codominant or dominant crown classes with poor crowns are more likely to die from defoliation than trees in the intermediate crown class with a good crown (Campbell and Valentine, 1972; Campbell and Sloan, 1977; Crow and Hicks, 1990). Models for estimating individual tree mortality related to gypsy moth defoliation have been developed for several regions including the Ridge and Valley area of Pennsylvania (Herrick and Gansner, 1987), New England (Campbell and Valentine,

Table 11.2.  Crown condition classes for ranking risk of tree mortality to gypsy moth defoliation. (Adapted from Gottschalk, 1993; Gottschalk and MacFarlane, 1993.) Crown condition class (mortality risk)

Crown characteristics

Poor (high)

≥ 50% of crown branches are dead; foliage density, size and coloration are subnormal; or epicormic bole sprouting is heavy 25–49% of crown branches are dead; foliage density, size and coloration are subnormal; or some epicormic bole sprouting is evident < 25% of crown branches are dead; healthy foliage; little or no epicormic bole sprouting

Fair (moderate) Good (low)

Crown condition

Good

Fair

Poor

Species

Aspect

Species

Other than white oak group

White oak group

N, NE, E SE or S

SW, NW, W, level

Other than oaks

Oaks

2%

9%

11%

43%

62%

86%

Fig. 11.4.  Estimated probabilities of defoliation-related mortality based on a model for the Pocono Mountains of Pennsylvania. Crown condition is defined in Table 11.2. (Adapted from Herrick, 1982; Gansner and Herrick, 1984.)

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Chapter 11

stand-level models require stand inventories that include the information necessary for model implementation. Stand vulnerability to gypsy moth and responses to ameliorative management practices can be modelled with the forest vegetation simulator (FVS) (see Chapter 15, this volume) (Crookston and Dixon, 2005; Gottschalk and Courter, 2007). Within the FVS framework, algorithms are used to prioritize stands for treatment or to estimate the potential impacts of gypsy moth outbreaks on a forested landscape. The species and sizes of trees in a stand can be used to compute a relative gypsy moth vulnerability index for that stand. Within a multi-year cycle, periodic gypsy moth outbreaks are simulated based on random draws from an outbreak probability distribution. During a simulated outbreak, decreased tree growth and survival due to the gypsy moth attack is simulated according to the severity of the outbreak and the relative susceptibility of the species in the stand. The greatest impact of an outbreak is on susceptible tree species, but for heavy outbreaks basal area growth of resistant species is also reduced. Thus, FVS can be used to proactively compare management alternatives intended to reduce a stand’s susceptibility to gypsy moth outbreaks and to estimate the probability of a gypsy moth event (see Gottschalk, 1993). The modelled responses of tree growth and mortality to gypsy moth defoliation do not yet capture the full range of variability observed in the field. Therefore the best use of the model is to compare relative (rather than absolute) differences in stand and landscape change among alternative management actions or inaction. Stand susceptibility and vulnerability ratings together with other stand information facilitate the application of silvicultural decision-making guidelines and prescriptions (Gottschalk, 1993). Some prescriptions for coping with the gypsy moth are preventative whereas others are designed to minimize timber losses after gypsy moth defoliation has occurred. Preventative silvicultural measures include presalvage cuttings, which are designed to anticipate and minimize the impact of defoliation. Presalvage cuttings focus on reducing stand vulnerability by harvesting highly vulnerable trees before they are defoliated and die. Gottschalk (1993) defined three types of presalvage cuttings applicable to stands susceptible to gypsy moth defoliation: (i) presalvage thinning; (ii) presalvage harvest; and (iii) presalvage shelterwoods.

Managing Forest Health

Presalvage thinning can be applied to well-stocked stands that are more than 15 years from maturity. Attaining the desired stand condition may require reducing relative stand density (stocking) by 50% or more. This exceeds the usual recommendation not to remove more than 35% of stocking in a single cut (Roach and Gingrich, 1968). However, in stands with more than 50% of basal area in preferred host species, normal thinning prescriptions are unlikely to sufficiently reduce stand vulnerability. Presalvage thinning is implemented 1–3 years before defoliation to allow stands to recover from possible (and usually temporary) stresses associated with thinning itself (Gottschalk, 1993). The prescription prioritizes removals (from highest to lowest) according to species and crown condition as follows: ●● ●● ●● ●●

oaks with poor crowns; non-oaks with poor crowns; oaks with fair crowns; and non-oaks with fair crowns.

These priorities can be integrated with normal thinning procedures for maintaining adequate residual stocking, stand structure and species composition to the extent possible but are implemented within constraints set by the gypsy moth control objectives. A presalvage harvest can be used in stands near maturity or that are poorly stocked (e.g. below C-level stocking, Fig. 6.9, this volume), but do have adequate regeneration potential. The objective is to harvest the stand in a single cut before defoliation occurs in order to take advantage of the presence of adequate advance reproduction and to preserve stump sprouting potential. A presalvage harvest can be applied to stands where defoliation is occurring or is expected to occur. Where regeneration potential is inadequate, a presalvage shelterwood cutting can be used to encourage the development of reproduction. This strategy may be appropriate when stand susceptibility and vulnerability are both low to moderate. Establishment of desirable reproduction may require 5 years or longer. The shelterwood should retain desirable species of low to moderate vulnerability, and trees in good crown condition that are potentially good seed producers. Depending on stand regeneration dynamics, this may require compromise in the composition of the regenerated stand and a willingness to accept fewer oaks. Where the latter is unacceptable, underplanting shelterwoods with oaks may be a viable option (see Chapter 10, this volume).

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Two types of sanitation cuttings have been proposed for gypsy moth problems: (i) sanitation thinning; and (ii) sanitation conversion (Gottschalk, 1993). By definition, sanitation cuttings are designed to prevent the spread and establishment of damaging organisms (Smith et al., 1997). Sanitation thinning, in contrast to presalvage thinning (designed to reduce stand vulnerability), is accomplished by cutting trees to reduce stand susceptibility. The method is applicable to stands where defoliation is occurring or where the imminence of defoliation is several years away. Sanitation thinnings eliminate trees that are current or prospective sources of infestation (Gottschalk, 1993). This process includes removing preferred host species and structural features that provide refuges for the insect, and improving habitat for predators and parasites of the gypsy moth. Sanitation thinning is applicable to stands with less than 50% of basal area in preferred host species. Susceptibility in such stands can be reduced by reducing the basal area of preferred host species to 20% or less (Gottschalk, 1993). Priorities for removing trees are (highest to lowest): ●● preferred host species with numerous structural features that provide refuges for larvae; ●● trees in poor crown condition; and ●● trees in fair crown condition. Sanitation conversion cuttings are intended to reduce susceptibility and/or vulnerability by converting stands from preferred host species to more resistant or immune species before infestations occur. They are applicable to highly susceptible or vulnerable stands, which are likely to incur either frequent, heavy defoliation or high mortality. On poor sites with a large oak component, such stands may be amenable to natural conversion to pines or pines mixed with resistant hardwoods. On the better sites, stands may be naturally convertible to mixed hardwoods containing a high percentage of resistant species. In either case, it may be necessary to create a shelterwood to obtain adequate reproduction of desirable species. The method is potentially adaptable to natural and artificial regeneration or a combination of the two. Although conversion away from the oaks may reduce stand value, conversion may be the more economically desirable strategy in the long run because of reduced gypsy moth impacts and lowered stand protection costs. The gypsy moth guidelines define five types of salvage cuttings: (i) salvage thinning; (ii) salvage cutting; (iii) salvage harvest; (iv) salvage shelterwood;

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and (v) salvage conversion. They are applicable to stands where defoliation has already occurred, and significant mortality has occurred or is imminent. The objective of salvage thinning is to capture the economic value of dead trees and to simultaneously thin remaining live trees to reduce stand susceptibility and vulnerability. Dead trees should be salvaged within a year of their death (Garges et  al., 1984). Suitable stands include those that have greater than C-level stocking (Fig. 6.9, this volume), are at least 10 years from maturity, and contain live trees comprising more than 60% stocking. Such stands therefore would be considered adequately stocked for management to maturity. Thinning the live trees provides an opportunity to improve stand vigour, growth and quality while generating an economic return that can increase the feasibility of salvaging dead trees. Recommendations are to remove no more than 35% of live-tree stocking in a single cut. Priorities for tree removal (from highest to lowest) are: ●● dead trees; ●● oaks with poor crowns that are likely to die; ●● other species with poor crowns that are likely to die; and ●● trees with fair crowns. These priorities can be integrated with the usual thinning objectives of maintaining adequate residual stand stocking, removing less desirable trees before higher quality trees, and maintaining a desirable stand structure. The occurrence of dead trees provides opportunities for maintaining or increasing numbers of den trees for mammals and cavity trees for birds. Trees with structural features associated with larvae refuges can be removed in thinning. Salvage cutting objectives are similar to salvage thinning objectives – both are aimed at the economic salvage of dead and dying trees. However, salvage cutting is specifically applicable to stands that have between 30% and 60% stocking of live trees. In this case, the live component of the stand is not thinned because stand density is already at or below full utilization of growing space. In stands below 30% stocking, recommendations are to defer cutting until they can be thinned or re-examined in the future. Cutting priorities are simply to remove dead and dying trees. Options include leaving a few dead trees per acre to provide animal dens and cavities (see Chapter 13, this volume). Regardless of whether stands are given a salvage thinning or

Chapter 11

are deferred from cutting due to understocking, the residual stands are likely to be less susceptible and less vulnerable to the gypsy moth than before the last defoliation. A salvage harvest is similar to a presalvage harvest in that both are applicable to stands with low stocking (e.g. < C-level, Fig. 6.9, this volume) and both are used to regenerate the stand by taking advantage of an existing adequate regeneration potential. In both cases, regeneration is accomplished by complete overstorey removal (clearcutting). However, a salvage harvest is used in stands in advanced stages of defoliation with current or imminent mortality. A salvage shelterwood can be used under conditions similar to those used in presalvage shelterwood cuttings. However, the former is applicable to stands in advanced stages of defoliation and mortality. Similarly, the application of salvage conversion cuttings can be applied to salvaging dead and dying trees and to convert those stands to species not preferred by the gypsy moth. The silvicultural guidelines for forests threatened by the gypsy moth (Gottschalk, 1993) also include recommendations for deferring cutting, stand reexamination and criteria for prioritizing the use of pesticides. However, silviculture is only one aspect of a comprehensive programme of gypsy moth management that also includes eradication, suppression, slowing the spread and biological control of the insect. Eradication involves detecting and destroying new, isolated populations outside the insect’s generally established area. Eradication methods vary, but include spraying with chemicals or biological pesticides, mating disruption or mass-trapping of insects (USDA Forest Service, 2018). Direct suppression is used to minimize gypsy moth effects within the established population area. Materials used in suppression include the chemical pesticide diflubenzuron (Dimilin®) and biological pesticides including a bacterium (Bacillus thuringiensis) and Gypchek (a formulation of a naturally occurring gypsy moth virus) (Podgwaite et al., 1991). Biological control involves the use of natural enemies of the gypsy moth (predators, parasites and diseases) to naturally control the insect. Vertebrate predators include mice, voles, shrews, toads and birds including the blue jay and blackbilled cuckoo; invertebrate predators include certain beetles and wasps (Campbell and Sloan, 1977; Campbell, 1981). Two disease enemies that are already established within the gypsy moth range in North America are the fungal pathogen

Managing Forest Health

Entomophaga maimaiga and the nucleopolyhedrosis virus (NPV) (Lewis, 1981; Onken, 1995), from which Gypchek is formulated. E. maimaiga and NPV are the principal natural enemies that kill large numbers of gypsy moth larvae (Reardon and Hajek, 1993). USDA Forest Service and its cooperators have instituted a national programme to manage the gypsy moth. Called Slow the Spread (STS) Project, its objective is to slow the spread of gypsy moth along its 1200-mile front (Fig. 11.1A) (Tobin and Blackburn, 2007). To do this, pheromone traps are systematically located within the insect’s expanding range to detect isolated colonies. These colonies are then suppressed or eradicated using the most efficacious methods available (Tobin et al., 2004). As a result of this programme, spread has been reduced by more than 70% from the historical rate of 13 miles/year to 3 miles/year. In just 6 years, this programme prevented the encroachment of the gypsy moth on to more than 40 million acres (Gypsy Moth Slow the Spread Foundation, 2008). A part of the success of STS is related to its disruption of the insect’s mating (Thorpe et  al., 2007; Tobin et al., 2007). Pheromone flakes are aerially applied to foliage, which interferes with the male gypsy moth’s ability to locate females and thus mate.

Oak Decline A malady of oaks called oak decline is a widespread and potentially serious threat to millions of acres of oak forests. Its effects range from partial crown dieback to tree death. Trees in the red oak group (hereafter called ‘red oaks’) are more severely affected than those in the white oak group (hereafter called ‘white oaks’) (Kromroy et al., 2008). Oak decline can affect hundreds of thousands of acres within a region (Oak et  al., 2004; Starkey et  al., 2004) and on some sites may kill 50% or more of the basal area of red oaks (Spetich, 2004; Starkey et al., 2004; Heitzman et al., 2007). However, oak decline normally occurs over smaller areas and at lower levels of mortality (Kromroy et al., 2008). It has recurrently appeared across the oak range with severe decline events linked primarily to ageing trees and episodic drought. Between 1956 and 1986, there were 57 incidents of landscape-scale oak mortality and/or decline reported in eastern USA (Millers et  al., 1989), with 15% of those decline incidents also associated with gypsy moth (see previous section).

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The oak decline complex has been described in terms of: (i) predisposing factors; (ii) inciting factors; and (iii) contributing factors (Wargo, et  al., 1983; Manion, 1991). Predisposing factors are long-term, underlying conditions that make certain oak trees and stands particularly susceptible to oak decline. These include age, species, growth rate, landform, site and climate. Slow-growing, mature trees in the red oak group on sites subject to drought, frost damage or flooding are often predisposed to oak decline (Wargo et al., 1983; Clatterbuck and Kauffman, 2006). In Missouri and Arkansas, for example, red oaks older than 65 years on southfacing slopes with poor soils are especially vulnerable. Also, stands on low-quality sites are more likely to have an abundance of black oaks and scarlet oaks, two of the red oak group species that are particularly vulnerable to decline (Kabrick et al., 2008). Because tree age and site quality are important predisposing factors, the ratio of site index to stand (or tree) age is a useful index of susceptibility; low ratios are associated with a high susceptibility to oak decline (Oak and Starkey, 1991). Inciting factors are specific incidents (or a series of incidents) that trigger an oak decline event. These include drought, frost, flooding and insect damage (Wargo et al., 1983; Manion, 1991). However, inciting factors do not always produce a rapid response; oak mortality often peaks 2–5 years following an inciting event (Wargo et al., 1983). Moreover, inciting factors may have a cumulative effect. Some studies have found trees with slow diameter growth are more likely to suffer from decline-related mortality incited by drought. In at least some cases a tree’s slow diameter growth can be linked to a prior drought event that appears to have set the tree on a long, slow course towards mortality that is eventually triggered by a subsequent inciting drought (Dwyer et al., 1995; Voelker et al., 2008). Thus, the propensity for periodic droughts across a landscape can be a predisposing factor, whereas within a given stand a specific drought acts as an inciting factor. Contributing factors increase the probability of tree damage and mortality for trees weakened by the combination of predisposing and inciting factors. Contributing factors include Armillaria root disease, red oak borers, twolined chestnut borers, Hypoxylon atropunctatum, other decay fungi and possibly Phytophthora species (Wargo et al., 1983; Manion, 1991; Schwingle et  al., 2007). These are often present but not usually lethal in healthy oak stands. However, in declining stands the associated

418

damage and mortality can be substantial (Bruhn et al., 2000; Starkey et al., 2004). In some Missouri decline areas where high-grading occurred in the 1980s, Armillaria subsequently attacked the live residual trees after the harvest. The presence of Armillaria-infected trees thus may be a constraint to thinning in declining stands. Symptoms Tree dieback associated with oak decline begins at the branch tips and progresses downwards. Foliage may appear tufted near branch tips as a result of reduced shoot growth; leaves may brown or wilt without falling, or prematurely change to autumn colours and then fall. Boles and branches may respond by producing epicormic sprouts, and partial mortality of tree crowns may result in a ‘staghead’ appearance. The symptoms of oak decline can occur rapidly, especially when exacerbated by contributing factors. After the first year of a severe oak decline incident in Arkansas with a severe, concomitant outbreak of red oak borers, the number of standing dead oaks doubled and the number of trees with epicormic branching increased fivefold (Spetich, 2004, 2006). Within a region, stands with readily visible symptoms of mortality and crown dieback often affect only a small proportion of oaks. For example, during a recent period of oak decline in the Missouri Ozarks, the number of trees with moderate or severe crown dieback was 6% and 10% for the white oak and red oak species groups, respectively (Fan et al., 2008). The crowns of 76% of the white oaks and 64% of black and scarlet oaks were classified as healthy. Given the periodic but largely unpredictable occurrence of inciting factors related to weather, oak decline might be considered as part of the natural cycle of development for predisposed oak stands (Manion and Lachance, 1992). Unlike competition-induced mortality associated with the essentially continuous and predictable self-thinning of stands (see Chapter 6, this volume), inciting factors and associated oak decline act intermittently and suddenly to change stand structure and composition. During periods favourable for growth, trees that are well positioned with respect to crown class and vigour can grow rapidly but may lose the capacity to endure future stress. This has been proposed as a possible consequence of rapid above-ground growth during periods of resource

Chapter 11

abundance that occurs at the expense of root growth (Jenkins and Pallardy, 1995). Periodic decline events thus may disproportionately affect large, vulnerable trees with few root reserves and leave residual trees that are more resistant to inciting and contributing factors. Although the concept that oak decline is part of the natural cycle of oak forest development may be scant comfort to forest managers faced with a massive regional oak decline event, it does emphasize the importance of considering predisposing factors and future risk of oak decline when developing silvicultural prescriptions and long-term management plans. Treatment Because inciting factors are virtually impossible to control, management efforts to minimize oak decline impacts should concentrate on: (i) reducing exposure to predisposing factors before oak decline occurs; and (ii) restoring desirable stand conditions after oak decline occurs. Accordingly, managers should routinely evaluate predisposing factors when preparing stand management plans. Predisposing factors related to landform, soils, site quality and hydrology are stationary and can even be mapped for large areas. In contrast, predisposing factors related to stand age, species composition or defoliators are dynamic and should be monitored over time. In the Ozark Highlands there are millions of acres of drought-prone forests with a high proportion of ageing red oaks. There, it has been shown that thinning can be effectively used to reduce the impact of oak decline, but not necessarily the incidence of oak decline. Although reducing stand density, by itself, may not preclude oak decline, thinning nevertheless can reduce the number of susceptible trees (e.g. mature red oaks) so that when decline does occur fewer susceptible trees remain to be affected, and those that do have ample growing space. In stands already showing symptoms of oak decline, improvement cuts can be used. In one example, removing declining and susceptible trees down to a residual basal area of 44 ft2/acre increased the growth rate of residual trees and also captured products from trees that otherwise would have died (Dwyer et  al., 2007). However, 14 years later, the thinned and comparable unthinned stands had similar rates of crown dieback. Despite the potential benefits associated with thinning, improvement cuts or salvage cuts, the product values associated with

Managing Forest Health

dying, declining or borer-afflicted trees may not be sufficient to make such treatments economically practical (Stringer et al., 1989). Before thinning stands in decline or at risk of decline, trees that are most likely to survive and thrive should be identified. Then after accounting for expected mortality based on tree and stand conditions (including site quality), thinning can follow general guidelines appropriate for even- or uneven-aged stands. The following guidelines are based on studies in both healthy and declining oak stands in the Missouri Ozarks (Fan et  al., 2006, 2008; Shifley et al., 2006; Dwyer et al., 2007). Pre-emptive thinning of oak stands to minimize future oak decline impacts should focus on sites that are predisposed and should concentrate on removing trees with the greatest risk of future mortality while retaining trees that help reduce future predisposition. When marking stands for thinning, remove trees in the approximate order listed below until the target residual stand density is obtained: ●● Remove red oaks in intermediate or suppressed crown classes when: ## they interfere with crowns of more desirable residual trees (especially white oaks with healthy crowns); and/or ## they are merchantable. ●● Remove white oaks in intermediate or suppressed crown classes when: ## they interfere with crowns of more desirable residual trees (including other white oaks with healthy crowns of intermediate crown class, which are at low risk in decline-prone stands); and/or ## they are merchantable. ●● On drought-prone sites, remove dominant and codominant red oaks. As red oaks increase in size and age, their predisposition to oak decline increases, and also as site quality decreases. Thus, relatively young dominant and codominant red oaks are at lower risk than older trees of the same crown class, other factors (such as current crown health) being equal. Although the current diameter growth of individual trees is usually unknown, slower growing red oaks are at higher risk to oak decline than faster growing red oaks, other factors being equal. ●● Remove dominant and codominant white oaks. For stands already in decline, one option may be to regenerate the stand as discussed later in this section. When this is not a viable option, the thinning

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process can proceed in the order listed below, ­subject to the usual considerations of residual tree spacing, residual stand density and product merchantability. As already noted, thinning may not eliminate future oak decline incidents, but it can reduce the number of vulnerable trees. ●● Remove oaks with moderate (34–66%) to severe (> 66%) crown decline. White oaks with declining crowns should be harvested whenever practical. The available information suggests that although white oaks are less vulnerable than red oaks to crown dieback, once afflicted the white oaks are unlikely to recover (Dwyer et al., 2007). Red oaks with less than 67% crown dieback tend to be more variable than white oaks in their recovery from dieback. ●● Remove remaining oaks showing symptoms of crown decline. As noted above, declining red oaks are more likely to recover than declining white oaks. ●● Remove additional trees in the order listed above for pre-emptive thinning in the absence of oak decline. ●● Because the stand has already shown vulnerability to oak decline, any decision to thin in the future should consider tree value (current and future) and the likelihood of post-thinning tree mortality. This consideration is especially important with respect to older and larger red oaks, which are the most vulnerable to decline. Symptoms of oak decline can take 2 or more years to fully develop. Thus, for the red oaks, a vigorous appearance now does not reflect their risk to decline. Stands that are highly predisposed to oak decline or already in decline may be better candidates for regeneration than for thinning. Other factors being equal, shorter rotations reduce decline-caused mortality of black and scarlet oaks, which reach physiological maturity at relatively young ages. However, stands that have reached advanced stages of oak decline also have reduced acorn production and sprouting capacity, both of which can seriously limit regeneration options. Regeneration prescriptions therefore should favour species best adapted to prevailing site conditions and that are resistant to future inciting factors. For example, on droughty sites, pines and white oaks can be favoured over red oaks. Both even-aged and uneven-aged methods are potentially useful for regenerating oak stands of advanced age that are predisposed to oak

420

decline or that are already affected by decline. The extent and pattern of crown dieback and mortality within stands and across the landscape will influence the choice of methods together with the oak regeneration process intrinsic to a stand or ecosystem (see Chapter 3, this volume). Clearcutting may be an appropriate choice in stands largely comprised of red oaks and where the current stand regeneration potential is sufficient to meet management objectives, or where enrichment oak planting is feasible (see Chapter 10, this volume). In stands already affected by oak decline, the stump sprouting potential of the overstorey has already been reduced by tree mortality. Expected contributions to future stocking from oak stump sprouts therefore need to be proportionately reduced to account for the number of declineaffected trees (see Fig. 2.25 and also ‘Regeneration models’, Chapter 8, this volume). However, Armillaria infected, live oak trees in decline stands that are harvested by clearcutting may still initially produce sprouts at the expected probabilities as healthy trees of similar species, diameter and age (Lee et al., 2016). Oak sprouts appear to be able to compartmentalize Armillaria infections to allow for normal development. Although, over time (e.g. 50 years) Armillaria may contribute to increased mortality as reproduction develops after clearcutting, especially in red oaks (Lee et  al., 2016). Where advance reproduction is inadequate, application of the shelterwood method may be appropriate. But that option assumes that the shelterwood itself is of adequate density, composition and of sufficiently low susceptibility to decline to meet regeneration objectives (see ‘The shelterwood method’, Chapter 8, this volume). In some situations, uneven-aged management (see Chapter 9, this volume) may also be a suitable method for addressing oak decline problems. It provides options for regular stand entry and removal of predisposed trees or scattered declining trees. When decline-related mortality is moderate and spatially dispersed, the group selection method can be used to remove declining trees and simultaneously regenerate stands in small patches or groups (see ‘The Group Selection Method’, Chapter 9, this volume).

Oak Wilt Oak wilt is a potentially serious disease of oaks in the eastern USA (Fig. 11.1B). A fungus that disrupts the oak’s vascular system and causes leaf wilting

Chapter 11

and tree death causes the disease. Although species in the red oak subgenus are the most vulnerable, the white oaks are not immune. The disease occurs throughout the Central Hardwood Region, the Forest–Prairie Region, parts of the Northern Hardwood Region, and to a limited extent in the Southern Pine–Hardwood Region (Gibbs and French, 1980). The disease is spread through root grafts that connect neighbouring trees, and overland by certain sap-feeding insects such as nitidulid beetles and tree-wounding insects such as oak bark beetles. The oak bark beetles are believed to be the most important vectors of the oak wilt fungus throughout the known range of the disease (Rexrode, 1977). The nitidulid beetles also may be important vectors in some areas, especially where spore-producing mats frequently form on diseased trees (Dorsey et  al., 1953; Gibbs and French, 1980). Red oaks usually die within a year of infection, and often within a few days to 2 weeks after wilting becomes visible (Bruhn et al., 1991). The severity of the disease varies geographically and is greatest in the north-western part of its range. Its rapid expansion in parts of Minnesota and Wisconsin during the last 60 years may be related to relatively new and highly susceptible host populations of northern pin oak (Gibbs and French, 1980). Northern pin oak commonly forms pure or nearly pure stands of coppice origin on sandy outwash plains and associated glacial landforms that are characterized by frequent droughts and low soil fertility. Many of these forests developed after logging and fire destroyed the original, more diverse forest communities of the region. Symptoms and spread Oak wilt is characterized by expanding clusters of dead trees, or epicentres caused by the transmission of the disease through naturally occurring root grafts between trees. These grafts are most common within the same oak species, but they can also occur between different oak species (Gibbs and French, 1980). In northern pin oak stands, the probability of occurrence of root grafts between any two trees is related to their size, the distance between them and soil characteristics (Bruhn et al., 1991; Bruhn and Heyd, 1992). The spread of oak wilt through root grafts is greater in shallower than deeper soils, and greater in sandy than in finer-textured soils (Garin, 1942; Gillespie and True, 1959; Bruhn et  al., 1991).

Managing Forest Health

In Minnesota, more than 95% of diseased oaks are infected via root grafts (Juzwik, 1983). The maximum observed annual rate of spread via root grafts in northern pin oak stands on sandy soils in Michigan was about 40 ft (Bruhn and Heyd, 1992). However, root transmission may be less important in the southern part of the oak wilt range than further north (Gibbs and French, 1980). In infected trees, fungal structures called mats can form on the boles and large branches of living oaks and also on cut logs with attached bark. In the north-western portion of the oak wilt range, these mats are common and typically form in the spring, usually in May and early June, on trees that died from the disease the previous summer. Insects are attracted to the mats, possibly by their fruity odour, and carry the fungal spores to fresh wounds on healthy oaks for up to a mile or more (Gibbs and French, 1980; Juzwik, 1983). Insects that carry the fungal spores from infected to uninfected trees thus create new epicentres. Spores carried to fresh wounds on tree boles and branches initiate infection. Humans often unwittingly spread the fungus by transporting diseased wood with attached bark to unaffected areas. Spring is the most active time of the year for overland spread of oak wilt because it is then that most mats are produced, insect vectors are most abundant, and trees are most susceptible and easily wounded (Bruhn and Heyd, 1992). Treatment and prevention Methods for controlling oak wilt fall into two categories: (i) controlling the expansion of established epicentres via root grafts; and (ii) reducing overland spread by insects. Established methods for controlling epicentre expansion have been developed for northern pin oak in Michigan (Bruhn and Heyd, 1992). Epicentres and individual diseased trees can be located from large-scale (e.g. 1:1250) infrared transparency photographs taken in late July or early August. On film, healthy trees appear bright red; sick trees appear pink; wilted trees appear tan; and leafless trees are black (Bruhn and Heyd, 1992). Once epicentres are located, their expansion can be controlled by constructing ‘root-graft barriers’. The objective of a barrier is to contain the underground spread of the pathogen. Such barriers are typically used in residential areas and parks where their relatively high cost of construction can be justified. They can be created by either of two methods:

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Distance between trees (ft)

(i) soil fumigants; and (ii) deep root-severing trenches dug with a vibratory plough of the type used to lay telephone cable. In urban areas with numerous underground utilities, a soil fumigant such as Vapam can be used to chemically kill trees with interconnected roots (Bruhn and Heyd, 1992). However, in addition to the environmental risks imposed by the use of a toxic chemical, another disadvantage of the fumigant treatment is that it can cause cankers on the lower boles of healthy trees inside the barrier. Although the cankers may not kill trees, they impair their health. Where feasible, the vibratory plough is therefore likely to be the treatment of choice. A plough blade capable of cutting a 5 ft-deep trench is required for effective barrier construction. A strategy for barrier placement was developed from research and experience in northern pin oak stands in Michigan (Bruhn et  al., 1991). The method is based on a model that estimates the probability of oak wilt transmission between a diseased and an apparently healthy tree. Application of the model requires knowing the distance between such pairs of trees and their diameters (dbh). The inter-tree distances for infection are determined from the sum of the paired-tree diameters. These distances have been calculated for 95% and 99% probabilities that an apparently healthy tree will not contract oak wilt from an infected tree within a year of the infected tree’s death (Fig. 11.5). Distances for multiple-stemmed sprout clumps are based on the sum of the diameters of all living stems within the clump. These distances vary with soil type, and are greater in sands than in finer-textured soils (Fig. 11.5). Using the inter-tree distances, effective root-graft barriers can be located and constructed around oak wilt epicentres. Recommendations are to create two concentric barriers, inner and outer, based on the inter-tree distances for 95% and 99% probabilities of disease containment, respectively (Fig. 11.5). The inner barrier is located just outside the distance to the apparently healthy tree based on the 95% probability. To better assure separating root systems of interconnected trees, recommendations are to position the inner barrier close to the border trees within the inner barrier, rather than midway between those trees and the closest trees outside the inner barrier. The outer barrier corresponds to distances associated with the 99% probability. The barrier boundaries then are marked with flagging to guide the plough operator or fumigant applicator.

120

G99%

100

P99% G95%

80

P95%

60 40 20 0

0

10

20

30

40

50

Sum of dbhs (in.) Fig. 11.5.  Relation of the distance between pairs of oaks and the sum of their dbhs for 95% and 99% probabilities that the apparently uninfected member of the pair will not contract oak wilt from the infected member within a year of the latter tree’s death. (Adapted from Bruhn et al., 1991; Bruhn and Heyd, 1992.) Relations are based on a model developed in the Upper Peninsula of Michigan (Northern Hardwood Region) in stands that were predominantly northern pin oak. The study sites occupied Grayling and Pemene soil series, which differ in texture and soil (solum) thickness. The Grayling soil is formed in sands occurring on glacial outwash and lake plains and has a solum thickness of 15–30 inches. The Pemene soil is formed in loamy sand glacial till on ground or end moraines and has a solum thickness of 24–48 inches. The two soils and associated probabilities are labelled G95% and G99% for the Grayling soil and P95% and P99% for the Pemene soil.

The barriers are constructed within 1 or 2 months of establishing inner and outer boundaries. Because oak roots usually do not grow beneath long-established and well-travelled roads, barriers along such roadways may be unnecessary (Bruhn and Heyd, 1992). Barriers should be created before trees in the epicentre are felled. After root-graft barriers are constructed, all oaks inside the inner (95%) barrier are felled as soon as possible; trees between the inner and outer barriers are not cut (Fig. 11.6). An appropriate herbicide should be applied to the stumps of all cut oaks to prevent sprouting and perpetuating living, diseased root systems within the treated area. Infected trees are marked for sanitary treatment to prevent overland spread of the disease. These treatments need to be completed before fungal mats

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99% barrier 95% barrier No trees are cut

Cut all oaks and apply sanitation treatments

Fig. 11.6.  Relation of inner (95%) and outer (99%) root-graft barriers to cutting and sanitation treatments for controlling oak wilt around epicentres of infection. Only trees within the 95% barrier (see Fig. 11.5) are cut. Appropriate sanitation treatments are then applied (see text). (Adapted from Bruhn and Heyd, 1992.)

form the following spring. Sanitary treatments include completely covering the ground on treated sites with plastic to prevent insects from reaching fungal mats. To be effective, the covering should be sealed around its edges. In this enclosed environment, competing fungi will kill the oak wilt fungus (Bruhn and Heyd, 1992). The covering then can be removed at the end of the following summer. Alternatively, the wood from infected trees can be removed to non-vulnerable sites several miles from the infection site. Oak wilt experts familiar with the local distribution of oaks should supervise this option. Infected wood also can be destroyed before mats form. This requires debarking or destruction of infected wood. Wood can be sold to a sawmill or other utilization plant provided that the buyer understands that the wood is diseased and should be utilized before the next spring. However, the oak wilt fungus cannot live on lumber. The mill preferably should be one that is several miles from the nearest vulnerable oak stand. Oaks within the inner barrier not showing disease symptoms also should be felled as soon as possible after barrier establishment. If they are felled before the next spring, fungal mats will not form on them and they can be marked and exempted

Managing Forest Health

from sanitary treatment. Additional details, explanations and precautions on implementing the rootgraft barrier method of oak wilt control are presented in Bruhn and Heyd (1992). Root-graft barriers may be impractical or unnecessary in many silvicultural applications. Precau­ tionary measures nevertheless may be advisable in known wilt-prone areas including those lying outside the extremely susceptible northern pin oak forests of the Lakes States. Such measures include avoiding pruning, stand and sprout-clump thinning, logging and other possible injury-producing activities to oaks from May through to July. Injuries create entryways for spore-carrying insects into the wood of boles and branches, which then can become sites for the establishment of new epicentres. In areas of known oak wilt activity, clump thinning during this period should be avoided because of possible disease transmission through interconnections of the stumps of cut stems with crop stems. Thinning sprout clumps in northern pin oak coppice stands in Wisconsin in late May and early June resulted in a 23% infection rate of residual stems; 19% became infected when thinning was done in mid-June (Kuntz and Drake, 1957). There also is evidence that injecting herbicides into trees already infected with oak wilt can reduce the overland spread of the disease. One study showed that the herbicide cacodylic acid (hyroxydimethylarsine oxide) when pressure injected into the xylem rendered infected oaks unsuitable sites for oak bark beetle breeding and the formation of fungus mats (Rexrode, 1977). The treatment reduced the number of infected trees with oak bark beetles by 75%, and the number of trees producing fungal mats by 61%. However, the method is not effective in containing disease transmission via root grafting. Although oak wilt has caused heavy damage to oak stands in some areas, it has not become a serious pathogen in many other areas where it occurs, possibly because the insect vectors are not efficient transmitters of the disease and the occurrence of mycelial mats is infrequent (Gibbs and French, 1980).

Rapid White Oak Mortality Most of the oak mortality in the Central Hardwood Forest Region is attributed to oak decline and oak wilt that principally affect the red oak group. A relatively new malady known as ‘rapid white oak mortality’ (RWOM) has been identified and mainly

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affects white oaks such as the species white oak, chestnut oak and post oak in the Central Hardwood Forest Region since the early 2000s (Balci et  al., 2010; Nagle et al., 2010; Reed et al., 2017). Symptoms Symptoms associated with RWOM include rapid bronzing of foliage at the beginning of the growing season or during late summer followed by the rapid death of entire tree crowns. Other symptoms or signs most often associated with RWOM include branch dieback, colonization by Biscogniauxia spp. causing Biscogniauxia (formerly hypoxylon) canker, fallen bark around the base of the tree, and wilted leaves attached to trees. At some locations, round insect holes 2–5 mm wide have been reported on affected trees. The white oaks most affected by this phenomenon are on lower slopes, in upland drainages, along ravine bottoms, and in bottomlands where soil water accumulates and is ordinarily abundant. Symptoms appear during or following acute drought after several consecutive years of above-average rainfall. The weather and soil moisture patterns along with the patchy nature of the symptoms within affected stands suggests the involvement of a pathogenic root rot such as that caused by the oomycete Phytophthora cinnamomi. This and other species of Phytophthora have been shown to cause substantial root damage in infected trees. Spread The presence of P. cinnamomi is generally greater in seasonally wet or poorly drained soils. There is some evidence supporting the current hypothesis that periods of excessive soil moisture favour the growth of P. cinnamomi and other species of this genus which feed on the fine roots of oaks and other tree species. The ill effects of the root damage and loss of the fine roots caused by the root feeding is not problematic for infected trees as long as there is sufficient soil moisture. However, the loss of the fine roots becomes a significant problem during drought because of the tree’s inability to take up sufficient soil water due to the compromised root system. Treatment and prevention The management actions required to mitigate or prevent RWOM remain unknown. Because RWOM

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is a relatively new syndrome, it has not occurred extensively nor has it been studied comprehensively. An additional complication is that there appear to be few early symptoms indicating that a stand is at risk of developing RWOM. Moreover, RWOM appears to affect tree roots, and these cannot be easily inspected. Consequently, there is insufficient information from which to draw conclusions at this time about prevention or mitigation through thinning practices and other management practices intended to promote vigorous growth. Research is ongoing and more information about this syndrome will become available in ensuing years.

Sudden Oak Death Sudden oak death was first reported in California in 1995 and was subsequently determined to be caused by Phytophthora ramorum, a previously unknown oomycete, a fungus-like water mould. Thus far, outbreaks of sudden oak death have been confined to coastal areas of California and southwest Oregon (Fig. 11.1C). Incidents of sudden oak death in native Oregon forests are relatively small compared with central and northern California where the pathogen is found at numerous locations within 50 miles of the Pacific coast from central to northern California. Some areas in California are heavily infested, and hundreds of thousands of oaks have been killed, including coast live oaks, California black oaks, canyon live oaks and Shreve oaks; tanoaks also are extremely susceptible to the pathogen. P. ramorum infects more than 100 other host plant species, usually causing shoot dieback or leaf blight rather than mortality. Douglas-fir, coast redwood and Pacific yew are widespread tree hosts (O’Brien et  al., 2002; Goheen et  al., 2006; Lee et al., 2011; Karel and Man, 2017). Symptoms Affected oaks are characterized by cankers on their boles. Cankers often have a red or black ooze that ‘bleeds’ from the cankers and stains the bark. Black lines called ‘zone lines’ are often found along the margins of the dead bark tissue, which can be seen by cutting away the cankers on infected oak boles. However, these symptoms also can be caused by other Phytopthora species and laboratory tests are required to confirm the identity of P. ramorum (Frankel, 2002; O’Brien et  al., 2002; Goheen et al., 2006).

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Infected oaks may survive 1 to several years, but cankers eventually girdle the bole followed by rapid crown dieback. Within a few weeks leaves turn from green to pale yellow to brown (O’Brien et al., 2002). Oak mortality may be accelerated by secondary infections from ambrosia beetles and sapwood decay fungi. Non-oak host species in the vicinity also may show symptoms ranging from cankers and mortality (tanoak) to shoot dieback or leaf blight (Goheen et al., 2006). Sudden oak death has not been observed in native eastern oak forests, but due to the potential for transport of P. ramorum via nursery stock and the potential for widespread damage and mortality among oaks and other host species, suspected infections of P. ramorum must be taken seriously. Eastern oak disorders that resemble sudden oak death include oak wilt, oak decline and red oak borer infestations (O’Brien et al., 2002). Oak wilt (see the earlier section ‘Oak Wilt’) can rapidly kill oaks but affected trees lack the cankers, the associated ‘bleeding’ and the zone lines in the necrotic bark tissues common to sudden oak death. The leaves of red oaks afflicted with oak wilt turn brown on the edges while the veins remain green, and leaves are rapidly shed. Conversely, living oaks affected by sudden oak death often retain dead leaves as the leaf veins turn yellow and then brown (O’Brien et al., 2002). Oak decline (see ealier section ‘Oak Decline’) is a slower acting disorder associated with physiologically mature trees. During oak decline, crown dieback occurs over several years beginning with the outer extremities of crowns and progressing inwards. Stem-boring insects also can cause oozing and bark staining, but cankers with zone lines are lacking. Red oak borers are often associated with red and white oaks affected by oak decline, and may produce moist, dark stains at the site of attack. Red oak borer damage can be distinguished from sudden oak death by the frass-packed insect burrows beneath the bark, the lack of cankers and the lack of zone lines beneath dead bark (O’Brien et al., 2002). Spread P. ramorum spores are spread by wind, wind-driven rain and rain splash. Spores can move among tree canopies, from canopies to tree boles or shrubs, and from understorey plants to overstorey trees.

Managing Forest Health

In California and Oregon, half of newly infected trees occur within about 300 ft of a previously infected tree. Although oaks are highly susceptible to mortality, they are not prolific spore producers. Prolific spore producers such as tanoak, California bay laurel and coast redwood appear to be important in sustaining the pathogen. California bay laurel acts as an advance host that facilitates introduction of P. ramorum to oak forests and woodlands (Rank et al., 2008). Long-distance pathogen transport by humans and animals is of particular concern. Spores in the soil near infected trees can be transported by feet and by vehicle tyres (Goheen et al., 2006). Moreover, P. ramorum is easily transported via rhododendrons, viburnums and other kinds of nursery stock. The pathogen has been isolated from plant nurseries in 24 US states (Oak et  al., 2008a). To date, eradication of infected nursery stock has been effective in preventing the further geographic extension of the disease. Testing indicates that many red oak species are susceptible to sudden oak death and that northern red oak and pin oak are highly susceptible (O’Brien et  al., 2002). Consequently, the potential for outbreaks of sudden oak death in the eastern USA is of great concern. The large volume of oak timber that would be at risk should the disease spread expresses the potential impact of the disease. P. ramorum also occurs in Europe where it is widely dispersed by the nursery trade. The native European oaks (which are exclusively white oaks) are unaffected, but red oaks introduced from North America have been infected (Goheen et al., 2006). Treatment and prevention Monitoring for early detection is essential and in place via coordinated state and national programmes. In addition to aerial and ground surveys for vegetation damage, P. ramorum spores can be detected in streams by floating bags of rhododendron leaves that show symptoms of infection if spores are present (Murphy et al., 2008; Oak et al., 2008b). Treatment of infected nursery stock consists of burning and burial of affected stock and the nearby stock of any susceptible species. Additional quarantine and testing of remaining nursery stock is required as well as monitoring surrounding native plant populations (Goheen et  al., 2008).

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Infestations of native forests can be treated by cutting, piling and burning infected trees and other host plants within 100 ft or more of infected plants. Herbicides can be used to eliminate sprouting from cut stumps. Tree species that are not susceptible to the pathogen can be retained. Subsequent monitoring at and around the site is necessary to evaluate the efficacy of treatments (Goheen et al., 2008; Lee et al., 2011). Sanitation is important for crews working at sites infested with P. ramorum. Mud and plant debris should be cleaned from vehicles and equipment. Equipment, personal gear and boots should be sanitized with a chlorine bleach solution before leaving the area. Vehicles should be confined to dry surfaces or pavement (Goheen et  al., 2008; Lee et al., 2011). Due to the relatively recent onset of sudden oak death and its demonstrated capacity for serious, widespread ecological and economic consequences (Frankel et al., 2008), much research and monitoring is ongoing and the latest information can be found online.

Deer Herbivory by white-tailed deer (Odocoileus virginianus) is considered to be a forest health issue because of the impact that browsing has on forest regeneration (Russell et  al., 2017). Consequently, browsing by deer is one of the most important barriers to successful oak regeneration where deer are abundant. Deer feed on a variety of forbs, shrubs, grasses and trees, including foliage and branches of oak trees and seedlings. Although forbs are considered preferred browse, deer will eat the leaves and branches of trees and shrubs when the availability of preferred species is limited due to seasonality or to high deer population densities that limit food availability. Deer also feed on acorns, thus limiting the number available for establishing oak seedlings. Browse damage can easily be detected by the appearance on the stem. Deer grasp, twist and pull the shoots that are being fed upon. This causes stems to have a slightly shredded appearance. This is in contrast to the feeding habit of rabbits that leave a clean-cut and sharply angled stem. During autumn, deer also rub the velvet from their antlers along the stems of large seedlings, saplings and small trees. This rubbing damages the tree’s cambium and often causes the shoot to die back and resprout from around the root collar.

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Planted bareroot seedlings are particularly vulnerable to deer browsing because they have to overcome stressors associated with establishment including: (i) potential imbalances of the root-toshoot ratio after planting; (ii) improper planting practices; (iii) stem damage by rodents, rabbits, squirrels and other small mammals; and (iv) competition by other plants (see Chapter 10, this volume). Thus browsing by deer is an additional stress that may reduce the survival or growth of oak seedlings. However, even vigorous and healthy seedlings are vulnerable to browsing because the repeated clipping prevents the terminal shoot from growing beyond the reach of deer. In fact, clipping of the terminal shoot stimulates the production of a large number of succulent new shoots that sprout from near the root collar. This sprouting in turn enhances the palatability of the seedling and increases the likelihood of future browsing. Oak seedlings may be vulnerable to browsing because of their slow juvenile shoot growth which leaves the terminal shoot at risk of clipping for several years until the stem grows beyond the reach of browsing deer. This may also exacerbate oak regeneration problems on mesic sites where oaks are competing with faster-growing tree species that can quickly grow beyond the reach of deer. Deer population densities of 15/square mile have been shown to substantially reduce tree seedling density in all forest types of the northern USA, including oak forests (Russell et  al., 2017). Others have reported that deer densities of 10/square mile are detrimental to trees (Alverson et al., 1988) although densities as high as 30/square mile are not as damaging to trees when alternative foods are available. Across the northern USA, the probability of seedling browsing exceeds 50% in nearly every state except in isolated areas in northern Minnesota, the Missouri Ozarks and eastern Maine (Plate 11). These browsing probabilities are closely related to the deer population which generally exceeds the 10/square mile threshold throughout this region. Although the deer population can be controlled through hunting, many cultural constraints limit the ability of regulatory agencies to alter hunting seasons or harvest limits to sufficiently reduce the deer density below critical population density thresholds associated with higher levels of browsing damage. Where browsing is adversely affecting regeneration success, protecting seedlings from browsing is critical. Throughout much of the Allegheny Plateau of Pennsylvania where deer populations exceed

Chapter 11

15/square mile, fencing is used to enhance oak regeneration success. These fences are constructed with woven or welded wire and are 8 ft tall. Because they are used to surround the entire perimeter of the regenerating stand, thousands of feet of fencing may be required. This imposes a considerable annual maintenance cost to check for and repair fence that is damaged by limbs and branches from nearby trees that routinely fall during the year. Despite the expense, fencing has been shown to be very effective for regenerating oaks where deer browsing has proven to be a significant barrier. A study by Long et  al. (2012) showed that exclosure fencing doubled the shoot length of 5-year-old northern red oak seedlings compared with those grown without fencing. In the same region, Miller et al. (2017) studied the 10-year fate of tagged northern red oak seedlings and found that fencing increased the number of surviving seedlings by more than 1.5 times, regardless of other silvicultural practices to make the overstorey and understorey more favourable for oak regeneration. For oak plantings with bareroot or container stock where fencing is not economical or logistically feasible, tree shelters or cages also can be used to protect individual trees from browse as discussed in detail in Chapter 10 (this volume).

Note 1

 Current information on the gypsy moth is available from the USDA Forest Service gypsy moth website at https://www.nrs.fs.fed.us/disturbance/invasive_species/ gm/ (accessed 1 July 2018).

References Alverson, W.S., Waller, D.M. and Solheim, S.L. (1988) Forests too deer: edge effects in northern Wisconsin. Conservation Biology 2, 348–358. https://doi. org/10.1111/j.1523-1739.1988.tb00199.x Balci, Y., Long, R.P., Mansfield, M., Balser, D. and MacDonald, W.L. (2010) Involvement of Phytophthora species in white oak (Quercus alba) decline in Ohio. Forest Pathology 40, 430–442. https://doi.org/ 10.1111/j.1439-0329.2009.00617.x Bess, H.A., Spurr, S.H. and Littlefield, E.W. (1947) Forest site conditions and the gypsy moth. Harvard Forest Bulletin 22. Harvard University, Harvard Forest, Petersham, Massachusetts. Bruhn, J.N. and Heyd, R.L. (1992) Biology and control of oak wilt in Michigan red oak stands. Northern Journal

Managing Forest Health

of Applied Forestry 9, 47–51. https://doi.org/10.1093/ njaf/9.2.47 Bruhn, J.N., Pickens, J.B. and Stanfield, D.B. (1991) Probit analysis of oak wilt transmission through root grafts in red oak stands. Forest Science 37, 28–44. https://doi.org/10.1093/forestscience/37.1.28 Bruhn, J.N., Wetteroff, J.J., Mihail, J.D., Kabrick, J. and Pickens, J.B. (2000) Distribution of Armillaria species in upland Ozark Mountain forests with respect to site, overstory species composition and oak decline. Forest Pathology 30, 43–60. https://doi.org/10.1046/ j.1439-0329.2000.00185.x Campbell, R.W. (1981) Population dynamics. In: Doane, C.C. and McManus, M.L. (eds) The Gypsy Moth: Research Toward Integrated Pest Management. USDA Forest Service Technical Bulletin 1584. USDA Forest Service, Washington, DC, pp. 65–214. Campbell, R.W. and Sloan, R.J. (1977) Natural regulation of innocuous gypsy moth populations. Environmental Entomology 6, 315–322. https://doi.org/10.1093/ ee/6.2.315 Campbell, R.W. and Valentine, H.T. (1972) Tree condition and mortality following defoliation by the gypsy moth. USDA Forest Service Research Paper NE-236. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/23662 (accessed 1 July 2018). Campbell, R.W., Hubbard, D.L. and Sloan, R.J. (1975a) Patterns of gypsy moth occurrence within a sparse and numerically stable population. Environmental Entomology 4, 535–542. https://doi.org/10.1093/ ee/4.4.535 Campbell, R.W., Hubbard, D.L. and Sloan, R.J. (1975b) Location of gypsy moth pupae and subsequent pupal survival in sparse, stable populations. Environmental Entomology 4, 597–600. https://doi.org/10.1093/ ee/4.4.597 Campbell, R.W., Miller, M.G., Duda, E.J., Biazak, C.E. and Sloan, R.J. (1976) Man’s activities and subsequent gypsy moth egg-mass density along with forest edge. Environmental Entomology 5, 273–276. https:// doi.org/10.1093/ee/5.2.273 Clatterbuck, W.K. and Kauffman, B.W. (2006) Managing oak decline. University of Kentucky Cooperative Extension Service Publication FOR-099. University of Kentucky, Lexington, Kentucky. Crookston, N. and Dixon, G. (2005) The forest vegetation simulator: a review of its structure, content, and applications. Computers and Electronics in Agriculture 49, 60–80. https://doi.org/10.1016/j.compag.2005.02.003 Crow, G.R. and Hicks, R.R., Jr (1990) Predicting mortality in mixed oak stands following spring insect defoliation. Forest Science 36, 831–841. https://doi.org/ 10.1093/forestscience/36.3.831 Dorsey, C.K., Jewell, F.F., Leach, J.G. and True, R.P. (1953) Experimental transmission of oak wilt by four

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species of Nitidulidae. Plant Disease Reporter 37, 419–420. Dwyer, J.P., Cutter, B.E. and Wetteroff, J.J. (1995) A dendrochronological study of black and scarlet oak decline in the Missouri Ozarks. Forest Ecology and Management 75, 60–75. https://doi.org/10.1016/03781127(95)03537-K Dwyer, J.P., Kabrick, J.M. and Wetteroff, J. (2007) Do improvement harvests mitigate oak decline in Missouri Ozark forests? Northern Journal of Applied Forestry 24(2), 123–128. Available at: https://www.fs.usda.gov/ treesearch/pubs/17264 (accessed 1 July 2018). Fan, Z., Kabrick, J.M. and Shifley, S.R. (2006) Classification and regression tree based survival analysis in oakdominated forests of Missouri’s Ozark highlands. Canadian Journal of Forest Research 36, 1740–1748. https://doi.org/10.1139/x06-068 Fan, Z., Kabrick, J.M., Spetich, M.A., Shifley, S.R. and Jensen, R.G. (2008) Oak mortality associated with crown dieback and oak borer attack in the Ozark Highlands. Forest Ecology and Management 255, 2297–2305.https://doi.org/10.1016/j.foreco.2007.12.041 Frankel, S. (2002) Sudden oak death caused by a new species, Phytophthora ramorum. USDA Forest Service Pest Alert NA-PR-06-01. USDA Forest Service, Washington, DC. Available at: https://www. invasive.org/publications/usfs/SODW.html (accessed 1 July 2018). Frankel, S.J., Kliejunas, J.T. and Palmieri, K.M. (tech. coords) (2008) Proceedings of the sudden oak death third science symposium. USDA Forest Service General Technical Report PSW-214. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-214 Gansner, D.A. and Herrick, O.W. (1984) Guides for estimating forest stand losses to gypsy moth. Northern Journal of Applied Forestry 1, 21–23. Available at: https://www.fs.usda.gov/treesearch/pubs/21604 (accessed 1 July 2018). Gansner, D.A. and Herrick, O.W. (1985) Host preferences of gypsy moth on a new frontier of infestation. USDA Forest Service Research Note NE-330. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/11055 (accessed 1 July 2018). Garges, L.D., Laboskey, P., Blankenhorn, P.R. and Rishel, L.E. (1984) Lumber recovery from gypsy moth-killed red and white oak trees. Forest Products Journal 34, 45–50. Garin, G.I. (1942) Distribution of roots of certain tree species in two Connecticut soils. Connecticut Agriculture Experiment Station Bulletin 454. University of Con­ necticut, Connecticut Agricultural Experiment Station, New Haven, Connecticut. https://doi.org/10.5962/bhl. title.51065

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Gibbs, J.N. and French, D.W. (1980) The transmission of oak wilt. USDA Forest Service Research Paper NC185. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10706 (accessed 1 July 2018). Gillespie, W.H. and True, R.P. (1959) Three factors which influence the local spread of oak wilt in five northeastern counties of West Virginia. Plant Disease Reporter 43, 588–593. Goheen, E.M., Hansen, E., Kanaskie, A., Osterbauer, N., Parke, J., Pscheidt, J. and Chastagner, G. (2006) Sudden oak death and Phytophthora ramorum: a guide for forest managers, Christmas tree growers, and forest-tree nursery operators in Oregon and Washington. Oregon State University Extension Service EM8877. Oregon State University, Corvallis, Oregon. Goheen, E.M., Hansen, E., Kanaskie, A., Sutton, W. and Reeser, P. (2008) Vegetation response following Phytophthora ramorum eradication treatments in southwest Oregon forests. USDA Forest Service General Technical Report PSW-214. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 301–303. Available at: https://www.fs.usda.gov/treesearch/ pubs/all/29915 (accessed 1 July 2018). Gottschalk, K.W. (1991) Gypsy moth: impacts and silvicultural options. In: Proceedings of The Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 159–169. Gottschalk, K.W. (1993) Silvicultural guidelines for forest stands threatened by the gypsy moth. USDA Forest Service General Technical Report NE-171. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4258 (accessed 1 July 2018). Gottschalk, K.W. and Courter, A.W. (2007) The gypsy moth even monitor for FVS: a tool for forest and pest managers. USDA Forest Service Northern Research Station NRS-P-10. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, p. 45. Available at: https://www.fs.usda.gov/treesearch/ pubs/12470 (accessed 1 July 2018). Gottschalk, K.W. and MacFarlane, W.R. (1993) Photographic guide to crown condition of oaks: use for gypsy moth silvicultural treatments. USDA Forest Service General Technical Report NE-168. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs. usda.gov/treesearch/pubs/4255 (accessed 1 July 2018). Gottschalk, K.W., Gansner, D.A. and Twery, M.J. (1989) Impacts of gypsy moth on oak timber resources or will there be oak in 2001? In: Proceedings of 17th Annual Hardwood Symposium of the Hardwood Research Council. Hardwood Research Council, Memphis, Tennessee.

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Gypsy Moth Slow the Spread Foundation (2008) Slow the Spread of Gypsy Moth. Available at: http://www. slowthespread.org (accessed 10 November 2008). Heitzman, E., Grell, A., Spetich, M. and Starkey, D. (2007) Changes in forest structure associated with oak decline in severely impacted areas of northern Arkansas. Southern Journal of Applied Forestry 31(1), 17–22. Available at: https://www.fs.usda.gov/ treesearch/pubs/26439 (accessed 1 July 2018). Herrick, W.O. (1982) Hazard rating forest trees threatened with gypsy moth invasion. In: Proceedings, Coping with the Gypsy Moth. The Pennsylvania State University, University Park, Pennsylvania, pp. 38–42. Herrick, O.W. and Gansner, D.A. (1986) Rating forest stands for gypsy moth defoliation. USDA Forest Service Research Paper NE-583. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/21753 (accessed 1 July 2018). Herrick, O.W. and Gansner, D.A. (1987) Mortality risks for forest trees threatened with gypsy moth infestation. USDA Forest Service Research Note NE-338. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/21596 (accessed 1 July 2018). Jenkins, M.A. and Pallardy, S.G. (1995) The influence of drought on red oak group species growth and mortality in the Missouri Ozarks. Canadian Journal of Forest Research 25, 1119–1127. https://doi.org/10.1139/ x95-124 Juzwik, J. (1983) Factors affecting overland transmission of Ceratocystis fagacearum in Minnesota. PhD dissertation, University of Minnesota, St Paul, Minnesota. Kabrick, J.M., Dey, D.C., Jensen, R.G. and Wallendorf, M. (2008) The role of environmental factors in oak decline and mortality in the Ozark Highlands. Forest Ecology and Management 255, 1409–1417. http:// doi.org/10.1016/j.foreco.2007.10.054 Karel, T.H. and Man, G. (comps) (2017) Major forest insect and disease conditions in the United States, 2015. USDA Forest Service Forest Health Protection Report FS-1093. USDA Forest Service, Washington, DC. Available at: https://www.fs.fed.us/foresthealth/ publications/ConditionsReport_2015.pdf (accessed 11 July 2018). Kegg, J.D. (1974) Quantitative classification of forest stands as related to their vulnerability to loss following initial outbreak of the gypsy moth, Porthetria dispar (L.), with equations for forecasting basal area losses. MSc. thesis, Rutgers University, New Brunswick, New Jersey. Kromroy, K.W., Juzwik, J., Castillo, P. and Hansen, M.H. (2008) Using Forest Service forest inventory and analysis data to estimate regional oak decline and

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oak mortality. Northern Journal of Applied Forestry 25(1), 17–24. Available at: https://www.fs.usda.gov/ treesearch/pubs/all/18484 (accessed 1 July 2018). Kuntz, J.E. and Drake, C.R. (1957) Tree wounds and long-distance spread of oak wilt. Phytopathology 47, 22. Lee, C., Valachnvic, Y. and Garbelotto, M. (2011) Protecting trees from sudden oak death before infection. University of California Agriculture and Natural Resources Publication 8426. University of California Agriculture and Natural Resources Communication Services, Richmond, California, 14 pp. Available at: http://anrcatalog.ucanr.edu/pdf/8426.pdf (accessed 1 July 2018). Lee, C.A., Dey, D.C. and Muzika, R-M. (2016) Oak stumpsprout vigor and Amillaria infection after clearcutting in southeastern Missouri, USA. Forest Ecology and Management 374, 211–219. http://dx.doi.org/10.1016/ j.foreco.2016.05.014 Leonard, D.E. (1981) Bioecology of the gypsy moth. In: Doane, C.C. and McManus, M.L. (eds) The Gypsy Moth: Research Toward Integrated Pest Management. USDA Forest Service Technical Bulletin 1584. USDA Forest Service, Washington, DC, pp. 1–29. Lewis, F.G. (1981) Gypsy moth nucleopolyhedrosis virus: introduction. In: Doane, C.C. and McManus, M.L. (eds) The Gypsy Moth: Research Toward Integrated Pest Management. USDA Forest Service Technical Bulletin 1584. USDA Forest Service, Washington, DC, pp. 503–514. Liebhold, A.M., Gottschalk, K.W., Muzika, R., Montgomery, M.E., Young, R., Day, K. and Kelley, B. (1995) Suitability of North American tree species to the gypsy moth: a summary of field and laboratory tests. USDA Forest Service General Technical Report NE-211. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 4327 (accessed 1 July 2018). Long, R.P., Brose, P.H. and Horsley, S.B. (2012) Responses of northern red oak seedlings to lime and deer exclosure fencing in Pennsylvania. Canadian Journal of Forest Research 42, 698–709. https://doi. org/10.1139/x2012-025 Manion, P.D. (1991) Tree Disease Concepts. PrenticeHall, Englewood Cliffs, New Jersey. Manion, P.D. and Lachance, D. (eds) (1992) Forest Decline Concepts. APS Press, St Paul, Minnesota. Mayo, J.H., Straka, T.J. and Leonard, D.S. (2003) The cost of slowing the spread of the gypsy moth (Lepidoptera: Lymantriidae). Journal of Economic Entomology 96, 1448–1454. https://doi.org/10.1093/ jee/96.5.1448 McManus, M.L. and Houston, D.R. (1979) The homeowner and the gypsy moth: guidelines for control. USDA Home and Garden Bulletin 227. United States Department of Agriculture (USDA), Washington, DC.

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Available at: https://naldc.nal.usda.gov/download/ CAT87213646/PDF (accessed 1 July 2018). McWilliams, W.A., Westfall, J.A., Brose, P.H., Dey, D.C., D’Amato, A., Dickinson, Y.L., Fajvan, M.A., Kenefic, L.S., Kern, C.C., Laustsen, K.M., Lehman, S.L., Morin, R.S., Ristau, T.E., Royo, A.A., Stoltman, A. and Stout, S.L. (2018) Subcontinental-scale patterns of large-ungulate herbivory and synoptic review of expected restoration management implications for forests of the Midwest and Northeast. USDA Forest Service General Technical Report NRS-182. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. Miller, G.W., Brose, P.H. and Gottschalk, K.W. (2017) Advanced oak seedling development as influenced by shelterwood treatments, competition control, deer fencing, and prescribed fire. Journal of Forestry 115, 179–189. https://doi.org/10.5849/jof.16-002 Millers, I., Shriner, D.S. and Rizzo, D. (1989) History of hardwood decline in the eastern United States. USDA Forest Service General Technical Report NE-126. USDA Forest Service, Northeastern Forest Experiment Station, Broomall, Pennsylvania, 75 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/4177 (accessed 1 July 2018). Murphy, S.K., Lee, C., Valachovic, Y., Bienapfl, J., Mark, W., Jirka, A., Owen, D.R., Smith, T.F. and Rizzo, D.M. (2008) Monitoring Phytophthora ramorum distribution in streams within California watersheds. USDA Forest Service General Technical Report PSW-214. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 409–411. Available at: https://www.fs.usda.gov/treesearch/ pubs/30228 (accessed 1 July 2018). Nagle, A.M., Long, R.P., Madden, L.V. and Bonello, P. (2010) Association of Phytophthora cinnamomi with white oak decline in southern Ohio. Plant Disease 94, 1026–1034. https://doi.org/10.1094/PDIS-94-8-1026 Naidoo, R. and Lechowicz, M. (2001) Effects of gypsy moth on radial growth of deciduous trees. Forest Science 47, 338–348. https://doi.org/10.1093/forestscience/ 47.3.338 Oak, S.W. and Starkey, D.A. (1991) Measuring physiological maturity from site quality and age: observations from oak decline areas in southern upland forests. In: Proceedings of Southwide Forest Disease Workshop. Duke University, Durham, North Carolina. Oak, S.W., Steinman, J.R., Starkey, D.A. and Yockey, E.K. (2004) Assessing oak decline incidence and distribution in the southern U.S. using forest inventory and analysis data. USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 236–242. Available at: https://www.fs.usda.gov/ treesearch/pubs/6552 (accessed 1 July 2018). Oak, S.W., Elledge, A.H., Yockey, E.K., Smith, W.D. and Tkacz, B.M. (2008a) Phytophthora ramorum

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early detection surveys for forests in the United States, 2003–2006. USDA Forest Service General Technical Report PSW-GTR-214. USDA Forest Service, Pacific Southwest Research Station, Albany California, pp. 413–416. Available at: https://www. fs.usda.gov/treesearch/pubs/all/30229 (accessed 1 July 2018). Oak, S.W., Hwang, J., Jeffers, S.N. and Tkacz, B.M. (2008b) 2006 Pilot Survey for Phytophthora ramorum in forest streams in the USA. USDA Forest Service General Technical Report PSW-214. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 59–64. Available at: https://www.fs.usda.gov/treesearch/ pubs/all/29810 (accessed 1 July 2018). O’Brien, J.G., Mielke, M.E., Oak, S. and Moltzan, B. (2002) Sudden oak death. USDA Forest Service Pest Alert NA-PR-02-02. USDA Forest Service, Northeastern Area State and Private Forestry, Newtown Square, Pennsylvania. Available at: https://www.fs. usda.gov/naspf/sites/default/files/publications/06_ na-pr-02-02_pest_alert_sudden_oak_death_eastern_ 508c.pdf (accessed 1 July 2018). Onken, A. (1995) Gypsy moth nucleopolyhedrosis virus (NPV) literature review. Gypsy Moth News 39, 7–10. Podgwaite, J.D., Reardon, R.C., Kolodny-Hirsch, D.M. and Walton, G.S. (1991) Efficacy of ground application of the gypsy moth (Lepidoptera: Lymantriidae) nucleopolyhedrosis virus product, gypchek. Journal of Economic Entomology 84, 440–444. https://doi. org/10.1093/jee/84.2.440 Rank, N., Cushman, H., Anacker, B., Rizzo, D. and Meentemeyer, R. (2008) Influence of oak woodland composition and structure on infection by Phytophthora ramorum. USDA Forest Service General Technical Report PSW-214. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 219–220. Available at: https:// www.fs.usda.gov/treesearch/pubs/all/29896 (accessed 1 July 2018). Reardon, R.C. and Hajek, A.E. (1993) Entomophaga maimaiga in North America: a review. USDA Forest Service Report NA-TP-15-93. USDA Forest Service, Northeastern Area State and Private Forestry, Newtown Square, Pennsylvania. Reed, S.E., English, J.T., Muzika, R.M, Kabrick, J.M. and Wright, S. (2017) Characteristics of sites and trees affected by rapid white oak mortality as reported by forestry professionals in Missouri. USDA Forest Service General Technical Report NRS-P-167. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 240–247. Available at: https://www.fs.usda.gov/treesearch/pubs/all/53780 (accessed 1 July 2018). Rexrode, C.O. (1977) Cacodylic acid reduces the spread of oak wilt. Plant Disease Reporter 61, 972–975.

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Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA Forest Service Agriculture Handbook 355. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Russell, M.B., Woodall, C.W., Potter, K.M., Walters, B.F., Domke, G.M. and Oswalt, C.M. (2017) Interactions between white-tailed deer density and the composition of forest understories in the northern United States. Forest Ecology and Management 384, 26–33. https://doi.org/10.1016/j.foreco.2016.10.038 Schwingle, B.W., Juzwik, J., Eggers, J. and Moltzan, B. (2007) Phytophthora species in soils associated with declining and nondeclining oaks in Missouri forests. Plant Disease 91(5), 633. https://doi.org/10.1094/ PDIS-91-5-0633A Shifley, S.R., Fan, Z., Kabrick, J.M. and Jensen, R.G. (2006) Oak mortality risk factors and mortality estimation. Forest Ecology and Management 229, 16–26. https://doi.org/10.1016/j.foreco.2006.03.033 Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, P.M.S. (1997) The Practice of Silviculture: Applied Forest Ecology, 9th edn. Wiley, New York. Spetich, M.A. (2004) Forest dynamics at the epicenter of an oak decline event in the Boston Mountains, Arkansas, year one. USDA Forest Service General Technical Report NE-316. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 21–26. Available at: https:// www.fs.usda.gov/treesearch/pubs/22683 (accessed 1 July 2018). Spetich, M.A. (2006) Early changes in physical tree characteristics during an oak decline event in the Ozark Highlands. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 424–427. Available at: https://www.fs.usda.gov/ treesearch/pubs/23432 (accessed 1 July 2018). Starkey, D.A., Oliveria, F., Mangini, A. and Mielke, M. (2004) Oak decline and red oak borer in the Interior Highlands of Arkansas and Missouri: natural phenomena, severe occurrences. USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 217–222. Available at: https://www.fs. usda.gov/treesearch/pubs/6547 (accessed 1 July 2018). Stringer, J.W., Kimmerer, T.W., Overstreet, J.C. and Dunn, J.P. (1989) Oak mortality in eastern Kentucky. Southern Journal of Applied Forestry 13(2), 86–91. https://doi.org/10.1093/sjaf/13.2.86 Thorpe, K., Tcheslavskaia, K., Tobin, P., Blackburn, L., Leonard, D. and Roberts, E. (2007) Persistent effects of aerial applications of disparlure on gypsy moth: trap catch and mating success. Entomologia

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Experimentalis et Applicata 125, 223–229. https:// doi.org/10.1111/j.1570-7458.2007.00613.x Tobin, P. and Blackburn, L. (2007) Slow the Spread: a national program to manage the gypsy moth. USDA Forest Service Technical Report NRS-6. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NRS-GTR-6 Tobin, P., Sharov, A., Liebhold, A., Leonard, D., Roberts, E. and Learn, M. (2004) Management of the gypsy moth through a decision algorithm under the STS Project. American Entomologist 50, 200–209. https:// doi.org/10.1093/ae/50.4.200 Tobin, P., Thorpe, K. and Blackburn, L. (2007) Multi-year evaluation of mating disruption treatments against gypsy moth. USDA Forest Service Technical Report NRS-P-10. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/12541 (accessed 1 July 2018). Twery, M.J. (1987) Changes in vertical distribution of xylem production in hardwoods defoliated by gypsy moth. PhD dissertation, Yale University, New Haven, Connecticut. Twery, M.J. (1991) Effects of defoliation by gypsy moth. USDA Forest Service General Technical Report NE-146. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 27–39. Available at: https://www.fs.usda.gov/ treesearch/pubs/4206 (accessed 1 July 2018). USDA Forest Service (2018) Gypsy Moth Digest. Available at: https://www.fs.usda.gov/naspf/programs/ forest-health-protection/gypsy-moth-digest (accessed 1 July 2018). USDA Forest Service (no date) Alien Pest Explorer. USDA Forest Service, Northern Research Station and Forest Health Protection. Available at: http:// foresthealth.fs.usda.gov/portal/Flex/APE (accessed 12 July 2018). Valentine, H.T. and Campbell, R.W. (1975) A simulation model of gypsy moth forest interaction. Forest Science 21, 233–238. https://doi.org/10.1093/forestscience/ 21.3.233 Voelker, S.L., Muzika, R. and Guyette, R.P. (2008) Individual tree and stand level influences on the growth, vigor, and decline of red oaks in the Ozarks. Forest Science 54, 8–20. https://doi.org/10.1093/ forestscience/54.1.8 Wargo, P.M., Houston, D.R. and LaMadeleine, L.A. (1983) Oak decline. USDA Forest Service Forest Insect and Disease Leaflet 165. USDA Forest Service, Washington, DC. Yahner, R.H. and Smith, H.R. (1991) Small mammal abundance and habitat relationships on deciduous forested sites with different susceptibility to gypsy moth defoliation. Environmental Management 15, 113–120. https://doi.org/10.1007/BF02393842

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12

Silvicultural Methods for Oak Savannahs and Woodlands Introduction

Oak savannahs and woodlands are plant communities that are ecologically transitional between prairies and forests. They thus share characteristics of both. In tree cover, they are typically (but not always) more open than forests. This open character is maintained by frequent fire and certain other disturbances that produce attributes in species composition and structure different from forests. Whereas savannahs, per se, are transitional from prairies and possess an understorey dominated by prairie grasses and forbs, woodlands, per se, are transitional to forests and include a sparse midstorey together with a herbaceous ground layer rich in grasses and forbs. In the eastern USA and in the absence of the disturbances that maintain them, the continuum usually culminates in closed-canopy oak forest through plant succession except on extremely dry sites. However, in the western states, a dry climate may preclude their transition to forest. Silvicultural practices for preserving, restoring and maintaining oak savannahs and woodlands traditionally have not been considered in oak silviculture. Although once common features of the North American landscape, oak savannahs and woodlands have largely disappeared. There nevertheless is growing interest in restoring them because of their contribution to landscape diversity and ecological resilience. They are well adapted to disturbances such as drought, fire, diseases, pathogens and pests, and thus may be well positioned for an uncertain future climate (see Chapter 14, this volume). Savannahs and woodlands also provide habitat for many species of mammals, reptiles, insects and other animals. They are aesthetically pleasing, can exist in rural and urban environments, and may even be adaptable to management with domestic livestock. For many ecologists and land managers, the herbaceous plants associated with savannahs and woodlands may be of greater interest than their

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woody plants. However, trees and shrubs play a major role in controlling sunlight reaching the herbaceous layer, which includes numerous lightdemanding species that require relatively high light intensities to survive, flower and produce seed. Managing the density and composition of woody plants, especially trees, thus is paramount to successful savannah or woodland preservation, restoration and maintenance.

Characteristics and Extent Oak savannahs and woodlands in the eastern USA are characterized by low to moderate overstorey densities dominated by oaks and a ground layer rich in grasses and forbs (Haney and Apfelbaum, 1990; Nelson, 2010; Hanberry et  al., 2014) (Fig. 12.1). Their biodiversity is largely concentrated in the herbaceous layer, which often includes 250 or more plant species. In contrast, the diversity of overstorey trees is usually low. The oaks that dominate the overstorey may include any of the species native to a region, but those with greater fire resistance are typically more abundant. The terms oak barrens, prairie grove, oak opening, oak woodland, open forest and brush prairie have all been used to describe what we here broadly define as oak savannahs and woodlands. Nuzzo (1986) defined their Midwestern counterpart as communities ‘dominated by oaks having between 10 and 80% canopy, with or without a shrub layer, and with a herbaceous predominantly grassy ground layer composed of species associated with both prairie and forest communities …’. Oak savannahs and woodlands thus possess characteristics of both prairies and forests and can occur in habitats ranging from dry (xeric) to wet (hydric) (Curtis, 1959). Others have distinguished between oak savannahs per se (less than 30% tree canopy cover) and woodlands (30–75% tree canopy cover) (Griffin, 1977; Nelson, 2010); even finer distinctions based

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

Fig. 12.1.  A dense and diverse herbaceous layer has been maintained by frequent controlled burning of this post oak– black oak–white oak savannah in the Ozark Highlands of Missouri (Ha Ha Tonka State Park). (Photograph courtesy of USDA Forest Service, North Central Research Station.)

on stand density have been offered (Dey and Kabrick, 2015). Oak savannahs, with their understorey of prairie grasses and forbs, are considered transitional from prairies. In contrast, oak woodlands have a sparse midstory of trees and a herbaceous layer rich in grasses and forbs. Both differ from forests, which usually have three distinct layers including a main canopy, midstorey and understorey (Fig. 5.1, this volume). Sometimes open woodlands are distinguish from closed woodlands based on a canopy closure threshold of approximately 50% and associated differences in the light environment on the forest floor (Nelson, 2010). Although distinctions between savannahs and woodlands and associated communities convey important information about their ecological state, especially understorey light, they in actuality form a structural/compositional plant community continuum. Classification of savannahs and woodlands is further complicated by the many transitional states from former savannah to fully stocked forest, often

Silvicultural Methods for Oak Savannahs and Woodlands

visually apparent as differences in the density of understorey and midstorey trees. Oak savannahs and woodlands once covered 27–32 million acres in the Forest–Prairie Transition Region that extended from Minnesota to Texas and eastwards into Ohio (Nuzzo, 1986) (Fig. 12.2). In south-eastern Minnesota and north-eastern Iowa, extensive oak savannahs and woodlands existed for thousands of years before the arrival of Europeans (Grimm, 1984; Chumbley et  al., 1990). Today, Midwestern savannahs and woodlands have been reduced to about 0.02% of their presettlement (pre-1840) area (Nuzzo, 1986). They also may have been more widespread further east in pre-colonial times than they are now (Day, 1953; Nowacki and Abrams, 2008; Keyser et al., 2016). In California, Oregon and Washington, they covered millions of acres before European settlement. Wherever savannahs and woodlands occurred, they were usually associated with humans and their use of fire (Beilmann and Brenner, 1951; Day, 1953; Cooper,

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Fig. 12.2.  The presettlement distribution of the major areas of oak savannahs in the USA before 1840. (From Nuzzo, 1986.)

1961; Wilhelm, 1973; Komarek, 1974; Little, 1974; Dorney, 1981; Pyne, 1982; Grimm, 1984; Biswell, 1989; Nelson, 2010; Arthur et al., 2012). In California today, the area of savannah exceeds 7 million acres. Hardwoods (primarily oaks) grow on an additional 1.6 million acres that are classified as grassland with trees. Of that grassland acreage, 7% carry more than five trees/acre and 38% carry between one and five trees/acre (Bolsinger, 1988; McPherson, 1997). In the Willamette Valley of Oregon, savannahs dominated by Oregon white oak once were common. Much of that area is now in agricultural use or has become closed-canopy forest (Thilenius, 1968). Oregon white oak savannahs also occur in northern California, while some valley oak counterparts occur on alluvial soils in the Central Valley of California. However due to agricultural and urban development, valley oak savannahs are now mainly limited to the foothills in the coast ranges and surrounding the Central Valley. On drier sites in this region, blue oak and valley oaks co-occur in some of savannahs. Blue oak savannahs cover extensive areas in the foothills surrounding California’s Central Valley and in the Coast Ranges (blue oak–digger pine type; Eyre, 1980). Non-native grasses, which often limit oak regeneration, often dominate the open understories

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of these ecosystems in California (Fig. 12.3). Although species associations of oaks vary over the geographic range of California oak savannahs, the commonly occurring species include valley, blue, California black, California live, canyon live, interior live, Oregon white and Engelmann oaks (Griffin, 1977). With their open canopies and grassy ground cover, many western oak savannahs are similar in appearance to their Midwestern counterparts (Fig. 12.3). In the west, however, the herbaceous vegetation originally dominated by bunchgrasses, has now often been displaced by introduced grasses and lacks the species diversity present before the era of livestock grazing and fire suppression.

Disturbance Processes Frequent low-intensity surface fires were probably instrumental in shaping the structure and composition of the original savannahs and woodlands (Nuzzo, 1986; Packard, 1991; Taft, 2009; Nelson, 2010; Kabrick et al., 2014a). Fire reduces the density of large trees and prevents or reduces the establishment of woody vegetation in midstorey and understorey layers. Over time, repeated burning shifts species composition through the gradual

Chapter 12

Fig. 12.3.  A California black oak woodland in Yosemite National Park. This open park-like woodland visually resembles the savannahs and open woodlands of eastern USA. The ecology of most western oak savannahs and woodlands has been altered by cattle grazing, the introduction of non-native herbaceous species and suppression of wildfires. These practices have reduced herbaceous diversity and increased the difficulty of regenerating oaks. (Photographs courtesy of USDA Forest Service, North Central Research Station.)

removal of fire-sensitive species and the retention of fire-resistant species (see Chapters 3 and 7, this volume). Low-intensity surface fires remove some or all of the leaf litter, enhancing the germination of many species of grasses, sedges and forbs. In the absence of fire, oak savannahs and woodlands historically declined in herbaceous species diversity and, with the cessation of burning became closedcanopy forests within a few decades (Beilmann and Brenner, 1951; Curtis, 1959; Bray, 1960; Thilenius, 1968; Griffin, 1976; Grimm, 1983; Nuzzo, 1986) (Fig. 12.4). In both the east and the west, the disappearance and decline of savannahs and woodlands was hastened by the building of roads and railroads (which often formed effective fire barriers), livestock and ungulate browsing, cultivation, land clearing for range and pasture improvement, herbaceous competition including the introduction of alien grasses, and urban expansion (White, 1966;

Silvicultural Methods for Oak Savannahs and Woodlands

Thilenius, 1968; Griffin, 1976; Rossi, 1980; Nuzzo, 1986; Barnhardt et  al., 1987; Reed and Sugihara, 1987; Adams et  al., 1991; Davis et  al., 1991). Many of today’s closed-canopy forests originated from former savannahs or woodlands, and scattered broad-crowned trees persist there as relicts from an earlier era (Thilenius, 1968; McClain et al., 1993). The transition from savannah or woodland to closed-canopy forest is more rapid on mesic than on xeric sites. The extensive area covered by presettlement oak savannahs and woodlands was largely the product of recurrent wildfires occurring over thousands of years before the arrival of Europeans (Pyne, 1982; Grimm, 1983; Chumbley et al., 1990; Ladd, 1991; Abrams, 1992; Nowacki and Abrams, 2008). During that period, indigenous people frequently set fire to forests and prairies to increase forage, improve habitat for game, drive game, harm enemies

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(A)

(B)

Fig. 12.4.  A black oak savannah in southern Wisconsin. (A) A diverse herbaceous layer has been maintained by periodic prescribed burning, which keeps the oak reproduction (foreground) in a shrub-like state. (B) After several years without burning, oaks and other hardwoods quickly develop into a closed-canopy forest. (Photographs courtesy of Dr Craig G. Lorimer, University of Wisconsin-Madison.)

and open visual corridors (Pyne, 1982; Whitney, 1994). Indigenous people and early European settlers were unencumbered by present-day legal constraints to burning (White, 1991), and they acted impromptu – without detailed burning plans. Thus, fires varied greatly in frequency, intensity and extent. Historical accounts indicate periods when vast areas were burned annually, often in the autumn after frost killed the herbaceous vegetation (Pyne, 1982; Ladd, 1991; McMurry et al., 2007). The few available records on historical fire frequency in Midwestern savannahs and woodlands are based on fire scars of old trees. Before settlement by Europeans, fires occurred at average intervals of 4–17 years (Henderson and Long, 1984; Guyette and Cutter, 1991; Jenkins, 1997; Guyette et  al., 2002; Stambaugh et al., 2016). But as human population increased, intervals between burns decreased (Guyette, 1995). As the new society evolved, and fire’s threat to life and property became more acute,

436

its frequency and severity rapidly declined (Guyette et al., 2002). Within a given savannah or woodland, the area affected by fire historically varied. During the 18th century, intense fires in Midwestern savannahs and woodlands that burned 20–80% of an area occurred at an average interval of 11 years based on a study of tree fire scars (Guyette and Cutter, 1991). Additional low-intensity ground fires that did not leave fire scars also probably occurred. Variation in weather, fuels, topography and the location of natural firebreaks also produced variation in fire intensity and impact (Bowles and McBride, 1998). Coves and lower slopes dampened fire spread, which in turn harboured pockets of dense tree cover surrounded by more flammable savannahs or woodlands with their dense herbaceous cover. This variation produced diverse plant communities variously described as savannahs, woodlands, dense patches of oak saplings (‘oak scrub’) and

Chapter 12

closed-canopy forests. Grimm (1984) suggested that such landscapes represent a ‘fire-probability pattern’ that reflects an intermingling of vegetation types produced by the frequency and severity of burning unique to each type. He hypothesized that the resulting mosaic formed a landscape-level equilibrium that sustained the prevailing state (cf. Anderson and Brown, 1986). Topography also can influence such mosaics. For example, an index of ‘topographic roughness’ was used to quantify how fire historically occurred more frequently on level or rolling topography than on rough topography (Guyette et  al., 2002; Stambaugh and Guyette, 2008). Moreover, topography can influence the distribution of other disturbance agents including wind, wildlife and people. It also can interact over time with anthropogenic fire ignitions to influence stand density and species composition, which in

turn affect rates of change from savannah to woodland to closed-canopy forest (Fig. 12.5). At the stand level, topography, soil properties and overall site quality affect development of savannah and woodland composition, structure and fuel characteristics. Dry ridges, south-facing slopes, nutrient-deficient soils and soils with low water-holding capacity normally support fewer tree species, shrubs and understorey plants than do north-facing slopes and nutrient-rich soils with their greater water-holding capacity (Kabrick et al., 2008, 2014b). Many tree and shrub species adapted to dry, fire-prone environments are associated with litter that dries rapidly, decomposes slowly and burns readily. Low site quality causes trees and shrubs to grow more slowly so that their canopies remain open for relatively long periods after disturbance, thereby allowing the development of the

(A) Presettlement vegetation Prairie High

Savannah Fire frequency gradient

Forest

Low

(B) European settlement: fire cessation and forest expansion

(C) Current conditions: maple invasion

Oak

Maple

Fig. 12.5.  Topography and anthropogenic fire ignitions interact affecting species composition and stand density over time. (A) Native Americans in Illinois strongly influenced interrelationships among fire, vegetation and topography. Highly flammable grasslands dominated flat areas lacking topographic barriers to fire and less flammable oak forest types dominated more rugged and dissected terrain. Sugar maple and other fire-sensitive trees were largely relegated to riparian zones and areas protected from fire. (B) Agricultural fields and roads accompanying settlement by Europeans greatly reduced landscape-scale burning, especially in grasslands. The burning of savannahs and woodlands to promote forage production was gradually eliminated as well. Forest vegetation expanded into areas that were not farmed. (C) Successional changes continue in the absence of fire. Heavy shading has reduced the presence and diversity of understorey plants adapted to high light conditions. Tree regeneration has largely shifted from oaks to shade-tolerant mesophytic species, particularly sugar maple. (Graphic and interpretation courtesy of Gregory J. Nowacki, USDA Forest Service.)

Silvicultural Methods for Oak Savannahs and Woodlands

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many light-demanding herbaceous species characteristic of these systems (Kinkead, 2013) (Fig. 12.6). Soil surveys and ecological classification systems (see Chapters 1 and 4, this volume) also are useful for identifying site conditions amenable to the management of savannahs or woodlands and predicting how different sites are likely to respond to management (Kabrick et al., 2014a). Variation in frequency, intensity and season of burning affects the composition, structure and biodiversity of oak savannahs and woodlands. For example, when a Minnesota oak savannah was burned at intervals from 1 to 12 years, the largest number of plant species occurred in areas burned every 2 years (Tester, 1989). Accordingly, an optimum burning schedule was proposed in which burning for 2 consecutive years would alternate with no burning for 2 years. The years without fire would allow fuels to accumulate to make the subsequent burn hotter than an annual burn and thereby more effective in limiting the invasion and survival of non-prairie species. However, this relationship may vary among types of oak savannahs or woodlands. Burning in different seasons also can produce different results (Van Lear and Waldrop, 1988). Most contemporary prescribed burning is conducted in the spring, even though autumn fires were common historically (Ladd, 1991).

In oak savannahs and woodlands in the eastern USA, the absence of fire usually increases tree and shrub populations and an attendant decrease in numbers of plant and bird species. For example in northern Illinois, the succession of tallgrass savannah to closed-canopy forest reduced the number of plant species from more than 300 to fewer than 25 and reduced bird species from 28 to four (Haney and Apfelbaum, 1990). In a mesic oak savannah in northern Illinois, the number of herbaceous species decreased with increasing canopy cover (Bowles and McBride, 1998). In the Ozark Highlands of Missouri, 64 species of birds occurred in a community described as ‘grass and shrub woodland’ in contrast to 39 species in a mature oak–hickory forest with little undergrowth (Evans and Kirkman, 1981). Where savannahs and woodlands were restored in the Ozark Highlands, greater densities of early-successional and generalist species were observed than in nearby unburned stands (Reidy et al., 2014). In western savannahs and woodlands in Mediterranean climates (e.g. in central California), hot and dry summers can limit canopy closure and also the regeneration of oaks, especially in the presence of competition from non-native grasses and the foraging of livestock and deer (White, 1966; Danielsen and Halvorson, 1991). There, the role of fire in maintaining or restoring savannahs

Oak savannah

Xeric sites

Oak Oak woodland forest

Mesophytic forest High

Low Fire frequency

No fire

Ecosystem state

Ecosystem state

Mesic sites

Oak savannah

High

Oak woodland

Oak forest

Low

Mesophytic forest

No fire

Fire frequency

Fig. 12.6.  Conceptualization of fire effects on alternative stable states for the savannah-to-forest continuum on mesic and xeric sites. In this ‘ball-in-cup’ model, the balls indicate community states under different fire frequencies. The depth and position of the ball-and-cup on the slope of basins along the surface indicate the degree of community stability. On mesic uplands, shifts from oak savannah to oak woodland to oak forest occur more readily with decreasing fire frequency as indicated by the shallower basins. On xeric uplands, shifts occur more slowly due to edaphic constraints such as by a limited water or nutrient supply as indicated by the deeper basins. Without fire, the shift to mesophytic forest can occur over time particularly where there are fewer edaphic constraints. Once this occurs, restoring oaks and associated fire-dependent species and maintaining more open woodland and savannah structures becomes difficult and costly, particularly on mesic sites. (Adapted from Nowacki and Abrams, 2008.)

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and woodlands is less well understood than in their Midwestern counterparts. Grazing and browsing by elk, bison and deer also have been historically significant in sustaining savannahs and woodlands (Jenkins, 1997). Bison require about 30 lb of forage/day, creating grazing disturbances that were probably locally severe (Evans and Probasco, 1977). In addition to reducing herbaceous cover by grazing, these animals affected vegetation by trampling and wallowing, and by distributing seeds via their dung.

Silvicultural Concepts and Methods The silvicultural concepts and methods used to manage oak forests for wood products and habitat also can be used to manage savannahs and woodlands. However, the application and timing of the tools used to do so differ (Kabrick et al., 2014a). In forests, thinning via tree harvest is usually the primary tool, which is used to control species composition and residual stand growth and quality. Fire may be optionally used to promote the accumulation of oak reproduction at certain times (Brose et al., 2013). In contrast, the goals of savannah or woodland management are to preserve, restore and maintain native biodiversity and habitat quality. Attaining those goals requires a strategy different from those used in managing forests (Table 12.1). Although fire historically was instrumental in creating savannahs or woodlands, today prescribed Table 12.1.  Reasons for thinning or burning in oak forests compared with those in savannahs and woodlands. Forests

burning is essential for preserving, restoring and maintaining them. Nevertheless, reintroducing a historical fire regime, per se, into a degraded savannah and woodland may be impractical or insufficient in obtaining its re-establishment; it also may require the cutting of trees. Effective use of fire thus requires a flexible burning schedule that simultaneously: ●● controls overstorey density and the accumulation of leaf litter; ●● encourages the establishment and accumulation of oak reproduction; and ●● promotes a diversity of native forbs, sedges and grasses yet limits the density of shrubs and trees. The objective of oak savannah and woodland management thus is to create understorey light intensities and other conditions favourable to native herbaceous species. To do this, an appropriate overstorey density consistent with sustaining oak regeneration also must be maintained. Because controlling light is central to all of the above objectives, silvicultural tools useful in estimating understorey light intensity are discussed below. Estimating light intensity Relative light intensity in the understorey of a savannah or woodland (i.e. light relative to that in the open or some other standard) can be estimated in several ways. Three methods are presented below, all of which provide measures or correlates of light availability in the understorey of woodlands and savannahs: (i) relative stand density (stocking per cent); (ii) relative crown cover; and (iii) relative light intensity.

Savannahs and woodlands

Thinning Improve stand quality

Thinning Reduce density and alter structure Concentrate growth Increase light reaching the ground Utilize trees that will be Provide growing space lost to mortality for ground flora Reduce disease and Reduce disease and infestation infestation Burning Burning Favour desirable tree Favour desirable species during ground flora species regeneration phase during tending phase Reduce fuel loading Maintain suitable during tending phase structure during the tending phase

Silvicultural Methods for Oak Savannahs and Woodlands

Relative stand density (stocking per cent) Stocking concepts such as those expressed by Gingrich’s (1967) stocking chart (Fig. 6.9, this volume) provide useful guides to creating preferred overstorey densities. Stocking per cent is a measure of relative stand density and expresses the relative amount of growing space utilized by trees (see Chapter 6, this volume). It is also related to crown canopy cover; reducing stocking decreases crown cover and simultaneously increases light reaching the ground (Blizzard et al., 2013). In managing forests, a stocking chart is normally used to control stocking between defined maximum and desired minimum levels. In Gingrich’s (1967) scheme, A-level stocking (100% stocking)

439

(c.1830) have been used to reconstruct stand characteristics from historical records of forests, woodlands and savannahs. As expected, these data show that there were fewer trees per acre in savannahs and woodlands then than in contemporary forests now (Table 12.2; Hanberry et al., 2014). However, trees at the time of the land surveys also were 1.4 times larger in mean diameter than those of contemporary forests. This results in stocking levels in historical woodlands comparable to contemporary forests (Hanberry et  al., 2014). One explanation for this seemingly unexpected relation may be that the overstorey utilization of growing space (i.e. stocking per cent) is not a linear correlate of understorey light in either quantity or quality, especially for that required by herbaceous species. Moreover, the high diversity of ground flora generally observed in savannahs and woodlands may depend on factors other than overstorey stocking, including the destruction of leaf litter and other unmeasured modifications of the understorey by fire (Kinkead et  al., 2013). Nevertheless, restoring today’s oak forests to savannah or woodland usually requires reducing stand density.

defines the level at which the maximum number of trees of a given mean stand diameter can coexist (referred to as average maximum stand density) and B-level stocking (about 57–59% stocking) defines the minimum number of trees of a given mean stand diameter required to fully occupy all of the growing space (see Chapter 6, this volume). Forests are usually managed at stand densities above B-level (see Chapter 8 and 9, this volume). Canopy gaps (and thus unused growing space) are defined by the region falling below B-level (Fig. 6.9). In contrast, stocking in savannahs and woodlands is often, if not usually, maintained below that level. In some cases, savannahs are distinguished from woodlands, with the term woodland reserved for those at 30–75% stocking, and savannah for those at < 30% stocking (Hanberry et  al., 2014) (Fig. 12.7). The stocking concept also can be used to interpret information on the nature of presettlement vegetation – information that is sometimes used to guide stand structure objectives in contemporary restoration and management. Witness-tree1 data from the USA General Land Office survey records

160

2220

18

16

140

14

Quadratic mean diameter (in.) 12 10

9

Basal area (ft2/acre)

120

8

st

100

7 110

A-line

Fore

100 90

Close

80

d wo

60

Ope

n wo

odla

40

nd

0

d

C-line

70

B-line 60 50

Stocking (%)

40

Sava

nnah

20

80

odlan

30

20

0

100

200

300

400

Trees/acre Fig. 12.7.  Stocking thresholds used for managing woodlands and savannahs. Stocking in woodlands is maintained between about 30% and 75% stocking. Closed woodlands have greater stocking (55–75%) compared with open woodlands (30–55%) and savannahs (< 30%). (Adapted from Dey and Kabrick, 2015.)

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Chapter 12

Table 12.2.  Historical and contemporary stand density and mean diameter of trees > 5 inches dbh for selected Missouri ecological subsections and land type associations from General Land Office (GLO) and Forest Inventory and Analysis (FIA) databases.a,b (From Hanberry et al., 2014.) Trees/acre

dbh (in.)

Stocking (%)

Land type association (and subsection)

GLO

FIA

GLO

FIA

GLO

FIA

Oak Woodland Plains and Hills (OZ7) Oak Woodland/Forest Hills (OZ7) Oak-Pine Hills (OZ7) Pine-Oak Woodland Plains (OZ7) Rugged Hills and Forest Breaks (OZ7) Oak Woodland Plains and Hills (OZ8) Oak Woodland/Forest Hills (OZ8) Oak-Pine Hills (OZ8) Pine-Oak Woodland Plains (OZ8) Oak-Pine Hills (OZ9) Pine-Oak Woodland Plains (OZ9) Rugged Hills and Forest Breaks (OZ9)

50 63 66 63 53 77 57 130 65 75 63 111

146 151 153 158 140 138 136 138 148 138 146 128

13 13 13 13 13 12 13 13 14 15 15 14

9 9 9 9 9 9 10 10 9 9 9 9

37 48 48 46 43 48 45 99 49 59 54 86

59 60 57 57 53 59 63 60 60 55 60 56

a

Values are averages adapted from Hanberry et al. (2014). In Missouri, General Land Office Surveys were made between 1815 and 1850.

b

Measures of stocking also can be used along with diameter distributions for monitoring effects of savannah and woodland management practices. For example, in fully stocked stands, two prescribed fires applied 2 years apart can reduce numbers of trees by 40% while only reducing stocking by 15–25% (Kinkead et  al., 2013). This occurs because: (i) the thin bark of small trees affords little protection from fire compared with larger trees (Dey and Hartman 2005; Fig. 3.11, this volume); and (ii) stocking per cent increases exponentially with tree diameter (Equation 6.17, this volume). Thus, effective reduction in stocking may require the cutting of trees (Hutchinson et  al., 2012). Reducing tree numbers nevertheless only temporarily decreases canopy cover, which continually rebounds as the residual stand grows and new trees enter the overstorey. For stands at B-level, stocking increases at an average rate of about 1.5% per year but may range from 1% on poor sites to 3% on more productive sites (Dale and Hilt, 1989). The required frequency of thinning for maintaining a target stocking level can be expected to increase as site quality increases, other factors being equal. Moreover, reducing stocking below B-level may stimulate the development of oak reproduction (Larsen et  al., 1997). Unless needed for regeneration, the resulting high density of reproduction may interfere with obtaining the required herbaceous species composition. In that case, more frequent burning or other methods of controlling understorey

Silvicultural Methods for Oak Savannahs and Woodlands

woody plants may be required. This is especially true on high-quality sites where tree reproduction rapidly develops (Kabrick et al., 2008). Relative crown cover For the Central Hardwood Region, a crown cover chart can be used to assess the current canopy cover of savannahs and woodlands and to monitor change with time (Fig. 12.8). Using the chart requires an estimate of stand basal area and number of trees. Although the chart is similar to the Gingrich stocking chart (see Chapter 6, this volume; Fig. 6.9), the 100% crown cover isoline is analogous to the ‘B’ line on the Gingrich chart (Fig. 6.9) and defines the stand conditions at which crown closure is imminent. The crown cover chart was developed from equations that estimate tree crown width from dbh for open-grown oaks (Krajicek et  al., 1961; Krajicek, 1967; also see Table 6.1, this volume). Studies have shown that for a given species, tree diameter explains 90% or more of the variation in crown width in open-grown trees (Krajicek, 1967) (Figs 12.9 and 12.10). On the crown cover chart (Fig. 12.8), the number, mean stand diameter and basal area of trees are used to estimate crown area. The chart provides an estimate of the maximum crown cover that trees can attain for a given basal area and mean diameter. However, an oak savannah and woodland, especially one that has been

441

80 14

70

12

10

9

8

Basal area (ft2/acre)

Ave

rage

7

60

6

tree

diam

eter

5

50

(in.) 4

40

3

30

80

20 10 0

10

0

30

20

100

40

200

%)

er (

v n co

w

Cro

300

70

60

50

100

90

400 Trees/acre

500

600

700

800

90

30 80

Average tree diameter (in 26 24 22 .) 20 18 16

14

Basal area (ft2/acre)

70

0 10

90

60

80

50

70 60

40 50

30

40 30

20 20

10

)

%

r(

e ov

nc

ow

Cr

10

0 0

10

20

30

40 Trees/acre

50

60

70

80

Fig. 12.8.  Relation between tree crown cover and basal area, number of trees per acre, and average diameter (dbh) in open-grown oak–hickory stands in the Central Hardwood Region. The relation assumes that trees are oaks (black, white and northern red) or hickories at maximum crown expansion for a given diameter. Stands at or above 100% crown cover are woodlands or closed-canopy forests. Average tree diameter is the diameter of the tree of average basal area. The line for 100% crown cover corresponds to the B-line in Figure 12.7. (From Law et al., 1994.)

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Chapter 12

Fig. 12.9.  Open-grown trees such as this bur oak develop predictable maximum crown areas that can be estimated from bole diameter. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

Crown area (% of an acre)

7 Elm Oak–hickory

6

Bur oak Sugar maple

5 4

Black cherry

3 2

Relative light intensity

1 0

in closed-canopy stands do not differ significantly from those that have always been open grown (McGill et  al., 1991). Thus, dominant trees originating from closed-canopy forests as well as those that have always grown under open conditions are likely to adequately represent their maximum possible crown diameter for their bole diameter.

0

5

10

15 20 Dbh (in.)

25

30

Fig. 12.10.  Estimated crown area (as a percentage of an acre) of open-grown trees in relation to bole diameter (dbh) for five species. (Adapted from Ek, 1974 (American elm, bur oak and black cherry); Krajicek et al., 1961 (oak–hickory); Smith and Gibbs, 1970 (sugar maple).)

recently reduced in density (naturally or by thinning), may have an actual crown cover below that shown by the crown cover chart. This seeming discrepancy can occur when after a stand is thinned the tree crowns have had insufficient time to expand to their maximum size. There nevertheless is evidence that, for a given dbh, crown areas of dominant oaks

Silvicultural Methods for Oak Savannahs and Woodlands

For the Ozark Highlands, understorey light relative to photosynthetically active radiation (PAR) can be estimated from residual stand density measured as numbers of trees, basal area, or stocking per cent. The related equations were developed from measurements of PAR expressed as a percentage of light above the forest canopy (Fig. 12.11). For a given light level, the appropriate stand density using any one of the three metrics (i.e. tree density, basal area or stocking per cent) can be silviculturally created. Although the equations were based on removing trees in lower canopy levels (‘low thinning’ and resultantly uniform overstories), their accuracy is nevertheless sufficient for application to most stand-level applications. Because the equations are based on actual measurements of light, they represent an improvement over more indirect methods based on presumed light correlations with stocking

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(A)

(B) 100 Above canopy PAR (%)

Above canopy PAR (%)

100 80 60 40 20 0 0

20

40

60

80

80 60 40 20 0

100

0

20

Stocking (%)

Above canopy PAR (%)

(C)

40

60

Basal area

80

100

120

(ft2/acre)

100 80 60 40 20 0

0

20

40

60

80

100 120 140

Density (trees/acre) Fig. 12.11.  Percentage of photosynthetically active radiation (PAR) (400–700 nm) reaching the understorey of oak stands after low thinninga for: (A) stand-level stocking, (B) stand-level basal area, and (C) stand-level tree density. Black lines show modelled relationships and hollow circles observed values. a

Low thinning removes trees from inferior crown classes to obtain a given stand density.

or crown cover per cent. Moreover, they confirm as suggested above, that there is a non-linear relation between light and the usual metrics of stand density (Fig. 12.11). A lack of knowledge of the specific light requirements for various prairie grasses, forbs and oaks nevertheless continues to beset savannah and woodland management using any of the methods discussed above (see also the discussion of shoot dieback and ‘compensation point’ in Chapter 2, this volume).

Restoration and Maintenance Methods for savannah and woodland restoration differ from those required for their maintenance

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(White, 1986; McClain et al., 1993). Nevertheless, both require flexible management strategies including monitoring. Once restored, an oak savannah or woodland is not a static condition and is never ‘finished’ (Packard, 1991). Burning or other appropriate treatments need periodic application to maintain the desired state lest it revert to a closedcanopy forest or other unwanted condition. This process usually requires simulating the disturbance regimes that historically created and perpetuated savannahs and woodlands. Restoration thus may take several decades. Current and former savannahs and woodlands have been modified by introduced species, increased canopy cover, and the loss of original herbaceous cover to such an extent that

Chapter 12

reintroducing burning and grazing at socially acceptable levels may not restore the desired species composition and structure. Although removing trees often can restore appropriate overstorey conditions, the task of restoring native herbaceous understorey species may be more difficult. Restoration and maintenance may necessitate controlling invasive plant species, both woody and herbaceous. The waves of European settlers moving across the North American landscape in earlier centuries introduced alien plant and animal species that have since displaced or become the dominant components of many natural communities. These invasives are especially problematic in oak savannah and woodland restoration and maintenance. They include smooth brome grass, Canada thistle, musk thistle, sericea lespedeza, autumn olive, multiflora rose, teasel, crown vetch, white sweetclover, yellow sweetclover, spotted knapweed, European buckthorn and Japanese honeysuckle (Dey and Kabrick, 2015). Often thriving and persisting in open environments, these species are adapted to fire and other disturbances that allow them to persist and thrive in savannahs and woodlands. During restoration, it is often prudent to control invasive plants in and around the restoration area before using fire, disturbing the forest floor, or reducing tree density, all of which can favour their spread. Effective methods for controlling many invasives include prescribed fire, herbicides, mechanical treatments and combinations thereof (Grace et al., 2001; DiTomaso et al., 2006; Zouhar et al., 2008). Because the complete eradication of invasive species is often impractical, monitoring and controlling localized invasions has now become a necessary part of restoring and sustaining savannahs and woodlands. Silvicultural prescriptions for savannahs and woodlands need to be tailored to the condition of each individual plant community and management objective. However, their management is often hindered by a lack of tested silvicultural prescriptions. Despite that, there is a growing body of knowledge based on experience. For the Midwest, much relevant information has been compiled in various proceedings (Burger et  al., 1991; Fralish, 1994; Stearns and Holland, 1995; US Environmental Protection Agency, 1995), papers (Kabrick et  al., 2014a; Dey et  al., 2017) and book chapters (Dey and Kabrick, 2015; Keyser et al., 2016; Hanberry et  al., 2017). For western oak savannahs and woodlands, similar compilations also are available

Silvicultural Methods for Oak Savannahs and Woodlands

(Plumb, 1980; Conrad and Oechel, 1982; Plumb and Pillsbury, 1987; Standiford, 1991; Ffolliott, et  al., 1992; McPherson, 1997; Pillsbury et  al., 1997; Ffolliot, 2002; Standiford, 2002; Hanberry et al., 2017). Restoration Although opinions differ about the management tools most appropriate for restoring oak savannahs and woodlands, they can be broadly divided into two strategies: (i) re-establishment of the disturbance processes that historically created savannahs and woodlands by gradually restoring the desired state (ecological mimicking); or (ii) quickly recreating the structure and composition typical of oak savannahs and woodlands by removing trees to a predetermined canopy density followed by burning, and/or artificially re-establishing an appropriate herbaceous flora by seeding or planting. These alternatives are not necessarily mutually exclusive. Following the principles of adaptive management, success with early results may suggest ways of integrating the two approaches. The first approach is a long-term strategy based on restoring some of the ecological processes that originally produced savannahs or woodlands. A restored community is accordingly expected to gradually emerge over perhaps several decades of management. The second approach relies more heavily on intensive management including some practices that historically were not associated with savannahs and woodlands. In either case it is essential to monitor progress and adapt cultural practices as necessary to improve chances that the desired outcome is obtained. Rapid restoration of the desired ecological state (e.g. within a single decade rather than several) may be important in areas where: ●● visual appearance and aesthetics are primary concerns; ●● there is an immediate need to provide a demonstration and teaching ‘laboratory’; and ●● quickly providing habitat is essential to saving an endangered species (e.g. the savannah and woodland-dependent Karner blue butterfly). Restoration can be accomplished most efficiently on sites where the vestiges of an earlier savannah or woodland remain. Indicators include: ●● the presence of old, dominant oaks with spreading crowns; these may be surrounded by a dense

445

growth of younger trees that developed after periodic burning ceased (Thilenius, 1968; McClain et al., 1993); ●● the presence of remnant populations of savannah and woodland herbaceous species (see Mead, 1846; Brendel, 1887; Daniels, 1904; Curtis, 1959; Packard, 1991); and ●● historical records for a site (e.g. Public Land Survey field notes) that provide evidence of past savannah or woodland. Selecting areas with low overstorey and midstorey tree densities and/or with a remnant herbaceous layer comprised of at least some savannah or woodland species may reduce the time, expense and difficulty of restoration. Potentially useful cultural practices include burning, controlled grazing, thinning by tree felling or tree deadening, seeding and planting. Some or all of those practices may not be required, depending on existing vegetation, the desired speed of restoration and the resources available for restoration. Fire is required in the eastern USA and in some western savannahs comprised of Oregon white oak in the Willamette Valley. However, the restorative effect of fire is uncertain in blue oak and valley oak woodlands where climate, an understorey of nonnative grasses, and abundant acorn consumers and seedling browsers limit oak regeneration (Griffin, 1976). Although an essential tool in savannah and woodland restoration and management, caveats in using prescribed fire include: ●● that burns be of sufficient frequency and intensity to kill some of the woody understorey and midstorey vegetation including trees. Historically, fire intensity varied across the landscape in both time and space. Occasional fires of relatively high intensity can effectively change overstorey and midstorey structure and composition. Although burning eliminates fire-sensitive woody plants, it can also promote the build-up of oak reproduction (see Chapters 3 and 7, this volume) sometimes to the detriment of desired herbaceous species; ●● selecting a burning interval likely to favour the development of forbs and grasses; ●● selecting a fire-free interval (from a few years to a few decades) as needed to sustain the periodic recruitment of oak reproduction into the overstorey to facilitate replacement of trees lost to fire, disease, senescence and other factors; and ●● considering both beneficial and deleterious effects on invasive species.

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Accordingly, a conservative approach to savannah or woodland restoration in the Midwest might begin with relatively light burns that reduce accumulated fuels and gradually eliminate fire-sensitive woody species (Jenkins, 1997). Reintroducing frequent burning in remnants of former savannahs or woodlands should, over time, help restore the desired ecological state provided that the desired herbaceous species have persisted in the seed bank or as remnant plants in the understorey. Where a closed-canopy forest has developed, restoration may require initial tree harvesting to create the desired overstorey structure and understorey light regime followed by frequent burning over several decades to effectively encourage the invasion and build-up of desired herbaceous flora (White, 1983; Dey et  al., 2017). In some cases, intense fires or thinning may be necessary to reduce the number of large trees (White, 1983; Dey et  al., 2017). When fuel loads are high, caution is required during the first prescribed burns because of the risk of excessive damage to overstorey trees. However, such risks need to be balanced against their failure to sufficiently reduce overstorey density and thus to re-establish and maintain desired herbaceous species. Periodic reductions in stand density to appropriate levels also help assure a cycle of recruitment of oak reproduction into the overstorey. To more quickly restore appropriate tree structure in savannahs and woodlands, tree felling or deadening is sometimes used (Dey and Kabrick, 2015; Dey et al., 2017). Because those practices usually precede prescribed burning, the resulting dead wood increases the fuel load. Concentrated fuels create hot spots that can destroy soil organic matter, retard the re-establishment of herbaceous and woody vegetation, and cause soil erosion (Biswell, 1989). Leaving standing dead trees can reduce this problem while also maintaining sufficient understorey light. However, timber harvesting in some cases can provide revenue and also reduce the hazards to humans associated with standing dead trees (especially in heavily used recreation areas). When timber harvesting or tree deadening is followed by burning, additional fire-related mortality of residual trees should be anticipated. Immediately after a fire, damage to trees can be estimated from the height of scorch marks on tree boles (Fig. 15.12, this volume). If fire damage to residual trees is extensive, a subsequent salvage harvest may be warranted. Controlled grazing provides another potentially useful tool in savannah and woodland management.

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Although there are few guidelines, ungulates historically were functional components of savannahs and woodlands that facilitated seed dissemination and nutrient cycling, and thereby the maintenance of herbaceous flora (Evans and Probasco, 1977; Fuhlendorf et al., 2008). Grazing nevertheless cannot eliminate the need for periodic burning in Midwestern savannahs and woodlands (Harrington and Kathol, 2008). Possible negative consequences from grazing that require monitoring include soil compaction and the introduction of undesirable seed in manure. In the west, livestock grazing has long been associated with savannahs, but usually not to the benefit of savannah preservation. Cattle consume acorns and browse oak seedlings, which often are scarce and slow to establish. Deer, gophers and other rodents may further exacerbate the effects of cattle grazing. However, even the use of exclosures to eliminate cattle, deer and gophers failed to satisfactorily establish oak reproduction in valley oak savannah in California’s Santa Lucia Mountains (Griffin, 1976). Where remnant plants or seed sources for herbaceous vegetation are lacking, they can be seeded or planted (Packard, 1991; McClain et  al., 1993). Such practices may be the only way to re-establish herbaceous species that have disappeared from a site. However, careful assessment of herbaceous vegetation and prudent selection of sites with high restoration potential may eliminate the need for costly measures by focusing efforts on sites with the fewest impediments to restoration. Savannahs and woodlands are often depicted as attractive, park-like settings comprised of scattered, wide-crowned trees rising above a dense growth of herbaceous vegetation (Figs 12.1, 12.3 and 12.4). Historical descriptions and old photographs confirm that at least some savannahs and woodlands conform to this perception. However, their visual qualities vary with time. During the course of restoration and maintenance, the presence of standing charred and dead trees, down wood and a brushy understorey may periodically become prominent features that, at least for some, reduce their aesthetic qualities. Success in their restoration therefore should be gauged more by progress towards attaining specific goals than by narrowly perceived aesthetic qualities. Maintenance In savannahs and woodlands in the eastern USA, burning at short intervals (e.g. biennially) over long

Silvicultural Methods for Oak Savannahs and Woodlands

periods favours the accumulation of oak seedlings and seedling sprouts (see Chapters 3 and 7, this volume). Low-intensity fires (e.g. fires with low flames moving slowly against the wind or downhill) are usually lethal only to small stems. Recurring fires often eliminate small oak seedlings and firesensitive woody species such as maples, birches, elms, cherries and ashes. However, oaks often survive fire by resprouting from the root collar, which may lie an inch or more below the soil surface (see Chapters 2 and 7, this volume). In ecosystems that are intrinsic accumulators of oak reproduction (see Chapter 3, this volume), oak sprouts favoured by fire may become sufficiently abundant to impede the development of the desired herbaceous layer. Some of the oak reproduction eventually develop large roots and a correlated capacity for rapid height growth during fire-free intervals (Johnson, 1979). The cycle that sustains the oaks in fire-dependent savannahs and woodlands over the long term is not continual burning at short intervals, but rather a series of fires at short intervals interrupted by a longer period without fire (Streng and Harcombe, 1982; Haney and Apfelbaum, 1990; Bowles and McBride, 1998; Arthur et  al., 2012; Knapp et  al., 2017). Where oak regeneration and recruitment of oak seedlings and seedling sprouts into the overstorey are needed, fire must be withheld long enough for reproduction to reach a size sufficient to resist top-kill when burning is resumed (Dey and Kabrick, 2015). This may take 10–30 years depending on the myriad factors that affect growth in hardwoods (Haney and Apfelbaum, 1990; Arthur et  al., 2012, Kabrick et al., 2014a). Based on fire-history studies, fire-free periods of this length commonly occurred in eastern forests after the mid-1600s (Guyette et  al., 2002, 2003; Guyette and Spetich, 2003; Stambaugh et al., 2006). The resulting intermittent recruitment process produces an overstorey of several age classes often separated by two or more decades. Even where the mean fire-free interval is only a few years, recruitment of oak reproduction into the overstorey can occur using a sequence of fires that are: (i) sufficiently variable over time so that long fire-free intervals (e.g. 5–20 years) occasionally occur; or (ii) sufficiently variable spatially so that some portions of a stand fail to burn in consecutive fires (Rebertus et al., 1993; Rebertus and Burns, 1997). The resulting burning regime accordingly would produce a continually shifting mosaic of tree cover representing a patchwork of recently recruited trees intermingled with trees of older age classes.

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Although the upper age limit for many oaks is about 250 years, individual trees may survive longer (Burns and Honkala, 1990). The eventual mortality of overstorey oaks in savannahs and woodlands requires their periodic replacement by oak reproduction that has accumulated in the understorey. In oak savannah and woodlands east of the Great Plains subjected to frequent burning over many decades, this pool of reproduction is likely to be adequate. Variation in either even-aged or uneven-aged regeneration methods (see Chapters 8 and 9, this volume) can be used in savannahs and woodlands depending on the size of the management area, the selected rotation or re-entry period, the desire to maintain continuous mature tree cover, ability to manage intensively and other factors. However, because of uncertainty in fire behaviour, even-aged silviculture (area regulation) is better suited for managing large savannahs or woodlands (Plate 12). With area regulation, specific stands or land units are selected for regeneration or tending. For stands requiring regeneration, prescribed fire can be withheld from stands or other land units by using fire lines, roads or natural fire breaks to allow recruitment of an existing population of reproduction into the overstorey. After a sufficient number and size of new trees have entered the overstorey and are no longer in danger of top-kill or severe damage, fire can be reintroduced along with other tending methods. Unless the fire-free interval is greater than 10 years, it may be difficult to manage woodlands using single-tree selection because that method requires the continuous establishment and recruitment of seedlings and small trees that are vulnerable to top-kill by fire. In general, the firefree interval will need to increase as residual stocking increases since higher overstorey stocking will slow the growth of reproduction thereby lengthening the time required for reproduction to enter size classes less vulnerable to fire damage. Prescribed burning guidelines are available for managing oak savannahs and woodlands in Minnesota (Irving, 1971; White, 1986). These and other experiences with prescribed burning have been documented for oak savannahs and woodlands in various regions (Thor and Nichols, 1973; Green, 1980; Van Lear and Waldrop, 1988; Brose and Van Lear, 1997; Kinkead et  al., 2013; Kabrick et  al., 2014a; Dey and Kabrick, 2015). Fire frequency and intensity tend to be self-­ regulating in savannahs and woodlands. Intense fires in an established stand will decrease fuel loads

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and thereby reduce the intensity of subsequent fires until fuels again build up (Stambaugh et al., 2006). Conversely, a long period without fire will increase the likelihood of an intense burn when the next fire occurs. Variation in weather, season of burning and topography introduce additional variation into effects of fire intensity and frequency (Fig. 12.5). Regenerating western savannahs and woodlands may be more complicated than in the eastern USA. For example, the decline of blue oak savannahs in California’s foothills appears to be related to recruitment rates of oak reproduction insufficient for replacing overstorey trees lost to mortality (White, 1966; Bartolome et  al., 1987; McClaran and Bartolome, 1989; Phillips et al., 1997; Swiecki et  al., 1997a, b). The problem is believed to be related to poor establishment, growth and survival of oak reproduction under prevailing conditions of cattle grazing, competition from non-native grasses, limited moisture and sometimes high overstorey density (Adams et al., 1991; Barnhardt et al., 1991; Danielsen and Halvorson, 1991; Davis et al., 1991). There is little evidence that other woody plants are invading these communities in the absence of fire. These conditions are pervasive, and grasslands typically replace western oak savannahs. The role of fire in regenerating oaks in western savannahs and woodlands may be less important than cattle grazing and other problems as a cause of regeneration failure (Swiecki et  al., 1997a). Burning may even benefit introduced grasses at the expense of oak reproduction. Although the role of fire in maintaining western savannahs and woodlands appears to be more complex than their eastern counterparts, it is nevertheless well established that fire plays a significant role in their historical development (Thilenius, 1968; Griffin, 1976; Rossi, 1980; Plumb and McDonald, 1981; McClaran and Bartolome, 1989). Thus, despite uncertainties in its application, prescribed burning remains a common denominator in the management of both eastern and western savannahs and woodlands. In both regions, monitoring needs to be an integral part of their preservation and maintenance. Monitoring should include the periodic assessment of not only the herbaceous layer, but also woody vegetation including oak reproduction and its recruitment into the overstorey. Regeneration guidelines (see Chapters 2, 3, 8 and 9, this volume) adapted to meet savannah or woodland management objectives combined with periodic assessments of light beneath tree crowns also are needed. The value of

Chapter 12

monitoring will increase over time if adequate records are maintained and there is timely analysis and application of findings. Designed experiments in savannah and woodland restoration are rare due to the long and highly variable sequence of disturbance factors required in management. Consequently, a better understanding of savannah and woodland management seems most likely to emerge, at least in the short term, by monitoring field practices and adopting new ones as information unfolds.

Note 1

  A witness tree (bearing tree) is one marked to identify the nearby location of a survey corner (Helms, 1998).

References Abrams, M.D. (1992) Fire and the development of oak forests. BioScience 42, 346–353. https://doi.org/ 10.2307/1311781 Adams, T.E. Jr, Sands, P.B., Weitkamp, W.H. and McDougald, N.K. (1991) Blue and valley oak seedling establishment on California’s hardwood rangelands. USDA Forest Service General Technical Report PSW126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 41–47. Available at: https://www.fs.usda.gov/ treesearch/pubs/28397 (accessed 1 July 2018). Anderson, R.C. and Brown, L.E. (1986) Stability and instability in plant communities following fire. American Journal of Botany 73, 364–368. https://doi.org/10.1002/ j.1537-2197.1986.tb12049.x Arthur, M.A., Alexander, H.D., Dey, D.C., Schweitzer, C.J. and Loftis, D.L. (2012) Refining the oak–fire hypothesis for management of oak-dominated forests of the eastern United States. Journal of Forestry 110, 257–266. https://doi.org/10.5849/jof.11-080 Barnhardt, S.J., McBride, J.R., Cicero, C., da Silva, P. and Warner, P. (1987) Vegetation dynamics of the northern oak woodland. USDA Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 53–58. https://doi.org/10.2737/PSW-GTR-100 Barnhardt, S.J., McBride, J.R. and Warner, P. (1991) Oak seedling establishment in relation to environmental factors at Annadel State Park. USDA Forest Service General Technical Report PSW–126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 25–30. Available at: https://www.fs.usda.gov/treesearch/pubs/ 28394 (accessed 1 July 2018). Bartolome, J.W., Muick, P.C. and McClaran, M.T. (1987) Natural regeneration of Californian hardwoods. USDA

Silvicultural Methods for Oak Savannahs and Woodlands

Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 26–31. https://doi.org/10.2737/PSW-GTR-100 Beilmann, A.P. and Brenner, L.G. (1951) The recent intrusion of forests in the Ozarks. Annals of the Missouri Botanical Garden 38, 263–281. https://doi. org10.2307/2394637 Biswell, H.H. (1989) Prescribed Burning in California Wildlands Vegetation Management. University of California Press, Berkeley, California. Blizzard, E.M., Kabrick, J.M., Dey, D.C., Larsen, D.R., Pallardy, S.G. and Gwaze, D.P. (2013) Light, canopy closure, and overstory retention in upland Ozark forests. In: USDA Forest Service e-General Technical Report SRS-P-175. USDA Forest Service, Southern Research Station, Ashville, North Carolina, pp. 73–79. Available at: https://www.fs.usda.gov/treesearch/pubs/ 43478 (accessed 1 July 2018). Bolsinger, C.L. (1988) The hardwoods of California’s timberlands, woodlands, and savannas. USDA Forest Service Resource Bulletin PNW-RB-148. USDA Forest Service, Pacific Northwest Research Station, Portland, Oregon. https://doi.org/10.2737/ PNW-RB-148 Bowles, M.L. and McBride, J.L. (1998) Vegetation composition, structure, and chronological change in a decadent Midwestern North American savanna remnant. Natural Areas Journal 18, 14–27. Bray, J.R. (1960) The composition of savanna vegetation in Wisconsin. Ecology 41, 721–732. https://doi. org/10.2307/1931806 Brendel, F. (1887) Flora Peoriana: the vegetation in the climate of middle Illinois. J.W. Frank and Sons, Peoria, Illinois. https://doi.org/10.5962/bhl.title.7818 Brose, P.H. and Van Lear, D.H. (1997) Responses of hardwood advance regeneration to seasonal prescribed fires in oak-dominated shelterwood stands. Canadian Journal of Forest Research 28, 331–339. https://doi.org/10.1139/x97-218 Brose, P.H., Dey, D.C., Phillips, R.J. and Waldrop, T.A. (2013) A meta-analysis of the fire-oak hypothesis: does prescribed burning promote oak reproduction in eastern North America? Forest Science 59(3), 322–334. https://doi.org/10.5849/forsci.12-039 Burger, G.V., Ebinger, J.E. and Wilhelm, G.S. (eds) (1991) Proceedings of the Oak Woods Management Workshop. Eastern Illinois University, Charleston, Illinois. Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. Volume 2, Hardwoods. USDA Forest Service Agriculture Handbook 654. USDA Forest Service, Washington, DC. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Chumbley, C.A., Baker, R.G. and Bettis, E.A. III (1990) Midwestern holocene paleoenvironments revealed

449

by floodplain deposits in northeastern Iowa. Science 249, 272–274. https://doi.org10.1126/science.249. 4966.272 Conrad, C.E. and Oechel, W.C. (tech. coords) (1982) Proceedings of Symposium on Dynamics and Management of Mediterranean-type Ecosystems. USDA Forest Service General Technical Report PSW-58. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-58 Cooper, C.F. (1961) The ecology of fire. Scientific American 204, 150–160. https://doi.org10.1038/ scientificamerican0461-150 Curtis, J.T. (1959) The Vegetation of Wisconsin. University of Wisconsin Press, Madison, Wisconsin. Dale, M.E. and Hilt, D.E. (1989) Stocking chart for upland central hardwoods. USDA Forest Service Central Hardwood Note 5.02. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/ pubs/11652 (accessed 1 July 2018). Daniels, F.P. (1904) Ecology of the flora of Sturgis, Michigan and vicinity. Report of Michigan Academy of Science, Arts and Letters 4, 145–159. Danielsen, K.C. and Halvorson, W.L. (1991) Valley oak seedling growth associated with selected grass species. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 9–13. Available at: https://www.fs.usda. gov/treesearch/pubs/28391 (accessed 1 July 2018). Davis, F.W., Borchert, M., Harvey, L.E. and Michaelsen, J.C. (1991) Factors affecting seedling survivorship of blue oak (Quercus douglasii H. & A.) in Central California. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 81–86. Available at: https:// www.fs.usda.gov/treesearch/pubs/28402 (accessed 1 July 2018). Day, G.M. (1953) The Indian as an ecological factor in the northeastern forest. Ecology 34, 329–346. https://doi. org/10.2307/1930900 Dey, D.C. and Hartman, G. (2005) Returning fire to Ozark Highland forest ecosystems: effects on advance reproduction. Forest Ecology and Management 217, 37–53. Dey, D.C. and Kabrick, J.M. (2015) Restoration of midwestern oak woodlands and savannas. In: Stanturf, J.A. (ed.) Restoration of Boreal and Temperate Forests, 2nd edn. CRC Press, Boca Raton, Florida, pp. 401–428. Available at: https://www.fs.usda.gov/ treesearch/pubs/49161 (accessed 1 July 2018). Dey, D.C., Kabrick, J.M. and Schweitzer, C.J. (2017) Silviculture to restore oak savannas and woodlands. Journal of Forestry 115, 202–211. https://doi.org/ 10.5849/jof.15-152

450

DiTomaso, J.M., Brooks, M.L., Allen, E.B., Minnich, R., Rice, P.M. and Kyser, G.B. (2006) Control of invasive weeds with prescribed burning. Weed Technology 20, 535–548. https://doi.org/10.1614/WT-05-086R1.1 Dorney, J.R. (1981) The impact of Native Americans on presettlement vegetation in southeastern Wisconsin. Transactions Wisconsin Academy of Science, Arts and Letters 69, 26–36. Ek, A.R. (1974) Dimensional relationships of forest and open grown trees in Wisconsin. University of Wisconsin Forestry Research Note July. University of Wisconsin, Madision, Wisconsin. Evans, K.E. and Kirkman, R.A. (1981) Guide to bird habitats of the Ozark Plateau. USDA Forest Service General Technical Report NC-68. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10132 (accessed 1 July 2018). Evans, K.E. and Probasco, G.E. (1977) Wildlife of the prairies and plains. USDA Forest Service General Technical Report NC-29. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10094 (accessed 1 July 2018). Eyre, F.H. (ed.) (1980) Forest Cover Types of the United States and Canada. Society of American Foresters, Bethesda, Maryland. Ffolliott, P.F. (2002) Ecology and management of evergreen oak woodlands in Arizona and New Mexico. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 304–316. Ffolliott, P.F., Gottfried, G.J., Bennett, D.A., Hernandez, V.M., Ortega-Rubio, A. and Hamre, R.H. (tech. coords) (1992) Ecology and management of oak and associated woodlands: perspectives in the southwestern United States and Northern Mexico. USDA Forest Service General Technical Report RM-218. Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado. https://doi.org/10.2737/ RM-GTR-218 Fralish, J. (comp.) (1994) Living in the edge. In: Proceedings of the North American Conference on Savanna and Barrens. US Environmental Protection Agency. Available at: https://archive.epa.gov/ecopage/web/html/index-5.html (accessed 27 September 2018). Fuhlendorf, S.D., Engle, D.M., Kerby, J. and Hamilton, R. (2008) Pyric herbivory: rewilding landscapes through the recoupling of fire and grazing. ConservationBiology3,588–598.https://doi.org/10.1111/j.15231739.2008.01139.x Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38

Chapter 12

Grace, J.B., Smith, M.D., Grace, S.L., Collins, S.L. and Stohlgren, T.J. (2001) Interactions between fire and invasive plants in temperate grasslands of North America. In: Tall Timbers Research Station Miscellaneous Publication No. 11. Tall Timbers Research Station, Tallahassee, Florida, pp. 40–65. Green, L.R. (1980) Prescribed burning in California oak management. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 136–142. Griffin, J.R. (1976) Regeneration in Quercus lobata savannas, Santa Lucia Mountains, California. American Midland Naturalist 95, 422–435. https:// doi.org10.2307/2424405 Griffin, J.R. (1977) Oak woodland. In: Barbour, M.G. and Major, J. (eds) Terrestrial Vegetation of California. Wiley, New York, pp. 383–415. Grimm, E.C. (1983) Chronology and dynamics of vegetation change in the prairie–woodland region of southern Minnesota, USA. New Phytologist 93, 311–350. https://doi.org/10.1111/j.1469-8137.1983.tb03434.x Grimm, E.C. (1984) Fire and other factors controlling the Big Woods vegetation of Minnesota in the mid-nineteenth century. Ecological Monographs 54, 291–311. https://doi.org/10.2307/1942499 Guyette, R.P. (1995) A History of Wildland Fire in the Current River Watershed. University of Missouri School of Natural Resources, Columbia, Missouri. Guyette, R.P. and Cutter, B.E. (1991) Tree-ring analysis of fire history of a post oak savanna in the Missouri Ozarks. Natural Areas Journal 11, 93–99. Guyette, R.P. and Spetich, M.A. (2003) Fire history of oak– pine forests in the lower Boston Mountains, Arkansas, USA. Forest Ecology and Management 180, 463–474. https://doi.org/10.1016/S0378-1127(02)00613-8 Guyette, R.P., Muzika, R.M. and Dey, D.C. (2002) Dynamics of an anthropogenic fire regime. Ecosystems 5,472–486.https://doi.org/10.1007/s10021-002-0115-7 Guyette, R.P., Dey, D.C. and Stambaugh, M.C. (2003) Fire and human history of a barren-forest mosaic in southern Indiana. American Midland Naturalist 149, 21–34. https://doi.org/10.1674/0003-0031(2003)149[0021:FAH HOA]2.0.CO;2 Hanberry, B.B., Jones-Farrand, D.T. and Kabrick, J.M. (2014) Historical open forest ecosystems in the Missouri Ozarks: reconstruction and restoration targets. Ecological Restoration 32, 407–416. https://doi. org10.3368/er.32.4.407 Hanberry, B.B., Kabrick, J.M., Dunwiddie, P.W., Hartel, T., Jain, T.B. and Knapp, B.O. (2017) Restoration of temperate savannas and woodlands. In: Allison, S.K. and Murphy, S.D. (eds) Rutland Handbook of Ecological and Environmental Restoration. Earthscan from Routledge, New York, pp. 142–157. Available at: https://www.fs.usda.gov/treesearch/pubs/54749 (accessed 1 July 2018).

Silvicultural Methods for Oak Savannahs and Woodlands

Haney, A. and Apfelbaum, S.I. (1990) Structure and dynamics of Midwest oak savannas. In: Sweeney, J.M. (ed.) Management of Dynamic Ecosystems. The Wildlife Society, North Central Section, West Lafayette, Indiana, pp. 19–30. Harrington, J.A. and Kathol, E. (2008) Responses of shrub midstory and herbaceous layers to managed grazing and fire in a North American savanna (oak woodland) and prairie landscape. Restoration Ecology 17, 234–244. https://doi.org/10.1111/j.1526100X.2008.00369.x Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland. Henderson, N.R. and Long, J.N. (1984) A comparison of stand structure and fire history in two black oak woodlands in northwestern Indiana. Botanical Gazette 145, 222–228. https://doi.org/10.1086/337450 Hutchinson, T.F., Yaussy, D.A., Long, R.P., Rebbeck, J. and Sutherland, E.K. (2012) Long-term (13-year) effects of repeated prescribed fires on stand structure and tree regeneration in mixed-oak forests. Forest Ecology and Management 286, 87–100. http://dx.doi.org/10.1016/j.foreco.2012.08.036 Irving, F.D. (1971) Field instruction in prescribed burning techniques at the University of Minnesota. In: Proceedings of the 10th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 323–331. Jenkins, S.E. (1997) Spatial demography of an Ozark savanna. PhD dissertation, University of Missouri, Columbia, Missouri. Johnson, P.S. (1979) Shoot elongation of black oak and white oak sprouts. Canadian Journal of Forest Research 9, 489–494. https://doi.org/10.1139/x79-079 Kabrick, J.M., Zenner, E.K., Dey, D.C., Gwaze, D. and Jensen, R.G. (2008) Using ecological landtypes to examine landscape-scale oak regeneration dynamics. Forest Ecology and Management 255, 3051–3062. https://doi.org/10.1016/j.foreco.2007.09.068 Kabrick, J.M., Dey, D.C., Kinkead, C.O., Knapp, B.O., Leahy, M., Olson, M.G., Stambaugh, M.C. and Stevenson, A.P. (2014a) Silvicultural considerations for managing fire-dependent oak woodland ecosystems. USDA Forest Service General Technical Report NRS-P-142. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 2–15. Available at: https://www.fs.usda.gov/treesearch/ pubs/47320 (accessed 1 July 2018). Kabrick, J.M., Villwock, J.L., Dey, D.C., Keyser, T.L. and Larsen, D.R. (2014b) Modeling and mapping oak advance reproduction density using soil and site variables. Forest Science 60, 1107–1117. https://doi. org/10.5849/forsci.13-006 Keyser, P.D., Harper, C.A., Anderson, M. and Vander Yacht, A. (2016) How do I manage for woodlands and savannahs? In: Keyser, P.D., Fearer, T. and Harper, C.A. (eds) Managing Oak Forests in the

451

Eastern United States. CRC Press, Boca Raton, Florida, pp. 223–246. Kinkead, C.O. (2013) Thinning and burning in oak woodlands. MSc. thesis, University of Missouri, Columbia, Missouri. Kinkead, C.O., Kabrick, J.M., Stambaugh, M.C. and Grabner, K.W. (2013) Changes to oak woodland stand structure and ground flora composition caused by thinning and burning. USDA Forest Service General Technical Report NRS-P-117. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, pp. 373–383. Available at: https://www.fs.usda.gov/treesearch/pubs/44102 (accessed 1 July 2018). Knapp, B.O., Hullinger, M.A. and Kabrick, J.M. (2017) Effects of fire frequency on long-term development of an oak–hickory forest in Missouri, U.S.A. Forest Ecology and Management 387, 19–29. https://doi. org/10.1016/j.foreco.2016.07.013 Komarek, E.V. (1974) Effects of fire on temperate forests and related ecosystems: southeastern United States. In: Kozlowski, T.T. and Ahlgren, C.E. (eds) Fire and Ecosystems. Academic Press, New York, pp. 251–277. https://doi.org/10.1016/B978-0-12-424255-5.50013-4 Krajicek, J.E. (1967) Maximum use of minimum acres. In: Proceedings of the 9th Southern Forest Tree Improvement Conference. Southern Forest Tree Improvement Conference, Knoxville, Tennessee, pp. 35–37. Krajicek, J.E., Brinkman, K.A. and Gingrich, S.F. (1961) Crown competition – a measure of density. Forest Science 7, 35–42. https://doi.org/10.1093/forestscience/ 7.1.35 Ladd, D. (1991) Re-examination of the role of fire in Missouri oak woodlands. In: Proceedings of Oak Woods Management Workshop. Eastern Illinois University, Charleston, Illinois, pp. 67–80. Larsen, D.R., Metzger, M.A. and Johnson, P.S. (1997) Oak regeneration and overstory density in the Missouri Ozarks. Canadian Journal of Forest Research 27, 869–875. https://doi.org/10.1139/x97-010 Law, J.R., Johnson, P.S. and Houf, G. (1994) A crown cover chart for oak savannas. USDA Forest Service Technical Brief TB-NC-2. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10999 (accessed 1 July 2018). Little, S. (1974) Effects of fire on temperate forests: northeastern United States. In: Kozlowski, T.T. and Ahlgren, C.E. (eds) Fire and Ecosystems. Academic Press, New York, pp. 225–250. https://doi.org/10.1016/B9780-12-424255-5.50012-2 McClain, W.E., Jenkins, M.A., Jenkins, S.E. and Ebinger, J.E. (1993) Changes in the woody vegetation of a bur oak savanna remnant in central Illinois. Natural Areas Journal 13, 108–114.

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McClaran, M.P. and Bartolome, J.W. (1989) Fire-related recruitment in stagnant Quercus douglasii populations. Canadian Journal of Forest Research 19, 580– 585. https://doi.org/10.1139/x89-091 McGill, D., Martin, J., Rogers, R. and Johnson, P.S. (1991) New stocking charts for northern red oak. University of Wisconsin Forestry Research Notes 277. University of Wisconsin, Madison, Wisconsin. McMurry, E.R., Stambaugh, M.C., Guyette, R.P. and Dey, D.C. (2007) Fire scars reveal source of New England’s 1780 Dark Day. International Journal of Wildland Fire 16, 266–270. https://doi.org/10.1071/ WF05095 McPherson, G.R. (1997) Ecology and Management of North American Savannas. University of Arizona Press, Tucson, Arizona. Mead, S.B. (1846) Catalogue of plants growing spontaneously in the state of Illinois, the principal part near Augusta, Hancock County. Prairie Farmer 6, 35–36, 60, 93, 199–122. Nelson, P.W. (2010) The Terrestrial Natural Communities of Missouri. Missouri Natural Areas Committee, Jefferson City, Missouri. Nowacki, G.J. and Abrams, M.D. (2008) The demise of fire and ‘mesophication’ of forest in the eastern United States. BioScience 58, 123–138. https://doi. org/10.1641/B580207 Nuzzo, V.A. (1986) Extent and status of Midwest oak savanna: presettlement and 1985. Natural Areas Journal 6, 6–36. Packard, S. (1991) Rediscovering the tallgrass savanna of Illinois. In: Proceedings of the Oak Woods Management Workshop. Eastern Illinois University, Charleston, Illinois, pp. 55–66. Phillips, R.L., McDougald, N.K., Standiford, R.B., McCreary, D.D. and Frost, W.E. (1997) Blue oak regeneration in southern Sierra Nevada foothills. USDA Forest Service General Technical Report PSW160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 177–181. Available at: https://www.fs.usda.gov/ treesearch/pubs/28172 (accessed 1 July 2018). Pillsbury, N.H., Verner, J. and Tietje, W.D. (tech. coords) (1997) Proceedings of Symposium on Oak Woodlands: Ecology, Management, and Urban Interface Issues. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-160 Plumb, T.R. (tech. coord.) (1980) Proceedings of the Symposium on the Ecology, Management, and Utilization of California Oaks. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-44

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Plumb, T.R. and McDonald, P.M. (1981) Oak management in California. USDA Forest Service General Technical Report PSW-54. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/ PSW-GTR-54 Plumb, T.R. and Pillsbury, N.H. (tech. coords) (1987) Proceedings of Symposium on Multiple-use Manage­ ment of California’s Hardwood Resources. USDA Forest Service General Technical Report PSW-100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi.org/10.2737/PSW-GTR-100 Pyne, S.J. (1982) Fire in America. Princeton University Press, Princeton, New Jersey. Rebertus, A.J. and Burns, B.R. (1997) The importance of gap processes in the development and maintenance of oak savannas and dry forests. Journal of Ecology 85, 635–645. https://doi.org10.2307/2960534 Rebertus, A.J., Williamson, G.B., Platt, W.J. and Glitzenstein, J.S. (1993) Impacts of temporal variation in fire regime on savanna oaks and pines. In: Proceedings of the 18th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 215–225. Reed, L.J. and Sugihara, N.G. (1987) Northern oak woodlands – ecosystem in jeopardy or is it already too late? USDA Forest Service General Technical Report PSW100. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 59–63. https://doi.org/10.2737/PSW-GTR-100 Reidy, J.L., Thompson, F.R. and Kendrick, S.W. (2014) Breeding bird response to habitat and landscape factors across a gradient of savanna, woodland, and forest in the Missouri Ozarks. Forest Ecology and Management 313, 34–46. https://doi.org/10.1016/j. foreco.2013.10.042 Rossi, R.S. (1980) History of cultural influences on the distribution and reproduction of oaks in California. USDA Forest Service General Technical Report PSW44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 7–18. Available at: https://www.fs.fed.us/psw/publications/documents/psw_gtr044/psw_gtr044.pdf (accessed 1 July 2018). Smith, H.C. and Gibbs, C.B. (1970) A guide to sugarbush stocking. USDA Forest Service Research Paper NE-171. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/23780 (accessed 1 July 2018). Stambaugh, M.C. and Guyette, R.P. (2008) Predicting spatio-temporal variability in fire return intervals using a topographic roughness index. Forest Ecology and Management 254, 463–473. https://doi.org/10.1016/j. foreco.2007.08.029

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Stambaugh, M.C., Guyette, R.P., Grabner, K.W. and Kolaks, J. (2006) Understanding Ozark forest litter variability through a synthesis of accumulation rates and fire events. USDA Forest Service Proceedings RMRS-P-41. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, pp. 321–332. Available at: https://www.fs.usda.gov/treesearch/ pubs/25958 (accessed 1 July 2018). Stambaugh, M.C., Guyette, R.P., Marschall, J.M. and Dey, D.C. (2016) Scale dependence of oak woodland and historical fire intervals: contrasting the Barrens of Tennessee and Cross Timbers of Oklahoma, USA. Fire Ecology 12, 65–84. https://doi.org10.4996/ fireecology.1202065 Standiford, R.B. (tech. coord.) (1991) Proceedings of Symposium on Oak Woodlands and Hardwood Rangeland Management. USDA Forest Service General Technical Report PSW-126. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-126 Standiford, R.B. (2002) California’s oak woodlands. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 280–303. Stearns, F. and Holland, K. (eds) (1995) Proceedings of the 1993 Midwest Oak Savanna Conference. US Environmental Protection Agency. Available at: http://www.epa.gov/glnpo/ecopage/upland/oak/ oak93/index.html (accessed 2 March 2009). Streng, D.R. and Harcombe, P.A. (1982) Why don’t East Texas savannas grow up to forest? American Midland Naturalist 108, 278–294. https://doi.org10.2307/ 2425488 Swiecki, T.J., Bernhardt, E.A. and Drake, C. (1997a) Stand-level status of blue oak sapling recruitment and regeneration. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 147–156. Available at: https://www.fs.usda.gov/treesearch/pubs/28169 (accessed 1 July 2018). Swiecki, T.J., Bernhardt, E.A. and Drake, C. (1997b) Factors affecting blue oak sapling recruitment. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 157–167. Available at: https://www.fs. usda.gov/treesearch/pubs/28170 (accessed 1 July 2018). Taft, J.B. (2009) Effects of overstory stand density and fire on ground layer vegetation in oak woodland and savanna habitats. USDA Forest Service General Technical Report NRS-P-46. USDA Forest Service, Northern Research Station, Newtown Square,

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Pennsylvania, pp. 21–39. Available at: https://www. fs.usda.gov/treesearch/pubs/17289 (accessed 19 September 2018). Tester, J.R. (1989) Effects of fire frequency on oak savanna in east-central Minnesota. Bulletin of the Torrey Botanical Club 116, 134–144. https://doi. org10.2307/2997196 Thilenius, J.F. (1968) The Quercus garryana forests of the Willamette Valley, Oregon. Ecology 49, 1124–1133. https://doi.org/10.2307/1934496 Thor, E. and Nichols, G.M. (1973) Some effects of fires on litter, soil, and hardwood regeneration. In: Proceedings of the 13th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 317–329. US Environmental Protection Agency (1995) Proceed­ ings of the Midwest Oak Savanna and Woodland Ecosystems Conferences. US Environmental Protection Agency, Washington, DC. Van Lear, D.H. and Waldrop, T.A. (1988) Effects of fire on natural regeneration in the Appalachian Mountains. Society of American Foresters Publication 88-03. Society of American Foresters, Bethesda, Maryland, pp. 56–70. White, A.S. (1983) The effects of thirteen years of annual prescribed burning on a Quercus ellipsoidalis community in Minnesota. Ecology 64, 1081–1085. https:// doi.org/10.2307/1937817

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White, A.S. (1986) Prescribed burning for oak savanna restoration in central Minnesota. USDA Forest Service Research Paper NC-266. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs. usda.gov/treesearch/pubs/10053 (accessed 1 July 2018). White, D.H. (1991) Legal implications associated with use and control of fire as a management practice. In: Proceedings of the 17th Tall Timbers Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 375–384. White, K.L. (1966) Structure and composition of foothill woodland in central Coastal California. Ecology 47, 229–237. https://doi.org/10.2307/1933769 Whitney, G.G. (1994) From Coastal Wilderness to Fruited Plain. Cambridge University Press, Cambridge. Wilhelm, G. (1973) Fire ecology in Shenandoah National Park. In: Proceedings of the 12th Tall Timbers Fire Ecology Conference. Tall Timbers Research Station, Tallahassee, Florida, pp. 445–488. Zouhar, K., Smith, J.K. and Sutherland, S. (2008) Effects of fire on nonnative invasive plants and invisibility of wildland ecosystems. USDA Forest Service General Technical Report RMRS-42. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, pp. 7–31. https://doi.org/10.2737/RMRSGTR-42-V6

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13

Silvicultural Methods for Selected Ecosystem Services Introduction

Contemporary management and protection of oak forests includes objectives that historically were not widely pursued in the practice of oak silviculture. These include managing oak forests for acorn production, managing wildlife habitat, producing biomass, sequestering carbon, maintaining old-growth oak forests and creating aesthetic oak forests. Collectively these practices fall under the umbrella of managing ecosystem services. Ecosystem services are broadly defined as the benefits people derive from ecosystems (Millennium Ecosystem Assessment, 2005), and they are often categorized as provisioning services (e.g. quantity of timber or water), regulating services (e.g. climate regulation, carbon sequestration, water quality) and cultural services (e.g. recreation, aesthetics, solitude). Sustaining ecosystem services is increasingly important in forest management considerations, and silviculture can be used to sustain or increase ecosystem services from oak forests. Many silvicultural practices benefit multiple ecosystem services simultaneously. For example, managing oak savannahs (see Chapter 12, this volume) may increase plant biodiversity and improve acorn production for wildlife while simultaneously creating a vegetation structure with high aesthetic appeal. This chapter presents silvicultural considerations for sustaining some common ecosystem services from oak forests.

Managing Stands for Acorn Production Oak forests are life support systems for the many animals that live there. Acorns, a staple product of oak forests, are eaten by many species of mammals and birds including deer, bears, squirrels, mice, rabbits, foxes, raccoons, grackles, turkeys, grouse, quail, blue jays, woodpeckers and waterfowl. The health and population densities of wildlife often rise and fall with the cyclic production of acorns. Wildlife reliance on acorns is related to several factors including their

widespread occurrence, palatability, nutritiousness and availability during the food-scarce autumn and winter months. Where maintaining high-quality wildlife habitat is a high priority, sustaining acorn production may be of paramount importance. Adequate acorn production is also critical for regenerating oak forests, whether that regeneration occurs through the gradual accumulation of oak seedlings over many years or through periodic bumper crops of acorns that result in significant regeneration events in a single year (see Chapter 3, this volume). In years with poor acorn production, acorn consumption by wildlife may be detrimental to the establishment of new oak reproduction. Regardless of the motivation for doing so, there are practical methods for sustaining and increasing acorn production. These are discussed in the following sections. Assessing and predicting acorn crops Estimating the quantity of acorns produced by trees or stands and predicting the size of future acorn crops are important tools in managing wildlife populations and scheduling regeneration practices to take advantage of good acorn crops. Variation in acorn production has been related to differences among oak species, differences among oaks of a single species, and differences among stands as a result of species composition, stand size structure and site characteristics. Differences among species Differences in acorn production among species are sometimes reflected in reported maximum numbers of acorns produced by single trees in 1 year (Table 13.1). However, such observations may not provide a reliable basis for comparing species’ potentials and differences if: (i) the observed numbers of trees and time intervals are too small to consistently include a

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Table 13.1.  Reported maximum numbers of mature acorns on a single tree/year for 12 species.a Species Blackjack oak Black oak Bluejack oak Chestnut oak Northern red oak Post oak Scarlet oak Southern red oak Swamp chestnut oak Water oak White oak Willow oak

Acorns/tree/year

Years observed (trees sampled)

3,100 9,640 5,500 2,000 4,020 2,630 46,000 5,000 1,000 93,000 23,788 52,200

3 (NA)b 4 (55) 3 (NA) 3 (55) 4 (13) 4 (4) 7 (NA) 3 (NA) 3 (NA) 3 (15) 4 (4) 3 (15)

State

Reference

Louisiana Missouri Louisiana New Jersey Missouri Missouri North Carolina Louisiana Louisiana Arkansas Virginia Arkansas

Moody et al. (1954) Christisen and Kearby (1984) Moody et al. (1954) Wood (1934) Christisen and Kearby (1984) Christisen and Kearby (1984) Downs (1944) Moody et al. (1954) Moody et al. (1954) Cypert and Webster (1948) Feret et al. (1982) Cypert and Webster (1948)

a

Maximum numbers from published sources. NA, not available.

b

value approaching the species’ maximum potential; (ii) the observed trees are of variable but unspecified size; or (iii) species’ population characteristics such as the frequency of occurrence of good and poor acorn crops and the proportion of trees that are inherently poor acorn producers are not considered. A more reliable basis for comparing acorn production among species is the average annual number of acorns produced per tree for a population of trees observed over several years (e.g. Christisen, 1955). However, even that method may not provide a satisfactory assessment of species’ differences because individual trees usually produce acorns in proportion to the cross-sectional areas of their crowns (Tryon and Carvell, 1962). Thus, comparative acorn production may be more meaningfully described by numbers of acorns produced per unit of crown area or per unit of basal area based on many trees observed over several years. A study in West Virginia showed that the average annual production of northern red oak acorns over 5 years was nearly twice that of white oak based on the number of mature acorns per unit of crown area (Tryon and Carvell, 1962). In southern Michigan, northern red oaks produced about three times more acorns than white oaks per unit of crown area over a 4-year study period (Gysel, 1957). Where the objective is to relate acorn production to food potentially available for wildlife, the most relevant expression of production may be the total weight of sound acorns per unit of crown area or basal area because acorns of different species differ greatly in size. For example, a bur oak acorn of average

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size weighs about six times more than a pin oak acorn of average size and an Oregon white oak acorn weighs about 12 times more than a huckleberry oak acorn (Olson, 1974). A study in North Carolina showed that, among five species, average annual acorn production of northern red oak was greater than the other species observed based on the fresh weight of mature acorns per square foot of basal area per year (Beck, 1977) (Table 13.2). However, consideration must be given to individual acorn size and whether the animal can consume the acorn. Comparing the acorn-producing capacities of different species based on average yields over only a few years also can be misleading. To resolve that problem, recognized differences among species in acorn-producing potential can be used to define indices of relative acorn production for each species. Sharp’s (1958) index bases acorn production on the average number of acorns per branch tip, where a branch tip is the terminal 24 inches of shoot growth on any branch in the upper one-third of the tree crown. For species in the red oak group, a 24 inch branch tip excludes the current year’s growth and is rated at 100% of potential yield if 32 or more acorns are present. For species in the white oak group, a 24 inch branch tip includes the current year’s growth and is rated at 100% of potential yield if 24 or more acorns are present (Table 13.3). This definition of 100% acorn yield thus considers differences between the two major species groups in the maximum numbers of acorns likely to occur on a typical branch. A similar acorn production index considers inherent differences among species in acorn-cluster

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size and percentage of the crown containing acorns (Christisen and Kearby, 1984). A cluster consists of a closely associated group of acorns (e.g. the cluster of second-year acorns in Fig. 2.4, this volume) rather than the entire branch tip. In application, a northern red oak with clusters of five to seven acorns and a black oak with clusters of twice that number both receive the maximum index rating of nine. Both cluster sizes express the average maximum potential for their respective species. The rating system also recognizes that white oaks produce acorns on limbs and branches midway down the trunk or lower, whereas black oak and northern red oaks produce acorns primarily in the upper one-third of their crowns. This index can be used to estimate numbers of acorns per unit crown area for each species. Depending on species, the production index accounts for 75–87% of the variation in the Table 13.2.  Average annual weight yield of mature acorns per square foot of basal area in North Carolina. (From Beck, 1977.)a Annual weight of mature acorns (lb/ft2 of basal area) Species

Mean

12-year range

Northern red oak White oak Scarlet oak Chestnut oak Black oak

12.2 8.3 3.0 2.4 1.6

0–62.2 0.1–39.8 0–15.4 0–17.6 0–5.4

a

Based on 12 years of observations from six 2/3-acre plots in 63- to 82-year-old even-aged stands. Reported values are gross production and include insect-infested acorns.

observed numbers of acorns per unit of crown area (Myers, 1978). Other visual acorn-crop rating systems also have been proposed (e.g. Graves, 1980; McDonald, 1992; Koenig et al., 1994). Koenig and others (1994) estimated acorn production based on 15 counts of acorns on each tree by each of two observers. They found close agreement between those estimates and numbers of acorns collected in acorn traps located beneath the same trees. They argued for recording and using acorn counts directly rather than categorizing them by index classes. Such counts, they pointed out, provide greater flexibility in statistical analyses. Moreover, they advocated counting acorns on the same trees each year to make among-year comparisons more accurate and thereby decrease the sample size needed to accurately estimate overall productivity. They found that estimating acorn crops based on their visual survey protocol was 16−25 times faster than estimating based on acorn traps. Such time savings potentially facilitate more intensive monitoring of acorn production for a given area or species. Visual surveys such as those described above do not, by themselves, directly yield data on the absolute numbers of acorns produced by trees. However, those numbers can be estimated by simultaneously monitoring acorns in traps randomly set out under a subset of the observed trees (Fig. 2.9, this volume). Then the relation between the two measures (i.e. the visual counts and numbers of acorns per unit area from the traps) can be established by regression methods (Koenig et al., 1994). However, this method is only as accurate as the trap data. Sources of error in acorn trap counts originate from the relatively

Table 13.3.  Sharp’s (1958) acorn production index.

Production index

Relative abundance rating of acorns

0 1 2 3 4 5

None Trace Poor Fair Good Bumper crop

Average number of acorns observed/branch tipa

Maximum potential number of acorns/branch tip (%)a

White oaks

Red oaks

0 < 10 10–25 26–50 51–75 76–100

0 < 2.4 2.4–6.1 6.2–13.2 13.3–18.1 18.2+

0 < 3.2 3.2–8.2 8.3–16.2 16.3–24.2 24.3+

a

A branch tip is any terminal 24 inches of shoot growth in the upper one-third of the tree crown. For species in the red oak group, a 24 inch branch tip excludes the current year’s growth and is rated at 100% of potential yield if 32 or more acorns are present. For species in the white oak group, a 24 inch branch tip includes the current year’s growth because all acorns maturing in the current year are on the current year’s shoot growth. White oak branch tips are rated at 100% of potential yield if 24 or more acorns are present.

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small proportion of ground area that can be practically sampled and animal pilfering of acorns from traps. The latter source of error can be reduced by counting acorn caps in addition to acorns (Auchmoody et al., 1993; Koenig et al., 1994). A method that, in application, largely reduces errors in acorn counting is based on estimating the number of acorns per square foot of basal area per acre for a given species from the percentage of trees bearing acorns (Greenberg and Parresol, 2000, 2002). The method was developed for black, northern red, scarlet, white and chestnut oaks in southern Appalachian forests and is based on observing acorn crops for 5 years for each species. In field application, the Greenberg–Parresol method requires an inventory of: (i) oaks in which acorns on individual trees are scored and recorded as present or absent during the late acorn maturation period; and (ii) stand measurements from which basal area per acre by species can be determined. This information is then applied to a set of pre-calculated equations or curves (Fig. 13.1). The resulting estimates of the number of acorns per square foot of basal area per acre by species then can be applied to stand basal area measurements to determine acorn yields by species. The method possesses relatively high statistical accuracy because of the high correlation between the predictor variable and numbers of acorns per square foot of basal area per acre (see Fig. 13.1). The oaks

in the stands from which the equations were derived were predominantly in codominant and dominant crown classes (Greenberg and Parresol, 2000). The method, in its application to such forests, is thus largely unconfounded by the occurrence of oaks in shaded, subordinate crown classes. Where oaks largely occur in superior crown classes, most of those that have an inherent capacity to produce acorns are unconstrained to do so when other factors are favourable for acorn production. This may not be the case in some uneven-aged oak stands where large numbers of oaks, and thus significant basal areas, occur in subordinate crown classes (see Chapter 9, this volume). The method nevertheless would appear to have wide application, with or without modification, to many oak species and regions. The method, unlike most others, is not disadvantaged in its application by requiring time-consuming and error-prone visual acorn counts, or errors in scoring acorn densityclasses or assumptions about their meaning. Information on the method’s application includes how to determine confidence limits for acorn production estimates and other information (Greenberg and Parresol, 2000, 2002). Methods are also presented for developing predictive equations. This requires information from: (i) stand inventories of tree diameters or basal area of each species of interest; and (ii) an acorn count from traps placed beneath acorn-bearing trees during the period of acorn maturation from

600

Acorns/ft2 of basal area/acre

White oak 500 Scarlet oak

400

Chestnut oak

300

Northern red oak Black oak

200 100 0

0

20

40

60

80

100

Trees bearing acorns (%) Fig. 13.1.  The number of acorns per square foot of basal area per acre by species estimated from the percentage of trees of a given species that bear acorns in a given year. The figure is derived from 5-year data from forests in the southern Appalachians. For method of field application, equations and goodness-of-fit, see related text and Greenberg and Parresol (2000, 2002). Observed data are based on data from acorn traps. (Adapted from Greenberg and Parresol, 2000, 2002.)

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mid-August until acorn fall is complete during each year of observation. Another approach to estimating numbers of acorns is based on the bifurcation rate of tree branching. Bifurcation rate (br) is the number of times a tree branches along the route from the ground to any given terminal bud. The number of branching bifurcations remained remarkably constant in a population of 23 oaks (12 white and 11 black oaks) in central Missouri that ranged from 8 to 23 inches dbh (Cecich and Larsen, 1997). The mean bifurcation rate of 8.9 (standard deviation 1.1) did not differ significantly between species. Bifurcation rate can be used to estimate the total number of potential acorn-producing branches per tree. If the average number of acorns per branch is known (e.g. as required in determining Sharp’s acorn production index), the total number of acorns per tree can be estimated by multiplication. For the observed population of Missouri trees, the average number of branch tips can be calculated as 2br (= 28.9 = 478). Thus, if the average number of acorns per branch is known (including lower branches not usually counted in applying Sharp’s method), this average can be multiplied by 2br to estimate whole-tree acorn production.1 To be useful, such estimates would require intensive measurements of branching in trees and accounting for possible sources of local or regional variation in the bifurcation rate of tree branching before they could be confidently applied to large populations of trees (e.g. an entire stand, forest or ecoregion). The method none the less deserves further testing in designing more accurate methods of estimating acorn crops. Even the most refined applications of the methods described above may not eliminate major errors in estimating acorn production. Errors are most likely to occur when estimates are applied to stands where the size of trees (stand structure) and other stand characteristics differ markedly from the stands in which the acorn production model was developed. Effects of tree size and stand characteristics The acorn-producing potential of individual trees in forests is partially related to stand density and the associated exposure of tree crowns to light. Other factors being equal, oaks growing in the open produce more acorns than those growing in closed-canopy forests (Sharp, 1958; Sharp and Sprague, 1967). Open-grown trees have: (i) maximum crown area for a given stem diameter (Krajicek et al., 1961); (ii) large

Silvicultural Methods for Selected Ecosystem Services

numbers of branches per unit of crown area and thus large numbers of buds from which acorns can arise (Sharp, 1958); and (iii) full crown exposure to light and thus maximum photosynthesis per unit leaf area (Verme, 1953). For black oaks, acorn production was 11 times greater in portions of the crown exposed to full light than in shaded portions (Verme, 1953). Similarly, exposed crown areas of white oak and northern red oak produced five times more acorns than shaded crown areas (Post, 1998). Northern red oaks shaded on one or more sides by other trees tended to produce more flowers and immature acorns on branches receiving full sunlight. Relative stand density (stocking) is an indirect measure of crown competition. Stocking can vary greatly even among stands with closed canopies (see Chapter 6, this volume). As stocking increases, the mean crown area per tree decreases, which in turn reduces the mean ratio of tree crown area to tree diameter. Stocking charts and equations that relate stand basal area and mean stand diameter to relative stand density (stocking percentage) are expressions of these relations (e.g. Figs 6.9 and 6.11, this volume). Healy (1997) related stocking percentage to differences in acorn production of individual northern red oaks in a mixed northern red oak stand in Massachusetts (initial age 42 years). Two levels of initial stocking were observed over 6 years: 40% and > 100% based on Gingrich’s (1967) stocking chart (Fig. 6.9). At 40% stocking, trees in the thinned stand did not fully utilize growing space, and thus represented a somewhat open-grown stand condition. Based on acorn sampling beneath the crowns of 60 dominant and codominant red oaks in each treatment, trees in the stand with 40% stocking produced significantly more acorns than trees in the unthinned stand based on 6-year average production. Although the proportion of trees bearing acorns and the proportion of sound acorns were similar in thinned and unthinned stands in most years, there was great year-to-year variation in acorn production at both stocking levels. In years of poor production, the proportion of sample trees bearing acorns was greater in the stand with lower stocking. A companion study of eight stands ranging from 62 to 82 years old compared stand-wide acorn production in thinned and unthinned stands (Healy, 1997). When averaged over the 3-year study period, acorn production in thinned stands (averaging 71% stocking) and unthinned stands (averaged 100% stocking) did not differ significantly. However, the lack of significance between the 3-year means was attributed to large year-to-year variation in acorn production.

459

When acorn yields for each of the 3 years were considered individually, annual yields were consistently greater (nominally) in thinned than in unthinned stands. The relative effects of thinning were greatest during years of poor acorn production and least during years of high production. Apparent gains from thinning ranged from 59% to 93% based on the dry weight of acorns per acre and from 42% to 94% based on numbers of sound acorns per acre (Healy, 1997). Thinning also can reduce acorn yields per acre even though individual tree yields may increase (e.g. Harlow and Eikum, 1963). This effect may be attributable to two factors: ●● a reduction in total oak crown area – however, if residual stand density is within the range representing full utilization of growing space (e.g. between A- and B-levels of stocking in Fig. 6.9), oak crowns can expand after thinning to capture the growing space vacated by thinned trees. At those stocking levels, reduction in total oak crown area should be temporary (assuming the retained trees are healthy). ●● the chance removal during thinning of one or more of the relatively few but inherently good acorn producers per acre – although the trees remaining after thinning may each increase in yield, the increase may not compensate for the removal of good producers. If increasing or maximizing acorn production is the primary goal of thinning, good producers should be identified before thinning and then retained (see the section below ‘Guidelines for sustaining acorn production’). Studies that report the effects of

thinning on acorn production do not uniformly show that thinning increases acorn production. Studies must be evaluated individually to identify treatments with thinnings applied without regard to individual-tree acorn production characteristics or with low acorn sampling intensity. Vertical stratification of trees produces recognizable canopy crown classes (Smith et al., 1997). Some investigators have noted that trees in dominant and codominant crown classes produce more acorns per unit crown area than trees in the intermediate and suppressed crown classes. The small, shaded crowns of trees in lower crown classes develop few branches per unit crown area, and this reduces their acornbearing potential (Kittredge and Chittenden, 1929; Moody, 1953; Gysel, 1956). Thus, expressing acorn production as yield per unit of crown area without considering variation in stand density and structure (i.e. the distribution of crown classes) obscures potential differences related to variation in crown class and crown exposure. In most stands, crown class is correlated with bole diameter, which in turn is correlated with crown area and acorn production. Thus, bole diameter can account for significant variation in acorn production that is related to both crown class and crown area. In southern Appalachian forests, this relation has been used to develop models for estimating average annual acorn production of dominant and codominant trees from tree diameter (Fig.  13.2). In the US south, similar models were developed to estimate average annual fresh-weight yields of mature acorns from tree diameters

4000 Scarlet oak

Acorns/tree

3000 Black oak

2000

White oak 1000

Chestnut oak Northern red oak

0

10

14

18

22 26 Dbh (in.)

30

34

Fig. 13.2.  Average annual production of mature acorns in the southern Appalachians in relation to tree diameter based on a 7-year study of dominant and codominant trees. (From Downs, 1944.)

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1956) and those habitats usually support more competing vegetation that interferes with oak reproduction. Wildlife that consume acorns also may prefer certain habitats and associated site quality factors over others (Gysel, 1957). All the predictive models discussed above express acorn yield on an average annual basis and ignore the enormous year-to-year variation in acorn production. Although such models provide useful estimates of average acorn production over relatively long periods such as a decade, they cannot accurately estimate acorn production for any given year. A basis for predicting yields in advance of a few weeks before acorn drop has yet to be developed (Cecich and Sullivan, 1999). Lack of progress in developing such models is due to inadequate knowledge of the flowering and fruiting process and the numerous factors that influence flowering and acorn production. Many of these factors, including weather and insect events, essentially occur at random and can only be accounted for probabilistically (Sullivan, 2001). One proposed model for predicting annual variation in populations of pistillate flowers and acorns in white oak is based on the probabilities of occurrence of events known to reduce acorn production

(Goodrum et  al., 1971). Crown radius explained 48–81% of the variation in acorn yield, depending on species. In the same study, predictive models based on crown radius explained 76–94% of the variation in total acorn yield (Fig. 13.3). Although crown radius produces more accurate estimates of acorn yield than bole diameter, the former is more difficult to measure. For that reason, bole diameter is usually the predictor of choice. Few studies have evaluated the effects of site quality (i.e. measures of forest productive capacity) on acorn production. A 4-year southern Michigan study indicated that site quality had no effect on white oak or black oak acorn production when production was expressed as numbers of fully developed acorns or acorn weight per unit of crown area (Gysel, 1957). Similarly, there was no evidence that site quality as measured by site index and topographic factors in West Virginia forests influenced the production of northern red and white oak acorns (Tryon and Carvell, 1962). Other factors correlated with site quality nevertheless may affect acorn survival and seedling establishment. For example, rich, moist habitats provide better conditions than drier forests for organisms such as fungi that can initiate decay in acorns after they have fallen (Winston, 50

White oak

Acorn yield (lb/tree)

40 Water oak 30

20

Post oak

Blackjack oak

Southern red oak

10

0

5

10

15

20

25

30

Crown radius (ft) Fig. 13.3.  Estimated average annual yield (fresh weight) of sound mature acorns in relation to crown radius for five species in the upper coastal plain of Texas and Louisiana. Estimates are based on 63–507 trees observed for 6–18 consecutive years, depending on species. Crown radius explained 48–81% of the variation in yield, depending on species. Models were based on the observed weight of acorns per size class (diameter or crown radius class) per year averaged over all trees in the size class. (From Goodrum et al., 1971.)

Silvicultural Methods for Selected Ecosystem Services

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(Larsen and Cecich, 1997). These include: (i) the relative frequency of low humidity (below 60%) during the 1-week pollination period; (ii) hail storms that destroy flowers; (iii) insect populations that reduce numbers of flowers (tree hoppers) and acorns (acorn weevils); (iv) summer droughts that cause premature flower and acorn abscission; and (v) genetic factors related to flower fertilization. A similar model incorporates these and other factors into a hierarchical framework that estimates acorn production at stand and landscape scales (Sullivan, 2001). The model considers the interactions between landform and the weather events listed above as well as tree sizes and ages; the relative proportion of red oaks and white oaks; and differences in their flowering and fruiting phenologies. Simulations through time consider the lagged effects of weather conditions on the 2-year development cycle of red oak acorns and on the annual development cycle of white oak acorns. Aggregate probability estimates of acorn production for stands can be linked to a landscape model that simulates future patterns of forest age structure and species composition in response to disturbance by wind, fire and timber harvest (e.g. see the section ‘Forest landscape models’ in Chapter 15, this volume). The result is a probabilistic model that can be applied to 10 acres or tens of thousands of acres. Although probabilistic models are not able to predict a given future year’s acorn crop with any certainty, they are useful in estimating the mean probability of a good (or poor) acorn crop in a given future year, the expected frequency of good and poor crops over a decade, and the probability of occurrence, by species, of 2 or 3 consecutive years of poor acorn crops. The latter estimates are useful to wildlife managers concerned with minimizing overall fluctuations in acorn production. Such models are also useful for assessing joint effects and the relative importance of the various factors known to limit acorn production and thus the regeneration of oak forests and wildlife nutrition. Guidelines for sustaining acorn production General guidelines Acorn production in established stands can be sustained, and perhaps even increased, by applying the following guidelines: ●● Before the first thinning (e.g. at stand age 25–30 years), identify and reserve the good acorn

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producers in each stand. To do this, record the acorn production of candidate trees for 5 years or more. If that is impractical, assess the acornproducing capacity of individual trees by observing production during a single year in which a good to excellent acorn crop occurs for one or more of the major species present. However, in the red oak group, many good producers may be overlooked because all trees of those species may not produce well in the same year. Criteria for identifying good acorn producers are given by Sharp (1958) (Table 13.4). In the oak–hickory region, the best time to rank trees is in the period 10–25 August, which is before acorn consumers have eaten or cached many acorns. Acorns are best observed with binoculars on bright days when they are silhouetted against the sky (Fig. 13.4). ●● During thinning, retain a mixture of oak species to minimize the impact of the large year-to-year fluctuation in acorn production in any one species. ●● Thin around the identified acorn producers to expose their crowns to full light on all sides. This facilitates crown expansion and increases branch density. Branch density increases acorn p ­ roduction per unit of crown area because of the increase in numbers (density) of acorn-bearing branches (Verme, 1953). Among potential acorn producers, dominant and codominant trees will be the most efficient producers. Area-wide thinning is not required because only 20 or fewer good seed producers are likely to occur per acre, even in pure oak stands. However, these seed producers typically will be dominant and codominant trees, and they may account for proportionately Table 13.4.  An acorn production ranking system for individual oaks. (Adapted from Sharp, 1958.)a Average number of acorns/branchb Ranking

White oak group

Red oak group

Excellent Good Fair Poor

18+ 12–17 6–11 ≤5

24+ 16–23 8–15 ≤8

a

Note that in any one year, excellent producers may not reach their potential because of unfavourable environmental factors. b Based on the terminal 24 inches of healthy branches in the upper one-third of crowns. For species in the red oak group, a 24 inch branch tip excludes the current year’s growth; for species in the white oak group, a 24 inch branch tip includes the current year’s growth.

Chapter 13

Acorn production in green tree reservoirs

Fig. 13.4.  If the 24 acorns on this northern red oak branch were average for the tree as a whole during a bumper acorn-crop year, the tree would be ranked as an excellent acorn producer (Table 13.4). In applying the ranking system, acorns are counted (with the aid of binoculars) on the terminal 24 inch lengths of healthy branches in the upper third of the crown. (Photograph courtesy of USDA Forest Service, Northern Research Station.)

more basal area and stocking than their numbers alone indicate. ●● Adjust the rotation age (or in uneven-age management, adjust the maximum tree diameter) to increase the number of trees in the size classes that typically produce the most acorns. Maximum production will vary by species. For example, production in northern red oak peaks when tree dbh reaches 20 inches and then it declines in larger trees (Fig. 13.2). In contrast, white oak production is maximized at about 26 inches. Many other species, however, do not exhibit well-defined diameter-related peaks in production, at least within the diameter ranges that have been reported (Downs, 1944; Goodrum et al., 1971). Large, senescent trees are usually poor acorn producers (Huntley, 1983). ●● Retain a selection of the known best acorn producers from one rotation to the next to avoid the loss in production for decades while the newly regenerating stand reaches sexual maturity. ●● Accelerate maturity to seed production by supplementing planting (Chapter 10, this volume) precocious seedlings grown as large bareroot or RPM® container seedlings using seed collected from known consistent and abundant seed producers (Kormanik et al., 1994, 2004; Grossman et al., 2003; Dey et al., 2004).

Silvicultural Methods for Selected Ecosystem Services

The guidelines presented above can be applied to many kinds of oak forests, both upland and lowland. However, management areas known as green tree reservoirs (GTRs) represent a special case. GTRs are mixed-hardwood bottomland forests dominated by oaks that are managed specifically for acorn production and waterfowl habitat. They are artificially flooded in winter (usually during November–February) to attract migrating waterfowl (Fig. 13.5). Many GTRs are adjacent to the Mississippi River and its major tributaries in southern USA, and some of them have been in operation for 50 years or more. During years of good production, acorns are a principal autumn and winter food for many species of ducks, especially mallards and wood ducks (McQuilkin and Musbach, 1977; Fredrickson, 2005). The waterfowl feed on fallen acorns, which sink to the bottom of the shallow pools. The combination of shallow water and abundant acorns makes GTRs a preferred autumn and winter habitat for ducks and provides opportunities for duck hunters. Controlled flooding is managed via artificial levees, and flooding depths are usually maintained at 1–3 ft. However, controlled flooding is commonly extended and intensified by natural flood events, which can occur at any time of year. Flooded areas are relatively flat with poor surface drainage, and soils are typically clayey with poor internal drainage even when not flooded. Because of their unique biotic and physical characteristics, both natural and constructed GTRs require special management methods not common to upland oak forests. Bottomland oaks common to GTRs include laurel, willow, overcup, water, cherrybark, pin and Nuttall (Texas) oaks. However, usually only one or two oak species predominate in a given GTR. Associated non-oaks include green ash, American elm, sweetgum, hackberry, blackgum, silver maple, red maple, water hickory, bald cypress and other species. Standing floodwater even during the dormant season is potentially harmful to established oaks, so the timing, duration, frequency and depth of flooding are of paramount importance in managing GTRs (Fredrickson and Batema, 1992; Fredrickson, 2005; Guttery and Ezell, 2006). Because bottomland tree species are not equally flood tolerant, excessive duration and/or frequency of flooding can alter forest composition (King, 1995; King and Allen, 1996; Ervin et al., 2006;

463

(A)

(B)

Fig. 13.5.  Pin oak stands in a managed green tree reservoir (GTR) in southern Illinois. (A) A flooded stand during the dormant season; continuous controlled flooding (to a depth of 1–3 ft) usually occurs from November through to February. (B) To minimize oak mortality, stands must remain unflooded during most of the growing season. Several years with no flooding may be required to establish oak reproduction for regenerating older stands that are declining in vigour and acorn production. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

Guttery and Ezell, 2006). Under the conditions typically prevailing in GTRs, the more flood- and shade-tolerant non-oaks eventually replace the oaks (Guttery and Ezell, 2006). Most of the bottomland oaks are categorized as only slightly tolerant or moderately tolerant of flooding (Table 13.5). Oak species accordingly stratify along the minor elevational gradients within bottomlands in response to their relative flood tolerances (Conner and Sharitz, 2005; McCurry et al., 2005). GTRs are flooded up to 2 months longer than unimpounded bottomland forests (Ervin et al., 2006). Such extended flooding can accelerate the mortality of overstorey oaks, shift species composition away from preferred species and complicate the

464

problem of regenerating oaks (Wigley and Filer, 1989; King, 1995; King and Allen, 1996; King et al., 1998). As a result, acorn production eventually declines in established GTRs. Like other oak forests, acorn production in GTRs varies from year to year. Over 12 years, annual acorn production by pin oaks in a Missouri GTR averaged 63,000 sound acorns (approximately 150 lb/acre/year). However, yields ranged from 8 to nearly 440 lb/acre/year (Fig. 13.6). Acorn crops failed completely every 3–4 years and there was only 1 year with a bumper crop (McQuilkin and Musbach, 1977). Stands that were not flooded produced about one-third more acorns than flooded stands. However, because insect predation

Chapter 13

Flood tolerance rating Species White oaka Swamp laurel oak Swamp chestnut oak Southern live oak Cherrybark oak Bur oak Swamp white oak Pin oak Water oak Willow oak Nuttall (Texas) oak Overcup oak

Slightly Moderately Intolerant tolerant tolerant Tolerant X X X

X X X X X X X X

X

a

Acorns/acre (1000s)

An upland oak occasionally present in bottomlands.

180

439

160

390

140

342

120

293

100

244

80

195

60

146

40

98

20

49

0 1956

1958

1960

1962

1964

1966

1968

Weight of acorns/acre (lb)

of acorns was greater in unflooded stands, the number of sound acorns in each case was nearly equal (Minckler and McDermott, 1960; Minckler and Janes, 1965; McQuilkin and Musbach, 1977). The percentage of sound acorns ranged from 60% to 90% in flooded stands and from 30% to 60% in unflooded stands (McQuilkin and Musbach, 1977). Although the reason for this difference was not apparent, the results demonstrated that annual flooding, at least in this case, did not reduce but instead increased the production of sound acorns. In flooded stands, the number of acorns that failed to mature or that were insect damaged was approximately the same each year, so good acorn yields occurred only when bumper crops overwhelmed losses to insects. In contrast, total acorn production of Nuttall (Texas) oak in a Mississippi GTR that was flooded annually was half that of an adjacent unflooded area during a 5-year study period; however, possible differences in acorn soundness were not reported (Francis, 1983). By the time pin oak stands in GTRs are 25–35 years old, they are capable of producing large acorn crops. In stands more than 25 years old, production increases as the number of large trees (> 11 inches dbh) increases. Acorn production also may increase with increasing stand basal area (up to 90 ft2/acre). However, GTRs with basal areas as low as 40 ft2/ acre can produce good acorn crops if stands are

Table 13.5.  Flood tolerance ratings for some bottomland oaks. (Adapted from McKnight et al., 1980; Hook, 1984; Burns and Honkala, 1990; Collins and Battaglia, 2008.)

0 1970

Year Fig. 13.6.  Fluctuation in annual production of sound, mature acorns (predominantly pin oak) over 12 years in a GTR in southern Missouri. Values are averages for stands with basal areas ranging from 40 to 90 ft2/acre. (Adapted from McQuilkin and Musbach, 1977.)

Silvicultural Methods for Selected Ecosystem Services

465

comprised of large-diameter trees (McQuilkin and Musbach, 1977). It is therefore possible to sustain good acorn production and simultaneously meet other management objectives if appropriate stand densities and large-crowned acorn-­producing trees are retained during thinning. Sustaining high acorn production in GTRs requires the periodic replacement of oaks lost to mortality or timber harvesting. Even when acorns are abundant and initial seedling establishment is successful, the recruitment of oak reproduction into the overstorey is usually problematic, especially when stands are annually flooded. Due to the limited size of GTRs and their high development costs, lengthy regeneration periods are often unacceptable to forest and wildlife managers who are under pressure to sustain annual hunting opportunities (Fredrickson, 2005). Nevertheless, the need to change the traditional approach to GTR management has been recognized. Recent guidelines for their management call for reductions in stand density combined with site preparation and with cessation of flooding for 1 or more years. This combination facilitates recruitment of oak reproduction into the overstorey and slows the mortality of overstorey oaks (Minckler and McDermott, 1960; Lhotka and Zaczek, 2003; Fredrickson, 2005; Guttery and Ezell, 2006) (also see ‘The shelterwood method’ in Chapter 8, this volume). Long-term flooding (> 7–20 days) that completely inundates seedlings during the growing season reduces their survival rate. However, shortterm flooding may promote oak seedling survival by reducing damage from girdling rodents (Chamberlain and Leopold, 2005). Short-term flooding is potentially practical in GTRs because flood control structures are already in place. New recommendations include limiting water depths to 5 inches or less and keeping GTRs flooded into late winter and then gradually removing the water. Shallow water combined with slow flooding and delaying drawdown into early spring also benefit breeding wood ducks and invertebrates. The shallow water and a more natural hydroperiod increases invertebrate populations (Batema et al., 2005), which are essential to egg laying by wood ducks (Drobney, 1990). Nevertheless, general recommendations are to avoid growing-season flooding at a given location on an unvarying annual schedule (Fredrickson, 2005). Although tested regeneration prescriptions are lacking for GTRs, shelterwood and group selection

466

methods have been recommended where recruitment of oak reproduction into the overstorey is inadequate for replacing trees lost to mortality or harvesting (Fredrickson and Batema, 1992; Hertlein and Gates, 2005). Uneven-aged silvicultural methods have been advocated as an appropriate method as well (Denman and Karnuth, 2005; see also Chapter 9, this volume). Underplanting oak seedlings in bottomlands may be a viable option (Gardiner and Yeiser, 2006; Krekeler et al., 2006, see also ‘Enrichment planting’ in Chapter 10, this volume). Methods of afforestation used in bottomlands also may have application to planting oaks in GTRs (Clatterbuck and Meadows, 1993; Dey et al., 2004; Kabrick et al., 2005).

Managing Oak Forests for Wildlife Hundreds of wildlife species spend all or part of their lives in oak forests. California oak woodlands and savannahs provide habitat for up to 282 terrestrial vertebrate species: 22 amphibians, 38 reptiles, 144 birds and 78 mammals (Tietje et al., 1997). Entire books have been written about managing forests for wildlife (e.g. DeGraaf et al., 1992, 2005; Dickson, 2001; McShea and Healy, 2002; Hunter and Schmiegelow, 2010). The literature describing habitat management for individual wildlife species is voluminous – particularly for mammals, birds and herpetofauna. Forest wildlife also includes invertebrates, which are rarely managed directly but which play important roles in forest health, nutrient cycling and food webs. Forests provide essential food, water, shelter and reproductive opportunities for wildlife, and changes in forest conditions inevitably favour some wildlife species over others. Thomas and Radke (1989) noted, ‘Timber management is wildlife management. The degree to which it is good wildlife management depends on how well the wildlife biologist can explain the relationship of wildlife to habitat and how well the forester can manipulate habitat to achieve wildlife goals.’ More generally, all forest management actions or inactions lead to changes in forest conditions that are tightly linked to present and future wildlife habitat suitability. Wildlife and timber management are also linked economically; proceeds from timber management are often a means of achieving wildlife management goals (or other ecosystem services) that otherwise would be economically prohibitive. Alternatively the value of wildlife as a focal point for recreation (e.g. observing, photographing, hunting, fishing) may generate financial support

Chapter 13

for application of silvicultural practices intended to improve habitat for desirable wildlife species. Changes in forest species composition and structure directly affect: (i) habitat temperature; (ii) shelter from unfavourable weather; (iii) primary food supply (e.g. leaves and acorns); (iv) secondary food supply (e.g. invertebrates that live and feed in oak forests); and (v) sites for hiding, roosting, nesting and foraging. Wildlife species affect oak forests through feeding on acorns, transporting acorns, burying and caching acorns, introducing seeds of desirable or undesirable plants, feeding on insects, excavating cavities, aiding in wood decomposition, and herbivory. Oak forest management is often directed at improving habitat for desirable wildlife species, but occasionally management is focused on reducing damage from undesirable wildlife (e.g. the gypsy moth or white-tailed deer) (see Chapter 11, this volume). Given the diversity of wildlife that utilizes oak forests, the focus in this section is not on management details for individual wildlife species, but rather on how to think about oak forests as dynamic, responsive ecosystems that affect and are affected by wildlife. Details on managing individual wildlife species are available from other sources and can be used in conjunction with the content here which is focused on managing forest structure, mast, dead wood and tree cavities. Forest stands are the primary focus of silviculture, and stand-scale changes are relevant to most forest-associated wildlife species – particularly species that have a small home range. However, habitat assessments for wide-ranging wildlife species also require consideration of landscape conditions (Dickson et al., 1995; Thompson, 2001; Shifley et al., 2006; Hunter and Schmiegelow, 2010). These may include interspersion patterns for stands that offer different size structures or tree species composition, the juxtaposition of forest and non-forest habitats, or proximity to special habitat features such as water or hibernacula. In the case of neotropical migrant songbirds, a full habitat assessment may span multiple continents. The cerulean warbler, for example, is commonly found in mature oak forests in the central USA where it spends summers feeding and breeding. Prior to the start of the North American winter, however, the cerulean warbler migrates thousands of miles to spend non-breeding periods in South America. Therefore, habitat conditions on both continents affect the cerulean’s population size. Standscale silvicultural prescriptions intended to improve

Silvicultural Methods for Selected Ecosystem Services

habitat for wildlife species must be considered in the context of a species’ entire range and requirements for survival and reproduction. For many wide-ranging, forest-associated wildlife species, forest landscape models can provide insight on how spatial patterns of forest stand conditions will change over time and across large landscapes (i.e. a few thousand to a few million acres in extent) (see ‘Forest landscape models’ and Plate 18 discussed in this section and in Chapter 15, this volume). Those results can support associated estimates of landscape-scale wildlife habitat suitability (e.g. Larson et al., 2003; Dijak et al., 2007; Rittenhouse et al., 2007; Pauli et al., 2015), but such landscape-scale habitat analyses fall in the realm of landscape ecology and are largely beyond the scope of this book. Consequently, the following sections describe tree- and stand-scale factors that affect wildlife habitat suitability with the admonition that long-term, landscape-scale, cumulative effects of forest management on wildlife also must be considered, as must the interspersion of forest and non-forest habitats. Managing stand structure for wildlife As described in Chapters 5 and 6, this volume, oak stands follow predicable patterns of structural change in the absence of disturbance, and some wildlife species can be matched with the stages of stand development that they frequent. The details of these relationships vary by ecoregion, forest cover type, physiography, hydrology and other stand or landscape conditions. Therefore, narrowing the geographical and ecological range of forest conditions allows greater specificity in associations of wildlife habitat with forest structure. The stages of stand development (Fig. 5.2, this volume) are a coarse filter for wildlife habitat suitability (Noss, 1990; Cushman et al., 2008; Hunter and Schmiegelow, 2010; Tingley et al., 2014). As such they allow one to identify wildlife species that are likely (or unlikely) to be found during a given stage of oak forest development, but they omit consideration of species-specific details of wildlife population dynamics. The relationship of wildlife species to forest structure has been illustrated by linking 161 bird species with upland oak (and other) habitats in the Missouri Ozarks (Evans and Kirkman, 1981). The number of associated avian species varies between 22 and 67 for different forest structural stages (Fig. 13.7).

467

Old-growth stage 67 avian species

Understory reinitiation stage 38 avian species

Late stem exclusion stage, xeric 39 avian species

Early stand initiation stage 30 avian species

Late stand initiation stage 22 avian species

Early stem exclusion stage 26 avian species

Fig. 13.7.  The number of bird species associated with different stages of stand development for oak–hickory forests on the Ozark Plateau. The changes in forest structure result in changes in avian habitat suitability. Birds may use a habitat for only part of a year or only for certain activities such as nesting or feeding. Stages of stand development are discussed in Chapter 5, this volume (the old-growth stage in this figure is equivalent to the complex stage in Chapter 5). (Data from Evans and Kirkman, 1981; illustrations by David A. Hamilton.)

Many of the 161 avian species are associated with two or three of the forest structural stages illustrated in Fig. 13.7, but only one species – the black-andwhite warbler – is associated with all stages. The old forest habitat supports 67 associated avian species, the most of any of the habitats. In contrast, the

468

late stand initiation stage has only 22 associated species, eight of which are also associated with old forest. For each structural stage of upland oak forests, Evans and Kirkman (1981) provide additional details about the relative abundance of avian species, their activities and foods they consume.

Chapter 13

Number of birds/100 acres of habitat

Forest

Open woodland

Savannah

6

Closed woodland

The degree of tree canopy cover also affects wildlife habitat suitability. In addition to the mature, closed-canopy forest illustrated as the understorey reinitiation stage of development in Fig. 13.7, other oak habitats with mature trees (e.g. trees 10 inches dbh and larger) include savannahs, open woodlands and closed woodlands. These habitats fall along a continuum of open to closed forest canopies. Habitat definitions are flexible, but savannahs typically have less than 30% stocking, open woodlands between 30% and 60% stocking, closed woodlands between 60% and 75% stocking, and closed-canopy forest greater than 75% stocking (see Chapter 12, this volume). In the eastern USA, habitat selection by resident winter birds has been shown to vary along a gradient of stocking per cent spanning open savannahs to closed-canopy forest (Fig. 13.8). In the western USA oak savannahs and open woodlands are far more common than in the east, and they support more than 100 avian species with opportunities for feeding (acorns, buds,

Hairy woodpecker

5

Red-bellied woodpecker

4 3 2

Northern flicker

1 0

0

20

40

60 80 100 Stocking (%)

120

140

Fig. 13.8.  Modelled winter bird densities by stocking per cent for selected species. Sampled sites varied from open-canopy savannahs to closed-canopy forests. All sites had a mature oak overstorey (e.g. dominant trees > 10 in. dbh), but sites differed in the number of overstorey trees and hence the stocking per cent and canopy closure. (Based on Kendrick and Thompson, 2013.)

Silvicultural Methods for Selected Ecosystem Services

leaves), foraging for insects, roosting and nesting (Verner, 1980; Block et al. 1992). Other sources link oak forest structure (as measured by size class, age class, stage of stand development or disturbance type) with other taxa of wildlife (e.g. Barrett, 1980; Verner, 1980; DeGraff and Rudis, 1986; DeGraff et al., 1992, 2005; Dickson, 2004). Thus, for oak forests in many regions it is relatively easy to know which of the common wildlife species are likely or unlikely to occur in association with a given structural stage of development. It is far more complicated to understand why a particular wildlife species does or does not occur within a given forest stand, the influence of landscape-scale factors beyond the stand, and how best to alter wildlife population size through management actions. By virtue of their longevity, large tree size, vertical stratification, abundant snags and abundant down wood in various stages of decay, old-growth oak forests provide wildlife habitat conditions that are not found in younger forests. Characteristics associated with old-growth oak forests are summarized later in this chapter in the section on managing old-growth oak forests. Oak forests become economically mature before they become ecologically mature, so most oak forests are harvested and regenerated before they can provide the characteristic structural and compositional features associated with old growth (Healy et al., 1989). Old-growth oak forests are now most abundant on xeric sites with low productivity where logging has been unprofitable (Stahle and Chaney, 1994), but scattered remnant old-growth, oak-dominated forests remain on more productive sites (Spetich et al., 1999). For reasons outlined in Chapters 2 and 3, this volume, it is difficult to establish and sustain oak reproduction in ageing, closed-canopy oak forests on mesic and hydric sites. On such sites, oak-dominated old-growth forests are usually transitional to non-oak tree species that successionally replace the oaks in the absence of disturbance. As noted later in this chapter, there are management practices that can help protect the existing remnant old-growth oak forests, and extend their influence. Moreover, due to historical harvesting practices, more than half of oak forests in the eastern USA are clustered in the 40- to 80-year age class. With current low rates of forest harvesting and disturbance, millions of acres of ageing oak forests will in time develop old-growth forest characteristics. In addition to forest structure, silvicultural systems (described in Chapters 8 and 9, this volume)

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favour a different suite of bird species (Thompson et al., 1996) (Fig. 13.9). There are also some avian generalists such as blue jays, American crows, and Carolina wrens that are ubiquitous. A Gingrich (1967) stocking guide is often used to guide thinning and other timber management decisions for oak stands (Fig. 6.9, this volume). The stocking guide framework, which graphically displays stand basal area, density, mean dbh and stocking per cent can also be customized as a guide to wildlife habitat.

have been used as a framework for identifying avian habitat associations. In the Central Harwood Forest region, for example, the 10-year period following regeneration by clearcutting (even-aged management) primarily favours a group of bird species adapted to forest openings. Many of the same bird species are common in glades or savannahs. Forests with less disturbance, such as those within 10 years of a prior group selection or singletree selection harvest (uneven-aged management),

Prairie warbler Indigo bunting Blue-grey gnatcatcher Field sparrow Brown-headed cowbird Northern cardinal Blue jay Yellow-breasted chat Great-crested flycatcher American crow Carolina wren Eastern wood-pewee Black-and-white warbler Red-eyed vireo Ovenbird Pine warbler Yellow-throated vireo Wood thrush Acadian flycatcher Mature forest, no harvest

Single-tree selection

Group selection

Shelterwood

Clearcut

Savannah

Glade

Worm-eating warbler Abundant Common Uncommon Occasional Rare or never

Fig. 13.9.  The relative abundance of selected breeding birds by habitat condition in oak-dominated Central Hardwood Forests. Open glade and savannah habitats were maintained by frequent fires and occasional tree cutting. Harvests occurred within the prior 10 years for habitats with regeneration harvests by clearcut, shelterwood establishment, group selection and single-tree selection. Mature forest habitat was even aged and 60–100 years old. In general, the degree of forest disturbance increases from habitat categories on the right of the diagram to those on the left. (Based on Thompson et al., 1996.)

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Different regions on the stocking guide represent early-, mid- and late-successional forests; woodlands; savannahs; and stages of stand development including stand initiation, stem exclusion, understorey reinitiation and old growth. These stand conditions can be mapped on a copy of the Gingrich stocking and linked with wildlife species that utilize each forest structural stage. This can be particularly convenient because stocking guides already are fully integrated with stand management decision making for forest products, and can also be used to track or forecast stand change over time (e.g. Fig. 6.14, this volume). Linking wildlife habitat suitability to stand conditions represented on the stocking guide can increase its utility to managers. However, there are some practical considerations when doing so: (i) the suite of wildlife species varies regionally; (ii) the number of wildlife species that can be mapped on to one stocking guide is limited by legibility; and (iii) wildlife habitat suitability may vary qualitatively across multiple habitats (e.g. see Fig. 13.8). Oaks as wildlife food Collectively, oaks are the most common source of food for forest-associated wildlife species in the USA. More than 100 vertebrate wildlife species regularly feed on oak acorns, foliage, buds, bark and/or wood (Martin et al., 1961; Barrett, 1980; Verner, 1980; Fearer, 2016) (Table 13.6). A detailed list of avian acorn consumers and their acorn dispersal habits is given in Table 2.2 (this volume). Those tables omit invertebrates that feed on oak trees or acorns and then become a food source elsewhere in the food web. Wildlife and acorns During periods when they are available, acorns provide concentrated nutrition. Acorns are low in protein compared with other common types of herbaceous and woody browse, but they are relatively high in fats and metabolizable energy. The crude fat percentage of acorns from the red oak group is three to four times greater than that of acorns in the white oak group. However, acorns from the white oak group contain fewer bitter tannins, and for this reason appear to be more palatable to some wildlife species than acorns from the red oak group (Kirkpatrick and Pekins, 2002). As noted earlier in this chapter and in Chapter 2, this volume, acorn crop yields have high tree-to-tree

Silvicultural Methods for Selected Ecosystem Services

and interannual variability. Populations of mice and other small rodents that consume acorns increase in years following good acorn crops and decrease following poor crops. These effects can ripple elsewhere in the food web with increases in rodent populations triggering increases in deer ticks which use rodents as hosts, increased rodent predation of eggs and nestlings of certain songbirds, decreased songbird reproduction, and increased predation of rodents by raptors (Healy, 2002; Fearer, 2016). The high metabolizable energy content of acorns allows wildlife to efficiently meet their daily energy requirements and minimize feeding periods when they may be exposed to predators. Acorns are a staple source of nutrition, even for species that prefer other foods when they are available. Abundant acorn crops help white-tailed deer and black-tailed deer gain necessary weight to get them through the winter months and are associated with greater reproductive success in deer. In autumn months, diets of black-tailed deer in western oak woodlands and savannahs may consist of 33% acorns and another 30% oak leaves, buds and twigs (Menke and Fry, 1980). Prior-year acorns can provide important early spring nutrition to deer that have overwintered (Kirkpatrick and Pekins, 2002). Where oak forests overlap with the home range of black bears, acorns are a preferred food, and they help bears develop fat reserves necessary to survive winter denning. In years with acorn crop failures, black bears may travel 50 miles or more to areas where acorns are abundant. Acorn crop failures are associated with increased nuisance-bear reports and lower reproductive success (Vaughan, 2002; Fearer, 2016). Acorn size and shape are important to bird species that consume acorns whole. For example, most jays and grouse are unable to consume large acorns. Wild turkeys will consume any acorn species, but when they have a choice, they favour small acorns such as those of post oak and blackjack oak (Steffen et al., 2002). Wild turkeys feed preferentially on acorns, even when other foods – including row crops – are available. Presumably that is due to the high energy content of acorns coupled with the protection that turkeys gain when foraging under an oak canopy rather than in an open field. Landscapes with an interspersion of equal parts oak forest and open land are considered ideal for wild turkeys (Kurzejeski and Lewis, 1990). Relationships are complex between oak species that produce acorns and wildlife species that consume acorns. In years with poor acorn production,

471

Table 13.6.  Common wildlife species listed by estimated proportion of diet supplied by oaks. Acorns are a staple food for most of these species, but some also consume oak buds, foliage, twigs or bark. Diets vary regionally for some widespread species such as white-tailed deer or wild turkeys, so they have repeated entries in the table. Diets also vary seasonally. (Based on Martin et al., 1961; Menke and Fry, 1980; Verner, 1980.) Proportion of diet supplied by oaks 50% or more

25–50%

10–25%

5–10%

2–5%

Up to 2%

Birds Acorn woodpecker Lesser prairie chicken Wood ducka Ant-eating woodpecker Band-tailed pigeon Blue jay California horned lark California jay Florida jay Scrub jay Steller’s jay Wild turkeya Brown thrasher Lewis’s woodpecker Mearns quail Purple gracklea Red-bellied woodpecker Red-headed woodpecker Ruffed grousea Varied thrush White-breasted nuthatch Wild turkeya Wood ducka Woodhouse’s scrub jay Bobwhite quail Common flicker Eastern towhee Meriam’s turkey Mountain quail Plain titmouse Purple gracklea Red-shafted flicker Spotted towhee Tufted titmouse Valley quail Wild turkeya California quail Greater prairie chicken Mallard duck Ring-necked pheasant Rufous-sided towhee Ruffed grousea Wood ducka Eastern crow California thrasher 18 others

Small mammals

Large mammals White-tailed deera

Grey squirrel Racoona Western fox squirrel Western grey squirrel

Black bear Black-tailed deer Peccary White-tailed deera

Beechey ground squirrel Fox squirrel Pocket gopher Racoona

Mule deera White-tailed deera

Columbian ground squirrel Eastern chipmunk Flying squirrel Racoona Red squirrel White-footed mouse Wood rat

Mule deera White-tailed deera

Eastern cottontail rabbit Mantled ground squirrel New England cottontail rabbit Opossum Western chipmunk

White-tailed deera

11 others

Elk Mountain sheep

a

Species is repeated in the table because acorn consumption varies by geographic region and is influenced by regional oak species abundances and availability of other foods.

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Chapter 13

wildlife may consume nearly the entire acorn crop and limit opportunities for oak regeneration. In years with abundant acorn crops some species of rodents and birds play essential roles in dispersing and burying acorns that subsequently germinate and become successful oak reproduction. Squirrels, mice and other rodents that feed on acorns will ground cache (i.e. transport and bury) acorns for later consumption. In the autumn, tree squirrels have been shown to preferentially consume white oak acorns (which germinate soon after they drop) and to preferentially cache red oak acorns (which do not germinate until the following spring). Tree squirrels often use their teeth to ‘notch’ the tip of acorns and destroy the embryo. When done properly it prevents acorn germination and retains more nutrition in the buried acorns (see Chapter 2, this volume). Jays, titmice, Clark’s nutcracker and yellow-billed magpies are among the avian species that cache acorns (Table 2.2, this volume). Blue jays have been observed transporting acorns 2.5 miles prior to ground caching (Fig. 2.14, this volume). Caching disperses acorns to new locations, and acorns that are cached and forgotten can be a significant source of future oak reproduction. Methods for improved acorn production are discussed in detail earlier in this chapter. In general, maintaining a mixture of white oak (1-year acorn development cycle) and red oak species (2-year acorn development cycle) helps buffer against a total acorn crop failure in a year with unfavourable weather conditions during critical periods of pollination and acorn development. Managing to produce a population of acorns that spans a range of sizes has the potential to feed a wide variety of wildlife species, some of which are physically unable to consume large acorns. Average acorn production per tree varies by tree species and within a species it increases with tree dbh – at least to a point (Fig. 13.2). Thus at the landscape scale it is important to continually regenerate oaks so there will always be a sufficient population of oaks entering their years of prime acorn production. This may require managing oaks to a larger size and greater age than necessary for contemporary timber production. More broadly, greater landscape diversity in oak species and sizes imparts greater resilience to oak forests and their associated wildlife populations following unanticipated or undesirable disturbances, including insect outbreaks, wildfire and climate change (see Chapter 14, this volume).

Silvicultural Methods for Selected Ecosystem Services

Oak herbivory Herbivory, particularly by deer, can be a severe detriment to oak regeneration (Plate 11) (also see Chapter 11, this volume). In many locations repeated deer browsing limits height growth of oak reproduction and acts as a significant barrier to successful regeneration of oak stands. Small mammals eat leaves, buds, bark and roots of young oaks. They may girdle oak stems and cause dieback or otherwise reduce the rate of tree growth. Silvicultural prescriptions could be designed to increase opportunities for herbivory by wildlife, but in practice silvicultural prescriptions are almost always aimed at reducing the impact of herbivory on tree growth and reproduction success. Where herbivory is severe, silvicultural prescriptions often include fencing stands or caging individual trees (Chapters 2, 10 and 11, this volume). Hunting regulations affect deer population sizes and the associated impact of deer herbivory. Herbivory as a damaging agent in oak forests is discussed in Chapter 11. Managing snags and coarse woody debris Standing dead trees are often referred to as snags, and they provide sites for feeding, perching and roosting by wildlife (Davis et al., 1983). Together, snags and tree cavities (discussed later in this chapter) provide essential habitat at some stage of life for at least 100 species of birds and mammals in the eastern USA (Titus, 1983). Coarse woody debris is utilized by 80 vertebrate species that inhabit western oak woodlands (Tietje et al., 1997). A large oak snag can remain standing for years, and it changes over time, gradually losing bark and branches as it decomposes. Newly formed snags are sometimes called hard snags, because they are comprised primarily of sound wood (Helms, 1998). Soft snags are comprised primarily of decaying wood that is more readily excavated by cavity builders and insectivores. Trees and branches that fall to the forest floor are termed woody debris, and material larger than 4 or 5 inches in diameter is commonly included in coarse woody debris inventories in oak forests. Coarse woody debris is often classified by decay class, ranging from newly fallen to almost fully decomposed. Coarse woody debris at different stages of decomposition differs in wood strength, moisture retention, retained bark and contact with the ground. These dynamic features of coarse woody debris affect its value to various wildlife

473

474

Coarse woody debris (ft 3/acre)

species. In general, large snags and large pieces of coarse woody debris are more desirable than small, because they are less common, they last longer, and by virtue of their size they provide more opportunities for perching, feeding and nesting (Hunter and Schmiegelow, 2010). Oaks and associated species differ in their propensity to produce coarse woody debris while alive (Tietje et al., 1997) and in the longevity of that coarse woody debris. Additions of coarse woody debris are episodic, with large pulses coming when large trees fall or when severe weather brings down numerous large branches (Rebertus et al., 1997). It may take decades of gradual decay until the newly fallen coarse woody debris is fully decomposed and incorporated into the forest floor. Harvesting also results in additions of coarse woody debris. Although boles of harvested trees are typically removed from a site, residual tops and branches add to the quantity of coarse woody debris. Jenkins, M.A. et al. (2004) reported that the amount of coarse woody debris resulting from clearcutting and group selection harvests in Indiana hardwood stands was highly variable, but on average the quantity of coarse woody debris was greater in group selection openings than in clearcuts and greater on mesic sites and bottomland sites than on dry slopes. They found that the volume of coarse woody debris declined rapidly in the 14 years following a harvest operation and then continued to decline at a slower rate through their subsequent 16 years of monitoring. Despite large variation among individual stands, there are predictable patterns of coarse woody debris volume that are related to stand age or time since the last disturbance (Spetich et al., 1999; Jenkins, M.A. et al., 2004). Coarse woody debris volume is typically high in the years immediately following a stand initiating disturbance (Fig. 13.10), because tops, boles or entire trees from the prior overstorey are left on the ground. Coarse woody debris volume declines as stands move into the stem exclusion stage of development. By that stage, coarse woody debris from the prior overstorey has largely decomposed, and even though tree mortality rates are high during the stem exclusion stage, the trees that die are small in size and decompose quickly. As mean tree size increases, biomass from large down trees and limbs accumulates on the forest floor, and the volume of coarse woody debris gradually increases as stands progress through the understorey reinitiation and old-growth stages of development. There is evidence that, other things being equal, the

2000 1600 1200 800 400 0

0

50

100

150

200

250

Age (years) Fig. 13.10.  Generalized pattern of coarse woody debris volume by stand age for oak forests in the US Midwest. (Based on Spetich et al., 1999.)

volume of coarse woody debris per acre increases with increasing site productivity (Spetich et al., 1999). This is not surprising because maximum live biomass per acre by age class also increases with increasing site productivity. Compared with eastern oak forests, coarse woody debris is rare in the open oak woodlands of California. A systematic sample spread across nearly 6 million acres of oak woodlands surrounding California’s Central Valley found that 60% of plots had no coarse woody debris, and the mean across all plots was 115 ft3/acre or 1.2 tons/acre (Tietje et al., 2002). Because coarse woody debris is utilized by at least 80 vertebrates in California oak woodlands, management practices intended to create, protect and retain coarse woody debris will generally be beneficial to wildlife. At the landscape scale, the number of snags by dbh class for oak-dominated forests that are in the understorey reinitiation or old-growth stages of development has been found to be roughly 10% of the number of live trees (Shifley et al., 1997a, b; Spetich et al., 1999; Herbeck, 2000) (Fig. 13.11). For sites in the US Midwest, the ratio of dead to live trees reported for old-growth oak forests averaged a little more than 10%, and for secondgrowth forests it was a little less than 10%, but the relative proportion of snags by dbh class was similar to that of live trees. Obviously, harvesting and other disturbances affect the ratio of snags to live trees, but in the absence of a site-specific snag inventory, the size distribution of live trees is an indicator of the size distribution of snags.

Chapter 13

An appropriate number and size of snags and quantity of coarse woody debris depends on the species of wildlife to be accommodated (Healy, 2002). Wildlife benefits of snags and coarse woody debris include those that are direct (e.g. loose bark on snags providing summer roost sites for the endangered Indiana bat) and those that are indirect (e.g. coarse wood debris supporting invertebrate populations that are fed upon by desired birds and herpetofauna). Interactions among coarse woody debris and wildlife species can be complex in oak forests. For example, in old-growth forests in Poland where oak recruitment is difficult, van Ginkel et al. (2013) conducted an experiment in which acorns were systematically cached in close proximity to coarse woody debris and then monitored. The acorns placed beneath an oak overstorey were entirely consumed by wild boars. Acorns cached in proximity to coarse woody debris beneath conifer forests were largely unaffected by boars but nearly 60% of acorns were consumed or otherwise moved and re-cached by rodents. Thus, sites in proximity to coarse woody debris afforded limited protection for acorns. However, among the few oak trees that regenerated from seed and survived, nearly all of the successful oak saplings were found in proximity to piles of coarse woody debris which helped protect them from repeated ungulate herbivory.

Managing tree cavities Tree cavities provide habitat for nesting, roosting, foraging, perching and denning by birds and small mammals. Oaks are particularly desirable as den trees because many of the species that use oak cavities as den sites also feed on acorns. Primary cavity nesters excavate cavities from live trees or from snags, while secondary cavity nesters use natural cavities or previously excavated cavities. A single cavity may host different species ­seasonally within a single year or in subsequent years. In general, cavity excavators have more specific cavity retirements than secondary cavity users, but reproductive success of both groups is diminished when cavities are few in number or ill-suited to the population of cavity users (Carey, 1983). Cavity nesting birds are common (Fig. 13.12). For example, there are 85 species of cavity nesting birds in the USA, and about 20% of birds in Missouri are cavity nesters (Hardin and Evans, 1977; Scott et al., 1977). Sustainable populations of cavity nesting birds and small mammals are considered essential to functioning forest ecosystems (Titus, 1983; Brawn et al., 1984). Cavity nesting birds have received much attention in the

Number of trees/acre

160 120 80 40 0

Live trees 10 × Snags 0

4

8 12 Dbh (in.)

16

20

Fig. 13.11.  Number of live trees and number of snags per acre by diameter class for a second-growth oak forest in the Missouri Ozarks. There is some variation, but the number of snags is approximately 10% of the number of live trees by diameter class. To facilitate comparison with live trees, the number of snags is plotted at 10× the observed number. This general relationship has been observed in many mature second-growth and old-growth oak forests. (Based on Shifley et al., 1997a, b.)

Silvicultural Methods for Selected Ecosystem Services

Fig. 13.12.  Wood ducks are cavity nesters and prolific acorn consumers. Commonly they inhabit wooded bottomlands, riparian corridors, green tree reservoirs and other sites in proximity to water. (Photograph © Steve Bloom, used with permission.)

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literature, but cavity investigations in oak forests have found far more cavities are used by mice and squirrels than birds (Healy et al., 1989). Cavity requirements are species specific and range from a few square inches for mice to 55 ft3 for turkey vultures (Giusti et al., 2015). Black bears are known to overwinter in cavities found in oak trees that are usually at least 36 inches in diameter (Fearer, 2016). Large cavities can only exist in large trees, and patterns of stand self-thinning and tree area relationships ensure that the number of trees in a stand decrease as trees increase in size (see Chapters 5 and 6, this volume). Consequently, large trees and large cavities are relatively few in number. Cavity location, elevation and internal structure are additional factors that affect cavity utilization by wildlife and affect protection from potential predators. In particular, cavities at the base of trees are highly accessible to species that prey on cavity nesters. Compared with live trees, the wood of decaying snags is relatively easy to excavate for cavities. However, snags are rare compared with live trees of comparable size. Moreover, snags are ephemeral; as they decay, they gradually disintegrate or may abruptly fall to the forest floor. Consequently, in most oak stands live trees provide more cavities per acre than snags, even though the probability of a snag having a cavity is greater than for a comparable live tree (Healy et al., 1989; Allen and Corn, 1990; Fan et al., 2003a, b). When live trees with a core of decaying wood are excavated to create a cavity, the cavity may persist for decades in the living tree. Thus, for cavity tree management, retaining all or most of the live and dead trees with existing cavities is more important than accumulating snags with the hope they will be prone to someday develop cavities. Cavity formation starts with a tree wound. Upper stem cavities are often initiated by the loss of a tree limb or top due to severe weather (Healy et al., 1989). Basal cavities commonly are initiated by fire scars (Stambaugh et al., 2017), or by mechanical damage such as from logging operations. Bark damage due to feeding by woodpeckers or squirrels also creates wounds. In all the above cases, the cavity provides access to decay-causing organisms, wood-destroying insects and vertebrate primary excavators that collectively may create a cavity over a period of years (DeGraaf and Shigo, 1985). Many wounds are closed by the scar tissue produced by the cambium surrounding the wound site; that process may take 2–4 years for a wound 2

476

inches in width or two decades for a wound 10 inches in width (Stambaugh et al., 2017). Even when wounds close, internal wood decay continues up and down the tree bole. In living trees, the column of decayed wood becomes compartmentalized and its diameter is limited to the diameter of the tree at the time the wound occurred (DeGraaf and Shigo, 1985). This internal decay produces future sites for primary cavity excavators to utilize. For unthinned, even-aged sawtimber oak stands in Massachusetts, Healy et al. (1989) reported 4% of trees had cavities which collectively amounted to 8% of stand basal area. Consequently, the diameter of cavity trees was on average larger than for trees without cavities. Silvicultural thinnings for timber production typically remove cavity trees, and Healy et al. (1989) found that unthinned stands had three times as many cavity trees and twice as many snags as comparable thinned stands. The probability of a tree having a cavity increases with increasing dbh. Within a stand or across a landscape, however, the total number of trees decreases with increasing tree dbh (see also Chapter 6, this volume). Thus, a sample of nearly 55,000 trees in mature, second-growth upland oak forest in Missouri showed that the greatest number of cavity trees occurred in the 15–20 inch dbh class, where the combined number of trees and probability of cavities was maximized (Jensen et al., 2002). Trees larger than 20 inches dbh were more likely to have cavities than smaller trees (e.g. 70% probability for 30 inch trees), but such large trees are relatively few in number in that ecosystem (Fig. 13.13). Tree species differ significantly in their probability of having one or more cavities. The same study indicated relative probabilities of cavity trees by species as blackgum > post oak and black hickory > black oak, scarlet oak, mockernut hickory and pignut hickory > white oak and shortleaf pine. A prior study found relative cavity probabilities by species to be black oak > scarlet oak and hickories > white oak (Allen and Corn, 1990). However, differences among species in typical longevity complicates these relationships. For example, black and scarlet oaks are more prone to have cavities than white oaks, but white oaks can live twice as long, and over time with long rotations they may provide more cumulative opportunities for cavity nesters. The distribution of cavity heights above ground differs by tree dbh class. On average, mean cavity height increases with increasing tree dbh but the relationship is complicated by the correlation between tree height and diameter. For example, trees with

Chapter 13

90 80

Proportion of all trees with cavities

70

Trees (%)

60 50 40

Proportion of all trees

30 20 Proportion of all cavity trees

10 0

5

10

15

20

25

30

35

Dbh class (in.) Fig. 13.13.  In oak-dominated forests the larger a tree’s diameter the greater its likelihood of having at least one cavity, but the total number of trees declines with increasing dbh class. As a consequence, the number of cavity trees peaks at about 18 inches dbh and gradually declines for larger dbh classes. (Based on Jensen et al., 2002.) ‘Proportion of all trees’ indicates the size distribution of all trees regardless of whether or not they have cavities. ‘Proportion of all trees with cavities’ indicates for a given diameter class, what percentage of all the trees are expected to have cavities (e.g. at least 70% of trees that are 30 inches dbh or larger are expected to have cavities). ‘Proportion of all cavity trees’ shows how the cavity trees are distributed by 2 inch dbh classes (e.g. of all the cavity trees, about 10% are in the 8–10 inch dbh class).

small diameters (e.g. 5–8 inches dbh) are relatively short and therefore any cavities they contain are relatively close to the ground (Jensen et al., 2002). The probability of a stand containing one or more cavity trees increases as stand age and basal area increase. In a systematic sample of more than 4000 inventory plots in Missouri, tree cavities were observed on roughly 35% of sites younger than 30 years of age, 60% of sites 30–50 years, and 70% of sites older than 50 years. Likewise, the probability of finding a cavity tree at a site increased with increasing basal area per acre. Cavities were found on approximately 15% of sites with less than 33 ft2 of basal area; 40% of sites between 33 and 54 ft2, 55% of sites between 54 and 80 ft2, and 70% of sites greater than 80 ft2 (Fan et al., 2003a). In addition, for plots that had cavity trees, the number of

Silvicultural Methods for Selected Ecosystem Services

cavity trees per acre increased with increasing stand age and basal area, as did the number of large cavities (Fig. 13.14). The number of cavity trees has also been related to site index. On good sites Carey (1983) found more trees, larger trees and more cavities than on poor sites. A study of 50- to 120-year-old oak–hickory forests in West Virginia reported that the number of cavity trees was 1–2% of the total number of trees and that cavity trees tended to be larger in diameter than the tree of average size. The majority of cavity trees occurred among the 25% of trees with largest dbh (Carey, 1983). Cavity formation takes time, so as trees become larger and older they are more likely to have a cavity (Fig. 13.13). Longer timber management rotations will accumulate more cavity trees, provided that cavity trees and potential cavity trees are retained during intermediate thinning operations. Retention of existing cavity trees to become part of the new stand following a regeneration harvest operation can help sustain a population of cavity trees during the stand initiation and stem exclusion stages of development when trees are relatively small and cavities are relatively few in number. If management of tree cavities is intended to favour particular wildlife species, it is necessary to learn the cavity requirements of those species (e.g. DeGraaf and Shigo, 1985) and tailor cavity management to match. If management is intended to support a broad range of cavity nesters, then sustaining a diverse population of cavity sizes and heights is paramount. In either case, it is necessary to retain existing cavity trees and replenish cavity trees that are lost through natural or anthropogenic disturbances. Forest management for wood products preferentially removes trees with cavities to increase the future yield of sound timber. Cavity retention improves the habitat for cavity-dependent wildlife, usually with some associated loss of potential for wood products. However, with attention to cavity management objectives (e.g. retention of suitable cavity trees) and associated silvicultural prescriptions, production of wood products and cavity resources can be pursued simultaneously (Welsh et al., 1992). Identification of cavity trees is best done when leaves are off the trees. Even then some cavities will be overlooked, and some recorded cavities will be misidentified (Jensen et al., 2002). Cavities in active use by mammals often show gnawing on the callous tissue surrounding the opening. Entrance holes for cavities used by nesting birds often have a smooth, worn surface (Healy and Houf, 1989).

477

Seedling and sapling

80 60

11–20 in. dbh

40 20 0

> 20 in. dbh

5–10 in. dbh 0

2

6

10 14 18 22 26 30 34 38 42 46 50 54 60 64 Pole

80 60 40 20 Plots (%)

0

0

2

6

10 14 18 22 26 30 34 38 42 46 50 54 60 64 Sawtimber

80 60 40 20 0

0

2

6

10 14 18 22 26 30 34 38 42 46 50 54 60 64 Old growth

80 60 40 20 0

0

2

6

10 14 18 22 26 30 34 38 42 46 50 54 60 64

Number of cavity trees/acre

Fig. 13.14.  The probability distribution for the number of cavity trees per acre by stand size class and the diameter distribution of cavity trees when they are present. For example, the column charts indicate that in seedling and sapling stands 67% of plots had zero or one cavity tree/acre, but in the old-growth stands more than 70% of the plots had at least 12 cavity trees/acre (i.e. the sum of percentages for categories with at least 12 cavity trees/acre is > 70%). The pie charts indicate that of the few cavity trees that occurred in the seedling sapling stands most were between 5 and 10 inches dbh, but in the old-growth stands most of the cavity trees were larger than 10 inches dbh. (Based on inventory data from Missouri, Illinois and Indiana as reported by Fan et al., 2005.)

Given the choice, it is usually desirable to retain live cavity trees. Live cavity trees continue to grow and support themselves, even after a decayed core of wood has been excavated. Although it may be

478

harder for animals to excavate cavities in live trees than in a decaying snags, live cavity trees usually remain upright longer than snags and provide more durable cavity habitats.

Chapter 13

Typically, the desired number of cavity trees will be a small proportion of all trees in a stand. For example, DeGraaf and Shigo (1985) estimated that the cavities needed to support maximum populations of nine species of woodpeckers could be met by about two cavity trees/acre, provided trees were thoughtfully selected and well distributed across the forest landscape. As a general guide, Titus (1983) recommends seven cavity trees/acre for optimal upland hardwood wildlife habitat. But along wooded watercourses, which can sustain a diversity of cavity-using species, he recommends a minimum of 12 cavity trees/acre and at least 25 cavity trees/ acre for optimal habitat. Cavity-tree densities of that magnitude can have a measurable impact on timber production. However, oaks and associated species in riparian areas are highly valued for the ecosystem services they provide – including improved water quality, wildlife habitat and mast. Consequently, in riparian zones where best management practices typically restrict the amount and type of harvesting, wildlife habitat considerations may trump timber production objectives. Large trees are necessary to produce the large cavities required by species such as the pileated woodpecker (Harris, 1983). Cavity trees should be retained in a range of sizes but with emphasis on retaining large cavity trees, because they are relatively rare and highly versatile in the habitat they provide. Cavity trees that are large in size have the potential to supply cavities in a range of sizes, but small trees simply cannot harbour large cavities (DeGraaf and Shigo, 1985). Titus (1983) provides guidelines for cavity retention by tree dbh class (Table 13.7). In general, those guidelines call for retaining cavity trees larger than 19 inches dbh whenever they are found, and, if possible, distributing the other retained cavity trees with about 60% in the 10–19 inch dbh range and the remainder less than 10 inches dbh. Those values are specific to

Missouri forests, but offer a framework for estimates in other regions. Due to spatial variability in cavity resources, targets for cavity tree and snag retention are best implemented and monitored at the landscape scale rather than for individual stands. Large cavity trees are relatively rare simply because in most cases the larger the diameter class the fewer the number of trees per acre (e.g. see Fig. 13.13). Thus, in practice it is usually desirable to retain any large cavity trees that are encountered, even though the per-acre target for large cavity trees is low (e.g. one to three cavity trees > 19 inches dbh/acre as shown in Table 13.7). Usually, cavity trees are more limiting than snags in meeting optimal wildlife habitat targets. Live cavity trees, which often have compromised root systems, may be subject to increased windthrow following harvesting operations that remove surrounding overstorey trees. Snags have dead root systems and are similarly subject to windthrow. Risk of windthrow can be reduced by retaining cavity trees in wind-protected locations including coves, drainages and northern or eastern slopes or by retaining desirable cavity trees within clumps of comparably sized surrounding trees (Titus, 1983). From a practical management perspective, cavity trees retained in clumps of trees that are one-fifth to one-third of an acre in size are easier than individual cavity trees to map and retain through subsequent decades following overstorey harvest operations. Clumps should include at least one actively used cavity tree 10 inches dbh or larger surrounded by three to five other dominant or codominant trees to protect existing cavity trees and provide the potential for future cavities. Titus (1983) recommends a minimum of one 0.2-acre clump but preferably one 0.3-acre clump per 5 acres of forest area. Although clumps are easier to track and manage over time, for a given total area of retained overstorey trees across a landscape, clumps include fewer total cavity trees than would retention

Table 13.7.  Optimal densities of dens and snags to support needs of 89 species of wildlife in Missouri, organized by broad habitat classes. (Based on Titus, 1983.) Forest interior

Tree dbh class (inches)

Wooded water courses and riparian zones (dens/acre)a

Semi-open or open areas, including savannahs (dens/acre)a

(Dens/acre)

(Snags/acre)

Greater than 19 10–19 Less than 10 Total

2 14 9 25

 3  4  3 10

1 4 2 7

0 4 2 6

a

Management recommendations for dens only. Tree deadening to create snags is not generally recommended in these habitat classes.

Silvicultural Methods for Selected Ecosystem Services

479

of individual cavity trees wherever they occur. The clumps include fewer total cavity trees than the practice of retaining cavity trees individually. Most cavity management practices are aimed at identifying, retaining and protecting existing cavity trees considered suitable for the wildlife species of interest (Healy and Houf, 1989). Over the long term, however, the population of cavity trees on the landscape is dynamic, and it is possible – theoretically at least – to gradually and intentionally create new cavity trees to replenish those that will be lost to timber harvest, inter-tree competition or natural disturbances. As noted earlier, wounding trees mechanically or with prescribed fire can introduce decay-causing organisms and result in a column of decaying wood with a diameter approximately equal to that of the tree at the time it was wounded (DeGraaf and Shigo, 1985). Repeated intentional wounding of trees of specific sizes can increase future opportunities for primary excavators and other wildlife seeking suitable den sites (Stambaugh et al., 2017). Likewise, tree deadening can be applied to create snags for wildlife use, but in general the ­number of snags per acre is less likely to be limiting than the number of cavity trees, whether live or dead (Carey, 1983). Thus, management recommendations for cavity trees and snags are focused primarily on retention of existing cavity trees (Table 13.7).

Managing Stands for Biomass Production and Carbon Sequestration Forest biomass is defined as the weight of living or dead organic matter in a tree, stand or forest (Helms, 1998). Biomass is often separated into components (bole, tops, bark, roots, foliage) to aid in biomass estimation and product marketing. Information on biomass stocks and biomass increment are important when oaks are utilized for bioenergy. Biomass is also the key metric used to estimate carbon sequestration in trees and forests, because the quantity of carbon in a tree is nearly equivalent to half its biomass dry weight. Information about biomass and carbon dynamics is important when managing oak forests to help mitigate climate change. Concerns over global climate change have increased interest in measuring carbon sequestration by forests and the role of forests in the global carbon cycle. Carbon sequestration occurs when trees and other forest vegetation absorb CO2 from the atmosphere, convert it to carbohydrates through photosynthesis, and use the carbohydrates to build their tissues. The quantity of carbon stored by a 480

forest is related to the rate of photosynthesis of its living plants and its net carbon balance. The net balance of carbon gains and losses among plants, atmosphere and soil determines a forest’s carbon budget. Carbon is difficult to measure directly, so estimates of sequestered carbon are derived from estimates of biomass, biomass change and forest productivity (see also Chapters 4 and 14, this volume). Sequestering carbon in trees Trees and other plants combine atmospheric CO2 with water (H2O) in the presence of sunlight, and through photosynthesis they convert the sun’s energy into chemical energy in the form of sugar (C6H12O6), a carbon-based molecule. Some sugar provides the energy for tree respiration, but most of the rest is used for plant growth and reproduction. The principal structural component of trees is wood, which is composed of various polymers of sugar in the form of cellulose, hemicellulose and lignin. Thus, wood is a natural, non-fossil repository of carbon suitable for long-term storage, especially in longlived trees or in durable products made from wood. Forests are globally important in sequestering atmospheric carbon and in helping to offset increasing concentrations of atmospheric CO2 from burning fossil fuels (Malmsheimer et al., 2008). Net annual carbon sequestration by forests in the USA currently offsets roughly 10% of the nation’s annual CO2 emissions (US Environmental Protection Agency, 2017). However, the annual rate of sequestration by forests is expected to gradually decline due to forest ageing and gradual loss of forest land to development (Woodall et al., 2015; USDA Forest Service, 2017). Interest in carbon sequestration in trees is usually associated with managing forests to offset (or mitigate) increases in atmospheric CO2 that are associated with climate change (see Chapter 14, this volume). Trees store carbon in woody tissues, so stocks of live and dead tree biomass are indicative of stocks of sequestered carbon in forests. Likewise, changes in biomass are related to changes in the quantity of sequestered carbon. But a full accounting of carbon stocks and carbon dynamics is complex (Woodall et al., 2015). Additional carbon is sequestered with increases in living biomass, but forests also accumulate dead woody biomass (e.g. as standing snags or woody debris on the forest floor; see the earlier sections on wildlife habitat in this chapter). This dead tissue gradually releases CO2 back into the atmosphere as it decomposes, but full decomposition for Chapter 13

mitigation (see Chapter 14, this volume), but such practices may simultaneously result in increases of other commodities and ecosystem services.

large tree boles may take decades. In contrast, forest fires quickly release carbon sequestered in wood back into the atmosphere as CO2. Wood removed from the forest and converted to products may continue to sequester carbon for decades or centuries (see Table 14.1, this volume). Carbon accounting is sometimes expanded to even consider the carbon emissions associated with fossil fuels used by forest harvesting and processing equipment. Soils also contain large quantities of carbon, often as much or more than the total for above-ground forest vegetation. The quantity of carbon sequestered in soil can be slowly altered by vegetation growth, death and decomposition, particularly that of roots. Accounting for these and other factors while projecting net carbon sequestration forward in time is a complicated task that is potentially fraught with errors. Forests sequester carbon whether they are managed or not, but properly managed stands are generally capable of sequestering more carbon in a shorter period of time than unmanaged stands. In principle, this is no different from managing stands to maximize the production of various useable wood products such as sawtimber and pulpwood. Management practices intended primarily to increase carbon sequestration are associated with climate change

Estimating biomass and carbon For practical reasons associated with sampling and/ or product utilization, forest biomass is often categorized as trees, shrubs and herbaceous vegetation. It may be further partitioned into above-ground and below-ground components, living and dead components, and tree boles, tops, stumps, bark, roots and foliage. The biomass of tree boles is relatively easy to estimate, because it is closely related to cubic foot bole volume and basal area. Less is known about other biomass components such as tree roots, which are difficult to measure, or herbaceous vegetation, which can rapidly change during a year. Converting tree volume to biomass (weight) is relatively simple because weight per unit volume is known for many species. The dry weight of oak wood is about 35 lb/ft3 and its green weight is about 60 lb/ft3. More exact conversion factors for weight and volume are available, and they differ by species and between bark and wood for a given species (Table 13.8). Green weight (weight at harvest measured in tons) is usually used when biomass is

Table 13.8.  Green weight, dry weight and carbon content of oaks and associated species. Species are listed in decreasing order of dry weight. (From Smith, 1985; Lamlom and Savidge, 2003.) Wood Species group Hickories White oak group Red oak group Hard maple groupd White ash and green ash Soft maple groupe Sweetgum Southern pines Black cherry Eastern redcedar Red pine Yellow-poplar White pine

Bark

Dry weight Green weight Dry weight Green weight Bark volume Moisture Carbon (lb/ft3) (lb/ft3)a (lb/ft3) (lb/ft3)a content (%)c (%)c (%)b 40 37 35 35 34 31 29 29 29 27 26 25 23

64 59 63 63 54 55 60 58 49 43 49 52 43

37 33 41 34 21 32 29 20 30 25 15 25 31

59 53 74 61 34 58 61 40 51 40 29 53 59

13 18 14 12 16 12 15 15 10 12 16 15 16

60 60 80 80 60 80 110 100 70 60 90 110 90

48 50 50 49 48 49 50f 50f 50 50f 53 50f 50

a

Typical values, but green weight varies with tree moisture content. Bark volume as a percentage of wood volume. For bark biomass multiply bark volume by bark weight. c As a percentage of wood dry weight. d Includes sugar maple and black maple. e Includes red maple and silver maple. f No species-specific information on carbon dry weight; 50% is the typical default value. b

Silvicultural Methods for Selected Ecosystem Services

481

sold for fibre or energy. Corresponding dry weights can be calculated from green weights using standard conversion factors derived from weighing green wood samples, thoroughly drying them in an oven, weighing them again, and calculating the ratio of dry weight to green weight. Hence, some references express dry weight as oven-dry weight and green weight as field weight. Dry weight is commonly used as the basis to compare energy yields among tree species that differ in wood density or to compensate for differences in the proportion of water to wood at harvest. Dry weight is always used to estimate the amount of sequestered carbon in wood; carbon constitutes about half the dry weight of wood. Tree biomass is related to tree diameter, height, form, wood density and moisture content. Tree basal area and stand basal area are correlated with tree and stand biomass, respectively. Thus, relative change in basal area is a good indicator of relative change in biomass. For oaks and associated species there are numerous local or regional biomass equations and tables that specifically estimate aboveground green and dry biomass as a function of: (i) tree dbh; (ii) dbh and site index; or (iii) dbh and height (Fig. 13.15) (also see Stanek and State, 1978;

Total green weight above stump (lb)

14,000 Height 110 ft

12,000 10,000 8,000 6,000

Height 90 ft

4,000

25 × basal area

2,000

Height 70 ft

0 5

10

15 Dbh (in.)

20

25

Fig. 13.15.  Total tree biomass (excluding foliage) of northern red oak trees in western North Carolina by dbh and tree height. (Based on Clark et al., 1980.) Plotted values are based on regression equations and span the range of observed data. The dashed reference line shows tree basal area (ft2) multiplied by 25. Tree basal area is strongly correlated with tree biomass and the proportional change in basal area is a simple guide to the proportional change in tree biomass.

482

Tritton and Hornbeck, 1982; Jenkins, J.C. et al., 2003, 2004; Cienciala et al., 2008; Chojnacky et al., 2014). Biomass equations commonly partition biomass into stump, bole, top and bark. Where available and applicable, local biomass equations are the preferred source of information because they incorporate information on local tree form and utilization standards. However, local volume equations and tables usually address a limited group of tree species and may be complicated to apply in mixed-species stands when estimates of standing biomass per acre are required. For large-scale applications, national (e.g. Jenkins, J.C. et al., 2003, 2004; Chojnacky et al., 2014) or regional biomass equations for common species or species groups are useful. The biomass equations for tree species in the North Central USA provide one example. They present cubic foot volume equations for tree boles in relation to tree species, dbh, and site index (Hahn and Hansen, 1991) and then add equations and conversion factors to estimate v­ olume and weight of other tree components (Smith, 1985). For oaks and other tree species, this system of equations estimates biomass of green and dry weights with or without bark for stumps, boles and tops (Fig. 13.16). These equations assume a 1 ft stump height and a bole height that extends to a 4  inch top diameter outside bark. The equations express the following general relationships concerning above-ground oak biomass: ●● A 5 inch dbh oak weighs about 100 lb (dry weight), a 13 inch oak is about 1000 lb, a 17 inch oak is about 2000 lb, and a 23 inch oak is about 4000 lb. ●● Green weights for the red oak group are about 80% greater than dry weights; green weights for the white oak group are about 60% greater than dry weights. ●● For trees larger than 5 inches dbh, boles are 60–80% of total biomass (increasing with increasing tree size), tops are 30–15% of total biomass (decreasing with increasing tree size), and stumps are 10–5% of total biomass (decreasing with increasing tree size). ●● Bark constitutes about 14% of total bole biomass and/or total stump biomass. ●● For a tree of given species and dbh, the tree height and thus tree biomass typically increases with increasing site index. ●● Estimated tree biomass is highly correlated with tree basal area.

Chapter 13

8000

Biomass (dry weight in lb)

Total tree Bole and stump

6000

4000

2000

0

Stump 0

5

10

15 20 Dbh (in.)

25

30

Fig. 13.16.  White oak biomass components by dbh for site index 65 ft (index age 50 years). (Estimates are based on equations and conversion factors by Hahn and Hansen (1991) and Smith (1985).)

The carbon sequestered in wood can be calculated from biomass dry weight, and for oaks and many associates the weight of the stored carbon is about half of the dry weight (Lamlom and Savidge, 2003). Conifers contain more lignin than oaks, and therefore store slightly more carbon per unit of dry weight (Table 13.8). However, the wood density of oaks (about 35 lb dry weight/ft3) is greater than that of conifers (about 25–30 lb dry weight/ft3). Conse­ quently, a given volume of oak has a greater dry weight and carbon content than a conifer. But a dry ton of conifer wood will hold more carbon and have a greater volume than a dry ton of oak wood. Simple conversion factors and rules of thumb for converting among green weight, dry weight and carbon are useful in field operations. However, in estimating sequestered carbon across large forest areas, small errors in estimated biomass per tree or in estimated carbon per unit of biomass can translate into enormous differences in the assumed tonnage of sequestered carbon (Lamlom and Savidge, 2003). All tree biomass equations incorporate the basic relation that biomass increases exponentially with increasing tree diameter. However, some equations tend to over- or under-estimate biomass for some tree components or over a portion of the dbh range. Large errors can occur if biomass equations are applied to species other than those used for equation development or applied with diameters larger than those used for equation development. When applying biomass equations and conversion factors to support important decisions or when preparing

Silvicultural Methods for Selected Ecosystem Services

large-scale estimates, it is important to examine the tree species, tree size classes, site classes, and ecoregions represented in the data used for equation development to ensure they are appropriate for application. Those details are published for most equations. In an operational setting it is relatively easy to periodically compare estimated and actual weights of delivered loads of biomass to check the accuracy and precision of biomass estimates. Live biomass increment is indicative of the carbon increment in living trees, but it is only one part of the total change in forest carbon. A more complete accounting of the carbon in a forest (Birdsey, 2006; Smith et al., 2006) estimates total carbon (carbon pools) and carbon change for: ●● live trees including coarse roots, stems, branches and foliage; ●● standing dead trees including coarse roots, stems and branches; ●● live understorey woody and herbaceous vegetation; ●● down dead wood, including stumps and associated coarse roots; ●● forest floor including fine woody material, litter, humus and fine roots in the humus layer; ●● soil carbon and fine roots in the soil; ●● harvested wood used to make forest products; ●● discarded forest products in landfills where they decompose slowly; ●● harvested wood used for energy production; and ●● harvested wood that is combusted or that decays and emits carbon to the atmosphere without energy capture.

483

There are guidelines and methodologies for estimating carbon sequestration for managed stands comprised of oaks and associated species (Hoover et al., 2000). Also there are tools for using national forest inventory data to estimate carbon stocks over large areas (Woodall et al., 2015; USDA Forest Service, 2017) (Fig. 13.17). Subsequent sections provide additional detail about managing forests for biomass production and carbon sequestration. Managing for biomass production Producing oak biomass as a forest product usually implies growing wood for energy production, but oak biomass is also marketed on a weight basis for manufacturing pulp or composite panels (International Energy Agency Bioenergy, 2002, 2005; National Renewable Energy Laboratory, 2006, 2007). Residual woody biomass from harvesting operations or from primary processing of oak and other hardwoods is often used for wood energy (e.g. via combustion) at the primary processing facility, for conversion to charcoal for cooking, or increasingly for wood pellets for domestic and international markets. Collectively, wood provides about 9% of all residential heating energy in the USA (Houck et al., 1998) and is increasing (Alliance for Green Heat, 2012). Woody biomass in the form of tops, branches, small trees or chips is bulky to transport and store, and for that reason biomass resources are usually utilized close to where they are grown. The exception

is woody biomass that is processed into pellets which are commonly exported from the USA to Europe (US Energy Information Administration, 2017). The term biofuel is applied to biomass that has been converted either mechanically or chemically into solid, liquid or gaseous forms that facilitate transport and storage (National Renewable Energy Laboratory, 2006, 2007). Oak biomass as a source of fibre or for energy production is likely to come from conventional forestry practices in natural stands rather than from oak plantations grown and managed specifically for biomass (International Energy Agency Bioenergy, 2002). Oaks are better suited to longrotation management schemes because of their relatively slow growth compared with species such as aspen, willow and eucalyptus, which can be more intensively and efficiently managed for biofuels on rotations as short as 3–15 years. The value of oak sawtimber is greater than the corresponding fuelwood value, so it is often advantageous to manage oaks for high quality sawtimber but still utilize tops, limbs, culls and processing residues for biomass. Normal silvicultural activities in established oak stands (see Chapters 8 and 9, this volume) can yield substantial volumes of woody residues that are suitable for biomass energy. These residues can make up between 25% and 45% of harvested wood (International Energy Agency Bioenergy, 2002). Harvesting mature oak stands or cleaning, weeding and thinning in young stands can yield

Estimated total carbon (tons/acre)

100

Live trees

80 60 40 Soil organic matter

20

Standing and down dead Forest floor

0 0

20

40

60

80

100

Stand age (years) Fig. 13.17.  Regional average sequestered carbon by stand age and forest component for oak–hickory forests in northeastern USA. (Based on values from Smith et al., 2006.) Values for live and standing dead trees include coarse roots (> 0.2 inches in diameter). Down dead wood includes material ≥ 3 inches in diameter, stumps and coarse roots of stumps.

484

Chapter 13

biomass from boles, tops and branches that otherwise would be left on the forest floor. Moreover, income from the sale of biomass can help offset the cost of certain silvicultural operations such as thinning in young stands to improve species composition and structure, or removing mid-storey and understorey trees during woodland or savannah restoration. Due to the relatively low value of biomass per unit volume, handling and transportation systems need to be carefully considered to make such operations cost-effective (International Energy Agency Bioenergy, 2002). If oak biomass production is the sole management objective and there are no merchantability standards beyond total weight of harvested b ­iomass, that objective will be supported by rapidly regenerating stands and maintaining full stocking of fastgrowing oak species (see Chapter 15, this volume). Thinning such stands presumably could produce larger oaks faster, result in earlier revenue than not thinning, and may even capture biomass that otherwise would be lost due to self-thinning. However, thinning oak forests managed solely for biomass production is unlikely to be cost-effective. Biomass is a low-value product with little or no premium for tree size, and the cost of thinning is typically high compared with the value of biomass removed in a thinning operation. Consequently, when biomass production is the primary objective, any net gains in total biomass yield over a relatively short rotation are unlikely to offset the cost of thinning – at least under current market conditions. Biomass harvesting has the potential to remove more wood from a given site than is typical of traditional harvesting for sawlogs or pulpwood. This raises concerns about sustaining productivity in forests managed for biomass. Although forest management practices affect a wide range of soil characteristics to varying degrees (Grigal, 2000), the main concerns related to biomass harvesting include the possible depletion of soil nutrients, loss of organic matter and increased soil disturbance. Soils low in nutrients represent the greatest risks (Hornbeck et al., 1990). Resolving such issues is complicated because: (i) silvicultural practices vary in the quantity, type and timing of biomass they remove; (ii) nutrient and carbon dynamics associated with cycles of forest harvesting, regeneration and growth are subject to regional and site variation and some uncertainty (Johnson and Todd, 1997; Grigal, 2000); (iii) utilization of oak biomass for energy may be largely replaced by other energy technologies before repeated intensive harvests occur (e.g. 50–100 years hence); and

Silvicultural Methods for Selected Ecosystem Services

(iv) uncertain effects on long-term site productivity must be balanced against the capability of woody biomass energy to offset the impacts of energy produced from fossil fuels with high net carbon emissions. For regions where they exist, best management practices for biomass harvesting should be consulted and followed (e.g. Herrick et al., 2009; Missouri Department of Conservation, 2010). Managing for carbon sequestration At one level, managing oak forests for carbon sequestration is very simple: more total biomass equates to more total carbon in forests. However, that logic relies on a short-term perspective of carbon sequestration. Conclusions about carbon sequestration differ depending on the length of the time interval considered, and whether the focus is on rates of change in carbon accumulation during a period or the total stock of carbon at the end of a period. Over long management periods (decades or centuries) carbon accounting is complex. Determining whether a given forest is a net carbon sink (accumulator) or net carbon source (emitter) depends in large part on the time interval evaluated, initial stand conditions, and what happens to woody biomass (and associated carbon) in trees that die or are harvested. These issues are also discussed in Chapter 14, this volume, in association with climate change mitigation practices that aim to reduce the magnitude of climate change by increasing the quantity of CO2 removed from the atmosphere and sequestered in forest ecosystems. Rotation length and thinning effects Information about rotation length and thinning effects on carbon sequestration in hardwood forests is available from a few sources. Schnur’s (1937) yield tables for oak stands estimate total above-ground cubic foot yield for live trees by age and site class. These can be used to convert cubic foot wood yield to biomass or carbon yield for wood and bark (Table 13.8) and thereby estimate change in biomass and carbon over time for the above-ground component of live trees. The results suggest that maximum mean annual increment for carbon in the live, above-ground component of upland oak forests occurs between 40 and 60 years, depending on site quality (Fig. 13.18). This represents a typical age range for maximizing both carbon and biomass yield. However, these estimates

485

Site index (ft)

Mean annual increment of carbon in live trees (lb/acre)

1400 1200

80

1000

70

800

60

600

50 40

400 200 0

0

20

40

60

80

100

Stand age (years) Fig. 13.18.  Estimated mean annual carbon increment for live trees on fully stocked upland oak–hickory forests in the eastern USA. (Based on Schnur, 1937; with additional computations.) Values are for boles and bark for live trees to a 0.6 inch top diameter, assuming species from the red oak and white oak groups occur in equal proportion. The maximum mean annual increment occurs at age 50 for site index 40 and at age 55 for higher site indices. The culmination of mean annual increment indicates the harvest age that achieves maximum carbon accumulation over time. Biomass increment in dry weight would be approximately double the weight of carbon shown on the vertical axis. Green biomass weights would be approximately 70% greater than dry weights. Branches, stumps, foliage, roots and dead trees are excluded, so total tree carbon and biomass would be greater.

do not account for dead trees, branches, stumps, roots, foliage or down wood, which are often estimated from live standing biomass or stand age. Thinning can alter species composition, stand structure, stocks of carbon and rates of carbon accumulation in forests. For example, the rate of above-ground carbon (and biomass) accumulation decreased for 1 year after thinning for upland oak stands in Ohio, but then rebounded to pre-harvest levels (Chiang et al., 2008).Thus, if a portion of the harvested wood biomass is converted to durable products, the carbon in the products is retained until they decompose, and the total quantity of sequestered carbon may be increased by thinning. A study based on 25 years of repeated measurements in Allegheny hardwood stands in Pennsylvania computed net carbon change associated with thinning treatments (Hoover and Stout, 2007). These stands were dominated by sugar and red maples, black cherry and American beech. Stands were thinned to a residual density of 60–70% stocking. Trees were removed from either the smallest, middle or largest diameter classes. The first thinning was made when stands were about 53 years old and repeated 15 years later. Unthinned stands served as a control. The carbon in the residual live biomass, dead wood, logging slash and harvested forest products was then estimated. Carbon sequestered in 486

harvested products was estimated using published conversion factors (Birdsey, 1996; Row and Phelps, 1996). In that study, thinning treatments did affect the average annual change in carbon sequestered by the forest and the associated products (i.e. the net difference in all stocks of carbon between the end and the start of the study period divided by the number of years). Thinning from below (removing smaller trees first) stored the most carbon. In contrast, thinning from above produced a net reduction in sequestered carbon (Fig. 13.19). Thinning treatments that resulted in larger quantities of sequestered carbon also produced larger merchantable board foot volume. However, estimation of carbon budgets is sensitive to time frame and both short- and long-term changes need to be considered in making management decisions. Because of their complexity, long-term carbon accounting methods and guidelines are still evolving. Other considerations Many unknowns and uncertainties remain regarding forest carbon sequestration. For example, elevated levels of atmospheric CO2 (such as those resulting from burning fossil fuels) actually increase tree growth and corresponding rates of carbon Chapter 13

(A)

Biomass change (tons/acre/year)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 –0.1 Below

Middle

Above

Control

Thinning treatment (B) 250

Board foot change (acre/year)

200 150 100 50 0 –50 –100 –150 Below

Middle

Above

Control

Thinning treatment Fig. 13.19.  Average annual change in (A) carbon stocks and (B) board feet per acre for the period 1975–2000 for four thinning treatments applied to a 53-year-old Allegheny hardwood stand. Over the 25-year period, thinning from above and thinning from the middle sequestered significantly less carbon per year than did thinning from above or no thinning. (From Hoover and Stout, 2007.) Carbon stocks include live biomass, dead wood, logging residue and harvested products. Thinning treatments reduced stand density to 60–70% stocking by thinning from above (largest dbh trees), thinning from below (smallest dbh trees first), or thinning from the middle (trees at the middle of the dbh distribution). The control sites were not thinned.

sequestration in living trees (Norby et al., 1986; Hättenschwiler et al., 1997; Iversen and Ledford, 2008). Likewise warmer temperatures and longer growing seasons associated with climate warming can result in increased tree growth and carbon sequestration, particularly in the absence of drought Silvicultural Methods for Selected Ecosystem Services

(Ainsworth and Long, 2005). Impacts of insects, disease and fire on forest biomass and carbon dynamics further complicate forecasting forest carbon sequestration. Despite the uncertainties in accounting for long-term forest carbon dynamics, some general 487

silvicultural guidelines for increasing stand-scale carbon sequestration have been proposed (Bosworth et al., 2008; Malmsheimer et al., 2008): ●● Keep forests forested – other land uses have less capacity to sequester carbon. ●● Regenerate stands quickly after harvest (see Chapters 3 and 10, this volume) – the sooner trees are re-established the sooner they begin to accumulate biomass and sequester carbon. Rapid reforestation also reduces the period of elevated soil temperatures which increase the activity of soil microorganisms that release CO2 into the atmosphere. ●● Minimize loss of soil and litter – both sequester carbon in the form of organic matter. ●● Consider even-aged management with fast-­growing species – this produces rapid, short-term carbon sequestration (over a few decades). ●● Consider managing long-lived, tree species – this can result in long-term carbon sequestration. Including shade-tolerant species in the mix helps maximize leaf area and photosynthesis. ●● Consider both the rate of carbon sequestration and the cumulative carbon sequestration – for example, managing an old-growth forest without harvesting accumulates large total quantities of carbon in large trees and down wood. For old forests, however, annual net increases in sequestered carbon may be small compared with younger, fast-growing forests. ●● Produce forest products – forest products sequester carbon during their useful life and can continue to sequester carbon when recycled or buried in a landfill. ●● Manage for forest health – minimize losses associated with insects, diseases, declines and other sources of mortality to reduce the associated wood decay and carbon release. Due to the many variables and uncertainties involved, management based on even-aged silviculture, uneven-aged silviculture, or no harvesting can all be appropriate methods for sequestering carbon in forests. Many options are thereby open for ­managing forests not only for carbon sequestration but also other objectives compatible with carbon sequestration. Ultimately it is important to maintain forest diversity along with carbon sequestration and other management goals. Holistic, multiple-use, longterm forest management goals require the maintenance of forest landscape diversity, species diversity and genetic diversity. Successfully attaining those goals requires a wide range of management practices. 488

Moreover, maintaining or increasing forest diversity is a recommended strategy for helping forests adapt to climate change (see Chapter 14, this volume).

Old-growth Oak Forests The term ‘old growth’ is used to denote forests or stands of trees that have remained largely undisturbed by humans over long periods (e.g. more than a century). Although old-growth oak forests comprise only a tiny fraction of today’s oak resource, they are potentially important features of the l­andscape because of their role in maintaining biodiversity. In  addition to the presence of relatively old trees, old-growth forests possess other characteristics that are not well represented in other stages of forest development. Moreover, ­ certain species and life forms are closely bound to these special attributes of old growth. Old-growth forests also serve as ecological ‘laboratories’ for studying natural processes of stand development and for establishing natural benchmarks (e.g. for biomass and carbon accumulation) against which the effects of silvicultural practices can be gauged. Old-growth forests also rank high aesthetically and serve as places where some people find psychological and spiritual solace and renewal (Schroeder, 1996). Nevertheless there are many questions concerning objectives and strategies for managing old-growth oak forests. Extent and characteristics Estimates of the extent of old-growth forests in eastern USA range from 500,000 to more than 1.5 million acres (Davis, 1996; Leverett, 1996). However, oaks dominate only a fraction of that. In 1937, an estimated 350,000 acres of ‘virgin’ upland oak forest remained within the Central Hardwood Region (Schnur, 1937). This amounted to 0.3% of the total oak forest acreage. Today, old-growth hardwood forests of all types in the Central Hardwood Region collectively cover about 100,000 acres (Parker, 1989). In Indiana, Illinois, Missouri and Iowa, oldgrowth forests cover about 22,000 acres, or approximately 0.08% of the forests of those states (Shifley, 1994). Most of these forests include oaks as a major component. In Michigan, Wisconsin and Minnesota, 138,000 acres of oak–hickory forest are at least 120 years old. However, only about 900 of these acres have not been logged to some extent. In some regions, remnants of old-growth oak forests may be more widespread than commonly recognized Chapter 13

because certain old-growth forest conditions are inconspicuous and have been overlooked. For example, about 19% of the oak forests on steep, droughty southfacing slopes in north-western Arkansas are dominated by post oaks ranging from 140 to 320 years old (Stahle and Chaney, 1994). These trees, which occupy sites categorized as non-commercial forestland, are typically less than 65 ft tall and between 10 and 24 inches dbh. The harsh environment of these sites naturally maintains small trees and low stand densities. Such oldgrowth remnants may comprise as much as 0.8% of the total forest area of north-western Arkansas. In addition to old trees, certain compositional, structural and dynamic features characterize oldgrowth forests (Fig. 13.20). These include the continual creation of senescent and standing dead trees, of canopy gaps and attendant forest regeneration, and of down wood in various states of decomposition. These conditions occur when major disturbances are excluded from a forest over a long period. Accordingly, old growth could be defined as forests that are ‘relatively old and relatively undisturbed by humans’ (Hunter, 1989). However, other criteria are needed for practical definitions that can be applied to specific forest types and geographic regions. These criteria include: ●● the attainment of tree ages sufficient to represent a relatively stable species composition; ●● mean net stand growth near zero; ●● some dominant trees of an age approaching their species-specific average life expectancy; and ●● the absence of timber harvesting at intensities or frequencies that can significantly alter species composition (Hunter, 1989). Forest scientists generally agree on the major characteristics associated with old-growth hardwood forests (Table 13.9). Nevertheless, the great variability of old growth, even within a forest type or region, makes accurate definition difficult if not impossible. This problem persists despite many written descriptions of individual old-growth stands (Nowacki and Trianosky, 1993; Tyrrell et al., 1998). The commonly held perception of old-growth forests as pristine plant communities unaffected by humans is largely a myth (Day, 1953; Pyne, 1982; Lorimer, 1985; Whitney, 1994; Hicks, 1997; also see Chapter 1, this volume). This is especially true of oak-dominated old-growth forests, most of which have been disturbed by humans. Although oldgrowth forests are noted for their low level of human disturbance, virtually every existing old-growth Silvicultural Methods for Selected Ecosystem Services

Fig. 13.20.  A large northern red oak dominates neighbouring sugar maples and other hardwoods in this mixed mesophytic old-growth forest in southern Indiana. Although smaller trees are more numerous than larger trees in old-growth forests, it is the grandeur of the largest trees that usually catches our eye. Old-growth stands often form and naturally maintain a negative exponential diameter frequency distribution. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

oak forest in the USA has been periodically burned by Native Americans or by European settlers (Parker, 1989; Ladd, 1991; Whitney, 1994; Guyette, 1995; White and White, 1996). Many also have been disturbed to various degrees by domestic livestock, and others have been selectively logged to a limited extent during the last two centuries. Individually or collectively, such disturbances do not negate the value of these old-growth remnants. In fact, the long history of such disturbances raises questions about the degree to which low-intensity disturbances such as fires should be considered a normal part of old-growth forest development. 489

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Table 13.9.  Characteristics used to define old-growth hardwood stands in eastern USA and associated values and qualities observed in old-growth oak stands. Observed values and qualities of defining characteristic for Defining characteristic Species diversity

Canopy structure

Source: Martin (1992) High species richness and diversity; species richness ≥ 20 canopy trees; Shannon– Weiner diversity index > 3; evenness > 0.5; Simpson’s diversity index < 0.3 Uneven-aged with canopy species in several size classes

Source: Meyer Source: Parker (1989) (1986) Tree species richness Diverse dominant 20–40; herbaceous tree species species richness composition 17–53; breeding bird species richness 18–33

Upland old-growth oak and mixed hardwood stands in IN, IL, MO, IAab

Mesic and wet-mesic old-growth oak standsc

Xeric old-growth oak standsc

24–39 woody species with dbh ≥ 0.8 inches

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Multi-layered Uneven-aged with canopy canopy; wide species present in many range of tree size and age classes heights and ages Largest trees Several large canopy Decadence evident Largest oaks 30–55 Largest oaks 50–77 trees in tops and boles inches dbh inches dbh of large trees Prior Large, high-quality Commercially important disturbance commercially important species abundant; trees present evidence of very limited (indicating no recent selective logging along harvest) perimeter of some tracts Oldest trees Oldest trees ≥ 200 years Mean age of Oldest trees ≥ 100 Oldest trees at least 112 Oldest oaks 200–400+ overstorey 135–210 years years and as old as 336 years years; maximum years age 190–375 years Tree density Overstorey density Overstorey density Density 770–2250 trees/ Density 50–245 trees/ approximately 100 approximately acre ≥ 1 inches dbh; acre ≥ 4 inches dbh; trees/acre ≥ 4 inches 65–1707 trees/acre density 90–190 trees/ density 2–70 trees/ dbh ≥ 4 inches dbh acre ≥ 4 inches dbh acre ≥ 20 inches dbh Basal area and Overstorey basal area Overstorey basal At least 25% Basal area 91–141 ft2/ Basal area 51–205 ft2/ stocking ≥ 110 ft2/acre area 110–150 stocking in acre for trees ≥ 1 inch acre for trees ≥ 4 ft2/acre; volume live trees ≥ 14 dbh; basal area 83–139 inches dbh 16,000–25,000 inches dbh ft2/acre for trees ≥ 4 board ft/acre inches dbh

Largest oaks 40–48 inches dbh

Oldest trees ≥ 150 years

Density 115–180 trees/acre ≥ 4 inches dbh Basal area 40–120 ft2/ acre for trees ≥ 4 inches dbh

Silvicultural Methods for Selected Ecosystem Services

Snags and coarse woody debris

Logs and snags present in various sizes and stages of decay

From 7 to 17 snags/ acre ≥ 4 inches dbh; dead wood on ground 7–11 t/acre; annual tree mortality 0.6–0.9%

Canopy gaps

Tree-fall gaps formed by windthrow; gaps usually < 1.2 acres

Gaps are 7–8% of forest; randomly distributed; range from 0.12 to 0.9 acres in size

Other

Plant and animals that prefer old growth; undisturbed soils and soil macropores; little or no evidence of human disturbance

a

Large snags and 37–128 snags/acre ≥ 4 45–68 snags/acre ≥ large down inches dbh; snag basal 1 inch dbh; 0–36 logs widely area 7–20 ft2/acre; snags/acre ≥ 4 distributed; large snags roughly 10% inches dbh; snag logs present in of live trees by dbh basal area 3–14 ft2/ streams and class; volume of snags acre for trees >4 drainages 140–510 ft3/acre; down inches dbh; 50–295 wood volume 350–1580 down logs/acre ≥ ft3/acre for stems ≥ 4 4 inches diameter; inches diameter down wood volume 300–980 ft3/acre for stems ≥ 4 inches diameter Various degrees Numerous tree-fall gaps 0.7–9% of area in of understorey canopy gaps density and of herbaceous ground cover Three to eight cavities/acre > 4 inches; organic matter 5–12% in first 2 inches of soil; litter dry weight 2–12 tons/acre; undisturbed by harvest; no appreciable fire or grazing in last 50 years

IA, Iowa; IL, Illinois; IN, Indiana; MO, Missouri. Source: Shifley (1994), Shifley et al. (1995, 1997a), Spetich (1995), Roovers and Shifley (1997) and Spetich et al. (1999). c Source: Tyrell et al. (1998). b

56–84 snags/ acre ≥ 1 inch dbh; down wood volume 350–500 ft3/ acre for stems ≥ 4 inches diameter

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Silvicultural options A silviculture for old-growth forests may at first appear to be an oxymoron. If the management objective is to simply maintain and protect existing old growth by minimizing human-caused disturbances, then any silvicultural treatment would be incompatible with that objective. However, other objectives may include: (i) maintaining a woody or herbaceous species composition that is best achieved through periodic burning; (ii) eradicating exotic species; (iii) proactively managing advanced second-growth forests for certain old-growth characteristics; or (iv) expanding the ­ effective area of existing old growth with silviculturally designed buffer zones. Strategies for applying these options are discussed below.

Existing old-growth forests For the few old-growth oak forests that remain, direct silvicultural manipulation will usually be inappropriate. Minimal human disturbance is, after all, a defining characteristic of old-growth stands. Given that all old-growth hardwood forests are exceedingly rare (whether dominated by oaks or by other species), there may be little basis to assume that an old-growth forest with high oak regeneration potential is of greater ecological or aesthetic value than one that is successional to other tree species. This may be especially true in landscapes comprised of a mosaic of stand ages and structural states that include mature second-growth oak stands that will continue to age and become old oak forests. Nevertheless, two reasons have been suggested for silvicultural manipulation in old-growth oak forests (Parker, 1989; Guldin, 1991). The first concerns the control of exotic species. In some cases, it may be feasible and appropriate to eliminate or control exotic plant or animal species that have invaded an old-growth forest. If practical, a lighthanded approach is preferable. The second exception concerns the role of fire in the development and maintenance of oak forests. The historical evidence points to fire as a major disturbance factor in oak forests not only through the first half of the 20th century, but for centuries earlier (Day, 1953; Little, 1974; Dorney, 1981; Pyne, 1982; Grimm, 1984; Lorimer, 1985; Chumbley et  al., 1990; Ladd, 1991; Whitney, 1994; Guyette, 1995). Many old-growth oak forests originated when fires occurred at a greater frequency than was

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the norm during much of the 20th century (Parker, 1989; Ladd, 1991; Guyette, 1995; White and White, 1996) (Chapter 7, this volume). These fires were predominantly of human origin and recurred at intervals as short as 2–4 years. Should fire therefore be considered a natural process associated with old-growth forests? And if so, at what frequency and intensity? Unless fires are of sufficient intensity to damage or kill a large proportion of overstorey trees, periodic fires will neither move a forest towards nor away from the old-growth state. Fire may, however, alter the species composition of an old-growth forest, especially the herbaceous or woody understorey ­layers. In eastern USA, old-growth oak forests on mesic sites are usually successional to species of greater shade tolerance such as sugar maple and beech (Parker et al., 1985; Schlesinger, 1989; Abrams, 1992; Shotola et al., 1992; Runkle, 1996; also see Chapters 3 and 5, this volume). On these sites, aggressive shade-intolerant species such as yellow-poplar also can displace oaks by invading the naturally formed canopy gaps typical of an advanced old-growth state (Abrams and Downs, 1990). The reduction in fire frequency during the last half of the 20th century coincides with a shift from dominance by oaks to other species in many oldgrowth forests, especially on mesic sites. Prescribed burning is a silvicultural tool that may increase the regeneration potential of oaks in mesic forests (Chapters 3 and 7, this volume) and in old-growth oak forests in particular (Parker, 1989; Guldin, 1991). Nevertheless, prescribed burning has seldom been used in old-growth forests for that purpose. There is little information to guide the detailed development of burning prescriptions to increase oak regeneration in old-growth forests. Furthermore, some scientists have suggested that mature eastern oak forests developing for decades in the absence of fire have been so ecologically altered that reintroduction of fire to favour oak recruitment will be increasingly difficult and ultimately may prove ineffective (Nowacki and Abrams, 2008) (see also Fig. 12.5, this volume). Depending on one’s objectives, old-growth forest management can accommodate a mix of practices for existing oak forests that are successional to non-oaks. The usual practice is to allow succession to proceed without concern about the successional displacement of the oaks. Another option is to intervene with prescribed burning and/or other cultural measures to simulate the disturbance regime of earlier eras that sustained oak forests. An

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argument for considering the latter is exemplified by old-growth northern red oak–sugar maple forests in southern Wisconsin. These mesic forests are among the most botanically diverse in the region (Peet and Loucks, 1977) and provide excellent habitat for some uncommon breeding birds (Mossman and Lange, 1982; Ambuel and Temple, 1983). Yet they are ecologically unstable and successional to less biologically diverse forests dominated by sugar maple and other shade-tolerant hardwoods. Results from limited prescribed burns in one oldgrowth oak stand in southern Wisconsin showed that burning alone is unlikely to sustain an oak component given the practical constraints in the frequency and intensity of its use (Will-Wolf, 1991). Suggested solutions included planting oaks and creating canopy gaps by girdling overstorey trees. Although such methods may be at odds with traditional views of old-growth forest management, their advocacy by those interested in sustaining specific characteristics within old-growth forests points out that there are few firm guidelines for managing oldgrowth oak forests. Managing old-growth forests to encourage oak regeneration will certainly need to be monitored for effectiveness and modified over time through adaptive management. Forests in transition to old growth If old-growth oak forests are to become a more prominent feature of the landscape, a substantial acreage of existing second-growth forests must be allowed to develop to the necessary age, size structure and species composition. To help meet this objective, more than 10% of second-growth forests within many National Forest jurisdictions have been set aside to develop old-growth characteristics. Over time, state and private forests, parks and wilderness areas will contribute additional oldgrowth acreage. Although it is possible to design silvicultural treatments to accelerate within secondgrowth oak forests the development of some oldgrowth characteristics, such treatments are rarely desirable. For example, selective girdling or felling (without removal of cut trees) theoretically could be used to accelerate the development of large overstorey trees and the accumulation of snags and down wood. However, tree deadening and the use of other silvicultural methods may be at odds with the common objective of minimizing human disturbance of old-growth forests. Natural patterns of development eventually produce an old-growth

Silvicultural Methods for Selected Ecosystem Services

stand without incurring the costs of silvicultural treatments. The issues related to maintaining oaks in secondgrowth oak forests that are in transition to old growth are essentially the same as those discussed in the previous section. Like many remnant oldgrowth forests, many second-growth oak forests are successional to shade-tolerant tree species. The large existing acreage of second-growth oak forests in transition to old growth provides ample opportunity to explore alternative fire regimes in conjunction with other silvicultural practices for ­ efficacy in sustaining oaks in maturing forests. Managing second-growth forests for old-growth characteristics Multiple management objectives such as producing timber while retaining selected old-growth features can be attained silviculturally with treatments designed to maintain or increase some of the oldgrowth forest characteristics outlined in Table 13.7. For example, uneven-aged silviculture using singletree or group selection methods can mimic the formation of natural canopy gaps with subsequent within-gap recruitment of trees into the overstorey. In stands managed according to a negative exponential diameter distribution, the q value can be set fairly low (e.g. 1.1−1.2 for 2 inch dbh classes) and the maximum tree size can be increased so that more large-diameter trees are retained (Guldin, 1991; also see Chapter 9, this volume). The number of large diameter trees in old-growth oak forests is usually small. For example, an old-growth oak forest may have ten or fewer trees/acre larger than 24 inches dbh. Nevertheless that is about twice the number that typically occur in mature secondgrowth oak forests (McComb and Muller, 1983; Shifley et al., 1995). Retention and/or creation of large snags will increase the amount of standing dead wood, and the number of den and nesting sites associated with dead standing trees (McComb and Muller, 1983; Allen and Corn, 1990). Old-growth oak forests at the landscape scale At the landscape scale, the remaining old-growth oak stands provide valuable ecological models of the old-growth state. Although there are similarities between old-growth and mature second-growth oak stands in terms of basal area, diameter distribution and number of snags, old-growth oak stands have

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greater volumes of down wood and larger mean and maximum tree diameters than second-growth stands (Fig. 13.21). The species composition of old-growth stands also tends to differ from second-growth stands. For example, the proportion of long-lived white oaks and shade-tolerant tree species is often greater in old-growth oak stands than in mature secondgrowth oak stands on similar sites (Muller, 1982; McComb and Muller, 1983; Shifley et al., 1995). Where old-growth stands already exist, it may be feasible to expand their effective area by adding adjacent forest acreage. The additional acreage can be dedicated partially or exclusively to the development of old-growth forest characteristics. Expanding the size of old-growth oak forests is desirable for two reasons. First, most existing oldgrowth oak forests are small − usually less than a few hundred acres. Remnant old-growth oak forests often occur as scattered small patches in landscapes dominated by second-growth or non-forest lands. Thus, increasing the total area of tracts managed for old-growth characteristics can increase landscape diversity. Secondly, larger old-growth tracts are more resilient in the sense that they are better able to absorb large-scale natural disturbances (e.g. tornadoes, insects, disease) and still retain their character. Surrounding an old-growth core area with a buffer zone of second growth can protect the core

Fig. 13.21.  Old-growth oak forests are characterized by large volumes of down wood – often twice that of mature second-growth forests on comparable sites. Wind is often a factor in tree fall, but trees also fall as a result of senescence, disease and insect damage. The fall of large trees such as the one shown often create canopy gaps that are quickly captured by tree reproduction. (Photograph courtesy of USDA Forest Service, North Central Research Station.)

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area while extending the effective size of the oldgrowth tract. The second-growth buffer reduces edge effects at the margins of the old growth and reduces the potential for invasion of undesirable plants adapted to edge environments. Such invasions can alter the composition of understorey vegetation inwards by 65 ft or more from the perimeter of the old-growth stand (Brothers and Spingarn, 1992). Modification of understorey microclimate may extend up to 150 ft from edges to interiors of old-growth hardwood forests (Jacquart et al., 1992). In small stands, this edge effect can greatly reduce the effective area of the old-growth core. Abrupt edges around old growth nevertheless produce management trade-offs. Edges usually produce greater densities and diversity of woody stems, and receive more solar radiation than forest interiors. The latter may be especially favourable to oak regeneration, in contrast to oldgrowth interiors where low light levels near the forest floor discourage the accumulation of oak reproduction, which in turn hastens the successional displacement of oaks (Brothers, 1993). In addition to protecting the old-growth core, an appropriately managed buffer zone provides other benefits including: ●● suitable habitat for certain plant and animal species that utilize old-growth forests; ●● temporary refuge for plants and animals after natural disturbances within the old-growth core; and ●● a source of replacement for old-growth core areas lost to catastrophic disturbances. Many of these benefits can be attained by conventional practices such as managing stands within the old-growth buffer zone with uneven-aged silviculture or with long, staggered, even-aged rotations (Hunter, 1989). Over time, as stands in the buffer zone are harvested and regenerated, the location of the oldest stands in the buffer zone will shift, although the total acreage by age classes remains fairly constant. Within a second-growth buffer zone, the number, size, shape and harvest sequence of stands can be manipulated to alter the amount and type of forest edge (ecotones), to regulate age differences among adjacent stands, and to create natural fire breaks (Harris, 1984; Hunter and Schmiegelow, 2010). The spatial arrangement of early- and late-successional stands in the buffer zone may significantly affect the zone’s wildlife habitat qualities, and management practices within an old-growth buffer zone

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are often strongly influenced by wildlife management objectives. Hunter and Schmiegelow (2010) and Thompson et al. (1995) present general principles and guidelines relevant to various wildlife objectives. A buffer zone managed using uneven-aged silviculture will often produce more similarity between the buffer and the old-growth core than a buffer designed using even-aged silviculture. The amount and type of edge in an uneven-aged buffer zone can be manipulated by controlling the size, number and spatial arrangement of harvested trees and timing of harvests (Guldin, 1991). Uneven-aged silviculture in buffer zones surrounding a core of old-growth forest provides additional advantages by eliminating large openings that can funnel damaging winds into the core old-growth area and by maintaining a continuous cover of high forest that minimizes edge effects around the perimeter of the old-growth core. Corridors of mature forest connecting oldgrowth tracts can increase ecosystem stability by providing plants and animals with pathways for migration (Harris, 1984). However, an intermingling of old growth with buffer zones and connective corridors may be a difficult condition to obtain where land ownership is highly fragmented and land use is diverse. The concept of managing buffer zones adjacent to old-growth forests can be applied to most landscapes containing both old-growth and secondgrowth forests to: (i) protect and buffer existing old growth; and (ii) extend some old-growth characteristics into surrounding second-growth forests. The net result can extend the effective size of oldgrowth forests (Mladenoff et al., 1994). But because landscape-scale management practices for oldgrowth forests are largely untested in practice, their application should be monitored for conformance with desired outcomes. Over time, field practices can be modified as experience and ecosystem-­ specific conditions dictate.

Aesthetics The aesthetic consequences of forest management are important factors in contemporary silvicultural decision making. In some situations, minimizing negative visual impacts may be the primary consideration in selecting a silvicultural method. It has been suggested that when people are present in a forest, their experiences are also a part of the ecosystem

Silvicultural Methods for Selected Ecosystem Services

(Schroeder, 1996). Although the aesthetic qualities of a forest are most commonly perceived visually, the sounds, fragrances, temperatures and textures of the forest are also parts of the experience. No two people entering a forest will have the same aesthetic experience because of differences in their emotional states, prior experiences, personal sense of beauty and many other factors. To some, a forest may be perceived as a place of darkness or a physical or economic barrier, while others may perceive the same forest as a place of beauty and source of emotional or spiritual renewal. Forests often represent stability, and old forests can provide a physical and emotional link to earlier times and places. Because human perceptions of a forest vary with individual experiences and values, aesthetic responses to silvicultural treatments may: ●● ●● ●● ●●

vary widely among individuals; be based on strongly held personal values; evoke strong emotions; and be emotionally linked to issues or past incidents that are not readily apparent or intuitive (Schroeder, 1996).

Even though aesthetic responses to silvicultural treatments vary among individuals, there are certain silvicultural practices that are generally associated with increasing the aesthetic appeal of a managed forest. There are also silvicultural practices that are widely viewed as decreasing the aesthetic appeal of a forest. Some of these practices apply to individual stands; others involve the spatial arrangement and timing of silvicultural treatments across a landscape. Even though the aesthetic appeal of a forest can be explored through any of the senses, here we consider only visual perceptions of scenic beauty. It is the aesthetic interaction between humans and forests that to date has been investigated quantitatively, and it is a primary means through which most people interact with natural environments (Gobster, 1994). Stand-level aesthetics Scenic beauty ratings have been used as a measure of the visual attractiveness of a forest. Asking observers to rate the relative attractiveness of various real or photographed forest settings can be used to derive scenic beauty ratings. The silvicultural practices used to create the observed forest conditions, the measured physical characteristics of the forest, and other elements in each view are

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analysed to identify the features that are associated with high or low scenic beauty ratings. Only a few such studies have been conducted specifically in oak and oak–pine forests, but some general trends are present in all studies (Table 13.10). Some silvicultural practices typically increase aesthetic appeal. For example, intermediate cuttings are associated with increased scenic beauty except for short periods immediately following timber harvesting (Rutherford and Shafer, 1969; Brush, 1979). In contrast, large amounts of dead and down wood on the forest floor of upland hardwood stands are associated with diminished scenic beauty (Vodak et al., 1985; Ribe, 1991). Likewise, scenic beauty decreases with increasing intensity of harvest from no harvest to intermediate thinnings to clearcuts (Vodak et al., 1985). Savannahs (Chapter 12, this volume) with large scattered trees, long lines of sight and abundant ground flora are consistently rated among the most aesthetically appealing settings (Balling and Falk, 1982). However, in any given setting the presence of roads, water, scenic vistas, geologic formations or people may overshadow the influence of vegetation structure and composition on perceptions of scenic beauty. The scenic quality of a stand changes over the course of stand development. Managers can plan for these changes in the same way they plan for changes in timber volume over time. Simulation studies have been used to explore changes in scenic beauty over a rotation for loblolly pine managed under alternative silvicultural regimes (Hull and Buhyoff, 1986), and silvicultural treatments in general strongly influence aesthetic preferences. Unlike timber, however, scenic beauty is not a tangible

commodity that can be transported for consumption elsewhere. Diminished scenic beauty in one stand will not necessarily be compensated by increased scenic beauty in another. For some people, the aesthetic value of a forest is intimately tied to its location (e.g. a forest that they regularly visit). In these situations, any change in the forest may be perceived negatively regardless of offsetting improvements in scenic beauty elsewhere. All timber harvesting tends at least temporarily to reduce the aesthetic appeal of a forest by creating slash, skid trails and log landings, and by causing some damage to the residual vegetation. However, some silvicultural practices produce fewer negative visual impacts than others (Table 13.11). Although few studies have addressed the aesthetics of oak or oak–pine silviculture, ponderosa pine stands with a Gamble oak understorey were associated with high scenic beauty (Brown and Daniel, 1986). Similarly, when oaks were retained as a component of mature shortleaf pine stands, the stands were perceived as more attractive than those with no hardwoods (Gramann and Rudis, 1994). In general, retaining oaks and other hardwoods in predominantly conifer forests will add colour and variation in vegetation structure to the stand. These effects are especially apparent in autumn, winter and spring (McDonald and Huber, 1995). Silvicultural systems can be evaluated with respect to their impacts on scenic beauty (Table 13.12). For a given system, scenic beauty ratings are usually lowest immediately after timber harvesting. In some cases, managing visual impacts during a single timber harvest may be of paramount importance. In other cases it may be more important to consider changes in scenic

Table 13.10.  Forest characteristics associated with high and low scenic beauty ratings. (From Brush, 1979; Litton and McDonald, 1980; Schroeder and Daniel, 1980; Vodak et al., 1985; Rudis et al., 1988; Ribe, 1991; Gobster, 1994.) Forest scenic beauty rating High

Low

•  Large trees; relatively old forest •  Open forests; understories allowing views deep into the forest •  Appearance of easy travel through the forest •  Herbaceous vegetation on the forest floor •  Vegetation diversity •  Large openings surrounded by trees •  Flowering trees or other flowering vegetation •  Vistas, overlooks, water bodies, unique geologic formations

•  A predominance of small trees; evidence of logging •  Dense, impenetrable understorey or mid-storey vegetation •  Large volumes of down wood; height of dead wood (logging slash or natural) greater than 3 ft above ground •  Exposed soil •  Uniform vegetation structure; few species •  Unbounded forest openings •  An unhealthy appearance; numerous dead trees •  Closed views

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Table 13.11.  Practices useful in reducing negative visual impacts of timber harvesting. (From Smith and Kuhr, 1989.)

•• Locate stand boundaries so they follow natural •• •• •• •• •• ••

landscape features Soften the contrast between harvest and nonharvest areas using partial harvests to provide a gradual transition from cut to uncut stands Reduce the apparent size of large openings by retaining groups of trees or shrubs Retain peninsulas of vegetation that extend from adjacent stands into harvested areas leaving evenly-spaced residual trees Retain vegetation with showy flowers Retain conifers to increase visual diversity in hardwood forests, especially during the dormant season Retain oaks and other hardwoods to increase visual diversity in predominantly conifer forests

•• Minimize the duration of harvest operations to reduce the duration of their visual impact

•• Harvest during the dormant season to minimize the •• •• •• •• ••

visibility of roads and skid trails and to minimize colour contrasts in vegetation among cut and uncut areas Remove understorey vegetation to increase visual penetration into the stand Select residual den trees or snags so they fall in clumps with other vegetation in the foreground or along edges of openings Design roads, skid trails and landings to minimize the amount of area disturbed and to minimize visibility to other forest users Lop, chip or scatter slash to reduce its height and visibility Utilize tops and limbs for products to minimize volume of residue

Table 13.12.  Silvicultural systems and their impacts on scenic beauty after timber harvesting. (Adapted from Smith and Kuhr, 1989.)a Silvicultural system

Positive impacts on scenic beauty

Clearcutting method

May open vistas. Can create visual variety in forest structure and species composition

Two-stage shelterwood method

Three-stage shelterwood method

Two-age management

Seed tree method

Negative impacts on scenic beauty

Creates slash and disturbed soils that may be visible for 2 years or more. Woody and herbaceous reproduction forms a brushy mass that may be visually impenetrable for years and low in scenic beauty The shelterwood stage may provide open, Logging activity, slash and soil disturbance park-like appearance with aesthetic appeal. occurs twice (i.e. shelterwood creation and Retained overstorey softens the visual impact shelterwood removal). Unsightly damage of the initial harvest. Advance growth of the to residual vegetation (overstorey or understorey during the shelterwood stage helps understorey) may result at both harvests mask the impact of the removal cut. Deferment cutting, which retains the overstorey indefinitely, may enhance aesthetic qualities. Can create visual variety in forest structure and species composition (Smith et al., 1989) Same as two-stage shelterwood but third stage Logging activity, slash and soil disturbance leaves some residual overstorey present for a occurs at three harvests. Unsightly damage longer period (up to one-third of the rotation) to residual vegetation (overstorey or and further reduces the visual impact of understorey) may result at each of the three harvest. Can create visual variety in forest harvests structure and species composition Logging activity, slash and soil disturbance Maintains continuous overstorey cover. Can occur twice per rotation. Unsightly damage create visual variety in forest structure and species composition. Residual forest cover can to residual vegetation (overstorey or mitigate visual impacts of harvest understorey) may result at the each harvest Seed trees retained after initial harvest may Logging activity, slash and soil disturbance occurs soften the visual impact of harvesting. May at two harvests (i.e. seed tree creation and open vistas. Can create visual variety in forest overstorey removal). Woody and herbaceous structure and species composition. Deferment reproduction may form a brushy mass that is cutting which retains the seed trees indefinitely visually impenetrable for years and of low scenic can enhance the aesthetic qualities (Smith beauty. The wide, regular spacing often desired et al., 1989) for seed trees may be less attractive than other (e.g. clumped) arrangements of residual trees Continued

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Table 13.12.  Continued. Silvicultural system Group selection

Single tree selection

Intermediate thinning

No harvest

Positive impacts on scenic beauty

Negative impacts on scenic beauty

Maintains nearly continuous forest overstorey vegetation throughout cutting cycles. Residual vegetation mitigates visual impact. Group opening can provide visual variety in forest structure and species composition. Associated thinning between groups increases visual penetration into the stand and may even increase the scenic beauty relative to stands with no harvest (Rutherford and Shafer, 1969) Maintains continuous forest overstorey vegetation throughout cutting cycles. Residual vegetation mitigates visual impact of harvest. Associated thinnings may increase visual penetration into the stand. Stands managed using selection harvest may be perceived as more attractive than stands with no harvest (Rutherford and Shafer, 1969) Creates views into forest interior. Accelerates development of large diameter trees. Lowintensity disturbance relative to regeneration cuts. Thinned stands may be perceived as more attractive than stands with no harvest (Rutherford and Shafer, 1969) Develops large tree character. No logging activity, slash or soil disturbance. No periodic fluctuations in scenic beauty due to harvest

Low-intensity logging activity occurs frequently (e.g. every decade). May require more roads or skid trails than other harvest methods or allow less opportunity for skid trails to recover between harvests. Group openings have the same characteristics as very small (e.g. up to 1 acre) clearcuts. May obscure scenic vistas Low-intensity logging activity occurs frequently (e.g. every decade). May require more roads or skid trails than other harvest methods or allow less opportunity for skid trails to recover between harvests. Periodic logging results in soil disturbance and creation of slash. May obscure scenic vistas Logging activity, slash and soil disturbance occur periodically. Unsightly damage to residual vegetation (overstorey or understorey) may result at the periodic harvests May accumulate relatively large volumes of snags and down wood

a

For each silvicultural system, the described impacts apply to years immediately following harvests except where otherwise noted. Silvicultural systems are listed in approximate order of decreasing impact on scenic beauty. In general, the aesthetic qualities of stands increase with time since harvest, regardless of the system used.

beauty over an entire rotation. Some silvicultural practices result in frequent but relatively small reductions in scenic beauty during and shortly after harvest (e.g. single-tree selection method). Other practices may produce infrequent but large, longer lasting reductions in scenic beauty (e.g. clearcutting). Silvicultural systems have been described and ranked in terms of positive and negative impact on scenic beauty after timber harvesting (Table 13.12). However, human perceptions are complex. For example, a clearcut that opens a spectacular vista may evoke a more favourable aesthetic response than, say, a single-tree selection harvest near a popular recreation area. A stocking chart (Chapter 6, this volume) provides a potentially useful tool for defining regions of stand density and average tree size associated with different levels of aesthetic appeal. Some provisional trends in scenic preferences can be illustrated with stocking charts and refined based on experiences in specific landscapes. Lowest levels of

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aesthetic appeal are generally associated with dense stands of small trees. From there, aesthetic appeal generally increases with increasing average tree size (Fig. 13.22). Landscape-level aesthetics Landscapes comprising thousands of acres with large vistas (‘viewsheds’), travel corridors and recreation areas present additional aesthetic concerns. For example, a travel corridor flanked by a continuous mature and unharvested forest may have less aesthetic appeal than a corridor containing occasional forest openings that provide a more varied roadside view (Schroeder and Daniel, 1980). Visual variety is known to be aesthetically appealing. Consequently, maximizing the scenic beauty of individual stands does not necessarily maximize the scenic beauty of the landscape, especially if the result is an unvaried landscape.

Chapter 13

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40 50 100 150 200 250 300 350 400 Trees/acre Fig. 13.22.  Inferred relations between scenic preference ratings (very low to very high) and stand stocking based on Gingrich’s (1967) stocking chart for upland oak stands in the Central Hardwood Region and other sources. Open stands with large diameter trees generally have higher scenic beauty than stands crowded with small trees. However, many other factors affect the perceived scenic beauty of a given site.

Silvicultural Methods for Selected Ecosystem Services

Managing landscapes and vistas for aesthetic appeal typically extends beyond the purview of the silviculturist and into that of the landscape architect. Forest managers nevertheless should be aware of the potential impacts of silvicultural treatments on landscape vistas and methods for increasing the aesthetic appeal of silviculturally treated areas when viewed from a distance. The following guidelines can be applied where the aesthetic appeal of landscape views and travel corridors are important (Smith and Kuhr, 1989; Palmer et al., 1995): ●● Create landscapes with visual variety; they usually have great appeal. That variety can come from vegetation but may be substantially enhanced by natural landforms, geologic features, water bodies and open spaces. ●● Create mosaics of vegetation that contain different size classes and densities of trees and other plants. ●● Concentrate efforts on viewsheds visible from travel corridors, recreation sites or other high-use areas. ●● Concentrate efforts on the foreground of a viewshed where visibility and potential impact is greater than in the viewshed background. ●● Design openings that are varied in shape and that have boundaries that follow natural topographic features. ●● Avoid openings that cross ridgelines; they visually emphasize differences between openings and adjacent areas. ●● Limit the total area of the landscape view that is harvested. ●● Retain conifers in areas that are predominantly hardwood and hardwoods in areas that are predominantly conifer to increase visual variety during the dormant season. ●● Vary the size of forest openings. ●● Select opening sizes that are appropriate for the landscape view. One study found that scenic beauty ratings were higher when the total harvested area in the middle ground of the scene was divided into cutting units of 10–14 acres in size rather than ones that were smaller (4–5 acres) or larger (20–30 acres). Management of forested landscapes for aesthetic values thus requires an awareness of the different kinds of human reactions to not only the stages of stand development, condition and spatial arrangement, but also to their larger landscape setting and how that will change over time. New technologies have provided opportunities for people to view forest landscapes remotely.

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Digital aerial images are now widely available and they provide the opportunity for anyone with a computer to view remote forest landscapes from above. Image resolution is often good enough to show timber harvest openings, skid trails and relative tree size and density. Products such as Google Earth (Google Earth, 2017) and emerging drone technologies allow viewers to virtually fly over or through digital landscapes comprised of full colour, high-resolution imagery and terrain. This provides an entirely new perspective for viewing forest landscapes, and from that perspective the buffers of mature trees typically used to screen roadside harvests are largely irrelevant. Managers concerned with forest aesthetics will increasingly need to be aware of these virtual viewsheds as well as the viewshed accessible to observers on the ground.

Notes 1   For species in the red oak group, the correct multiplier is 2br−1 because acorns are borne on 2-year-old branches. Also, the multiplier 2br can be adjusted to account only for branches in the upper one-third of the crown as required in using Sharp’s (1958) acorn inventory system. 2   However, see ‘Bird predation and dispersal’ in Chapter 2, this volume, to learn how blue jays and other birds facilitate the rapid migration of oaks.

References Abrams, M.D. (1992) Fire and the development of oak forests. BioScience 42, 346–353. https://doi.org/ 10.2307/1311781 Abrams, M.D. and Downs, J.A. (1990) Successional replacement of old-growth white oak by mixed mesophytic hardwoods in southwestern Pennsylvania. Canadian Journal of Forest Research 20, 1864– 1870. https://doi.org/10.1139/x90-250 Ainsworth, E.A. and Long, S.P. (2005) What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytologist 165, 351–372. https://doi.org/10.1111/j.1469-8137.2004.01224.x Allen, A.W. and Corn, J.G. (1990) Relationships between live tree diameter and cavity abundance in Missouri oak–hickory forest. Northern Journal of Applied Forestry 7, 179–183. https://doi.org/10.1093/njaf/7.4.179 Alliance for Green Heat (2012) 2011 Census Shows Wood Heat Continues to Rise in U.S. Available at: https://forgreenheat.blogspot.com/2012/10/2011-census-shows-wood-heat-continues.html (accessed 10 July 2017).

500

Ambuel, B. and Temple, S.A. (1983) Area-dependent changes in the bird communities and vegetation of southern Wisconsin forests. Ecology 64, 1057–1068. https://doi.org/10.2307/1937814 Auchmoody, L.R., Smith, H.C. and Walters, R.S. (1993) Acorn production in northern red oak stands in northwestern Pennsylvania. USDA Forest Service General Technical Report NE-680. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-680 Balling, J.D. and Falk, J.H. (1982) Development of visual preference for natural environments. Environment and Behavior 14, 5–28. https://doi.org/10.1177/ 0013916582141001 Barrett, R.G. (1980) Mammals of California oak habitats – management implications. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 275–291. https://doi.org/10.2737/PSW-GTR-44 Batema, D.L., Kaminski, R.M. and Magee, P.A. (2005) Wetland invertebrate communities and management of hardwood bottomlands in the Mississippi AlluvialValley. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 173–190. Beck, D.E. (1977) Twelve-year acorn yield in southern Appalachian oaks. USDA Forest Service Research Note SE-244. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/ pubs/9237 (accessed 1 July 2018). Birdsey, R.A. (1996) Carbon storage for major forest types and regions in the conterminous United States. In: Sampson, R.N. and Hair, D. (eds) Forests and Global Change: Forest Management Opportunities for Mitigating Carbon Emissions, Vol. 2. American Forests, Washington, DC, pp. 1–26. Birdsey, R.A. (2006) Carbon accounting rules and guidelines for the United States forest sector. Journal of Environmental Quality 35, 1518–1524. https://doi. org/10.2134/jeq2005.0193 Block, W.M., Ganey, J.L., Severson, K.E. and Morrison, M.L. (1992) Use of oaks by neotropical migratory birds in the Southwest. USDA Forest Service General Technical Report RM-218. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado, pp. 65–70. https://doi. org/10.2737/RM-GTR-218 Bosworth, D., Birdsey, R., Joyce, L. and Millar, C. (2008) Climate change and the nation’s forests: challenges and opportunities. Journal of Forestry 106(4), 214– 221. Available at: https://www.fs.usda.gov/treesearch/ pubs/18499 (accessed 1 July 2018).

Chapter 13

Brawn, J.D., Tannenbaum, B. and Evans, K.E. (1984) Nest site characteristics of cavity nesting birds in central Missouri. USDA Forest Service Research Note NC-314. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11219 (accessed 1 July 2018). Brothers, T.S. (1993) Fragmentation and edge effects in central Indiana old-growth forests. Natural Areas Journal 13, 268–275. Brothers, T.S. and Spingarn, A. (1992) Forest fragmentation and alien plant invasion of central Indiana oldgrowth forests. Conservation Biology 6, 91–99. https://doi.org/10.1046/j.1523-1739.1992.610091.x Brown, T.C. and Daniel, T.C. (1986) Predicting scenic beauty of timber stands. Forest Science 32, 471– 487. https://doi.org/10.1093/forestscience/32.2.471 Brush, R.O. (1979) The attractiveness of woodlands: perceptions of forest landowners in Massachusetts. Forest Science 25, 495–506. https://doi.org/10.1093/ forestscience/25.3.495 Burns, R.M. and Honkala, B.H. (tech. coords) (1990) Silvics of North America. Volume 2, Hardwoods. USDA Forest Service Agriculture Handbook 654. USDA Forest Service, Washington, DC. Available at: https://www.srs.fs.usda.gov/pubs/misc/ag_654/ table_of_contents.htm (accessed 1 July 2018). Carey, A.B. (1983) Cavities in trees in hardwood forests. USDA Forest Service General Technical Report RM-99. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, pp. 167–184. https://doi.org/10.2737/RM-GTR-99 Cecich, R.W. and Larsen, D.R. (1997) Bud demography and bifurcation in Quercus alba L. and Quercus ­velutina Lam. In: Genetics of Quercus, Diversity and Adaptation in Oak Species. Proceedings of Working Party 2.08.05, 12–17 October, International Union of Forestry Research Organizations, Pennsylvania State University, University Park, Pennsylvania, pp. 68–75. Cecich, R.A. and Sullivan, N.H. (1999) Influence of weather at time of pollination on acorn production of Quercus alba and Q. velutina. Canadian Journal of Forest Research 29, 1817–1823. https://doi. org/10.1139/x99-165 Chamberlain, M.J. and Leopold, B.D. (2005) Survival and cause-specific mortality of hardwood seedlings in the Mississippi Alluvial Valley. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 137–141. Chiang, J.M., Iverson, L.R., Prasad, A. and Brown, K.J. (2008) Effects of climate change and shifts in forest composition on forest net primary production. Journal of Integrative Plant Biology 50, 1426–1439. https:// doi.org/10.1111/j.1744-7909.2008.00749.x

Silvicultural Methods for Selected Ecosystem Services

Chojnacky, D.C., Heath, L.S. and Jenkins, J.C. (2014) Updated generalized biomass equations for North American tree species. Forestry 87, 129–151. https:// doi.org/10.1093/forestry/cpt053 Christisen, E.M. (1955) Ecological notes on the mountain brush in Utah. Proceedings of Utah Academy of Science, Arts, and Letters 32, 107–111. Christisen, D.M. and Kearby, W.H. (1984) Mast measurement and production in Missouri (with special reference to acorns). Missouri Department of Conservation Terrestrial Series 13. Missouri Department of Conservation, Jefferson City, Missouri. Chumbley, C.A., Baker, R.G. and Bettis, E.A. III (1990) Midwestern holocene paleoenvironments revealed by floodplain deposits in northeastern Iowa. Science 249, 272–274. https://doi.org/10.1126/science.249. 4966.272 Cienciala, E., Apltauer, J., Exnerová, Z. and Tatarinov, F. (2008) Biomass functions applicable to oak trees grown in Central-European forestry. Journal of Forest Science 54, 109–120. https://doi.org/10.17221/2906-JFS Clark, A. III, Phillips, D.R. and Schroeder, J.G. (1980) Predicted weights and volumes of northern red oak trees in Western North Carolina. USDA Forest Service Research Paper SE-209. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Clatterbuck, W.K. and Meadows, J.S. (1993) Regenerating oaks in the bottomlands. USDA Forest Service General Technical Report SE-84. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 184–195. https://doi. org/10.2737/SE-GTR-84 Collins, B. and Battaglia, L.L. (2008) Oak regeneration in southeastern bottomland hardwood forest. Forest Ecology and Management 255(7), 3026–3034. https://doi.org/10.1016/j.foreco.2007.09.023 Conner, W.H. and Sharitz, R.R. (2005) Forest communities of bottomlands. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 93–120. Cushman, S.A., McKelvey, K.S., Flather, C.H. and McGarigal, K. (2008) Do forest community types provide a sufficient basis to evaluate biological diversity? Frontiers in Ecology and the Environment 6, 13–17. https://doi.org/10.1890/070039 Cypert, E. and Webster, B.S. (1948) Yield and use by wildlife of acorns of water and willow oaks. Journal of Wildlife Management 12, 227–231. Davis, J.W., Goodwin, G.A. and Ockenfels, R.A. (tech. coords) (1983) Snag habitat management: proceedings of the symposium. USDA Forest Service General Technical Report RM-99. USDA Forest Service, Rocky Mountain Forest and Range Experiment

501

Station, Fort Collins, Colorado. https://doi.org/10.2737/ RM-GTR-99 Davis, M.B. (1996) Eastern Old-Growth Forests. Island Press, Washington, DC. Day, G.M. (1953) The Indian as an ecological factor in the northeastern forest. Ecology 34, 329–346. https://doi. org/10.2307/1930900 DeGraaf, R.M. and Rudis, D.D. (1986) New England wildlife: habitat, natural history, and distribution. USDA Forest Service General Technical Report NE-108. USDA Forest Service, Northeastern Forest Experiment Station, Broomall, Pennsylvania. https://doi.org/10.2737/ NE-GTR-108 DeGraaf, R.M. and Shigo, A.L. (1985) Managing cavity trees for wildlife in the Northeast. USDA Forest Service General Technical Report NE-101. USDA Forest Service, Northeastern Forest Experiment Station, Broomall, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/4138 (accessed 1 July 2018). DeGraaf, R.M., Yamasaki, M., Leak, W.B. and Lanier, J.W. (1992) New England wildlife: management of forested habitats. USDA Forest Service General Technical Report NE-144. USDA Forest Service, Northeastern Forest Experiment Station, Radnor, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/4195 (accessed 1 July 2018). DeGraaf, R.M., Yamasaki, M., Leak, W.B. and Lester, A.M. (2005) Landowner’s Guide to Wildlife Habitat Forest Management for the New England Region. University Press of New England, Lebanon, New Hampshire. Denman, J.B. and Karnuth, L. (2005) Fifty years of uneven-aged regeneration of oak in bottomland hardwood forests. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 461–465. Dey, D.C., Lovelace, W., Kabrick, J.M. and Gold, M.A. (2004) Production and early field performance of RPM® seedlings in Missouri Floodplains. USDA Forest Service General Technical Report NC-243. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 59–65. Available at: https://www.fs.usda.gov/treesearch/pubs/14707 (accessed 1 July 2018). Dickson, J.G. (comp. ed.) (2001) Wildlife of Southern Forests Habitat and Management. Hancock House Publishers, Blaine, Washington. Dickson, J.G. (2004) Wildlife and upland oak forests. USDA Forest Service General Technical Report SRS-73. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 106–115. Available at: https://www.fs.usda.gov/treesearch/pubs/ 6506 (accessed 1 July 2018).

502

Dickson, J.G., Thompson F.R. III, Conner, R.N. and Franzred, K.E. (1995) Silviculture in Central and Southern oak–pine forests. In: Martin, T.E. and Finch, D.M. (eds) Ecology and Management of Neotropical Migratory Birds: a Synthesis and Review of Critical Issues. Oxford University Press, New York, pp. 245–268. Dijak, W.D., Rittenhouse, C.D., Larson, M.A., Thompson, F.R. III and Millspaugh, J.J. (2007) Landscape habitat suitability index software. Journal of Wildlife Management 71, 668–670. https://doi.org/10.2193/ 2006-341 Dorney, J.R. (1981) The impact of Native Americans on presettlement vegetation in southeastern Wisconsin. Transactions Wisconsin Academy of Science, Arts and Letters 69, 26–36. Downs, A.A. (1944) Estimating acorn crops for wildlife in the southern Appalachians. Journal of Wildlife Management 8, 339–340. https://doi.org/10.2307/ 3796028 Drobney, R.D. (1990) Nutritional ecology of breeding wood ducks: a synopsis for wetland management. In: Fredrickson, L.H, Burger, G.V., Havera, S.P, Graber, D.A., Kirby, R.E. and Taylor, T.S. (eds) Proceedings of the 1988 North American Wood Duck Symposium. North American Wood Duck Symposium, St Louis, Missouri, pp. 62–67. Ervin, G.N., Majure, L.C. and Bried, J.T. (2006) Influence of long-term greentree reservoir impoundment on stand structure, species composition, and hydrophytic indicators. Journal of the Torrey Botanical Society 133, 468–481. Available at: https://www.jstor. org/stable/20063860 (accessed 1 July 2018). Evans, K.E. and Kirkman, R.A. (1981) Guide to bird habitats of the Ozark Plateau. USDA Forest Service General Technical Report NC-68. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs. usda.gov/treesearch/pubs/10132 (accessed 1 July 2018). Fan, Z., Larsen, D.R., Shifley, S.R., Spetich, M.A. and Thompson, F.R. III (2003a) Estimating cavity tree abundance by stand age and basal area. Forest Ecology and Management 179, 231–242. https://doi. org/10.1016/S0378-1127(02)00522-4 Fan, Z., Shifley, S.R., Spetich, M.A., Thompson, F.R. and Larsen, D.R. (2003b) Distribution of cavity trees in midwestern old-growth and second-growth forests. Canadian Journal of Forest Research 33, 1481– 1494. https://doi.org/10.1139/x03-068 Fan, Z., Shifley, S.R., Spetich, M.A., Thompson, F.R. and Larsen, D.R. (2005) Abundance and size distribution of cavity trees in second-growth and old-growth Central Hardwood Forests. Northern Journal of Applied Forestry 22, 162–169. Available at: https:// www.fs.usda.gov/treesearch/pubs/22215 (accessed 1 July 2018).

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Fearer, T. (2016) Oak forest’s value to wildlife. In: Keyser, P.D., Fearer, T. and Harper, C.A. (eds) Managing Oak Forests in the Eastern United States. CRC Press, Boca Raton, Florida, pp 41–49. Feret, P.P., Kreh, R.E., Merkle, S.A. and Oderwald, R.G. (1982) Flower abundance, premature acorn abscission, and acorn production in Quercus alba L. Botanical Gazette (Chicago) 143, 216–218. https:// doi.org/10.1086/337292 Francis, J.K. (1983) Acorn production and tree growth of Nuttall oak in a green-tree reservoir. USDA Forest Service Research Note SO-289. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, 3 pp. https://doi.org/10.2737/SO-RN-289 Fredrickson, L.H. (2005) Greentree reservoir management: implications of historic practices and contemporary considerations to maintain habitat values. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 479–486. Fredrickson, L.H. and Batema, D. (1992) Greentree Reservoir Management Handbook. Wetland Man­ agement Series 1. Gaylord Memorial Laboratory, Puxico, Missouri, 88 pp. Gardiner, E.S. and Yeiser, J.L. (2006) Underplanting cherrybark oak (Quercus pagoda Raf.) seedlings on a bottomland site in the southern United States. New Forests 32, 105–119. https://doi.org/10.1007/ s11056-005-4168-2 Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Giusti, G.A., Keiffer, R.J., Feirer, S. and Keiffer, R.F. (2015) Oak tree selection by nesting turkey vultures (Cathartes aura). USDA Forest Service General Technical Report PSW-251. USDA Forest Service, Pacific Southwest Research Station, Berkeley, California, pp. 107–110. Available at: https://www.fs.usda.gov/treesearch/pubs/49975 (accessed 1 July 2018). Gobster, P.H. (1994) The aesthetic experience of sustainable forest ecosystems. USDA Forest Service General Technical Report RM-247. Rocky Mountain Forest and Range Experiment Station, Ft Collins, Colorado, pp. 246–255. https://doi.org/10.2737/RM-GTR-247 Goodrum, P.D., Reid, V.H. and Boyd, C.E. (1971) Acorn yields, characteristics, and management criteria of oaks for wildlife. Journal of Wildlife Management 35, 520–532. https://doi.org/10.2307/3799707 Google Earth (2017) Available at: https://www.google. com/earth/index.html (accessed 12 March 2017). Gramann, J.H. and Rudis, V.A. (1994) Effects of hardwood retention, season of year, and landform on the perceived scenic beauty of forest plots in the Ouachita

Silvicultural Methods for Selected Ecosystem Services

Mountains. USDA Forest Service General Technical Report SO-112. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 223–228. Available at: https://www.fs.usda.gov/ treesearch/pubs/493 (accessed 1 July 2018). Graves, W.C. (1980) Annual oak mast yields from visual estimates. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 270–274. Available at: https://www.fs.fed.us/psw/publications/documents/ psw_gtr044/index.shtml (accessed 1 July 2018). Greenberg, C.H. and Parresol, B.R. (2000) Acorn production characteristics of southern Appalachian oaks: a simple method to predict within-year acorn crop size. USDA Forest Service Research Paper SRS-20. USDA Forest Service, Southern Research Station, Asheville, North Carolina, 14 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/1770 (accessed 1 July 2018). Greenberg, C.H. and Parresol, B.R. (2002) Dynamics of acorn production by five species of southern Appalachian oaks. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems, Ecology and Management for Wildlife. Johns Hopkins University Press, Baltimore, Maryland, pp. 149–172. Available at: https://www.fs.usda.gov/treesearch/pubs/33015 (accessed 1 July 2018). Grigal, D.F. (2000) Effects of extensive forest management on soil productivity. Forest Ecology and Management 138, 167–185. https://doi.org/10.1016/ S0378-1127(00)00395-9 Grimm, E.C. (1984) Fire and other factors controlling the Big Woods vegetation of Minnesota in the mid-nineteenth century. Ecological Monographs 54, 291–311. https://doi.org/10.2307/1942499 Grossman, B.C., Gold, M.A. and Dey, D.C. (2003) Restoration of hard mast species for wildlife in Missouri using precocious flowering oak in the Missouri River floodplain, USA. Agroforestry Systems 59, 3–10. https://doi.org/10.1023/A:1026124717097 Guldin, J.M. (1991) Silvicultural practices applied to old-growth stand management. In: Proceedings of Restoration of Old Growth Forests in the Interior Highlands of Arkansas and Oklahoma Conference. Winrock International, Morrilton, Arkansas, pp. 171–190. Guttery, M.R. and Ezell, A.W. (2006) Characteristics of a bottomland hardwood forest under greentree reservoir management in east central Arkansas. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 409–411. Available at: https://www.fs.usda.gov/treesearch/pubs/23429 (accessed 1 July 2018). Guyette, R.P. (1995) A History of Wildland Fire in the Current River Watershed. University of Missouri School of Natural Resources, Columbia, Missouri.

503

Gysel, L.W. (1956) Measurement of acorn crops. Forest Science 2, 305–313. https://doi.org/10.1093/ forestscience/2.4.305 Gysel, L.W. (1957) Acorn production on good, medium, and poor oak sites in southern Michigan. Journal of Forestry 55, 570–574. https://doi.org/10.1093/ jof/55.8.570 Hahn, J.T. and Hansen, M.H. (1991) Cubic and board foot volume equations for the Central States. Northern Journal of Applied Forestry 8, 47–57. https:// doi.org/10.1093/njaf/8.2.47 Hardin, K.I. and Evans, K.E. (1977) Cavity nesting birds in the oak–hickory forest … a review. USDA Forest Service General Technical Report NC-30. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https:// www.fs.usda.gov/treesearch/pubs/10095 (accessed 1 July 2018). Harlow, R.F. and Eikum, R.L. (1963) The effects of stand density on the acorn production of turkey oaks. In: Proceedings of the Annual Conference of Southeast Game and Fish Commissioners 17. Southeast Association of Game and Fish Commissioners, Hot Springs, Arkansas, pp. 126–133. Harris, L.D. (1984) The Fragmented Forest. University of Chicago Press, Chicago, Illinois. Harris, R.D. (1983) Decay characteristics of pileated woodpecker nest trees. USDA Forest Service General Technical Report RM-99. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, pp. 125– 129. https://doi.org/10.2737/RM-GTR-99 Hättenschwiler, S., Miglietta, F., Raschi, A. and Körner, C. (1997) Thirty years of in situ tree growth under elevated CO2: a model for future forest responses? Global Change Biology 3, 463–471. https://doi. org/10.1046/j.1365-2486.1997.00105.x Healy, W.M. (1997) Thinning New England oak stands to enhance acorn production. Journal of Forestry 14(3), 152–156. https://doi.org/10.1093/njaf/14.3.152 Healy, W.M. (2002) Managing eastern oak forests for wildlife. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. The John Hopkins Press, Baltimore, Maryland, pp. 317–341. Healy, W.W. and Houf, G.F. (1989) Treating immature stands for wildlife. In: Hutchinson, J.G. (ed.) Central Hardwood Notes. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, Note 9.04. Available at: https://www.nrs. fs.fe d.us/pubs/1437 (accessed 1 July 2018). Healy, W.M., Brooks, R.T. and DeGraaf, R.M. (1989) Cavity trees in sawtimber-size oak stands in central Massachusetts. Northern Journal of Applied Forestry 6, 61–65. https://doi.org/10.1093/njaf/6.2.61 Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland.

504

Herbeck, L.A. (2000) Analysis of down wood volume and percent ground cover for the Missouri Ozark forest ecosystem project. USDA Forest Service General Technical Report NC-208. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 124–131. Available at: https://www.fs.usda.gov/ treesearch/pubs/12501 (accessed 1 July 2018). Herrick, S.K., Kovach, J.A., Padley, E.A., Wagner, C.R. and Zastrow, D.E. (2009) Wisconsin’s Forestland Woody Biomass Harvesting Guidelines. Publication PUB-FR-435-2009. Wisconsin Department of Natural Resources Division of Forestry and Wisconsin Council on Forestry, Madison, Wisconsin, 51 pp. Hertlein, D.M. and Gates, R.J. (2005) Condition, species composition, and oak regeneration at Oakwood Bottoms Greentree Reservoir in southern Illinois. In: Fredrickson, L.H., King, S.L. and Kaminski, R.M. (eds) Ecology and Management of Bottomland Hardwood Systems: the State of Our Understanding. Gaylord Memorial Laboratory Special Publication No. 10. University of Missouri, Columbia, Missouri, pp. 495–508. Hicks, R.R., Jr (1997) A resource at the crossroads: a history of the central hardwoods. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 1–22. Available at: https://www.fs.usda.gov/treesearch/pubs/15603 (accessed 1 July 2018). Hook, D.D. (1984) Waterlogging tolerance of lowland tree species in the South. Southern Journal of Applied Forestry 8, 136–148. https://doi.org/10.1093/ sjaf/8.3.136 Hoover, C. and Stout, S. (2007) The carbon consequences of thinning techniques: stand structure makes a difference. Journal of Forestry 105, 266– 270. Available at: https://www.fs.usda.gov/treesearch/ pubs/12575 (accessed 1 July 2018). Hoover, C.M., Birdsey, R.A., Heath, L.S. and Stout, S.L. (2000) How to estimate carbon sequestration on small forest tracts. Journal of Forestry 98, 13–19. Available at: https://www.fs.usda.gov/treesearch/pubs/ 12556 (accessed 1 July 2018). Hornbeck, J.W., Smith, C.T., Martin, C.W., Tritton, L.M. and Pierce, R.S. (1990) Effects of intensive harvesting on nutrient capitals of three forest types in New England. Forest Ecology and Management 30, 55–64. https://doi.org/10.1016/0378-1127(90)90126-V Houck, J.E., Tiegs, P.E., McCrillis, R.C., Keithley, C. and Crouch, J. (1998) Air emissions from residential heating: the wood heating option put into environmental perspective. In: The Proceedings of a US EPA and Air Waste Management Association Conference: Emission Inventory: Living in a Global Environment, Vol. 1. US Environmental Protection Agency, Washington, DC, pp. 373–384. Hull, R.B. IV and Buhyoff, G.J. (1986) The scenic beauty temporal distribution method: an attempt to make

Chapter 13

scenic beauty assessments compatible with forest planning methods. Forest Science 32, 271–286. https://doi.org/10.1093/forestscience/32.2.271 Hunter, M.A. and Schmiegelow, F.K.A. (2010) Wildlife, Forests, and Forestry: Principles of Managing Forest for Biological Diversity, 2nd edn. Pearson, London. Hunter, M.L., Jr (1989) What constitutes an old-growth stand? Journal of Forestry 87, 33–35. https://doi. org/10.1093/jof/87.8.33 Huntley, J.C. (1983) Squirrel den tree management: reducing incompatibility with timber production in upland hardwoods. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 488–495. https://doi.org/10.2737/SE-GTR-24 International Energy Agency Bioenergy (2002) Sustainable production of woody biomass for energy. In: International Energy Agency, ExCo: 2002:03. International Energy Agency Bioenergy, Rotorua, New Zealand, pp. 1–12. International Energy Agency Bioenergy (2005) Benefits of bioenergy. International Energy Agency, ExCo: 2005:01. International Energy Agency Bioenergy, Rotorua, New Zealand, pp. 1–10. Iversen, C.M. and Ledford, C.M. (2008) CO2 enrichment increases carbon and nitrogen input from fine roots in a deciduous forest. New Phytologist 179, 837–847. https://doi.org/10.1111/j.1469-8137.2008.02516.x Jacquart, E.M., Armentano, T.V. and Spingarn, A.L. (1992) Spatial and temporal tree responses to water stress in an old-growth deciduous forest. American Midland Naturalist 127, 158–171. https://doi. org/10.2307/2426331 Jenkins, J.C., Chojnacky, D.C., Heath, L.S. and Birdsey, R.A. (2003) National-scale biomass estimators for United States tree species. Forest Science 49, 12– 35. Available at: https://www.fs.usda.gov/treesearch/ pubs/6996 (accessed 1 July 2018). Jenkins, J.C., Chojnacky, D.C., Heath, L.S. and Birdsey, R.A. (2004a) A comprehensive database of diameter-based biomass regressions for North American tree species. USDA Forest Service General Technical Report NE-319. USDA Forest Service, Northeastern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-GTR-319 Jenkins, M.A., Webster, C.R., Parker, G.R. and Spetich, M.A. (2004b) Coarse woody debris in managed Central Hardwood forests of Indiana, USA. Forest Science 50, 781–792. Available at: https://www.fs.usda.gov/treesearch/pubs/9028 (accessed 1 July 2018). Jensen, R.G., Kabrick, J.M. and Zenner, E.K. (2002) Tree cavity estimation and verification in the Missouri Ozarks. USDA Forest Service General Technical Report NC-227. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 114–129. Available at: https://www.fs.usda.gov/treesearch/pubs/19045 (accessed 1 July 2018).

Silvicultural Methods for Selected Ecosystem Services

Johnson, D.W. and Todd, D.E. (1997) Effects of harvesting intensity of forest productivity and soil carbon storage in a mixed oak forest. In: Kimble, J.M., Follett, R.F., Stewart, B.A. and Lal, R. (eds) Management of Carbon Sequestration in Soil. CRC Press, Boca Raton, Florida, pp. 351–363. Kabrick, J.M., Dey, D.C., Van Sambeek, J.W., Wallendorf, M. and Gold, M.A. (2005) Soil properties and growth of swamp white oak and pin oak on bedded soils in the lower Missouri River floodplain. Forest Ecology and Management 204, 315–327. https://doi.org/10.1016/j.foreco.2004.09.014 Kendrick, S.W. and Thompson, F.R. III (2013) Tree stocking affects winter bird densities across a gradient of savanna, woodland, and forest in the Missouri Ozarks. Wildlife Society Bulletin 37, 577–584. https:// doi.org/10.1002/wsb.286 King, S.L. (1995) Effects of flooding regimes on two impounded bottomland hardwood stands. Wetlands 15(3), 272–284. https://doi.org/10.1007/BF03160707 King, S.L. and Allen, J.A. (1996) Plant succession and greentree reservoir management: implications for management and restoration of bottomland hardwood wetlands. Wetlands 16(4), 503–511. https://doi. org/10.1007/BF03161340 King, S.L., Allen, J.A. and McCoy, J.W. (1998) Long-term effects of a lock and dam and greentree reservoir management on a bottomland hardwood forest. Forest Ecology and Management 112(3), 213–226. https://doi.org/10.1016/S0378-1127(98)00344-2 Kirkpatrick, R.L. and Pekins, P.J. (2002) Nutritional value of acorns for wildlife. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems, Ecology and Management for Wildlife. The Johns Hopkins University Press, Baltimore, Maryland, pp. 173–181. Kittredge, J. and Chittenden, A.K. (1929) Oak forests of northern Michigan. USDA Forest Service Lake States Forest Experiment Station Special Bulletin 190. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Koenig, W.D., Knops, J.M.H., Carmen, W.J., Stanback, M.T. and Mumme, R.L. (1994) Estimating acorn crops using visual surveys. Canadian Journal of Forest Research 24, 2105–2112. https://doi.org/10.1139/x94-270 Kormanik, P.P., Sung, S.-J. and Kormanik, T.L. (1994) Toward a single nursery protocol for oak seedlings. In: Proceedings of 22nd Southern Forest Tree Improvement Conference. Southern Forest Tree Improvement Committee, Atlanta, Georgia, pp. 89– 98. Available at: https://www.fs.usda.gov/treesearch/ pubs/1040 (accessed 1 July 2018). Kormanik, P.P., Sung, S.-J.S., Kormanik, T., Tibbs, T. and Zarnock, S.J. (2004) Northern red oak from acorns to acorns in 8 years or less. In: USDA Forest Service General Technical Report SRS-71. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 555–558. Available

505

at: https://www.fs.usda.gov/treesearch/pubs/all/6700 (accessed 1 July 2018). Krajicek, J.E., Brinkman, K.A. and Gingrich, S.F. (1961) Crown competition – a measure of density. Forest Science 7, 35–42. https://doi.org/10.1093/ forestscience/7.1.35 Krekeler, N., Kabrick, J.M., Dey, D.C. and Wallendorf, M. (2006) Comparing natural and artificial methods for establishing pin oak advance reproduction in bottomland forests managed as greentree reservoirs. USDA Forest Service General Technical Report SRS-92. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 224–228. Available at: https://www.fs.usda.gov/treesearch/pubs/all/23382 (accessed 1 July 2018). Kurzejeski, E.W. and Lewis, J.B. (1990) Home ranges, movements, and habitat use of wild turkey hens in northern Missouri. In: Healy, W.M. and Healy, G.B. (eds) Proceedings of the Sixth National Wild Turkey Symposium. National Wild Turkey Federation, Edgefield, South Carolina, pp. 67–71. Ladd, D. (1991) Re-examination of the role of fire in Missouri oak woodlands. In: Proceedings of Oak Woods Management Workshop. Eastern Illinois University, Charleston, Illinois, pp. 67–80. Lamlom, S.H. and Savidge, R.A. (2003) A reassessment of carbon content in wood: variation within and between 41 North American species. Biomass and Bioenergy 25, 381–388. https://doi.org/10.1016/ S0961-9534(03)00033-3 Larsen, D.R. and Cecich, R.A. (1997) Model of white oak flower survival and maturation. USDA Forest Service General Technical Report NC-188. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 262–268. Available at: https:// www.fs.usda.gov/treesearch/pubs/15670 (accessed 1 July 2018). Larson, M.A., Dijak, W.D., Thompson, F.R. III and Millspaugh, J.J. (2003) Landscape-level habitat suitability models for twelve species in southern Missouri. USDA Forest Service General Technical Report NC-233. USDA Forest Service, Northern Central Forest Experiment Station, St Paul, Minnesota. https://doi.org/10.2737/NC-GTR-233 Leverett, R. (1996) Definitions and history. In: Davis, M.B. (ed.) Eastern Old-Growth Forests. Island Press, Washington, DC, pp. 3–17. Lhotka, J.M. and Zaczek, J.J. (2003) Soil scarification effects on oak reproduction in two mixed-oak bottomland stands of southern Illinois. Southern Journal of Applied Forestry 27(3), 164–171. https://doi. org/10.1093/sjaf/27.3.164 Little, S. (1974) Effects of fire on temperate forests: northeastern United States. In: Kozlowski, T.T. and Ahlgren, C.E. (eds) Fire and Ecosystems. Academic Press, New York, pp. 225–250. https://doi.org/10.1016/ B978-0-12-424255-5.50012-2

506

Litton, R.B., Jr and McDonald, P.M. (1980) Silvicul­ ture and visual resources. In: Proceedings of the 1979 Society of American Foresters Convention. Society of American Foresters, Bethesda, Maryland, pp. 97–102. Lorimer, C.G. (1985) The role of fire in the perpetuation of oak forests. In: Proceedings of Challenges in Oak Management and Utilization. University of Wisconsin, Madison, Wisconsin, pp. 8–25. Malmsheimer, R.W., Heffernan, P., Brink, S., Crandall, D., Deneke, F., Galik, C., Gee, E., Helms, J.A., McClure, N., Mortimer, M., Ruddell, S., Smith, M. and Stewart, J. (2008) Forest management solutions for mitigating climate change in the United States. Journal of Forestry 106, 115–171. https://doi.org/10.1093/ jof/106.3.115 Martin, A.C., Zim, H.S. and Nelson, A.L. (1961) American Wildlife and Plants: a Guide to Wildlife Food Habits. Dover Publications, New York. Martin, W.H. (1992) Characteristics of old-growth mixed mesophytic forests. Natural Areas Journal 12, 127–135. McComb, W.C. and Muller, R.N. (1983) Snag densities in old-growth and second-growth Appalachian forests. Journal of Wildlife Management 47, 376–382. https:// doi.org/10.2307/3808510 McCurry, J.R., Gray, M.J., Franklin, J.A. and Mercker, D.C. (2005) Relationship of oak seedling height and diameter with bottomland elevation. In: Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies 59. Southeastern Association of Fish and Wildlife Agencies, Tallahassee, Florida, pp. 173–182. McDonald, P.M. (1992) Estimating seed crops of conifer and hardwood species. Canadian Journal of Forest Research 22, 832–838. https://doi.org/10.1139/ x92-112 McDonald, P.M. and Huber, D.W. (1995) California’s hardwood resource: managing for wildlife, water, pleasing scenery, and wood products. USDA Forest Service General Technical Report PSW-154. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-154 McKnight, J.S., Hook, D.D., Langdon, O.G. and Johnson, R.L. (1980) Flood tolerance and related characteristics of trees of the bottomland hardwood forests. In: Proceedings of Workshop on Bottomland Hardwood Forest Wetlands of the Southeast United States. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 29–69. Available at: https://www.fs.usda.gov/treesearch/ pubs/42960 (accessed 1 July 2018). McQuilkin, R.A. and Musbach, R.A. (1977) Pin oak acorn production on green tree reservoirs in southeastern Missouri. Journal of Wildlife Management 41, 218–225. https://doi.org/10.2307/3800598

Chapter 13

McShea, W.J. and Healy, W.M. (2002) Oak Forest Ecosystems: Ecology and Management for Wildlife. The John Hopkins Press, Baltimore, Maryland. Menke, J.W. and Fry, M.E. (1980) Trends in oak utilization – fuelwood, mast production, animal use. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 297–305. https://doi.org/10.2737/PSW-GTR-44 Meyer, J. (1986) Management of old growth forests in Missouri. Missouri Department of Conservation Habitat Management Series 3. Missouri Department of Conservation, Jefferson City, Missouri. Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC, 137 pp. Available at: https://www. millenniumassessment.org/documents/document.356.aspx.pdf (accessed 1 July 2018). Minckler, L.S. and Janes, D. (1965) Pin oak acorn production on normal and flooded areas. University of Missouri Agricultural Experiment Station Research Bulletin 898. University of Missouri Agricultural Experiment Station, Columbia, Missouri. Minckler, L.S. and McDermott, R.E. (1960) Pin oak acorn production and regeneration as affected by stand density, structure and flooding. University of Missouri Agricultural Experiment Station Research Bulletin 750. University of Missouri Agricultural Experiment Station, Columbia, Missouri. Available at: https://www.fs.usda. gov/treesearch/pubs/49747 (accessed 1 July 2018). Missouri Department of Conservation (2010) Missouri Woody Biomass Harvesting Best Management Practices Manual. Available at: https://mdc.mo.gov/ sites/default/files/resources/2010/09/woody_biomass_ harvesting_bmp_book.pdf (accessed 17 March 2017). Mladenoff, D.J., White, M.A., Crow, T.R. and Pastor, J. (1994) Applying principles of landscape design and management to integrate old-growth forest enhancement and commodity use. Conservation Biology 8, 752–762. https://doi.org/10.1046/j.1523-1739.1994. 08030752.x Moody, R.D. (1953) Mast production of certain oaks in Louisiana. Paper presented at the Southeast Association of Game and Fish Commissioners Conference. Southeast Association of Game and Fish Commissioners, Chattenooga, Tennessee. Moody, R.D., Collins, J.O. and Reid, V.H. (1954) Oak production study under way. Louisiana Conservationist 6(9), 6–8. Mossman, M.J. and Lange, K.I. (1982) Breeding Birds of the Baraboo Hills, Wisconsin: their History, Distribution, and Ecology. Wisconsin Department of Natural Resources and Wisconsin Society for Ornithology, Madison, Wisconsin. Muller, R.N. (1982) Vegetation patterns in the mixed mesophytic forest of eastern Kentucky. Ecology 63, 1901–1917. https://doi.org/10.2307/1940129

Silvicultural Methods for Selected Ecosystem Services

Myers, S.A. (1978) Insect impact on acorn production in Missouri upland forests. PhD dissertation, University of Missouri, Columbia, Missouri. National Renewable Energy Laboratory (2006) From Biomass to Biofuels. US Department of Energy, NREL/ BR-510-39436 (August). US Department of Energy, National Renewable Energy Laboratory, Golden, Colorado, pp. 1–6. https://doi.org/10.2172/891596 National Renewable Energy Laboratory (2007) Research advances cellulosic ethanol. US Department of Energy, NREL/BR-510-40742 (March). US Depart­ ment of Energy, National Renewable Energy Laboratory, Golden, Colorado, pp. 1–8. https://doi. org/10.2172/902034 Norby, R.J., O’Neill, E.G. and Luxmoore, R.J. (1986) Effects of atmospheric CO2 enrichment on the growth and mineral nutrition of Quercus alba seedlings in nutrient-poor soil. Plant Physiology 82, 83–89. https:// doi.org/10.1104/pp.82.1.83 Noss, R.F. (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4, 355–364. https://doi.org/10.1111/j.1523-1739.1990. tb00309.x Nowacki, G.J. and Abrams, M.D. (2008) The demise of fire and ‘mesophication’ of forest in the eastern United States. BioScience 58, 123–138. https://doi. org/10.1641/B580207 Nowacki, G.J. and Trianosky, P.A. (1993) Literature on old-growth forests of the eastern North America. Natural Areas Journal 13, 87–105. Olson, D.F., Jr (1974) Quercus L. Oak. USDA Forest Service Agriculture Handbook 450. USDA Forest Service, Washington, DC, 88 pp. Palmer, J.F., Shannon, S., Harrilchak, M.A., Gobster, P.H. and Kokx, T. (1995) Esthetics of clearcutting: alternatives in the White Mountain National Forest. Journal of Forestry 93(5), 37–42. https://doi.org/ 10.1093/jof/93.5.37 Parker, G.R. (1989) Old-growth forests of the central hardwood region. Natural Areas Journal 9, 5–11. Parker, G.R., Leopold, D.J. and Eichenberger, J.K. (1985) Tree dynamics in an old-growth, deciduous forest. Forest Ecology and Management 11, 31–57. https://doi.org/10.1016/0378-1127(85)90057-X Pauli, B.P., Zollner, P.A., Haulton, G.S., Shao, G. and Shao, G. (2015) The simulated effects of timber harvest on suitable habitat for Indiana and northern long-eared bats. Ecosphere 6, 1–24. https://doi.org/10.1890/ ES14-00336.1 Peet, R.K. and Loucks, O.L. (1977) A gradient analysis of southern Wisconsin forests. Ecology 58, 485–499. https://doi.org/10.2307/1938999 Post, L.S. (1998) Seed management in Tennessee: development of seed zones for Tennessee and distribution and protection of northern red oak (Quercus rubra L.) acorns. MSc. thesis, University of Tennessee, Knoxville, Tennessee.

507

Pyne, S.J. (1982) Fire in America. Princeton University Press, Princeton, New Jersey. Rebertus, A.J., Shifley, S.R., Richards, R.H. and Roovers, L.M. (1997) Ice storm damage to an old-growth oak–hickory forest in Missouri. American Midland Naturalist 137, 48–61. https://doi.org/10.2307/2426754 Ribe, R. (1991) The scenic impact of key forest attributes and long-term management alternatives for hardwood forests. USDA Forest Service General Technical Report NE-148. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 34–54. Available at: https://www.fs.usda.gov/treesearch/pubs/4226 (accessed 1 July 2018). Rittenhouse, C.D., Dijak, W.D., Thompson, F.R. III and Millspaugh, J.J. (2007) Development of landscapelevel habitat suitability models for ten wildlife species in the Central Hardwoods region. USDA Forest Service General Technical Report NRS-4. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NRS-GTR-4 Roovers, L.M. and Shifley, S.R. (1997) Composition and dynamics of Spitler Woods, an old-growth remnant forest in Illinois (USA). Natural Areas Journal 17, 219–232. Row, C. and Phelps, R.B. (1996) Wood carbon flows and storage after timber harvest. In: Sampson, R.N. and Hair, D. (eds) Forests and Global Change: Forest Management Opportunities for Mitigating Carbon Emissions, Vol. 2. American Forests, Washington, DC, pp. 27–58. Rudis, V.A., Gramann, J.H., Ruddell, E.J. and Westphal, J.M. (1988) Forest inventory and managementbased visual preference models of southern pine stands. Forest Science 34, 846–863. Available at: https://www.fs.usda.gov/treesearch/pubs/30597 (accessed 1 July 2018). Runkle, J.R. (1996) Central mesophytic forests. In: Davis, M.B. (ed.) Eastern Old-Growth Forests. Island Press, Washington, DC, pp. 161–177. Rutherford, W., Jr and Shafer, E.L., Jr (1969) Selection cuts increased natural beauty in two Adirondack forest stands. Journal of Forestry 67, 415–419. https:// doi.org/10.1093/jof/67.6.415 Schlesinger, R.C. (1989) Dynamics of the sugar maple component of a white oak–yellow-poplar community. USDA Forest Service General Technical Report NC-132. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 262– 266. Available at: https://www.fs.usda.gov/treesearch/ pubs/19622 (accessed 1 July 2018). Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc. nal.usda.gov/download/CAT86200555/PDF (accessed 1 July 2018).

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Schroeder, H. (1996) Ecology of the heart: restoring and sustaining the human experience of ecosystems. In: Ewert, A.W. (ed.) Natural Resources Management: the Human Dimension. Westview Press, Boulder, Colorado, pp. 13–27. Schroeder, H. and Daniel, T.C. (1980) Predicting the scenic quality of forest road corridors. Environment and Behavior 12, 349–366. https://doi.org/10.1177/ 0013916580123004 Scott, V.E., Evans, K.E., Patton, D.R. and Stone, C.P. (1977) Cavity-nesting Birds of North American Forests. Agriculture Handbook No. 511. USDA Forest Service, Washington, DC. Available at: https:// www.gutenberg.org/files/49172/49172-h/49172-h. htm (accessed 1 July 2018). Sharp, W.M. (1958) Evaluating mast yields in the oaks. Pennsylvania State University Agricultural Experiment Station Bulletin 635. Pennsylvania State University, University Park, Pennsylvania. Sharp, W.M. and Sprague, V.G. (1967) Flowering and fruiting in the white oaks. Pistillate flowering, acorn development, weather, and yields. Ecology 48, 243–251. https://doi.org/10.2307/1933106 Shifley, S.R. (1994) Old-growth forests in the Central States: observations on a unique resource. In: Proceedings of the 1993 Society of American Foresters National Convention. Society of American Foresters, Bethesda, Maryland, pp. 527–528. Shifley, S.R., Roovers, L.M. and Brookshire, B.L. (1995) Structural and compositional differences between old-growth and mature second-growth forests in the Missouri Ozarks. USDA Forest Service General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 23–36. Available at: https://www.fs.usda.gov/treesearch/pubs/all/4301 (accessed 1 July 2018). Shifley, S.R., Brookshire, B.L., Larsen, D.R. and Herbeck, L.A. (1997a) Snags and down wood in Missouri old-growth and mature second-growth forest. Northern Journal of Applied Forestry 14, 165–172. https://doi.org/10.1093/njaf/14.4.165 Shifley, S.R., Brookshire, B.L., Larsen, D.R., Herbeck, L.A. and Jensen, R.G. (1997b) Snags and down wood on upland oak sites in the Missouri Ozark Forest Ecosystem Project. USDA Forest Service General Technical Report NC-193. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp 248–256. Available at: https:// www.fs.usda.gov/treesearch/pubs/53575 (accessed 1 July 2018). Shifley, S.R., Thompson, F.R. III, Dijak, W.D., Larson, M.A. and Millspaugh, J.J. (2006) Simulated effects of forest management alternatives on landscape structure and habitat suitability in the Midwestern United States. Forest Ecology and Management 229, 361–377. https://doi.org/10.1016/j.foreco.2006.04.030

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Shotola, S.J., Weaver, G.T., Robertson, P.A. and Ashby, W.C. (1992) Sugar maple invasion of an old-growth oak–hickory forest in southwestern Illinois. American Midland Naturalist 127, 125–138. https://doi. org/10.2307/2426328 Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, P.M.S. (1997) The Practice of Silviculture: Applied Forest Ecology, 9th edn. Wiley, New York. Smith, H.C., Lamson, N.I. and Miller, G.W. (1989) An esthetic alternative to clearcutting? Journal of Forestry 87(3), 14–18. https://doi.org/10.1093/jof/87.3.14 Smith, J.E. and Kuhr, J.F. (1989) Visual management of Central Hardwoods. USDA Forest Service Central Hardwood Notes Note 10.03. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11693 (accessed 1 July 2018). Smith, J.E., Heath, L.S., Skog, K.E. and Birdsey, R.A. (2006) Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. USDA Forest Service General Technical Report NE-343. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-GTR-343 Smith, W.B. (1985) Factors and equations to estimate forest biomass in the North Central Region. USDA Forest Service Research Paper NC-269. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10055 (accessed 1 July 2018). Spetich, M.A. (1995) Characteristics and spatial pattern of old-growth forests in the Midwest. PhD dissertation, Purdue University, West Lafayette, Indiana. Spetich, M.A., Shifley, S.R. and Parker, G.R. (1999) Regional distribution and dynamics of coarse woody debris in Midwestern old-growth forests. Forest Science 45, 302–313. Available at: https://www.fs.usda.gov/ treesearch/pubs/all/900 (accessed 1 July 2018). Stahle, D.W. and Chaney, P.L. (1994) A predictive model for the location of ancient forests. Natural Areas Journal 14, 151–158. Stambaugh, M.C., Smith, K.T. and Dey, D.C. (2017) Fire scar growth and closure rates in white oak (Quercus alba) and the implications for prescribed burning. Forest Ecology and Management 391, 396–403. https://doi.org/10.1016/j.foreco.2017.02.005 Stanek, W. and State, D. (1978) Equations predicting ­primary productivity (biomass) of trees, shrubs and lesser vegetation based on current literature. Canadian Forestry Service Information Report BC-X183. Environment Canada Forestry Service, Pacifc Forest Research Center, Victoria, British Columbia. Available at: https://cfs.nrcan.gc.ca/publications?id=399 (accessed 19 September 2018). Steffen, D.E., Lafon, N.W. and Norman, G.W. (2002) Turkeys, acorns, and oaks. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology

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and Management for Wildlife. The John Hopkins Press, Baltimore, Maryland, pp. 241–255. Sullivan, N.H. (2001) An algorithm for a landscape model of mast production. PhD dissertation, University of Missouri, Columbia, Missouri. Thomas, J.W. and Radke, R.E. (1989) Effects of timber management practices on forest wildlife management. In: Burns, R.M. (comp.) USDA Forest Service, General Technical Report WO-55. USDA Forest Service Washington Office, Washington, District of Columbia, pp 107–117. Thompson, F.R. III (2001) Principles of landscape ecology for conservation of wildlife and biodiversity. In: Dickson, J.G. (comp. ed.) Wildlife of Southern Forests Habitat and Management. Hancock House Publishers, Blaine, Washington, pp. 74–82. Thompson, F.R. III, Probst, J.R. and Raphael, M.G. (1995) Impacts of silviculture: overview of management recommendations. In: Martin, T.E. and Finch, D.M. (eds) Ecology and Management of Neotropical Migratory Birds. Oxford University Press, New York, pp. 201–219 Thompson, F.R. III, Robinson, S.K., Whitehead, D.R. and Brawn, J.D. (1996) Management of Central Hardwood landscapes for the conservation of migratory birds. USDA Forest Service General Technical Report NC-187. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 117–143. Available at: https://www.fs.usda.gov/treesearch/ pubs/10251 (accessed 1 July 2018). Tietje, W.D., Berlund, T.C., Garcia, S.L., Halpin, C.G. and Jensen, W.A. (1997) Contribution of downed woody material by blue, valley and coast live oaks in central California. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Research Station, Albany, California, Colorado, pp. 423–430. Available at: https://www.fs.usda. gov/treesearch/pubs/28203 (accessed 1 July 2018). Tietje, W.D., Waddell, K.L., Vreeland, J.K. and Bolsinger, C.L. (2002) Coarse woody debris in oak woodlands of California. Western Journal of Applied Forestry 17, 139–146. Available at: https://www.fs.usda.gov/treesearch/pubs/4853 (accessed 1 July 2018). Tingley, M.W., Darling, E.S. and Wilcove, D.S. (2014) Fine- and coarse-filter conservation strategies in a time of climate change. Annals of the New York Academy of Sciences 1322, 92–109. https://doi. org/10.1111/nyas.12484 Titus, R. (1983) Management of snags and den trees in Missouri – a process. USDA Forest Service General Technical Report RM-99. Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, pp. 51–59. https://doi.org/10.2737/RM-GTR-99 Tritton, L.M. and Hornbeck, J.W. (1982) Biomass equations for major tree species of the Northeast. USDA Forest Service General Technical Report NE-69. USDA Forest Service, Northeastern Forest Experiment

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Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/4082 (accessed 1 July 2018). Tryon, E.H. and Carvell, K.L. (1962) Acorn production and damage. West Virginia University Agricultural Experiment Station Bulletin 466T. West Virginia University Agricultural Experiment Station, Morgantown, West Virginia. Tyrrell, L.E., Nowacki, G.J., Crow, T.R., Buckley, D.S., Nauertz, E.A., Niese, J.N., Rollinger, J.L. and Zasada, J.C. (1998) Information about old growth for selected forest type groups in the eastern United States. USDA Forest Service General Technical Report NC-197. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/ pubs/10261 (accessed 1 July 2018). US Energy Information Administration (2017) UK’s Renewable Energy Targets Drive Increases in U.S. Wood Pellet Exports. Available at: https://www.eia. gov/todayinenergy/detail.php?id=20912 (accessed 24 February 2017). US Environmental Protection Agency (2017) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2015. Report number EPA 430-P-17-001. US Environmental Protection Agency, Washington, DC. Availableat:https://www.epa.gov/ghgemissions/inventoryus-greenhouse-gas-emissions-and-sinks-1990-2015 (accessed 20 September 2018). USDA Forest Service (2017) Forest Carbon Estimation. Available at: https://www.fia.fs.fed.us/forestcarbon/ (accessed 28 February 2017). van Ginkel, H.A.L., Kuijper, D.P.J., Churski, M., Zub, K., Szafranska, P. and Smit, C. (2013) Safe for saplings not safe for seeds: Quercus robur recruitment in relation to coarse woody debris in Bialowieza Primeval Forest, Poland. Forest Ecology and Management 304, 73–79. https://doi.org/10.1016/j.foreco.2013.04.037 Vaughan, M.R. (2002) Oak trees, acorns, and bears. In: McShea, W.J. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. The John Hopkins Press, Baltimore, Maryland, pp. 224–240. Verme, L.J. (1953) Production and utilization of acorns in Clinton County, Michigan. MSc. thesis, Michigan State University, East Lansing, Michigan.

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Verner, J. (1980) Birds of California oak habitats – ­management implications. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 247–264. https://doi.org/10.2737/PSW-GTR-44 Vodak, M.C., Roberts, P.L., Wellman, J.D. and Buhyoff, G.J. (1985) Scenic impacts of eastern hardwood management. Forest Science 31, 289–301. https:// doi.org/10.1093/forestscience/31.2.289 Welsh, C.J.E., Healy, W.M. and DeGraaf, R.M. (1992) Cavity-nesting bird abundance in thinned versus unthinned Massachusetts oak stands. Northern Journal of Applied Forestry 9, 6–9. Available at: https://www.fs.usda.gov/treesearch/pubs/53925 (accessed 1 July 2018). White, P.S. and White, R.D. (1996) Old-growth oak and oak–hickory forests. In: Eastern Old-Growth Forests. Island Press, Washington, DC, pp. 178–198. Whitney, G.G. (1994) From Coastal Wilderness to Fruited Plain. Cambridge University Press, Cambridge, UK. Wigley, T.B.W., Jr and Filer, T.H., Jr (1989) Cha­ racteristics of greentree reservoirs: a survey of managers. Wildlife Society Bulletin 17, 136–142. Available at: https://www.jstor.org/stable/3782639 (accessed 1 July 2018). Will-Wolf, S. (1991) Role of fire in maintaining oaks in mesic oak maple forests. In: Proceedings of the Oak Resource in the Upper Midwest Conference. University of Minnesota, St Paul, Minnesota, pp. 27–33. Winston, P.W. (1956) The acorn microsere, with special reference to arthropods. Ecology 37, 120–132. https://doi.org/10.2307/1929675 Wood, O.M. (1934) A brief record of seed productivity for chestnut oak in southern New Jersey. Journal of Forestry 32, 1014–1016. https://doi.org/10.1093/ jof/32.9.1010 Woodall, C.W., Coulston, J.W., Domke, G.M., Walters, B.F., Wear, D.N., Smith, J.E., Andersen, H., Clough, B.J., Cohen, W.B., Griffith, D.M. and seven others (2015) The U.S. forest carbon accounting framework: stocks and stock change, 1990–2016. USDA Forest Service General Technical Report NRS-154. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NRSGTR-154

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14

Managing Oak Forests in a Changing Climate Introduction

Changes in the earth’s climate are occurring as a consequence of human activity. A primary cause has been ascribed to burning fossil fuels, which in turn increases atmospheric carbon dioxide (CO2). CO2 and other so-called ‘greenhouse gases’ such as methane and nitrous oxide allow solar radiation to penetrate the atmosphere while simultaneously trapping a portion of the heat that otherwise would be re-radiated back into space. The build-up of greenhouse gases warms the earth’s biosphere (i.e. the enveloping surface/atmospheric layer to which all life has adapted). Increases over time in atmospheric CO2 have been well documented and anthropogenic CO2 emissions are projected to continue in the absence of major reductions in the use of fossil fuels. Even in the unlikely event that anthropogenic greenhouse gas emissions were greatly curtailed or ended, the climate would continue to warm for decades due to the longevity of greenhouse gases in in the atmosphere (IPCC, 2014). In some respects, climate change is simply one more anthropogenic forest disturbance added to a plethora of others including timber harvesting, tree planting, wildfire, livestock grazing and invasive species. Like climate change, those disturbances alter forest dynamics to favour some tree species or forest types at the expense of others. Given adequate time and funding, silviculturists and forest managers can control the occurrence and impact of many types of human-caused disturbances. But that is not the case with climate change; resource managers themselves have limited capacity to regulate or modify climate change. Instead they must consider anticipated global, national and regional climate impacts and prepare stand-scale silvicultural prescriptions that enable forests to adapt to gradual changes in climate. Thus consideration of climate change is the new normal in preparing silvicultural prescriptions. Silviculturists and forest managers must be aware of how warmer temperatures and altered precipitation

­ atterns will affect the future suitability of a site for current p and for alternate tree species. Climate change is a gradual process with impacts that will increase decade by decade and be necessary to consider in long-term silvicultural prescriptions for oak forests. This is especially the case during stand regeneration events that provide some of the best opportunities to direct a stand’s species composition along a trajectory compatible with anticipated climate conditions. Fortunately, new tools are available to help anticipate consequences of climate change on forests (e.g. Prasad et al., 2007– ongoing; Wang et al., 2014; Hargrove et al., 2018). Climate change will gradually and persistently alter the details of oak silviculture and ecology at a given location, and the magnitude of climate change is expected to increase over at least the next century. The underlying ecological processes and the basic principles governing oak reproduction, competition, growth and survival presented elsewhere in this book are unlikely to change, but for a given region the details of oak species suitability and response to disturbance will be altered by climate change. Projected climate change must be considered in the context of other disturbances that influence forest size structure and species composition, because those disturbances will themselves be affected by climate change. For example, wildfire probabilities are likely to increase with a warmer climate and more variable precipitation patterns (Guyette et al., 2014; Tang et  al., 2015). Likewise the impacts of disturbances from insects, disease and timber harvesting in oak forests may be amplified or dampened by climate change. Climate change complicates our understanding of oak ecology and silviculture, because climate is a primary factor regulating where oaks occur (see Chapter 1, this volume) and the ability of oaks to compete among themselves and with other tree species. This chapter addresses: (i) how climate change is likely to shift the geographic locations of suitable habitat for oaks and associated species; and

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(ii) proactive management strategies that can help oak forests mitigate climate change or adapt to climate change (Spittlehouse and Stewart, 2003; Millar et al., 2007; D’Amato et al., 2011).

Climate Change: When, Where and How Much There is clear evidence of climate change associated with increasing concentrations of atmospheric greenhouse gases (IPCC, 2014, 2016). The remaining uncertainty is in the specifics of how much and how quickly climate will change globally and locally. These unknowns, in turn, depend in large measure on the quantity of greenhouse gases that will be released in the future, and the associated influence of many anthropogenic factors (social, economic, political and technological) that affect the rate of greenhouse gas emissions but cannot be fully anticipated. Nevertheless there is wide agreement that the concentration of atmospheric greenhouse gases will continue to rise for at least five decades, and there is evidence that climate warming could continue for centuries, even if emissions of greenhouse gases were halted immediately (Frölicher et al., 2014). Climate scientists have examined a plausible range of greenhouse gas emissions scenarios and produced future climate estimates applicable to each scenario (IPCC, 2014, 2016). The modelled climate patterns all indicate that the climate will warm, although for a given locality the estimates differ in how quickly and how much it will warm. Likewise, future precipitation patterns are expected to change, with changes in the annual amount of precipitation sometimes accompanied by shifts in seasonality of precipitation (e.g. in some places more total annual precipitation is expected but with less occurring during the growing season). Rare but extreme events (e.g. severe droughts coupled with increased wildfire) may prove more influential in altering tree species composition than the gradual changes in average temperature and precipitation (Mette et al., 2013; Clark et al., 2016). Projections of climate change are available online from various sources and are regularly updated as new information becomes available. For example, the Intergovernmental Panel on Climate Change (IPCC) 2016) provides multiple scenarios of global greenhouse gas emission and modelled climate responses. These have been downscaled so that for a particular geographic region, one can learn the anticipated patterns and variation of the future climate (US ­

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Geological Service, 2018). Estimates of climate change will continue to be refined as new information and modelling methods evolve. Therefore, prudent ecologists, silviculturists and forest managers will periodically review the best available climate predictions and syntheses as they consider future impacts on the forests they manage. More important than knowledge of climate change per se is information about how climate change is likely to affect forests (e.g. Vose et al., 2012; Melillo et  al., 2014; Iverson et  al., 2017; USDA Forest Service, 2018). Regional assessments of forest vulnerability to climate change (e.g. Brandt et  al., 2014; Butler et  al., 2015; Butler-Leopold et  al., 2018; Janowiak et al., 2018a) and other sources that summarize regional and local impacts of climate change on the future spatial distributions of tree species (e.g. Prasad et al., 2007–ongoing; Potter et al., 2017) can be particularly helpful in guiding silvicultural decisions. These are discussed in the following section.

Climate Change and the Distribution of Oaks Projected global climatic conditions for the next century have been downscaled and mapped at country, state and ecoregion levels. These anticipated changes in climate have been combined with data on soil and site variables to model and map locations where suitable habitat for individual tree species are likely to be gained, lost or unchanged (e.g. Prasad et  al., 2007–ongoing; Hargrove et  al., 2018). The models anticipate that climate change will increase the area of suitable habitat for many oak species, primarily – but not exclusively – through the northern expansion of suitable habitat resulting from a warmer climate (e.g. Plates 13, 14). In some scenarios the southern limit of suitable oak habitat for some species is simultaneously expected to retreat northwards. In addition to temperature, the amount and timing of precipitation and the elevation are also influential in habitat shifts. There is evidence that changes in precipitation patterns observed over the past three decades already have resulted in greater westward than northern range expansion for some oaks (Fei et al., 2017). Online maps of anticipated range shifts have been published for more than 50 oak species (e.g. Prasad et al., 2007–ongoing; Hargrove et al., 2018) and for dozens of oak associates (Plate 15). This provides a wealth of information about expected shifts in the geographic location of suitable habitat for oaks and

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potential competitors. Important considerations when interpreting estimates of habit change for tree species include the following: ●● The magnitude of climate change is expected to increase over time, and longer evaluation periods typically result in greater shifts in a species’ habitat. Evaluation periods are not standardized, but periods of 50 or 100 years are typical. ●● Climate estimates and species responses are mathematically modelled, and they are subject to uncertainty inherent in the modelling process. Long-term, large-scale trends in habitat change are meaningful; site-specific estimates for a particular stand or ownership generally are not. ●● Estimates of future habitat suitability differ based on assumptions about future greenhouse gas emissions and climate responses. As illustrated in Plates 13–15, different responses are expected for scenarios of relatively low versus high greenhouse gas emissions. ●● Although climate change may expand the area of suitable oak habitat by the year 2100, there are no guarantees that oaks will actually occupy the expanded habitat by that year, or ever. Oak migration to new locations with suitable habitat may be limited by the relatively large size and low mobility of acorns or by other species competing for the same space. These limitations have been partially addressed by other types of analyses including regional climate vulnerability assessments (e.g. Brandt et al., 2014; Butler et al., 2015; Butler-Leopold et  al., 2018; Janowiak et  al., 2018a) based on landscape-scale analyses of tree species competition and response to climate change (e.g. Wang et  al., 2014) and on models that estimate the rate at which oaks migrate to colonize new habitat (e.g. Prasad et al., 2013). Over time, new online tools will be developed that standardize assumptions, synthesize collective results for multiple species, and make other improvements that simplify interpretation of mapped climate change effects over time. Silviculturists and mangers will find it necessary to periodically reacquaint themselves with the best available online tools for interpreting effects of climate change on oak forests.

Managing Oak Forests in a Changing Climate The relation between forest change and climate change is complex. As they grow, forests capture

Managing Oak Forests in a Changing Climate

CO2 and sequester large quantities of carbon in live and dead woody biomass as cellulose, hemicellulose and lignin. Decaying roots and other tree parts add to the organic carbon sequestered in forest soils. The rates at which atmospheric CO2 is pulled from the atmosphere and sequestered in trees (a carbon sink that reduces the greenhouse effect) or is returned to the atmosphere through wood decay or combustion (a carbon source that increases the greenhouse effect) are in turn affected by many climate-dependent processes including tree photosynthesis, respiration, mortality and decomposition. The complexity is further compounded by endogenous disturbances that affect carbon sequestration including timber harvesting, wood product utilization, wildfires, insects and diseases. In the USA, forests collectively are a carbon sink. Carbon sequestration associated with the growth of existing US forests is 8–10% of US carbon emissions. Afforestation and deforestation also affect carbon dynamics, and carbon sequestration estimates vary slightly depending on underlying assumptions (Ryan et  al., 2010; McKinley et  al., 2011; Vose et  al., 2012; US Environmental Protection Agency, 2017; USDA Forest Service, 2017). Forest management options, to a limited extent, can affect climate change by altering the rate of atmospheric greenhouse gas accumulation. Simple forest management choices could conceivably increase by a few per cent the rate of carbon sequestration in forests (e.g. Hoover and Stout, 2007), and with extraordinary measures by perhaps 10% (Skog et al., 2014). Although forests are an important component of the carbon cycle, they can offset through sequestration only a fraction of anthropogenic atmospheric carbon emissions. There are two general strategies for managing forests under a changing climate: (i) mitigation; and (ii) adaptation. Mitigation strategies increase the quantity of carbon sequestered by forests and thus reduce the rate of atmospheric greenhouse gas accumulation and the associated magnitude of future climate change. Mitigation strategies are applicable to a wide range of species and forest types. Adaptation strategies are adjustments to forest management intended to reduce potential harm or exploit potential benefits associated with actual or expected climate change. Adaptation includes the concepts of resistance, resilience and transition used to (Millar et al., 2007):

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●● increase resistance of a forest to future impacts of anticipated climate change; ●● increase resilience of a forest to allow it to return to desired conditions following climate impacts or other disturbances; and ●● facilitate forest transition to conditions suitable for an anticipated future climate. Mitigation and adaptation practices represent different viewpoints on how to address climate change through forest management, but they are not mutually exclusive. It is possible to conceive of climate change adaptation practices that simultaneously increase carbon sequestration and therefore aid with mitigation. Although it is possible to design silvicultural prescriptions focused exclusively on climate change mitigation and/or adaptation benefits, in most cases those benefits will be pursued as a component of a prescription focused on goals such as providing clean water, forest products or wildlife habitat (Janowiak et al., 2017a). Mitigation strategies Silviculture for climate change mitigation aims to increase the quantity of carbon sequestered in a forest and thereby slow the rate of CO2 accumulation in the atmosphere and thus the associated rate of climate change. Via photosynthesis, trees absorb atmospheric CO2, and as they grow, trees and stands become carbon sinks (accumulators) to partially offset anthropogenic CO2 emissions. In general, more total living biomass equates to more total carbon sequestered in forests at a given point in time, but conclusions about the periodic rate of carbon sequestration differ depending on: (i) the time interval considered; (ii) stand stocking; (iii) how and when a stand is regenerated; (iv) the dynamics of carbon in soil and dead wood; and (v) the disposition of any wood removed from the site. Long-term mitigation strategies are necessarily concerned with net carbon sequestration over the entire cycle of forest growth, death, removals, product manufacturing, transportation and recycling. For practical purposes the conversion between tree biomass and carbon is a constant (i.e. about half the dry weight of wood is carbon; see Chapter 13, this volume), so changes in carbon are proportional to changes in woody biomass. Stand basal area is highly correlated with biomass and carbon, so proportional changes in basal area are indicative of relative change in both biomass and carbon.

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Yield tables from Schnur (1937) and other forest inventory data suggest that above-ground live biomass and carbon continue to accumulate in oak stands to at least 150 years of age and possibly longer. The expectation for ageing stands is that total biomass and carbon will continue to accumulate, but at a decreasing annual rate as the trees reach maturity. Thus, maximizing the total quantity of carbon sequestered per acre in a given stand comes about by maintaining the stand at full stocking, allowing it to grow and age, and protecting it from disturbances such as timber harvesting, insect and disease epidemics, and wildfire. Although the total quantity of carbon per acre typically increases with stand age, the annual rate of carbon sequestration does not. For example, the mean annual rate of above-ground carbon increment reaches a peak at about age 55 for the fully stocked oak forests described by Schnur (1937) (see Fig. 13.18, this volume). Older stands, if undisturbed, still increase in total biomass and sequestered carbon, but the annual rate of carbon sequestration declines. Thus, other factors being equal, managing an upland oak forest on a 55-year, even-aged rotation could maximize the long-term annual rate of carbon sequestration per acre. In theory, two 55-year rotations would accumulate more carbon in live trees than one 110-year rotation. But from a carbon accounting perspective when the goal is to reduce the quantity of CO2 in the atmosphere, it matters what happens to the stand’s accumulated carbon at the end of a rotation. Carbon accounting methods for forests are complex and incomplete (Hoover et  al., 2014). Over long management periods (decades or centuries), determining whether a given forest is a net carbon sink (accumulator from the atmosphere) or net carbon source (emitter to the atmosphere) depends in large part on the time interval evaluated, initial forest conditions, what happens to wood (and associated carbon) in trees that die or are harvested, disposition of logging residue, effects of prescribed fires, and when or if stands are regenerated (Skog et  al., 2014). Even carbon emitted by fossil fuels used to power wood products’ harvesting, processing and transportation equipment is a factor in estimates of net carbon sequestration associated with forests. Cut or dead above-ground biomass that remains on site decomposes and releases CO2 back into the atmosphere. Wood that is converted to short-lived products such as paper will continue to sequester

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carbon for a few months or a few years. Then those products (through decomposition or combustion) may release their stored carbon back to the atmosphere as CO2, or the carbon may be sequestered longer if the products are recycled or buried in landfills. Durable wood products continue to sequester carbon for periods ranging from a few decades to more than a century (Skog and Nicholson, 2000; Janowiak et al., 2018b) (Table 14.1). Consequently, managing oak forests to produce long-lived wood products is generally beneficial for carbon sequestration, and that practice typically requires rotation lengths greater than 60 years to get trees to sawtimber size. Moreover, wood-based construction materials can have a compounding effect in reducing carbon emissions if they are specifically selected to replace other building materials (e.g. steel or cement) that require more ­carbon-emitting fossil fuel to manufacture and that may have lower insulating value over their lifetime (mgb ARCHITECTURE + DESIGN et al., 2012). When wood is used for energy (in a variety of forms) in place of fossil fuels, the net effect of the avoided carbon emissions from fossil fuels can be substantial (Malmsheimer et  al., 2008). Like fossil fuels, burning wood for energy releases CO2 into the atmosphere, but the expectation is that most of the carbon released from wood combustion will eventually be recaptured and sequestered again as forests regrow following harvest of biofuels. In contrast, when fossil fuels are unearthed and burned, it is extraordinarily difficult to recapture that newly released atmospheric carbon and return it to its source. Table 14.1.  Estimated longevity of carbon stored though various end uses of forest products. (From Skog and Nicholson, 2000.) End use Single-family homes Multi-family homes Non-residential construction Furniture Railroad ties Mobile homes Manufacturing Pallets Free-sheet papera Other paper

Half-life of sequestered carbon (years) 100 70 67 30 30 20 12 6 6 1

a

Produced with a chemical (rather than mechanical) pulping process. It is commonly used in better quality books and in office printers, copiers and forms.

Managing Oak Forests in a Changing Climate

In oak forests there is little reason to expect widespread application of silvicultural prescriptions focused solely on maximizing carbon sequestration to mitigate atmospheric carbon emissions because: ●● Nearly all forest management practices and associated silvicultural prescriptions result in some carbon sequestration over time. ●● Despite their widespread occurrence, oak forests (or any forests for that matter) have a limited capacity to offset the massive global anthropogenic carbon emissions from fossil fuels. ●● Carbon accounting methods are complex and incomplete, so they provide little motivation for widespread application of silvicultural prescriptions focused solely on maximizing carbon sequestration. ●● Markets for carbon credits have been unstable, so they provide little economic incentive for carbon sequestration. ●● It is difficult to anticipate risk. Wildfire, insects, disease and weather events are all probabilistic factors that can rapidly change a forest from a carbon sink to a carbon source if trees are burned or they die and decompose. ●● Oak forest management usually has multiple objectives. Despite these constraints, management practices and associated silvicultural prescriptions can emphasize carbon sequestration in conjunction with other objectives. Field trials of carbon sequestration in oak forests are limited, but in 50-year-old Allegheny hardwood (cherry–maple) stands, for example, thinning from below sequestered as much carbon and produced 38% more board feet in the following 25 years than a corresponding untreated stand (Hoover and Stout, 2007). Chapter 13, this volume, provides additional information on silvicultural practices to manage carbon sequestration. Adaptation strategies Adaptation strategies are intended to help forests respond pre-emptively to a changing climate. They include practices that: (i) increase a forest’s resistance to effects of climate change; (ii) increase a forest’s resilience so it can recover from impacts of climate change and other disturbances; and (iii) help a forest transition to a species composition and size structure compatible with the future climate (Millar et al., 2007). Resistance and resilience strategies are intended to help current forest conditions

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persist through climate changes. Transition strategies change the forest to be compatible with future climate conditions. Adaptive practices for addressing climate change are likely to change over time as experience and new information are acquired (Janowiak et al., 2014). Currently there are few mechanisms to fund forest management solely for forest adaptation. Thus, silvicultural prescriptions that include adaptation strategies are more likely to be implemented if they also provide other complementary benefits such as forest products, improved wildlife habitat or recreational opportunities. The following sections describe adaptation strategies incorporating resistance, reliance and transition.

composition (DeRose and Long, 2014). An example of a resistance strategy would be thinning to reduce stand density while removing trees most vulnerable to insects and disease or otherwise at high risk of mortality (D’Amato et al., 2013). Thinning increases the availability of light, water and nutrients to residual trees so that they are better able to endure drought or temperature extremes. Otherwise the existing forest structure and species composition are maintained intact. As oak stands managed with a resistance strategy inevitably age and change over time through growth and natural succession, they may reach a point where their unique and desirable features are lost, and shifting management to a resilience or transition strategy becomes a logical next step.

Resistance strategies

Resilience strategies

Resistance practices focus on maintaining (to the extent possible) current forest structure and species composition. They employ silvicultural prescriptions intended to minimize climate impacts (Table 14.2). Resistance practices are often targeted at unique sites or stands that are highly valued for products (e.g. veneer or cooperage) or habitat (e.g. for endangered species). Resistance is a near-term strategy to protect stands until their product values can be realized or until their desirable habitat conditions are created elsewhere. Silvicultural prescriptions will emphasize intensive management to suppress wildfires, control insects and diseases, and remove valuable products as appropriate while maintaining (to the extent possible) the unique services or products the stand provides (Millar et  al., 2007). Silvicultural practices act to minimize the ability of climate change to alter forest structure and species

Resilience strategies help a stand absorb a disturbance (e.g. changing climate) and still return to a prior (or otherwise acceptable) stand condition, with or without post-disturbance management intervention (Millar et  al., 2007). In contrast to resistance strategies described above, resilience strategies acknowledge that stands inevitably will be altered by climate change but assume they can nevertheless be made resilient enough to maintain their important characteristics. For example, a sawtimber-size oak stand managed under a resilience strategy might be expected to experience increased mortality due to the effects of climate change and related disturbances such as increased drought and wildfire. However, appropriate resilience management practices would permit it to continue to function as a sawtimber-size oak stand with respect to wood products, habitat quality,

Table 14.2.  Overview of practices included in three climate change adaptation strategies. (Adapted from Swanston et al., 2016.) Practice Sustain fundamental ecological functions Reduce the impact of existing biological stressors Protect forests from severe fire and wind disturbance Maintain or create refugia Maintain and enhance species and structural diversity Across the landscape, increase replicates (redundancy) of ecosystems vulnerable to climate change Promote landscape connectivity Enhance genetic diversity Facilitate adjustments to future climate through species transitions Plan for and embrace disturbance

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Resistance

Resilience

Transition

x x x x x

x x x

x x

x x x x

x x x x x

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water quality and other ecosystem services. Management practices can proactively alter stand conditions to be ready to absorb inevitable disturbances and/or reactively restore desired stand composition and structure after a disturbance. Appropriate silvicultural prescriptions include practices intended to minimize forest health problems, favour native tree species adapted to the future climate regime, gradually eliminate tree species deemed ill-adapted to a future climate regime, control invasives, and limit losses of desirable trees to wildfire (DeRose and Long, 2014). Silvicultural prescriptions focused on resilience usually manage native tree species within their historic geographic ranges and explicitly favour natives that are best suited to the anticipated future climate regime. Likewise stand structure is maintained within the historic range of variability for the region (e.g. open savannahs in the US west and woodlands or closed-canopy forests in the east), but with emphasis on increasing the total acreage of stands that can tolerate climate change. The forest regeneration period is especially amenable to shifting species composition towards native species well suited to the anticipated climate. It is widely held that increases in forest biodiversity will increase resilience under a changing climate or under other anthropogenic disturbances (Thompson et al., 2009). Silvicultural prescriptions to promote resilience primarily address stand-scale genetic and species diversity. However, landscapescale and regional metrics of diversity come into play (e.g. Hunter and Schmiegelow, 2011) if they are used to guide silvicultural prescriptions for individual stands. An example of a resilience strategy includes thinning, much like in the resistance strategy described in the previous section, but with the additional goals of removing species that are considered poorly suited to long-term climate change scenarios and retaining or recruiting native species considered well suited. Restoring closed-canopy forests to woodlands or savannahs maintained by thinning and prescribed fire (see Chapter 12, this volume) is another strategy that on suitable sites can proactively alter forest structure and species composition to be better adapted to stressful climate conditions (e.g. warmer and more droughty). Transition strategies Transition strategies (sometimes called response strategies) use proactive management to create forests

Managing Oak Forests in a Changing Climate

with composition and structure well adapted to anticipated climate changes. The intent is to preemptively modify forest conditions and to avoid lengthy, uncontrolled or catastrophic responses when ill-adapted forests suffer consequences of significant climate change. Transition practices often will be more intensive than those designed to increase resistance or resilience. Practices include: (i) planting and other methods for assisting migration of tree species within or beyond their current native range; (ii) maintaining landscapes with connected forest corridors that enable natural translocation of tree species beyond their current ranges; (iii) expanding the genetic diversity within a species; and (iv) experimenting with novel species mixtures that may be better suited to the expected climate (Millar et al., 2007). As with other adaptation practices, implementation of transition practices requires physical and monetary inputs, and therefore may be most practical when paired with other compatible objectives that help defray costs. An example of a transition strategy would be supplemental planting to introduce tree species that are projected to become naturalized in future decades as the climate changes. Implementation Implementation of resilience and transition practices, in particular, requires assumptions about future climate at locations where forests of interest are located. As noted elsewhere in this chapter, numerous sources provide mapped estimates of climate change over time. Although scenarios of future climate conditions are subject to uncertainty due to assumptions about changes in the concentration of atmospheric greenhouse gases, comparisons made among multiple climate models indicate a likely range for changes in seasonal temperature and precipitation. More importantly, using these estimates in forest impact models can indicate for a given climate scenario which tree species are likely to be winners (and thus gain suitable habitat) and which are likely to be losers (Prasad et al., 2007– ongoing; Crookston, 2016; Iverson et  al., 2017; Hargrove et al., 2018) (also see Plates 13–15). This information is essential to support practical decisions regarding adaptation practices. Climate change will shift the locations of suitable habitat for oak species, but there is no guarantee that oaks will be able to migrate to occupy new habitats via natural seed dispersal. Expected patterns of oak

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migration to new habitats in response to climate change are illustrated by Prasad et al. (2013) for four oak species whose average acorn dispersal is about 30 miles (48 km)/century (Plate 16). For these species, the natural patterns of acorn dispersal are unlikely to populate the majority of newly available habitat. The establishment of oaks on newly suitable sites may require intensive management, planting or an abundance of patience. Acorn predation and dispersal by birds and rodents (see Chapter 2, this volume) further complicate patterns of oak migration across the landscape. Long-distance acorn dispersal and caching by birds is generally beneficial in moving oaks to new habitats. Acorn predation by rodents reduces the quantity of viable acorns, but may result in increased germination of forgotten acorns near the parent tree. Even when acorn dispersal is not limiting, oak regeneration can be problematic due to competing vegetation. Establishing oaks on new mesic habitats – even with the benefit of an increasingly favourable climate – may require years of intensive management including thinning, prescribed burning, applying herbicides, planting, controlling invasive species and controlling herbivores. During the next century, potential bottlenecks in the migration of oaks beyond their current ranges should be considered when making decisions about investing in assisted migration of oaks to new habitats via artificial regeneration (see also Chapter 10, this volume). Oak genetic material in the form of pollen is much more mobile than that in acorns. Oak pollen spreads as much as 20–60 miles from a parent tree (Schueler and Schlünzen, 2006). Moreover, oaks possess relatively high genetic diversity (Kremer, 2010). Thus, pollen’s ability to increase genetic diversity within the current geographic range of an oak species may improve that species’ ability to adapt to climate change, but oak pollen dispersal does not facilitate the dispersal of oak species into new habitats beyond their current ranges.

Metrics for Assessing Climate Change Vulnerability Increasingly, quantitative metrics are available to assess the potential vulnerability of oaks and other tree species to climate change. Species-specific attributes have been used to estimate the degree to which a tree species will be: (i) compatible with anticipated climate change; (ii) able to adapt to a changing climate due to factors such as high reproductive capacity and high genetic variability; and

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(iii) able to persist through disturbances, including climate change. It is necessary to examine potential climate change vulnerabilities hierarchically at continental, national, regional and local scales. Due to the large geographic range of many oak species, continental or national vulnerability assessments are required to identify species that are threatened range-wide with a significant reduction in suitable habitat or are at risk of extirpation. These species may need special monitoring and other types of intensive conservation assistance. Ideally broad-scale assessments would be conducted at the continental scale, because national boundaries are irrelevant to oaks and to climate change. However, differences among nations in forest inventory protocols and data availability have limited opportunities for continental assessments, and national or regional assessments are the norm. Regional vulnerability assessments (e.g. Brandt et al., 2017; Butler-Leopold et al., 2018; Swanston et al., 2018) provide a subnational geographic focus that can incorporate localized (sometimes referred to as downscaled) information about: (i) anticipated temperature and precipitation trends; (ii) spatial distribution of species on the landscape; (iii) species-specific reproductive capacities; and (iv) exogenous disturbance factors including harvesting, wildfire and prescribed fire. Regional climate change assessments help set the context for forest management decisions and associated silvicultural prescriptions. Finally, local or stand-scale climate vulnerability assessments are required to prepare silvicultural prescriptions. For a given silvicultural prescription, there are tree- and stand-scale metrics that can be used to help determine if the proposed treatments are likely to make the future stand more (or less) compatible with anticipated changes in climate. Subsequent sections present examples of national, regional and local metrics of anticipated climate change impacts on oaks trees and oak stands. Metrics for national climate change vulnerability assessments Oaks and other tree species in the USA have been categorized by their vulnerability to climate change based on their expected exposure to climate change (i.e. climate pressure), their sensitivity to climate change, and their ability to adapt to climate change (Potter et al., 2017). Climate change exposure for each species has been quantified as a function of

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the anticipated proportional change in area of suitable habitat, stability of the suitable habitat range over time, and distance to sites expected to have future suitable habitat. Sensitivity to climate change has been estimated from a species’ rarity, range extent, dispersal ability and disturbance tolerance. Finally, a species’ potential for adaptation to climate change has been estimated from its seeding characteristics, seed abundance, genetic diversity

indicators and the size of its ecological niche. Potter et  al. (2017) clustered tree species into groups based on the number and type of climate vulnerabilities they possess (Table 14.3). These vulnerability classifications are useful in distinguishing oaks and other tree species that are of high nationwide conservation concern under climate change. Three oaks – Arkansas, swamp white and Lacey – are considered highly vulnerable

Table 14.3.  Estimated vulnerability to climate change for US oaks and common associated species. Species are clustered into vulnerability groups based on their expected climate change exposure, sensitivity and adaptability. Each species’ composite vulnerability ranking is shown parenthetically. Higher rankings indicate greater vulnerability, but rankings are comparable only within a given grouping. (Based on Potter et al., 2017.) High exposure and high sensitivity with low capacity for adaptation (most vulnerable) Arkansas oak (70) nutmeg hickory (65)

swamp white oak (61) Lacey oak (59)

High exposure and low capacity for adaptation, but with low sensitivity (vulnerable) dwarf chinquapin oak (67) Shumard oak (65) shellbark hickory (64) sand hickory (63) dwarf post oak (60) chinkapin oak (60) pecan (57)

shingle oak (56) black walnut (55) pin oak (55) Durand oak (55) water hickory (53) overcup oak (52) bitternut hickory (48)

delta post oak (48) northern pin oak (48) black hickory (48) cherrybark oak (46) Emory oak (44) shagbark hickory (44) scrub oak (44)

High exposure and high sensitivity, but with capacity for adaptation (vulnerable) bluejack oak (67) Boynton oak (66) Mexican white oak (60)

Nutall oak (56) netleaf oak (54) chestnut oak (49)

grey oak (49) turkey oak (44)

Low exposure but high sensitivity and low capacity for adaptation (low vulnerability now, potential future vulnerability with increased exposure) swamp chestnut oak (57) Graves oak (56) slender oak (53)

Mexican blue oak (53) Arizona white oak (52) Engelmann oak (50)

live oak (46)

northern red oak (37) southern red oak (36) scarlet oak (36) black oak (34) California white oak (34) Virginia pine (33) canyon live oak (32) interior live oak (32) red maple (31) shortleaf pine (30) longleaf pine (30) loblolly pine (29)

laurel oak (29) American beech (29) yellow-poplar (28) California live oak (28) Oregon white oak (26) blue oak (26) black cherry (26) Gambel oak (24) sweetgum (23) water oak (17)

Low current vulnerability (least vulnerable) bur oak (54) blackjack oak (47) pignut hickory (42) silverleaf oak (42) dwarf live oak (41) eastern white pine (40) white oak (39) willow oak (38) mockernut hickory (38) post oak (38) sugar maple (37) Calfornia black oak (37)

Managing Oak Forests in a Changing Climate

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to climate change due to high exposure, high sensitivity and low capacity for adaptation. Another 21 oaks are expected to have high exposure to climate change but they have species-specific traits that give them low sensitivity to climate or the ability to adapt to climate change. The remaining oak species have low exposure to climate change, and this group includes the oak species with the greatest abundance and the largest ranges. Metrics for regional- and stand-scale vulnerability assessments For some locales, mapped data are available indicating approximately where oaks and associated tree species are expected to gain or lose suitable habitat under various climate change scenarios. These include online maps of current tree species’ ranges and importance values paired with maps of anticipated ranges and importance values 50 or 100 years in the future under alternative climate scenarios (Prasad et  al. 2007–ongoing; Crookston, 2016; Potter et al., 2017; Hargrove et al., 2018) (also see Plates 13–15). These maps generally include associated metrics that quantify the mapped changes and explain modifying or mitigating factors that can make a species more (or less) susceptible to impacts of climate change. By exploring these interactive atlases of anticipated species responses to climate change, silviculturists and ecologists can better understand which species are likely to be winners or losers under a range of climate change scenarios. Estimates of relative changes in the average importance value of tree species during the next century can be used to quantify outcomes for climate change scenarios based on low and high greenhouse gas emissions rates. Here importance value is defined as the combined abundance and range of a species. The associated compatibility score is the expected relative change in the importance values by species if current climate conditions were replaced by a future climate regime. The importance value for white oak, for example, is expected to remain about the same or decrease slightly over the next century (Table 14.4) with compatibility scores of 0.9 and 1.0 for high and low emissions scenarios, respectively. Under a high emissions scenario, northern red oak (compatibility score 0.7) appears likely to decline in importance more so than white oak, and post oak will increase in importance (compatibility score 4.1). Currently white oak and northern red oak are two of the most

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dominant oaks in the USA based on importance value. Shumard oak, currently one of the least dominant oaks, is expected to have a huge relative gain in suitable habitat (compatibility scores 13.8 and 7.6 for high and low emission alternatives, respectively). Despite the large relative gain with climate change, Shumard oak is expected to remain comparatively uncommon, because the increase is relative to its small current abundance and range. Most of the oaks in Table 14.4 gain in compatibility (suitable habitat) under both high and low emissions; loblolly, shortleaf and longleaf pines also gain. In contrast, red maple, sugar maple, black cherry, yellow-poplar and American beech appear likely to lose suitable habitat for both low and high emissions. Compatibility scores for a species vary by geographic location and climate scenario. The metrics for relative change in importance value from Table 14.4 (columns 2 and 3) are composites for data spanning the eastern USA. However, local, speciesspecific compatibility scores have been summarized for individual states, ecoregions and National Forests (Prasad et  al., 2007–ongoing). In most applications, local estimates of compatibility scores will be more meaningful than those based on the east-wide values. For example, with climate change a species may increase in compatibility (or importance value) along the northern extent of its current range and decrease along the southern extent. The species-specific adaptability scores (Table 14.4) do not vary by geographic location or climate scenario. Species with high adaptability scores are considered tolerant of climate change and other disturbances. Red maple has the highest adaptability score among species listed in Table 14.4. It is a prolific seed producer and prolific sprouter, and it tolerates a wide range of site conditions. Thus, even though the quality of habitat for red maple is expected to decline in importance with climate change, the species may persist in most of its current range and thrive at some locations. One approach to evaluating silvicultural treatment options under climate change is to compute weighted, composite, compatibility and adaptability scores (Table 14.4) for the current trees in a stand and then compare the scores to those expected following a proposed silvicultural treatment that alters stand species composition and size structure (Kabrick et  al., 2016). Thus, a climate-change compatibility and adaptability score for a given stand can be computed as the weighted mean of the individual species scores

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Table 14.4.  Predicted species’ compatibility scores (for high and low emissions) and adaptability scores calculated over the century 2000–2100 in the eastern USA. (Based on Prasad et al., 2007–ongoing; and Matthews et al., 2011.) Species by groupa Oaks White oak Northern red oak Black oak Post oak Bur oak Water oak Chestnut oak Southern red oak Laurel oak Scarlet oak Willow oak Blackjack oak Live oak Chinkapin oak Cherrybark oak Pin oak Shingle oak Overcup oak Turkey oak Northern pin oak Swamp white oak Swamp chestnut oak Nuttall oak Bluejack oak Shumard oak Bear oak: scrub oak Durand oak Hardwood competitors Red maple Sweetgum Sugar maple Black cherry Yellow-poplar American beech Mockernut hickory Pignut hickory Black walnut Pine competitors Loblolly pine Shortleaf pine Eastern white pine Virginia pine Longleaf pine

Compatibility score for high emissionsb

Compatibility score for low emissionsb

Adaptability scorec

0.9 0.7 1.3 4.1 1.8 1.6 0.7 1.9 1.1 0.7 1.6 4.7 2.5 2.0 1.5 2.1 1.7 1.8 1.2 1.5 0.9 0.7 1.6 2.2 13.8 1.4 1.2

1.0 1.0 1.2 2.7 1.3 1.4 0.8 1.4 1.2 0.7 1.3 2.9 1.6 1.7 1.3 1.6 1.5 1.1 1.3 1.1 1.2 0.9 1.1 1.5 7.6 1.5 0.8

6.1 5.4 4.9 5.7 6.4 3.7 6.1 5.3 4.5 4.6 4.7 4.8 5.0 4.8 3.9 2.8 4.9 3.2 6.0 6.0 4.9 4.6 6.5 5.6 5.8 4.6 4.2

0.6 1.2 0.4 0.6 0.5 0.5 1.3 1.0 1.0

0.8 1.1 0.6 0.8 0.6 0.7 1.2 0.9 1.3

8.5 4.1 5.8 3.0 5.3 3.6 5.4 4.7 4.0

1.2 2.6 0.6 0.6 1.1

1.2 1.9 0.8 0.6 1.4

3.4 3.6 3.3 3.8 4.2

a

Species within each group are listed in order of greatest to least initial importance value. Compatibility scores are equivalent to relative changes in species’ importance values. A compatibility score of 1.0 is equivalent to no change. Values less than 1.0 indicate a decrease in importance, and values greater than 1.0 indicate an increase. Values are averages of three different models of climate responses to low or high future greenhouse gas emissions. Additional details and interactive maps are available at: http://www.fs.fed.us/nrs/atlas/ (accessed 1 July 2018). c Adaptability scores are relative indicators of a species’ ability to adapt to climate change based on biological factors including: (i) reproductive capacity; (ii) seed dispersal; (iii) shade tolerance; (iv) habitat specificity; and (v) ability to tolerate disturbances including drought, flooding, fire, browsing, insects and disease. b

Managing Oak Forests in a Changing Climate

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with weighting based on basal area, number of trees, stocking per cent, or importance value by species. Stands with higher compatibility and adaptability scores would be considered more tolerant of future climate change than stands with lower scores. Likewise silvicultural practices that increase stand-level compatibility and adaptability scores (e.g. through selective thinning) would be expected to improve a stand’s capacity to tolerate climate change. Because they are indices of a stand’s ability to tolerate climate change, stand-scale compatibility and adaptability score can be used to supplement information supporting the preparation of silvicultural prescriptions. However, maximizing future compatibility or adaptability scores would rarely be a primary management goal. For example, eastern US stands with exceptionally high compatibility and adaptability scores could consist of an unlikely two-species mixture of Shumard oak and red maple (i.e. the species with the highest compatibility and adaptability scores, respectively). However, it is hard to envision situations where creating that species mix would be a management priority. Nevertheless applying composite, stand-scale compatibility and adaptability scores to assess a stand’s current and future fitness for climate change provides a set of quantitative indicators that can guide practical silvicultural decisions. Other metrics also have been proposed for evaluating stand-scale implications of regional climate trends (e.g. Janowiak et al., 2017b). In the future such metrics are likely to be further refined and routinely applied to support silvicultural decisions. The compatibility scores in Table 14.4 are summaries across entire species’ ranges in the eastern USA (e.g. see Plates 13–15). But compatibility scores computed for a particular ecoregion, state or management area may differ from east-wide values. Regional vulnerability assessments (e.g. Brandt et al., 2014; Butler et al., 2015; Butler-Leopold et al., 2018; Janowiak et al., 2018a) provide a more localized view (e.g. across 40 million acres) of future climate change scenarios and potential impact on oaks and associated species. For regions where they are available, these are key documents synthesizing driving forces and mitigating factors that determine which tree species are likely to increase (or decrease) in importance with climate change. Interactive maps of climate change responses by species and forest type provide insight into the likely spatial patterns of expanding or contracting suitable habitat (Prasad et  al., 2007–ongoing; Crookston, 2016; Iverson et al., 2017; Hargrove et al., 2018). Although white oak, for example, shows little climate-­associated change in

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importance value when summarized across the entire eastern USA (Table 14.4), along the northern boundary of the current white oak range (Plate 13) knowledge of the projected northward expansion of white oak habitat may be of great importance in designing appropriate site-specific silvicultural prescriptions. There are now mathematical models that project regional changes in habitat suitability for oaks and other tree species including their responses to moderate and severe climate changes over a century or more (Wang et  al., 2016; Iverson et  al., 2017) (Table 14.5). Trends for Midwestern and northeastern forests of the USA indicate that: ●● Large climate changes within a region produce large differences in the area of suitable habitat for many tree species, including oaks. ●● The future magnitude of climate change is not known with certainty so consideration of multiple future climate scenarios is prudent. ●● For a given assumption about climate change, species predicted to be winners or losers may vary among modelling methods. Predictions among alternative models are more consistent where climate change is expected to be most severe. ●● Largest predicted gains for oaks occur in the northern parts of their ranges. For example, importance values for oaks are expected to increase most consistently in the New England states. In the Central Hardwood Region where oaks are already common, predicted changes in oak importance values are less pronounced, although southern red oak is expected to increase in importance while northern red oak declines. ●● Mapped changes in tree species habitat associated with climate change are not precise. They indicate large-scale, spatial trends. ●● In the absence of other disturbances, effects of climate change on forest composition are likely to occur gradually over decades or centuries, but most notably in conjunction with regeneration events. It is essential when interpreting estimates of oak habitat gains and losses to also consider responses of non-oak competitors (e.g. see Mette et al., 2013). As noted elsewhere in this chapter, an increase in the area of suitable habitat for oak species does not ensure that oaks can readily migrate to occupy that habitat. Likewise projected decreases in suitable habitat do not ensure a species will quickly disappear from those sites. Oak migration across the landscape is affected by acorn dispersal, oak seedling establishment, and other biotic and abiotic factors (e.g. see prior sections

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Table 14.5.  Predicted relative change in importance value of oaks and associated species (potential oak competitors) in four ecoregions of the eastern US. Rankings of increased dominance (), no change () and decreased dominance () are composites of three modelsa for a moderate level of climate change. Entries are blank for species not evaluated due to their rarity in a given region. Based on Iverson et al. (2017). Species by group Oaks White oak Black oak Chestnut oak Scarlet oak Northern red oak Southern red oak Post oak Oak associates Loblolly pine Yellow-poplar Red maple Sugar maple Shortleaf pine

Central Hardwood Region

Central Appalachian Region

MidAtlantic Region

New England Region

    

    

    

    

New England

Mid Central Appalachian Atlantic

 Central Hardwood

     

   

   

a Averaged output from TreeAtlas (Prasad et al., 2007–ongoing), models.

of this chapter and Chapter 2, this volume). Oak range expansion and oak growth may be limited by non-oak competitors that are more mobile or better adapted to the changing climate conditions. These considerations are best evaluated at the local scale, provided supporting data are available. Regional climate vulnerability assessments (e.g. across 40 million acres) (Brandt et  al., 2014; Butler et  al. 2015; Butler-Leopold et al., 2018; Janowiak et al., 2018a) employ modelling methods that take into consideration seed dispersal and inter-species competition (Wang et  al., 2016) when estimating which species area is expected to gain or lose habitat for a given climate change scenario. Over time these methods will be applied at finer spatial resolutions which will provide more guidance for forest-scale and standscale silvicultural prescriptions that execute climate mitigation or adaptation strategies.

Practical Management Considerations Despite the growing body of knowledge about climate change and its anticipated effects on oaks and oak competitors, detailed silvicultural prescriptions

Managing Oak Forests in a Changing Climate

   

linkages

(Dijak et al., 2017) and

landis pro

(Wang et al., 2014)

designed to help oak stands mitigate or adapt to climate change are largely lacking. The uncertainty associated with slowly evolving impacts of climate change on oak forests favours learn-as-you-go adaptive management practices that can be modified as knowledge improves about the effects of climate change. The following additional considerations have been discussed by others (e.g. Bosworth et  al., 2008; Malmsheimer et  al., 2008; Kremer, 2010; Ryan et  al., 2010; Kolström et  al., 2011; Janowiak et al., 2014; Swanston et al., 2016). General considerations: ●● Stay current on climate change information – available maps and data illustrate where different tree species are likely to gain or lose favourable habitat under anticipated future climate scenarios (e.g. Prasad et  al., 2007–ongoing; Crookston, 2016; Iverson et al., 2017; Hargrove et al., 2018). In addition to the oaks, consider potential changes for oak competitors. Information on climate effects on tree species will continue to improve over time. ●● Recognize the variability in estimates of climate change – differences among alternative predictions

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of climate impart variation in the predicted spatial distribution of future suitable habitat for various tree species. The general trends are similar (e.g. the area of suitable habitat in the eastern USA increases for most oak species), but predictions differ in details of how far and how fast suitable habitat will change and which oak species will gain (or lose) the most habitat. Monitor forest change (over decades) – especially track climate and forest metrics that could be early indicators of whether observed climate change (e.g. growing season length) and observed forest change (e.g. species composition of forest regeneration with and without management) match expectations and assumptions. Whenever possible, monitor the differential responses associated with different climate adaptation treatments to determine which are most effective. Understand the difference between suitable oak habitat and occupied oak habitat – climate change that creates new, suitable habitat locations for a given oak species does not ensure that the species will be able to migrate to occupy the expanded area of suitable habitat (e.g. Plate 16). Reduce risk of wildfires – they pre-emptively release sequestered carbon back into the atmosphere and reduce the rate of forest carbon sequestration. Fire partially consumes litter and can oxidize organic matter in the mineral soil. Regaining this carbon, especially by sequestering it in mineral soil, takes decades. Prescribed fire is commonly used in oak forests, woodlands and savannahs to shape and maintain tree species composition and size structure. But like wildfires, prescribed fires also release carbon sequestered in vegetation and on the forest floor. The assumption is that the benefits associated with prescribed fires outweigh the negative consequences of increased carbon emissions (see Chapter 7, this volume). Keep forests healthy – minimize losses associated with insects, diseases, declines and other sources of mortality to reduce the associated wood decay and carbon release. Keep forests forested – conversion of forest to other uses releases sequestered carbon and reduces options for future increases in carbon sequestration through tree growth. Develop awareness of carbon dynamics – aboveground carbon is the most dynamic component of forest carbon and the most amenable to management, but forest soils and roots also contain large amounts of carbon (e.g. see Fig. 13.17, this

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volume). Consider also the disposition of carbon in trees removed for products. Produce long-lived forest products – forest products store carbon during their useful life and can continue to store carbon when recycled or buried in a landfill. Wood construction materials ­generally require less fossil fuel to produce than alternatives such as steel and concrete (mgb ­ ARCHITECTURE + DESIGN et al., 2012), and carbon is stored in wooden building materials for the life of the building. Production of forest products may be the most practical way to fund silvicultural treatments that address climate change. Utilize woody biomass to produce energy – using woody biomass in place of fossil fuels can lower net atmospheric carbon emissions. The quantity of carbon released when wood is used for energy is ultimately recycled back into the next forest crop if the harvested stands are regenerated. Stay flexible – due to the many variables and uncertainties involved, management based on even-aged silviculture, uneven-aged silviculture or no-harvesting can all be appropriate methods for mitigation or adaptation strategies, individually or in association with other objectives. One of the few certainties with climate change is that the future will not mirror the past. Consider climate change as a complicating rather than a dominating factor in silvicultural decisions – climate change is likely to be a complicating factor in all future silviculture and management decisions. In most situations climate mitigation and adaptation strategies will be addressed in association with other management objectives. Often climate change considerations will be secondary to other management goals or constraints.

Silvicultural considerations: ●● Consider favouring species that readily resprout from stumps when cut – they rapidly revegetate a disturbed site, and stump sprouts tend to be more drought tolerant than seedlings. ●● Consider managing for mixed species – under some circumstances mixed species can obtain maximum leaf area and maximum photosynthesis for rapid carbon sequestration. Where feasible, include shade-tolerant species that can efficiently capture sunlight in lower canopy layers. ●● Consider assisted migration – assisted migration in anticipation of climate change can include mixing genotypes (e.g. from south to north) within the current range of a species, planting species

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beyond their current range along the leading edge of expanding suitable habitat, and planting species at risk of extinction well outside their current range in seed orchards or artificial refugia (Williams and Dumroese, 2013). Some thought has been given to theoretical and practical aspects of assisted migration (Spittlehouse and Stewart, 2003; Kremer, 2010; Pedlar et al., 2012). The practice is common in urban environments where tree species are commonly planted outside their native range. See Chapter 10, this volume, for information about oak artificial regeneration. Regenerate stands quickly after harvest or other disturbances – the sooner trees are re-established the sooner they begin to accumulate biomass and sequester carbon, and the better they control soil erosion and loss of soil carbon. Rapid reforestation also reduces the period of elevated soil temperatures. High soil temperatures increase the activity of soil microorganisms that release CO2 into the atmosphere. Minimize loss of soil and litter – soil and litter sequester carbon in the form of organic matter. Losses are often associated with anthropogenic disturbances that can be controlled. Use afforestation to increase forest area – forests typically have higher rates of carbon sequestration than other land uses. Stay abreast of emerging markets for carbon credits – carbon credits may provide a supplemental source of revenue to support forest management. Use commercial thinning when practical – it can increase net growth by reducing mortality from inter-tree competition, and it can store carbon in forest products obtained by thinning. In contrast, precommercial thinning costs money to implement and does not produce products that store carbon. Consider how geography affects species movement in response to climate change – for example, the oceanic barriers to oak species’ migration into or out of the British Isles differ greatly from the barriers for the large contiguous land masses of continental Europe and North America where climate change associated with mountainous terrain can be limiting (Duncan et al., 2010).

Perspective on Managing Oaks in a Changing Climate For oak forests, climate change is but one disturbance among many that require consideration when developing silvicultural prescriptions. In most cases,

Managing Oak Forests in a Changing Climate

prescriptions will continue to focus on sustaining flows of traditional forest products and ecosystem services, but prescriptions can be modified as necessary to mitigate climate change by increasing carbon sequestration, resisting climate change, increasing resilience to climate change, or transitioning to forest conditions well suited to the future climate. Doing so may require favouring species best suited to an anticipated future climate, sustaining species diversity, keeping forests healthy, and considering carbon dynamics – while also staying committed to ongoing forest management. Assisting the migration of oaks by planting in areas projected to become suitable habitat is one way to accelerate their movement across the landscape in step with climate change. But doing so is not trivial. It requires consideration of when and where new habitat will be available, identification of suitable genotypes, and contemplation of scale and timing so that a new population of oaks is ready to bear acorns at a time when the future climate is suitable. Mixing genotypes within the range of oaks is another form of assisted species migration that can improve the ability of the population to adapt to climate change (Kremer, 2010; Williams and Dumroese, 2013). This can be accomplished with practices as simple as mixing acorns from different climate zones when planting nursery stock. Any type of assisted migration may require substantial changes in forest regeneration practices (see Chapters 8, 9 and 10, this volume), or at least a substantial change in emphasis. Currently less than 4% of oaks in US forests are planted, and at this time the practice of assisted migration is small in scale. Predictions from modelling studies for the US Central Hardwood Region have illustrated the relative contributions of forest succession, harvest and climate change to anticipated differences in future number and basal area of trees by species. The results showed that over the next 50 years normal forest growth and succession is expected to account for about 78% of the change in tree species composition, timber harvest about 17% and climate change about 1% (Wang et al., 2015). For a 300year evaluation period, the relative influence of climate change increased to 20% but remained less than that anticipated via normal forest succession (46%) and timber harvest (26%). Thus, while climate change and its potential impacts on forest change are expected to be significant, in the short term those effects are expected to be relatively

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small compared with other traditional forces of forest change. Anticipated effects of climate change nevertheless can be routinely incorporated into silvicultural prescriptions in a manner similar to other drivers of forest change. What the existing maps and models of forest response to climate change do not consider is the potential impact of extreme events linked to climate change – droughts, flooding, windstorms, ice damage, frost damage and wildfires. In reality these rare, extreme events may be more disruptive than the gradual shifts in mean temperature and precipitation associated with climate change scenarios. Climate change is usually considered undesirable and disruptive, but from an oak regeneration perspective it may be beneficial under certain conditions. For example, in mesic forests where oak recruitment into the overstorey is essential but challenging, a warming climate and greater variability in precipitation with more frequent droughts and fires may increase the probability of successful oak regeneration. In the USA, many oak species appear poised to gain suitable habitat under projected climate change, especially along the northern limits of their ranges (Plates 13–16) (Prasad et al., 2007–ongoing; Crookston, 2016; Iverson et  al., 2017; Hargrove et al., 2018). This expectation does not imply, however, that relying on climate change to resolve the widespread oak regeneration problem is a rational alternative to well-crafted, traditional silvicultural prescriptions, in part because widespread effects of climate change on tree dynamics are expected to take perhaps a century to fully materialize. Moreover the cascading, secondorder effects of climate change on pests, diseases, invasives or storms may affect oak forest dynamics in ways that are difficult to anticipate. Although climate change is a global phenomenon, anticipated changes in temperature and precipitation are site specific. Associated impacts on forests are expected to increase over time with continued global greenhouse gas emissions, even if future rates of emission are reduced. Thus the challenge to silviculturists is to anticipate future changes in tree species habitat suitability across a range of anticipated future climates. Tools for doing so are mostly web-based maps illustrating how habitats for individual tree species are expected to change under alternative climate scenarios (Prasad et  al., 2007– ongoing; Crookston, 2016; Iverson et  al., 2017; Hargrove et  al., 2018). Although climate-induced changes in suitable habitat have been projected for

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the major oaks and common associates, understanding the local habitat suitability changes for multiple species simultaneously for a particular forest, county, state or ecoregion does require serious study by silviculturists and ecologists. However, the tools for doing this continue to improve, and regional, multi-species analyses of probable tree habitat gains and losses under alternative climate scenarios are increasingly available (Brandt et  al., 2014; Butler et  al., 2015; Butler-Leopold et  al., 2018; Janowiak et  al., 2018a). For the western USA, climate effects have been incorporated into the forest vegetation simulator (FVS), a growth and yield model that can forecast tree and stand dynamics under various silvicultural options (Crookston, 2014; USDA Forest Service, 2016) (see also Chapter 15, this volume). Quantitative methods for predicting climate change and associated effects in forests are rapidly evolving in the form of publications, maps and other tools. The rate of change in this information emphasizes the necessity for silviculturists, ecologists and resource managers to stay abreast of current information on climate change in regions where they work. Under­ standing the implications of climate change for forest dynamics and learning how to incorporate that new information into silvicultural prescriptions has become a requisite silvicultural skill.

References Bosworth, D., Birdsey, R., Joyce, L. and Millar, C. (2008) Climate change and the nation’s forests: challenges and opportunities. Journal of Forestry 106, 214–222. Available at: https://www.fs.usda.gov/treesearch/ pubs/18499 (accessed 1 July 2018). Brandt, L., He, H., Iverson, L., Thompson, F.R. III, Butler, P. et al. (2014) Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods Climate Change Response Framework project. USDA Forest Service General Technical Report NRS-124. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 254 pp. https://doi.org/10.2737/NRSGTR-124 Brandt, L.A., Butler, P.R., Handler, S.D., Janowiak, M.K., Shannon, P.D. and Swanston, C.W. (2017) Integrating science and management to assess ­forest ecosystem vulnerability to climate change. Journal of Forestry 115, 212–221. https://doi.org/10.5849/ jof.15-147 Butler, P.R., Iverson, L., Thompson, F.R. III, Brandt, L., Handler, S. et al. (2015) Central Appalachians forest ecosystem vulnerability assessment and synthesis: a

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report from the Central Appalachians Climate Change Response Framework project. USDA Forest Service General Technical Report NRS-146. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 310 pp. https://doi.org/ 10.2737/NRS-GTR-146 Butler-Leopold, P.R., Iverson, L.R., Thompson, F.R. III, Brandt, L.A., Handler, S.D. et al. (2018) Mid-Atlantic forest ecosystem vulnerability assessment and synthesis: a report from the Mid-Atlantic Climate Change Response Framework project. USDA Forest Service General Technical Report NRS-181. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/nrs-gtr-181 Clark, J.S., Iverson, L., Woodall, C.W., Allen, C.D., Bell, D.M. et al. (2016) The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Global Change Biology 22, 2329– 2352. http://dx.doi.org/10.1111/gcb.13160 Crookston, N.L. (2014) Climate-FVS version 2: content, users guide, applications, and behavior. USDA Forest Service General Technical Report RMRS319. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, 38 pp. https://doi.org/10.2737/RMRS-GTR-319 Crookston, N.L. (2016) Plant Species and Climate Profile Predictions. Available at: http://charcoal.cnre. vt.edu/climate/species/index.php (accessed 31 August 2016). D’Amato, A.W., Bradford, J.B., Fraver, S. and Palik, B.J. (2011) Forest management for mitigation and adaptation to climate change: insights from longterm ­ silviculture experiments. Forest Ecology and Management 262, 803–816. https://doi.org/10.1016/j. foreco.2011.05.014 D’Amato, A.W., Bradford, J.B., Fraver, S. and Palik, B.J. (2013) Effects of thinning on drought vulnerability and climate response in north temperate forest ecosystems. Ecological Applications 23, 1735–1742. https://doi.org/10.1890/13-0677.1 DeRose, R.J. and Long, J.N. (2014) Resistance and resilience: a conceptual framework for silviculture. Forest Science 60, 1205–1212. https://doi.org/10.5849/ forsci.13-507 Dijak, W.D., Hanberry, B.B., Fraser, J.S., He, H.S., Wang, W.J. and Thompson, F.R. III (2017) Revision and application of the linkages model to simulate forest growth in Central Hardwood landscapes in response to climate change. Landscape Ecology 32, 1365–1384. https://doi.org/10.1007/s10980-016-0473-8 Duncan, R., Morison, J. and Broadmeadow, M. (2010) Climate change: impacts and adaptation in England’s woodlands. Forestry Commission Research Note (FCRN) 201. Forestry Commission England, 16 pp. Available at: https://www.forestry.gov.uk/pdf/FCRN201. pdf/$FILE/FCRN201.pdf (accessed 19 September 2018).

Managing Oak Forests in a Changing Climate

Fei, S., Desprez, J.M., Potter, K.M., Jo, I., Knott, J.A. and Oswalt, C.M. (2017) Divergence of species responses to climate change. Science Advances 3(5), e1603055. https://doi.org/10.1126/sciadv.1603055 Frölicher, T.L., Winton, M. and Sarmiento, J.L. (2014) Continued global warming after CO2 emissions stoppage. Nature Climate Change 4, 40–44. https://doi. org/10.1038/nclimate2060 Guyette, R.P., Thompson, F.R., Whittier, J., Stambaugh, M.C. and Dey, D.C. (2014) Future fire probability modeling with climate change data and physical chemistry. Forest Science 60, 862–870. https://doi. org/10.5849/forsci.13-108 Hargrove, W.W., Potter, K.M. and Koch, F.H. (2018) The ForeCASTS Project: Forecasts of Climate-associated Shifts in Tree Species. Available at: http://www. geobabble.org/~hnw/global/treeranges3/climate_ change/atlas.html (accessed 9 January 2018). Hoover, C. and Stout, S. (2007) The carbon consequences of thinning techniques: stand structure makes a difference. Journal of Forestry 105, 266– 270. Available at: https://www.fs.usda.gov/treesearch/pubs/12575 (accessed 1 July 2018). Hoover, C., Birdsey, R., Goines, B., Lahm, P., Marland, G. et al. (2014) Quantifying greenhouse gas sources and sinks in managed forest systems. In: Eve, M., Pape, D., Flugge, M., Steele, R., Man, D. et al. (eds) Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-scale Inventory. US Department of Agriculture Technical Bulletin 1939. Office of the Chief Economist, Washington, DC, pp. 6.1–6.114. Available at: https://www.usda.gov/oce/ climate_change/Quantifying_GHG/Chapter6S.pdf (accessed 1 July 2018). Hunter, M.A. and Schmiegelow, F.K.A. (2011) Wildlife, Forests, and Forestry: Principles of Managing Forest for Biological Diversity, 2nd edn. Prentice Hall, Boston, Massachusetts. Intergovernmental Panel on Climate Change (IPCC) (2014) Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Inter­ governmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 1–32. https://doi. org/10.1017/CBO9781107415416.005 Intergovernmental Panel on Climate Change (IPCC) (2016) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Available at: http://www.ipcc.ch/ report/ar5/wg2/ (accessed 7 September 2016). Iverson, L.R., Thompson, F.R. III, Matthews, S., Peters, M., Prasad, A. et al. (2017) Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models. Landscape Ecology 32, 1327–1346. https:// doi.org/10.1007/s10980-016-0404-8

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Janowiak, M.K., Swanston, C.W., Nagel, L.M., Brandt, L.A., Butler, P.R. et al. (2014) A practical approach for translating climate change adaptation principles into forest management actions. Journal of Forestry 112, 424–433. https://doi.org/10.5849/jof.13-094 Janowiak, M., Connelly, W.J., Dante-Wood, K., Domke, G.M., Giardina, C. et al. (2017a) Considering forest and grassland carbon in land management. USDA Forest Service General Technical Report WO-95. USDA Forest Service, Washington, DC, 68 pp. Available at: https://www.fs.usda.gov/treesearch/ pubs/54316 (accessed 1 July 2018). Janowiak, M.K., Iverson, L.R., Fosgitt, J., Handler, S.D., Dallman, M. et al. (2017b) Assessing stand-level climate change risk using forest inventory data and species distribution models. Journal of Forestry 115, 222–229. https://doi.org/10.5849/jof.2016-023R1 Janowiak, M.K., D’Amato, A.W., Swanston, C.W., Iverson, L., Thompson, F.R. III et al. (2018a) New England and northern New York forest ecosystem vulnerability assessment and synthesis: a report from the New England Climate Change Response Framework project. USDA Forest Service General Technical Report NRS-173. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 234 pp. Available at: https://www.fs.usda.gov/treesearch/pubs/ 55635 (accessed 1 July 2018). Janowiak, M., Swanston, C. and Ontl, T. (2018b) Carbon Benefits of Wood-based Products and Energy. Climate Change Resource Center, USDA Forest Service, Washington, DC. Available at: https://www. fs.usda.gov/ccrc/topics/forest-mgmt-carbon-benefits/ wood (accessed 11 January 2018). Kabrick, J.M., Clark, K.L., D’Amato, A.W., Dey, D.C., Kenefic, L.S. et al. (2016) Managing hardwood – softwood mixtures for future forests in eastern North America: assessing suitability to projected climate change. Journal of Forestry 115, 190–201. https:// doi.org/10.5849/jof.2016-024 Kolström, M., Lindne, M., Vilén, T., Maroschek, M., Seidl, R. et al. (2011) Reviewing the science and implementation of climate change adaptation measures in European forestry. Forests 2, 961–982. https://doi.org/10.3390/f2040961 Kremer, A. (2010) Evolutionary responses of European oaks to climate change. Irish Forestry 67, 53–65. Malmsheimer, R.W., Heffernan, P., Brink, S., Crandall, D., Deneke, F. et al. (2008) Forest management solutions for mitigating climate change in the United States. Journal of Forestry 106, 115–173. https://doi. org/10.1093/jof/106.3.115 Matthews, S.N., Iverson, L.R., Prasad, A.M., Peters, M.P. and Rodewald, P.G. (2011) Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history factors. Forest Ecology and Management 262, 1460–1472. https://doi.org/10.1016/j.foreco.2011.06.047

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McKinley, D.C., Ryan, M.G., Birdsey, R.A., Giardina, C.P., Harmon, M.E. et al. (2011) A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications 21, 1902– 1924. https://doi.org/10.1890/10-0697.1 Melillo, J.M., Richmond, T.C. and Yohe, G.W. (eds) (2014) Climate change impacts in the United States: the third national climate assessment. US Global Change Research Program, Washington, DC, 841 pp. https://doi.org/10.7930/J0Z31WJ2 Mette, T., Dolos, K., Meinardus, C., Bräuning, A., Reineking, B. et al. (2013) Climatic turning point for beech and oak under climate change in central Europe. Ecosphere 4, 1–19. https://doi.org/10.1890/ ES13-00115.1 mgb ARCHITECTURE + DESIGN, Equilibrium Consulting, LMDG Ltd and BTY Group (2012) The Case for Tall Wood Buildings: How Mass Timber Offers a Safe, Economical, and Environmentally Friendly Alternative for Tall Building Structures. Available at: http://cwc.ca/ wp-content/uploads/publications-Tall-Wood.pdf (accessed 22 September 2016). Millar, C.I., Stephenson, N.L. and Stephens, S.L. (2007) Climate change and forests of the future: managing in the face of uncertainty. Ecological Applications 17, 2145–2151. https://doi.org/10.1890/06-1715.1 Pedlar, J.H., McKenney, D.W., Aubin, I., Beardmore, T., Beaulieu, J. et al. (2012) Placing forestry in the assisted migration debate. BioScience 62, 835–842. https://doi.org/10.1525/bio.2012.62.9.10 Potter, K.M., Crane, B.S. and Hargrove, W.W. (2017) A United States national prioritization framework for tree species vulnerability to climate change. New Forests 48, 275–300. https://doi.org/10.1007/ s11056-017-9569-5 Prasad, A.M., Iverson, L.R., Matthews, S. and Peters, M. (2007–ongoing) A Climate Change Atlas for 134 Forest Tree Species of the Eastern United States [database]. Available at: http://www.nrs.fs.fed.us/ atlas/tree (accessed 13 September 2016). Prasad, A.M., Gardiner, J.D., Iverson, L.R., Matthews, S.N. and Peters, M. (2013) Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Global Change Biology 19, 2196–2208. https://doi.org/10.1111/gcb.12204 Ryan, M.G., Harmon, M.E., Birdsey, R.A., Giardina, C.P., Heath, L.S. et al. (2010) A synthesis of the science on forests and carbon for U.S. Forests. Issues in Ecology 13, 1–16. Available at: https://www.fs.usda.gov/treesearch/pubs/35006 (accessed 1 July 2018). Schnur, G.L. (1937) Yield, stand, and volume tables for even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture, Washington, DC, 87 pp. Schueler, S. and Schlünzen, K.H. (2006) Modeling of oak pollen dispersal on the landscape level with a

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mesoscale atmospheric model. Environmental Modeling and Assessment 11, 179. https://doi.org/ 10.1007/s10666-006-9044-8 Skog, K.E. and Nicholson, G.A. (2000) Carbon sequestration in wood and paper products. In: Joyce, L.A. and Birdsey, R. (eds) The impact of climate change on America’s forests: a technical document supporting the 2000 USDA Forest Service RPA assessment. USDA Forest Service General Technical Report RM-59. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, pp. 79–88. Available at: https://www.fs.usda.gov/treesearch/pubs/ 21428 (accessed 1 July 2018). Skog, K.E., McKinley, D.C., Birdsey, R.A., Hines, S.J., Woodall, C.W. et al. (2014) Managing carbon. In: Climate change and United States forests. Advances in Global Change Research 57. Springer Netherlands, Dordrecht, the Netherlands, pp. 151–182. https://doi. org/10.1007/978-94-007-7515-2_7 Spittlehouse, D.L. and Stewart, R.B. (2003) Adaptation to climate change in forest management. BC Journal of Ecosystems and Management 4, 1–11. Swanston, C.W., Janowiak, M.K., Brandt, L.A., Butler, P.R., Handler, S.D. et al. (2016) Forest adaptation resources: climate change tools and approaches for land managers, 2nd edn. USDA Forest Service General Technical Report NRS-87-2. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, 161 pp. http://dx.doi.org/10.2737/NRS-GTR-87-2 Swanston, C.W., Brandt, L.A., Janowiak, M.K., Handler, S.D., Butler-Leopold, P. et al. (2018) Vulnerability of forests of the Midwest and Northeast United States to climate change. Climate Change 146, 103–116. https://doi.org/10.1007/s10584-017-2065-2 Tang, Y., Zhong, S., Luo, L., Bian, X., Heilman, W.E. and Winkler, J. (2015) The potential impact of regional climate change on fire weather in the United States. Annals of the Association of American Geographers 105(1), 1–21. https://doi.org/10.1080/00045608.201 4.968892 Thompson, I., Mackey, B., McNulty, S. and Mosseler, A. (2009) Forest resilience, biodiversity, and climate change. A synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Secretariat of the Convention on Biological Diversity, Technical Series 43. Secretariat of the Convention on Biological Diversity, Montreal, Canada, 67 pp.

Managing Oak Forests in a Changing Climate

USDA Forest Service (2016) Climate-FVS. Available at: http://www.fs.fed.us/fmsc/fvs/whatis/climate-fvs. shtml (accessed 1 September 2016). USDA Forest Service (2017) Forests and Carbon Storage. Available at: https://www.fs.usda.gov/ccrc/ topics/forests-carbon (accessed 29 June 2017). USDA Forest Service (2018) Climate Change Resource Center. Available at: http://www.fs.usda.gov/ccrc/ homel (accessed 7 September 2018). US Environmental Protection Agency (2017) Inventory of U.S. greenhouse gas emissions and sinks 1990–2015. US Environmental Protection Agency Report EPA430-P-17-001. US Environmental Protection Agency, Washington, DC. Available at: https://www.epa.gov/ sites/production/files/2017-02/documents/2017_complete_report.pdf (accessed 19 September 2018). US Geological Service (2018) USGS Geo Data Portal. Available at: https://cida.usgs.gov/gdp/ (accessed 22 January 2018). Vose, J.M., Peterson, D.L. and Patel-Weynand, T. (eds) (2012) Effects of climatic variability and change on forest ecosystems: a comprehensive science synthesis for the U.S. forest sector. USDA Forest Service General Technical Report PNW-870. USDA Forest Service, Pacific Northwest Research Station, Portland, Oregon, 265 pp. https://doi.org/10.2737/PNW-GTR-870 Wang, W.J., He, H.S., Fraser, J.S., Thompson, F.R. III, Shifley, S.R. and Spetich, M.A. (2014) landis pro: a landscape model that predicts forest composition and structure changes at regional scales. Ecography 37, 225–229. https://doi.org/10.1111/j.1600-0587.2013.00495.x Wang, W.J., He, H.S., Thompson, F.R. III, Fraser, J.S., Hanberry, B.B. and Dijak, W.D. (2015) Importance of succession, harvest, and climate change in determining future composition in U.S. Central Hardwood Forests. Ecosphere 6, 1–18. https://doi.org/10.1890/ ES15-00238.1 Wang, W.J., He, H.S., Thompson, F.R. III, Fraser, J.S. and Dijak, W.D. (2016) Landscape-and regionalscale shifts in forest composition under climate change in the Central Hardwood Region of the United States. Landscape Ecology 31, 149–163. https://doi. org/10.1007/s10980-015-0294-1 Williams, M.I. and Dumroese, R.K. (2013) Preparing for climate change: forestry and assisted migration. Journal of Forestry 111, 287–297. https://doi.org/ 10.5849/jof.13-016

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15

Growth and Yield

Introduction This chapter discusses the annual growth phenology of oaks, factors that affect oak growth and survival, patterns of stand growth and yield, and growth and yield models for oaks. The latter are presented in their order of historical development and increasing complexity beginning with stand-level models and proceeding through individual-tree-based models and forest landscape models (FLMs). Physiological models of tree development are not addressed. Most silvicultural decisions implicitly or explicitly consider tree and stand growth and yield over time. Growth and yield estimates are relevant to oak silviculture because they forecast changes in stand structure and composition, and thus are useful in predicting commodity and ecosystem service values that potentially differ among silvicultural options. Some growth and yield estimates are quantitative and based on yield tables or mathematical equations. Other approaches include qualitative judgements about which trees to retain while marking a stand for thinning. Although most trees increase in height and diameter each year, some become smaller in response to adverse conditions, and others die. These responses all represent growth in the broader context of forest growth and yield. Yield is the net result of accumulated changes over time. Although yield is usually expressed in units of merchantable wood volume, yield also can refer to other tree and stand attributes such as mast (see Chapter 13, this volume) or carbon (see Chapter 14, this volume). When forest yield is expressed as a mathematical function of time (or age), growth can be computed as the first derivative of the yield function with respect to time. Conversely, yield over a specified time interval can be determined through numerical or analytical integration of a mathematical growth function (often called a growth curve). Growth and yield relations can be modelled at different scales, for example the tree, the stand, the forest or the region. Collectively, the combined

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changes at any given scale produce the composite changes observed at the next larger scale. Although it might seem logical to simply combine predictions of growth at fine scales (e.g. the growth of a tree) to compile estimates of growth at coarser scales (e.g. the growth of a forest or a forested landscape), the enormous detail required often renders the approach impractical. Instead, predictive growth models that operate at different scales have evolved more or less independently. The appropriate choice of model scale depends on: (i) the question or problem to be addressed; (ii) the availability of models applicable to different scales; (iii) the availability of data necessary to apply the models; and (iv) the time and other resources required for model implementation. Although mathematical models are useful in predicting forest growth and yield, they have limitations. For example, a mathematical model may not predict forest growth as accurately as the ‘mental model’ of an experienced forester who can account for effects of subtle differences in site quality or forest conditions that are not explicitly considered by a mathematical model. Also, mathematical models may omit factors that can ultimately prove important in future forest development (e.g. insects, disease, wildfire or climate change). Mathematical models nevertheless have the advantage of speed and consistency. They can be quickly applied to large forest inventories and adjusted or modified to reflect local patterns of growth as new information becomes available. Moreover, improvements in computing hardware and software continue to simplify the application of mathematical growth and yield models. A prerequisite to understanding quantitative models is a basic understanding of the phenology of oak growth.

Growth of an Oak Annual phenology The annual cycle of an oak’s growth begins with root elongation in the spring. Root elongation can

© CAB International 2019. The Ecology and Silviculture of Oaks, 3rd Edition (Paul S. Johnson et al.)

above 40°F (4°C) had accumulated for the season (Fuller et  al., 1988). Oaks in upper canopy layers continue diameter growth later into the growing season than those in subordinate crown positions. Water-stressed trees in the understorey may even decrease in diameter during the growing season, but they return to original size when moisture is more abundant (Buchanan et al., 1962). Shoot growth in oaks begins from 1 to 5 weeks after the start of cambial growth (Longman and Coutts, 1974; Reich et al., 1980) (Fig. 15.1). Unlike root elongation and cambial growth, which continue for months, shoot growth of non-juvenile trees is complete within 2–4 weeks (Buech, 1976; Reich et al., 1980). The amount of shoot growth can vary from 5 ft or more annually for vigorous young sprouts to virtually none for subordinate branches in mature trees (Reich et  al., 1980). When conditions are favourable, oak reproduction may produce two or more flushes of shoot growth after a rest period of 1 to several weeks between flushes (Johnson, 1975, 1979; Reich et al., 1980; see also Chapter 2, this volume). However, by the age of 5 years the number of flushes is limited to one or two, with multiple flushes occurring only occasionally on the most vigorous seedlings or sprouts (Johnston, 1941; Cobb et al., 1985).

continue through the entire growing season and well into autumn (Longman and Coutts, 1974). The rate of root growth of seedlings slows during periods of rapid shoot elongation, but that of mature trees continues throughout the period of shoot elongation (Reich et al., 1980) (Fig. 15.1). For trees with a large root system, the rate of root growth changes at various depths in the soil in response to temperature and moisture conditions (Teskey and Hinkley, 1981). As the soil warms in the spring, root growth may be greatest in the upper soil layers. Maximum root growth in deeper soil layers typically occurs when moisture in the surface layers becomes limiting later in the growing season. Cambial growth begins simultaneously throughout the bole and branches shortly after the onset of root growth and approximately 2 weeks before bud enlargement (Zasada and Zahner, 1969; Longman and Coutts, 1974). Radial growth is initially very slow. It is not until well after bud break, when oak leaves are ‘squirrel-ear size’, that diameter growth is sufficient to be recorded with dendrometer bands, which can detect a 0.01 inch increase in circumference (Buchanan et al., 1962). The development of earlywood (large-diameter water-conducting vessels) continues for approximately 10 weeks before changing to small-diameter latewood vessels (Zasada and Zahner, 1969). Measurable diameter growth of red oaks in New York began in mid-April and continued at a nearly constant rate until the end of July (Karnig and Stout, 1969). Diameter growth of black oak in Missouri also commenced in April and was 80–90% complete within the first 3 months of the growing season; most of that growth occurred in the first 2 months (Buchanan et al., 1962). About 80% of annual diameter growth for northern red oak in Michigan was complete when 1000 degree-days

0

2

4

6

Diameter growth Sources of variation More measurements of the diameter growth of oaks have been made than of any other tree dimension. Despite this wealth of data, only two facts about oak diameter growth are known with certainty: (i) it is influenced by many factors; and (ii) it is only partially predictable. Diameter growth varies among

Weeks since initiation of tree growth 8 10 12 14 16 18 20

22

24

26

Root growth Shoot growth Cambial growth Early wood

Late wood

Fig. 15.1.  The phenologies of root, stem and shoot growth in oaks. (Adapted from Longman and Coutts, 1974; and other sources.) Thicker bars indicate periods of relatively greater growth. Weather, site conditions, species, tree age or size, and root:shoot ratio can all affect patterns of development.

Growth and Yield

531

oak species and is further influenced by tree size, competition, stand density, crown position, site quality and weather. Whereas factors such as site quality remain relatively fixed, others such as stand density are continually changing, albeit in a more or less predictable way. Factors such as weather are inherently unpredictable and contribute to the observed variation in diameter growth (e.g. a fundamental principle of dendrochronology). Despite the many factors that affect diameter growth, general trends emerge from statistics averaged over large numbers of trees. Northern red oak is among the fastest growing of the upland oaks and its diameter growth rate averages about twice that of chinkapin or post oak (Table 15.1). Although on average the red oaks as a group grow faster in diameter than the white oaks, both groups are characterized by large variation in growth. The diameter growth of oaks is influenced more by crown position (Fig. 5.1, this volume) than by differences among species. For example, a change in crown class from codominant to intermediate typically reduces diameter growth by 20–50% (Fig. 15.2). Table 15.1.  Observed 10-year diameter growth rates of oaksa averaged over a wide range of initial tree diameters. (Compiled from Dunlap, 1921; McIntyre, 1933; Bull, 1945; Trimble, 1960, 1969; Gemmill, 1980; McDonald, 1980; Shifley and Smith, 1982; Smith and Shifley, 1984; Kershaw and Fischer, 1985; McDonald and Vaughn, 2007.)

Species Northern red oak Black oak Shingle oak Scarlet oak White oak Chestnut oak Swamp white oak Bur oak Chinkapin oak Blackjack oak Post oak California black oak Overcup oak Nuttall oak Willow oak a

Average dbh growth (in./ decade)

Years to grow 1 inch in dbh

1.63–2.90 1.50–2.26 1.82 1.55–1.92 1.12–1.78 1.05–1.60 1.4 1.3 0.82–1.09 0.93 0.79 0.67 2.4b 2.3b 2.3b

3–6 4–7 5 5–7 6–9 6–10 7 8 9–12 11 13 15 4b 4b 4b

Presented in approximate order of decreasing growth rate. Based on well-formed dominant trees and not directly comparable to data for other species.

b

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The influence of crown class on diameter growth is so great that, for trees in intermediate and overtopped crown classes, species-related differences in dbh growth may be indiscernible (Trimble, 1960). Diameter growth of dominant upland oaks can continue at rates of 2–3 inches in dbh/decade through age 70 and for trees up to 30 inches dbh (Patton, 1922; McKnight and Johnson, 1980). In bottomlands, dominant oaks can grow more than 4 inches dbh/decade. Wherever oaks occur, rapid diameter growth largely depends on maintaining a superior crown position. Based on data averaged across a wide range of tree and stand conditions, the mean diameter growth rate of oaks larger than 8 inches dbh remains relatively constant or may decline slightly with increasing dbh (Fig. 15.3). However, such averages ignore effects of crown class. Overtopped trees tend to have small diameters and small diameter increments. Con­ sequently, growth rates for trees less than 8 inches dbh that are based on averages composited across heterogeneous stand conditions tend to be low because the smaller trees largely occur in inferior crown classes. When trees within a single species and crown class (e.g. all intermediate white oaks) are considered separately, the correlation between initial diameter and diameter growth is seldom statistically significant (Trimble, 1960). Site quality also influences the rate of diameter growth. An increase in site index from 50 to 80 ft (base age 50) can increase diameter growth by 1 inch/decade for dominant and codominant upland northern red, black, chestnut and scarlet oaks (Trimble, 1960). But site effects are not always expressed in trees in intermediate and overtopped crown classes. Diameter growth of oaks in bottomlands generally exceeds those in uplands. In southern bottomlands, dominant trees in the red oak group may grow more than 4 inches dbh/decade, and trees in the white oak group may grow nearly 3 inches dbh/decade (McKnight and Johnson, 1980). Fertilizers, although not widely applied to oak stands, also can increase diameter growth. Fiftyyear-old northern red and white oaks grew 30–40% faster in diameter following application of nitrogen and phosphorus (Ward and Bowersox, 1970; Graney, 1987). White oak and black oak responded similarly but were less responsive to fertilization than northern red oak (McQuilkin, 1982). Maximum responses to fertilization usually occur the second year after application, but growth gains may persist for 6 years or longer (Karnig, 1972;

Chapter 15

Ten-year average dbh growth (in.)

2

1

0 Northern red oak Dominant

Black oak Codominant

Chestnut oak

White oak

Intermediate

Overtopped

Fig. 15.2.  Ten-year average dbh growth by crown classes of some common oaks. (From Trimble, 1969; Shifley and Smith, 1982; Smith and Shifley, 1984.) Red oaks are from Indiana; black and white oaks from Missouri; and chestnut oaks from West Virginia.

Ten-year dbh growth (in.)

2.0 Black oak 1.5

1.0

White oak

0.5

Post oak

0.0 2

6

10 14 18 22 Initial dbh (in.)

26

30

Fig. 15.3.  Ten-year diameter growth of oaks in relation to initial dbh, based on data from more than 9000 trees in Missouri. (From Shifley and Smith, 1982.)

Lamson, 1978; McQuilkin, 1982; Graney, 1987). Thinning combined with fertilization can further accelerate diameter growth (Graney, 1987). Responses to thinning Reducing stand density by thinning usually increases the diameter growth of the remaining trees. This response results from the expansion of tree crowns

Growth and Yield

and roots into space previously occupied by harvested trees (e.g. see Fig 3.22, this volume). This expansion, in turn, increases the leaf area and root surface area of individual residual trees and thus their photosynthetic capacity, their access to water and nutrients, and their diameter growth. Thinning also directly affects the relative crown positions of trees, and differences in crown position greatly affect diameter growth (Fig. 15.2). Diameter growth of pole-size and larger oaks increases as residual stand density decreases, even when stand density is reduced to 30% stocking or less (Dale, 1968; Hilt, 1979; Mitchell et al., 1988) (Fig. 15.4). This response occurs across a wide range of tree ages and site classes (Dale, 1968; Hilt, 1979; Graney, 1987; Meadows and Goelz, 2001). Diameter growth responses to thinning depend on species, site quality, tree size, tree age, tree crown condition, intensity of thinning and time since thinning. Although most trees respond to thinning with increased diameter growth, young, well-formed trees in dominant and codominant crown classes usually respond the most. Trees in overtopped and intermediate crown classes often have small, poorly formed crowns and stems, and they do not respond to thinning as much or as consistently as younger trees of better form and superior crown position (McGee, 1981; McGee and Bivens, 1984; Clatterbuck, 1993).

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For dominant and codominant pole-sized white oaks, responses to thinning are often greatest the second year after thinning. However, for trees with small crowns that have developed in high-density stands, the response to thinning may be delayed for 3–5 years. Although diameter growth usually declines gradually after reaching a peak response to thinning, effects of thinning can persist for 20 years or longer (Minckler, 1957, 1967; Dale, 1968; Schlesinger, 1978; Mitchell et  al., 1988). This long-term response to thinning occurs when residual trees capture and retain the unused growing space created by harvesting (see Chapter 6, this volume).

Ten-year dbh growth (in.)

4

3

2

1

0 Missouri Stocking level < 32% 40%

Iowa 55% 70%

> 80%

Fig. 15.4.  Ten-year dbh growth of the 40 largest oaks/ acre in relation to stand density (expressed as stocking percentage based on Gingrich’s (1967) stocking equation). (Adapted from Hilt, 1979.)

Crop-tree thinning concentrates the effect of thinning on selected trees (see Chapter 8, this volume). After crop-tree thinning, the diameter growth of dominant and codominant pole-sized oaks may increase by 50% and the effect may last for more than 10 years (Minckler, 1967; Dale and Sonderman, 1984; Lamson et  al., 1990; Ward, 1995; Miller, 2000; Perkey and Wilkins, 2001) (Table 15.2). However, increases in diameter growth of 15–30% sustained over several decades are more typically observed, especially at the residual stand densities commonly maintained after thinning (Hilt, 1979; Mitchell et al., 1988). Most oak thinning studies in North America have focused on eastern oak species. However, western oaks also respond to thinning (Pillsbury et  al., 2002; McDonald and Vaughn, 2007). For example, crown thinning doubled the rate of diameter growth of 60-year-old California black oaks and associated hardwoods within 3 years of treatment. The increased growth rate was sustained for 15 years, but the growth difference between thinned and unthinned trees declined with time. Consequently repeated thinning within 10 years was recommended for sustaining rapid diameter growth (McDonald and Vaughn, 2007). The effects of thinning in stands younger than 10 years (often termed weeding) are less predictable than in older stands. Unlike oak stands thinned during the pole-stage, weeding around sapling-size oaks does not consistently improve or maintain their crown class after treatment (Trimble, 1974). Sprouts from cut stems can quickly reoccupy the growing space that is only temporarily freed by weeding or early thinning. Consequently, weeding does not consistently increase diameter growth of

Table 15.2.  Typical growth response following crop-tree thinning for oaks and associated species on average sites in Ohio, West Virginia, Pennsylvania and western Maryland. (From Perkey and Wilkins, 2001.)a Species Northern red oak Black oak Scarlet oak Chestnut oak White oak Hickories Yellow-poplar Sugar maple Red maple

Typical diameter growth of unthinned dominant and codominant trees (in./decade)

Typical diameter growth of dominant and codominant following crop-tree release (in./decade)

1.8–2.2 1.5–2 1.7 1.5–1.7 1.2 N/Ab 2–2.9 1.4 2.2

2.8–4 1.8–3.5 2.3 2.3 2–2.5 1.5 3.6–4 2–3.5 2.8

a

See Perkey and Wilkins (2001) for additional information about crop-tree response to thinning. N/A, not available.

b

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Chapter 15

dominant and codominant saplings (Trimble, 1974; Lamson and Smith, 1978). Although this practice often increases diameter growth of intermediate and suppressed saplings (Allen and Marquis, 1970), the height growth response of trees in these crown classes is rarely sufficient to move them into dominant or codominant crown classes. Weeding in conjunction with herbicides to control resprouting of cut stems can extend the thinning effect and thus may be more effective than mechanical thinning alone in accelerating the diameter growth of young stands (Wendel and Lamson, 1987). Although weeding or thinning around individual oaks of young age is not always effective in increasing diameter growth of residual trees, thinning within young clumps of oak stump sprouts does accelerate the diameter growth of the retained sprouts (see Chapter 8, this volume, ‘Tending oak coppice (stump sprouts)’). When red oak sprout clumps ranging from 4 to 22 years in age were thinned to a single stem, the residual stem grew about 30% faster in diameter than a stem of the same size in an unthinned clump (4.1 versus 3.1 inches/decade). When thinned to two stems, residual red oak sprouts grew 14% faster in diameter than sprouts in unthinned clumps (Johnson and Rogers, 1980). Twenty-five-year-old white oak stump sprouts thinned to a single stem grew nearly 60% faster than the largest sprout in comparable unthinned sprout clumps (1.6 versus 1.0 inches/ decade) (Haney, 1962). Thinning during the stem exclusion stage of stand development (see Chapter 5, this volume) accelerates the natural pattern of self-thinning and quickly redirects additional growing space to the residual trees. This increases their rate of diameter growth. The stem exclusion stage usually begins when oak stands are about 10–20 years old. By that time, trees are well established, the canopy is closed, inter-tree competition is intense, and trees are rapidly stratifying into crown classes. During the stem exclusion stage, stems within clumps of oak stump sprouts also respond to thinning. By this time, stems within sprout clumps are tightly clustered around a single stump, and competition among stems within a clump begins soon after sprouts emerge. Thus, the equivalent of the stem exclusion stage of development within sprout clumps starts earlier than among individual trees. Consequently, thinning clumps of oak stump spouts can accelerate diameter growth of the residual sprouts when sprout clumps are only a few years

Growth and Yield

old (see Chapter 8, ‘Tending oak coppice (stump sprouts)’), so the usual recommendation for eastern oaks is to thin sprout clumps to a single stem as early as practical. However, oak sprouts in 60-yearold stands of mixed western hardwoods (California black oak, tanoak and Pacific madrone) thinned to two to four stems per clump responded differently (McDonald and Vaughn, 2007). After crown thinning reduced basal area from 200 to 110 ft2/acre, the diameter growth of residual sprouts was equal to that of single-stemmed non-sprouts. In contrast, multiple-stemmed sprouts in unthinned stands grew slower than non-sprouts. Thus, in these older stands, area-wide thinning effectively accelerated the diameter growth of sprouts. Removal of competing understorey vegetation alone has not consistently increased the diameter growth of oaks. Northern red oaks in thinned stands in New York grew at the same rate whether or not competing understorey vegetation was eliminated with herbicide (Karnig and Stout, 1969). In thinned stands in Missouri and Iowa, black oaks and white oaks increased in diameter growth by 10–20% when understorey vegetation was removed. However, little or no response to understorey vegetation removal was observed in oak stands in Ohio and Kentucky that were similarly treated. These seemingly disparate results may be explained by the drier sites in Missouri and Iowa, where understorey removal may have reduced moisture stress in overstorey trees (Dale, 1975). Although thinning virtually assures an increase in diameter growth of at least some residual trees, the net benefits from thinning depend on several factors including the duration of the response, expected changes in tree quality, and the resultant net economic gain (or loss). In general, heavier thinnings produce the greatest increases in the diameter growth of residual trees. However, thinning oak stands to less than 55% stocking (based on Gingrich’s (1967) stocking equation) is rarely recommended because the increased growth of residual trees may not compensate for the loss of growing stock and related unused growing space. Moreover, epicormic branching of oaks tends to increase with thinning intensity, which may reduce the value of residual trees (Dale and Sonderman, 1984; Sonderman, 1984; Sonderman and Rast, 1988). Periodic thinning beginning early in the life of a stand can greatly increase the rate of diameter and volume growth of residual trees. That also usually

535

results in increased merchantable volume and value per tree. Thinnings are usually intended to increase total merchantable stand yield per acre. But attaining that goal depends on residual stand density and the desired products. For example, thinning will have less impact on total biomass yield per acre than on board foot lumber yield per acre (see the section on thinning stands later in this chapter and also Chapter 8, this volume). Value growth An oak’s economic value often greatly increases as soon as it grows into the minimum size that defines a product class or log grade. Conversely, a tree’s value can greatly decrease when degraded by epicormic branches, logging damage, insects, disease or other factors. Changes in tree value thus may provide a more meaningful measure of growth than changes in tree size. But coupling varying market values with the many variables that influence tree growth and quality makes it difficult to generalize about value growth. Mean annual value increases of 4.6% (net of inflation) have been reported for northern red and black oaks in New England (Gansner et al., 1990) and Pennsylvania (Herrick and Gansner, 1985). For well-formed northern red oak and black oaks between 9 and 13 inches dbh, average rates of value growth exceeded 9% (net of inflation). In contrast, average rates for white oak ranged from 0.3 to 1.3%, depending on region, and did not exceed 1.6% (net of inflation). In general, rates of value increase were greatest for trees of good form growing in stands of high site quality and where basal area was less than 60 ft2/acre. Among tree size classes, small sawlog-size oaks (8–16 inches dbh) yielded the highest percentage growth rates. The annual rate of value growth for a tree usually declined after it attained large size and high quality. This is at least partially explained as a result of expressing the increase in tree value as a percentage. Because small trees have low volume and low value, a given increase in wood volume usually produces a greater percentage increase in value for small trees than for large trees. In some cases, larger trees may have a lower percentage increase in value than small trees but may nevertheless produce a greater absolute increase in value. In Wisconsin, annual value growth rates for northern red oak averaged between 1.2 and 1.9% (net of inflation) if increases in tree grade accruing

536

with increased diameter are ignored (Buongiorno and Hseu, 1993). The value growth rates for oaks were the highest among all the species included in the study, but due to differences in methodology, those results are not directly comparable to the value growth rates cited above for New England and Pennsylvania. At two sites in Indiana, the average annual value increase for surviving white, northern red and black oaks retained after applying two-stage, irregular shelterwood cuttings ranged from 22 to 28% (including inflation) for more than a decade after the last harvest (Fischer et al., 1980). Residual trees averaged 11 inches dbh, and the value increase for northern red and black oaks was 2% higher than for white oak. However, windthrow at the site destroyed nearly 60% of the shelterwood trees greater than 15 inches dbh. This reduced the combined increase for all residual trees (surviving and windthrown) to between 17 and 21% annually (including inflation). Value growth rates (including inflation) for white, northern red and chestnut oaks in West Virginia exceeded 40% in some cases, but the rate decreased with increasing tree diameter and with decreasing tree vigour and site quality. Increases tended to level off at values between 4 and 12% annually (including inflation) when trees reached 24 inches dbh on site index 80. On average, increases in tree quality over time (as opposed to increases in tree volume alone) accounted for at least half the increase in tree value. Other factors being equal, value growth of northern red oak exceeded chestnut oak, which in turn exceeded white oak (Trimble and Mendel, 1969). Site quality also influences the value growth of individual trees. Knot-free wood is produced at earlier ages and in greater quantity on good sites than on poor sites. The formation of clear wood on a tree bole occurs as concentric sheaths surrounding the knotty core (Fig. 15.5). Dominant and codominant upland oak saplings and poles typically produce a clear bole length that is approximately one-third their total height. Dominant and codominant trees grow faster in height and produce greater clear bole length at a given age on good sites than on poor sites. For dominant and codominant black oaks, a branch-free 17 ft log is expected at age 27 for site index 70, but takes nearly twice as long to develop (age 52) for site index 50. Trees growing on good sites also produce more clear wood at a given height on the bole

Chapter 15

7

18

6

15

5

12

4

9

3

6

2

3

1

0

0

Tree age (years)

Tree height (ft)

21

Clear bole length at age 7

Knotty wood Clear wood Fig. 15.5.  The formation of clear (knot-free) wood as the base of the crown moves up the bole is illustrated for the first 7 years of tree growth. Oak crowns typically extend over the upper two-thirds of the bole. Clear wood thus forms only on the lower third of the bole after dead branches are shed and branch scars are healed. (Adapted from Carmean and Boyce, 1973.)

(Carmean and Boyce, 1973). On good sites, the rapid development of a clear log allows more rings of knot-free wood to be added at a given age. For a given age, dominant and codominant trees on good sites produce, on average, logs of greater value than dominant and codominant trees on poor sites. On the better sites, the value increment associated with the additional bole length and knot-free logs boosts the value growth of individual trees and ultimately the value growth per acre.

Growth and Yield

Height growth Much information about the height growth of oaks is contained in site index curves (see Chapter 4, this volume). These curves describe the effect of site quality on maximum height growth, and also confirm that rates of height growth differ among the oaks (e.g. scarlet oak > black oak > white oak) (Doolittle, 1958; McQuilkin, 1974). Unfortunately, site index curves are applicable only to dominant

537

538

are commonly used to estimate tree height (Fig. 15.6). Although other factors such as weather, mechanical damage from adjacent trees, browsing or damage from other biotic agents also can significantly influence height growth, relatively little is known quantitatively about how those factors are related to oak height growth. A comprehensive and widely used equation for estimating heights of oaks was developed for the Central Hardwood Region from 2300 felled trees in Illinois, Indiana, Iowa, Kentucky, Missouri and Ohio (Hilt and Dale, 1982). This equation, which is a component of the oaksim growth and yield model, predicts total tree height from dbh, age and site index by species (Hilt, 1985a, b, 1987). The equation, which is applicable to white oak and black oak, is constrained to ensure that, other factors being equal: ●● Height approaches 4.5 ft as dbh approaches zero. ●● Height growth decreases as dbh increases. ●● Height growth decreases with increasing age. ●● Height increases with increasing site index. ●● Height, diameter and site index relationships are jointly consistent with Schnur’s (1937) stand tables. Because of these constraints, the resulting equation is mathematically complex. The equation form is given by: 100

Total tree height (ft)

and codominant trees that have not been previously suppressed. In any stand, most trees will experience periods of suppression and therefore will be shorter than indicated by site index curves of tree height over age. Height growth of dominant and codominant trees is relatively independent of stand density (see Chapter 4, this volume). The utility of site index as a measure of site quality is premised on this assumption. But independence of height growth and stand density does not hold for very young oaks or trees growing in the lower canopy. After stands younger than 20 years old are thinned, suppressed oaks can double their periodic rate of height growth, whereas those in superior crown classes in the same stands do not respond (Haney, 1962; Ward, 1995). In young stands, thinning may even reduce the height growth of dominant and codominant trees. In 7–9-year-old oak sapling stands, thinning to less than 50% stocking can decrease the rate of height growth by 60% relative to trees in unthinned stands (Allen and Marquis, 1970). The greatest negative impact in height growth from early thinning occurs among dominant and codominant saplings. At that stage of tree development, thinning encourages crowns to expand laterally at the expense of height growth. However, the reduced height growth of young oaks after thinning is partially compensated by increases in diameter growth (Gevorkiantz and Scholz, 1948; Stout and Shumway, 1982; see also Chapter 8, this volume). Oaks more than 30 years old may show little or no height growth response after thinning. In Tennessee for example, the average height growth of overtopped white oaks that were released from the main canopy was only 0.5 ft/year (McGee, 1981; McGee and Bivens, 1984; Clatterbuck, 1993). This rate of growth was less than that of comparable overtopped white oaks in stands that were not thinned. However, increased diameter growth from thinning may increase net merchantable volume despite slow height growth. Nevertheless, older, overtopped white oaks usually show little response thinning. Moreover, they often develop and retain epicormic branches after thinning (McGee and Bivens, 1984; Clatterbuck, 1993). The difficulty of measuring tree heights is often an incentive to estimate tree height indirectly from other variables. For a given species, a tree’s height is related to its age, diameter, site quality and sometimes stand density. Because tree diameter and site quality are relatively easy to measure, these factors

80

60

40

20

6

10

14

18

22

26

Dbh (in.) Fig. 15.6.  Heights and diameters of 500 white oaks in southern Missouri. Although the general shape of the height-diameter curve is apparent, each diameter class includes a wide range of heights. Approximately 65% of the variation in oak height can be explained by tree diameter.

Chapter 15

Equations that predict tree height in the absence of information on stand age are required when stand and tree ages are unknown. This is a common problem in relatively undisturbed stands and in stands under uneven-aged management. Total oak tree height has been modelled as a function of tree dbh for many species with the following equation:

  kH s − H s  D    H = 1.37 + (kH s − 1.37 ) 1 − exp ln       kH s − 1.37  Ds   

[15.1] where: H = total tree height (m) k = 1.07 for black oaks or 1.12 for white oaks D = tree dbh (cm) Hs = 1.80408 S0.932097 [1 – exp(–0.0240308A)]2.55529S–0.280445 Ds = 5.49927S0.744034[1–exp(–0.0192593A)]1.25342 for black oak or Ds = 6.40146S0.631893[1–exp(–0.0227614A)]1.21892 for white oak S = site index (m) A = stand age (years). This equation produces a height-diameter curve similar to Fig. 15.6. However, Equation 15.1 (presented as a graph in Fig. 15.7) expands the heightdiameter relation to account for age and site index effects. Although the equation was developed using data from unthinned stands, its application to thinned stands showed that height predictions were within 10% of observed values (Hilt and Dale, 1982).

H = 4.5 + exp ( b0 + b1Db2 ) [15.2] where: H = total tree height (ft) D = tree dbh (inches) bi = regression coefficients that differ by species and region (Table 15.3). When b2 is < 0, this equation has an upper asymptote of 4.5 + exp (b0), and it is sufficiently flexible to fit a wide variety of height-age patterns. It has been applied to oaks and associated species in western (Larsen and Hann, 1987; Wang and Hann, 1988) and eastern USA (Colbert et al., 2002; Lynch et al., 2005; Lootens et  al., 2007) (Fig. 15.8). When applied to Oregon white oak, California black oak and canyon live oak in south-western Oregon (Larsen and Hann, 1987), tree height to the base of

Tree height (ft)

120

80

40

200

0 30 20

50 Dbh (

in.)

10 0

Ag e

(y e

100

ar s)

150

0

Fig. 15.7.  Estimated white oak heights by dbh and stand age for site index 70, based on Equation 15.1. (Adapted from Hilt and Dale, 1982.)

Growth and Yield

539

Table 15.3.  Published species coefficients for the height-diameter Equation 15.2. Coefficients Species Canyon live oak California black oak Oregon white oak Pin oak Black oak Scarlet oak White oak Midwest upland oak Cherrybark oaka

b0

b1

b2

Reference

3.82509 4.82214 4.69891 5.6812 4.3702 4.5004 4.5024 4.5409 3.7997

–2.08385 –3.27676 –3.39164 –3.9049 –13.0002 –9.1643 –5.0009 –6.7095 –14.9173

–0.497143 –0.507032 –0.615259 –0.3965 –1.8022 –1.4756 –1.0845 –1.2405 –0.9

Larsen and Hann (1987) Larsen and Hann (1987) Wang and Hann (1988) Colbert et al. (2002) Lootens et al. (2007) Lootens et al. (2007) Lootens et al. (2007) Lootens et al. (2007) Lynch et al. (2005)

a

These coefficients are for metric values with height estimated in metres for dbh given in centimetres and with the equation’s breast height constant (4.5 ft) converted to 1.37 m.

120

Cherrybark oak Pin oak

100 Scarlet oak White oak Black oak Oregon white oak

Tree height (ft)

80

60

40

Canyon live oak

20

0

0

5

10

15 Dbh (in.)

20

25

30

Fig. 15.8.  Relation between height and diameter for seven species of oaks, based on Equation 15.2 and the coefficients in Table 15.3.

the live crown also can be estimated from total height and other stand characteristics (Ritchie and Hann, 1987). Although it does not explicitly consider site quality, it is a versatile approach for rapid estimation of total tree heights in association with forest inventories. Others have shown how the equation can be modified so that height and diameter measurements observed for about ten trees in a given stand can be used to adjust the height predictions for any other tree in that stand (Lynch et al., 2005).

540

When total tree height is known or estimated, merchantable heights and merchantable volumes can be estimated with taper functions. These functions use information about tree dbh and total tree height to estimate the height where a given upper stem merchantable diameter would occur (e.g. height to a 9 inch upper log diameter and corresponding merchantable sawlog length) (Thomas and Parresol, 1991; Kershaw et al., 2016). Although versatile, taper equations are not widely available for oaks.

Chapter 15

The following model has an implied taper function that allows users to predict merchantable heights of oaks in north-eastern USA to top diameters of either 4 or 9 inches (Yaussy and Dale, 1991): H m = b1S b2 éë1 - exp {b3 ( D - T )}ùû [15.3] where: Hm = (a) merchantable tree height (ft) of the sawlog portion of the tree to a minimum 9 inch top diameter outside bark (dob) or where terminated by limbs, crook or breakage (T is set to 9); or (b) merchantable height (ft) of the bole to a minimum 4 inch top dob (T is set to 4) D = tree dbh (inches) T = top diameter (inches) outside bark for the height measurement with (a) T = 9 for sawlog height or (b) T = 4 for bole length S = site index (feet at base age 50) bi = species-specific coefficients (Table 15.4). This equation was developed from data on more than 4000 oaks. It accounts for 25% of the observed variation in merchantable heights of sawlog-size trees and 50% of the variation in tree bole length. The unexplained variation is due to differences in tree form that cause trees of the same diameter to vary in merchantable height. Predicting merchantable heights with an equation is not recommended as a substitute for field measurement of heights when inventorying high-value products in individual stands. However, in large-scale applications such as forest-wide inventories, the equationgenerated heights are often adequate (Fig. 15.9). In some situations total tree height can be estimated when only merchantable height is known. The following model can be used to predict total height of upland oaks and hickories in Illinois based on observed merchantable height: H = 0.85H m + 30 [15.4]

where H is total height (ft) and Hm is merchantable height (ft) to a 5 inch top dob (Myers and Belcher, 1981). Similarly, when merchantable height is known to a 4 inch top diameter inside bark (dib), total heights of Lake States’ oaks can be estimated using a similar methodology described by Gevorkiantz and Olsen (1955: Table 11). A height-diameter equation applicable to five oak species in mixed hardwood stands in the Appalachians of Georgia, North Carolina, Tennessee and Virginia accounts for changes in the height-diameter relation as the average height of dominant and codominant trees in the stand increases (Harrison et al., 1986). In applying the model, the average height of dominant and codominant trees can be determined from field measurements or, for even-aged stands, can be estimated from site index curves when site index and stand age are known. The equation is of the form: é æ -b D ö ù H = 4.5 + H d éë1 + b1 exp ( b2 H d ) ùû ê1 - exp ç 3 ÷ ú  êë è H d ø úû [15.5] where: H = total tree height (ft) Hd = average height of dominant and codominant trees (ft), which can be measured in the field or estimated as é æ 1 1 öù H d = S × exp ê -22.0271 ç ÷ú è A 50 ø û ë bi = species-specific coefficients D = tree dbh (inches) S = site index (feet at age 50) A = stand age (years). Other height equations for oaks include a model that estimates the total height of blue oak in California based on dbh, relative crown size and

Table 15.4.  Species coefficients for Equation 15.3. (From Yaussy and Dale, 1991.) Coefficients Sawlog length (9 in. top) Species group Black oak Chestnut oak Northern red oak Scarlet oak White oak

Growth and Yield

Pulpwood length (4 in. top)

b1

b2

b3

b1

b2

b3

16.636 10.405 25.095 22.206 11.050

0.201 0.308 0.086 0.132 0.283

–0.328 –0.379 –0.403 –0.370 –0.387

13.901 10.573 34.608 27.724 19.406

0.291 0.338 0.062 0.141 0.206

–0.283 –0.301 –0.294 –0.239 –0.247

541

Merchantable tree height (ft)

50

tree mortality is less predictable. Survival rates of individual trees vary by species, tree size, stand density, site quality and crown class. Comparison of survival rates (or conversely mortality rates) requires the use of a common time interval, usually 1 year or a decade. Relations between annual and periodic survival and mortality rates with respect to time interval are as follows:

Pulpwood

40

Sawlogs

30

Annual survival rate = (periodic survival rate)1/ n  [15.6]

20

10

4

8

12

16

20

24

28

32

Dbh (in.) Site index 70

Site index 50

Fig. 15.9.  Estimated merchantable tree heights for sawlog-size (9 inch top diameter outside bark (dob)) and pulpwood-size (4 inch top dob) white oaks in the north-eastern USA (see Equation 15.3). (From Yaussy and Dale, 1991.)

site index (Standiford, 1997), and another that uses stand basal area as a predictor(Ek et al., 1984). In the past, the ability to calibrate models for tree height was hampered by a lack of tree height measurements. Tree heights were difficult to measure and, as a result of a primary interest in producing sawtimber, merchantable tree heights rather than total heights were usually measured. In recent years, technological advances in tools for measuring tree heights and expanding markets for whole-tree products such as biomass and sequestered carbon have increased the availability of total height measurements. Developing local height-diameter equations from local data (e.g. data available online from the Forest Inventory and Analysis Program (USDA Forest Service, 2015)) using a model such as Equation 15.2 provides a practical approach to tree height estimation. Models that predict total tree height and that subsequently use taper information to estimate height to user-defined merchantable top diameters also provide versatile tools for estimating product volumes in conjunction with forest inventories. Survival rates Although competition results in regular and predictable reductions in numbers of trees per acre as stands develop (see Chapters 5 and 6, this volume), individual

542

Periodic survival rate = (annual survival rate)n  [15.7] Annual mortality rate = 1 − annual survival rate  [15.8] Periodic mortality rate = 1− periodic survival rate [15.9] where n is the number of years in the period and survival and mortality rates are expressed decimally. Mortality rates are computed in terms of the corresponding survival rate and then converted to a mortality rate. When averaged across a wide range of stand conditions, annual survival rates are lowest for small trees. For example, the annual survival of 2  inch dbh oaks in Indiana and Illinois averages 94%, whereas the average rate for 6 inch dbh trees exceeds 99% (Fig. 15.10). Trees in the white oak group generally have higher survival rates than trees in the red oak group (Fig. 15.11). This characteristic is consistent with the greater shade tolerance and longevity of the white oaks. Survival rates for oaks also vary by region. For example, survival rates for oaks smaller than 6 inches dbh are lower in Indiana and Illinois than in Missouri; the pattern is reversed for trees larger than 10 inches dbh (Fig. 15.10). Such relations may be related to site productivity, which on average is higher in Indiana and Illinois than in Missouri. These patterns of survival reflect the faster tree growth, earlier stand closure and earlier competition-induced self-thinning on better sites. Although oaks die from many causes, the death of a tree often follows a long downward spiral in health and vigour (Franklin et  al., 1987; Ammon et  al., 1989; Starkey et  al., 1989; Wargo and Haack, 1991). Lee (1971) suggests separating mortality into two classes: regular and irregular. Regular mortality results from competition and self-thinning as a consequence of stand development through time.

Chapter 15

Annual survival rate (%)

100 Indiana and Illinois

99

Missouri

98 97 96 95 94

2

6

10 14 18 22 Initial dbh (in.)

26

30

Fig. 15.10.  Annual survival rates for oaks (all species) in Indiana and Illinois, and Missouri. Higher average site quality in Indiana and Illinois results in faster stand development, greater competition and lower survival rates for small diameter trees than in Missouri. For both data sets, the white oak group comprised 53% of trees, and the red oak group comprised 47%. (From Shifley and Smith, 1982; Smith and Shifley, 1984.)

It is the most prevalent type of tree mortality and is more predictable than other causes of mortality (see the section on ‘Self-thinning’ in Chapter 6, this volume). Irregular mortality is the abrupt, largely unpredictable mortality that results from events such as wildfire, windstorms, lightning, ice, snow, drought, insect and disease epidemics, and other factors. Although the two classes of mortality are not always separable (e.g. intense competition may predispose a tree to insect attack), it is useful to recognize that the largest and most predictable proportion of tree mortality results from competition. Tree survival following fire varies by species, tree size and fire intensity. Post, white and chestnut oak are more fire resistant than black, northern red and scarlet oaks (Loomis, 1973; Regelbrugge and Smith, 1994). Fire intensity is often reflected in the scorch height on the bole. When the scorch height in feet is equal to the tree diameter in inches, the probability of mortality ranges from 50 to 90%, depending on species and tree diameter (Fig. 15.12). Oaks are usually better able to survive a dormantseason fire than a growing-season fire of the same

20 White/post oaks (MO)

White oak group

99

15 Red oak group

98

Scorch height (ft)

Annual survival rate (%)

100

97 96

Red oaks (MO) 10

Chestnut oaks (VA)

5

95

Red oaks (VA)

94 2

6

10

14

18

22

26

30

Initial dbh (in.) Fig. 15.11.  Mean annual survival rate in relation to initial diameter for white oak and red oak species groups in Missouri. (From Shifley and Smith, 1982.) Rates are based on remeasurement intervals ranging from 7 to 11 years. The white oak group is predominantly white oak (52%) and post oak (40%); the red oak group is predominantly black (57%), northern red (13%), scarlet (13%) and blackjack (12%) oaks. Based on more than 14,000 trees in stands of variable age, density and structure.

Growth and Yield

0 1

5

9

13

Dbh (in.) Fig. 15.12.  Bole scorch heights associated with the death of 50% of trees of a given dbh following growing-season fires in Missouri (MO) and Virginia (VA). Species in the white oak group (white, post and chestnut oaks) survived higher scorch heights than species in the red oak group (black, scarlet and northern red oaks) for a given diameter. However, the relation varied greatly within species groups in both states. (Adapted from Loomis, 1973 (Missouri); Regelbrugge and Smith, 1994 (Virginia).)

543

intensity. Chapter 7 of this volume includes detailed information about fire effects on oaks.

Stand Growth Growth and yield in even-aged stands Normal stands (100% stocking) Early yield tables for oaks were based on data from so-called ‘normally’ stocked, even-aged stands. These yield tables were derived from inventories of relatively undisturbed stands that were thus at or near maximum basal area and volume for their age and site quality. Normal yield tables present expected mean volumes per acre by stand age and site classes. The change in volume by age class thus can be used to infer volume growth over time. Normal yield tables therefore represent simple models of stand change over time. Stand tables, which list numbers of trees by diameter classes for different stand ages and site classes, also can be compiled from the same data. Normal yield and normal stand tables apply to stands at or near the 100% stocking line (sometimes termed average maximum relative density) on Gingrich-style stocking charts1 (e.g. Figs 6.9 and 6.11, this volume). Reported yields for normal stands thus represent the special case where stands are at 100% stocking. Yields will be different for stands with stocking below 100%. Stand volume growth and yield depend largely on five factors: (i) stand age; (ii) tree heights; (iii) site quality; (iv) stocking; and (v) merchantability standards (Gevorkiantz and Scholz, 1948). The basal area of even-aged, normally stocked stands increases over time as mean stand diameter increases. These increases are accompanied by decreases in numbers of trees per acre (see Chapter 6, this volume). A widely cited and comprehensive source of information on the growth and yield of oak stands is Schnur’s (1937) compilation of normal yield and stand tables for upland oaks of the eastern USA. It includes stand and yield tables by age and site index for even-aged, normally stocked stands, and it is based on data gathered across much of the oak range of eastern USA. These tables were compiled from data collected in even-aged stands that were at least 30% oak (predominantly white, black, scarlet, chestnut and northern red oaks). Selected stands spanned a wide range of ages, and many originated after clearcutting. The stands were fully stocked with uniformly distributed trees. Because Schnur’s yield tables are referenced to stand age

544

and site index, they can be presented graphically (Fig. 15.13). However, their derivation limits their application to stands near 100% stocking (i.e. undisturbed stands with normal stocking). Similar stand and yield tables were developed for normal, even-aged oak stands in south-western Wisconsin (Gevorkiantz and Scholz, 1948). On average, 85% of the merchantable volume in those stands was comprised of northern red and white oak. Numerous mesic species were also present, including sugar maple, American basswood, elm, black walnut, black cherry and ash. The transitional nature of these forests to more northern forest types is reflected in the paucity of hickories compared to Schnur’s (1937) study region, where hickories occurred on 70% of sample plots. Gingrich’s (1971a) yield tables for the Central Hardwood Region provide a third comprehensive source of growth and yield data for even-aged oak stands. These tables include thinning effects by age and site index. They also include yield tables for unthinned stands at 100% stocking. Yield estimates from the three sources cited above can be compared graphically (Fig. 15.14). Although estimated basal areas are similar for all sources, volume estimates differ substantially. Volume differences result from differences in merchantability standards, quantitative methodology and stand selection criteria. Because yield tables are simple models of average forest growth and yield, they cannot be expected to accurately predict changes in individual stands. They nevertheless provide useful information on general patterns of oak stand development and provide reasonable average yield estimates for oak stands at 100% stocking. When yield tables (or any other source of forest yield data) are used to make silvicultural and management decisions, their applicability to a specific stand or area should be verified. It is especially important to consider merchantability standards and the minimum dbh used in yield table construction, because these can greatly affect volume estimates. Although stand volume increases with age, the number of trees decreases dramatically. Tree mortality is thus relentless in unthinned oak stands. Typically, only about 5% of the trees in a normally stocked 10-year-old oak stand will live to age 100 (Schnur, 1937). The rate of decrease in numbers of trees can be graphically illustrated for an age series of unthinned even-aged oak stands (Fig. 15.15). This decrease is the result of competition-induced tree mortality or self-thinning. Self-thinning occurs

Chapter 15

Basal area (ft2/acre)

(A)

80 60 40

140 100 60 20

(B) 30,000 20,000

70 60

10,000

50 40

0

Cubic ft/acre

(C) 6,000

80 70 60 50 40

4,000 2,000

Site index (ft)

Board ft/acre

80

0

Cords/acre

(D)

80

80 70 60 50 40

60 40 20 0 10

20

30

40

50

60

70

80

90 100

Stand age (years)

at an earlier age on the better sites (where trees grow rapidly) than on the poorer sites (see Chapter 6, this volume). Whereas annual survival rates for oaks are about 93% in 10-year-old stands, survival rates increase to 99% by age 80 (Fig. 15.16). Even though these survival rates exceed 90%, they represent exponentially decreasing numbers of trees over time. For example, an annual survival rate of 93%/ year reduces the tree population by almost half in one decade (0.9310 = 0.48, the proportion of initial trees that survive); this rate would be typical of 10to 20-year-old stands. Even when the annual sur-

Growth and Yield

Fig. 15.13.  Basal area and volume yields of normal (100% stocked) stands in the eastern USA. (A) Stand basal area for trees ≥ 0.6 inch dbh; (B) board foot (Scribner) volume to an 8 inch top diameter inside bark (dib); (C) cubic foot volume excluding bark for trees ≥ 0.6 inch dbh; and (D) cordwood volume to a 4 inch top dob. (From Schnur, 1937.)

vival rate is 98%, about half the original number of trees in a stand will die in 35 years (0.9835 = 0.49). Thinned stands Thinning results in recovery of the volume in trees that otherwise would be lost to mortality due to crowding and self-thinning. Moreover, growth in thinned stands is concentrated on fewer, fastergrowing trees that have been specifically retained for their potential to produce valuable products or ecological services (e.g. Dale, 1968; Clatterbuck,

545

6000

120 80

Trees/acre

Basal area (ft2/acre) Cubic ft/acre

160

40 0 5,000

4000

2000

4,000 3,000

0

2,000 1,000

20

0

60

Site index (ft) 40 60

20,000 Board ft/acre

40

80

100

Stand age (years)

15,000

80

Fig. 15.15.  Trees per acre by stand age and oak site index in normally stocked even-aged upland oak stands in the eastern USA; includes all trees 0.6 inches dbh and larger. (From Schnur, 1937.)

10,000 5,000

100

60 40 20 0 20

40

60

80

100

120

140

Stand age (years) Gingrich (1971a) Schnur (1937) Gevorkaintz and Scholz (1948) Fig. 15.14.  Reported yields for normal (100% stocked) upland oak stands. (From three sources: Schnur (1937) for site index 70; Gevorkiantz and Scholz (1948) for a medium site; and Gingrich (1971a) for site index 65.) The Schnur and Gingrich volumes are based on data from mixed oak stands in central and eastern USA. The Gevorkiantz and Scholz volumes are based on data from the driftless area of south-western Wisconsin, which are predominantly northern red oak. Schnur’s volumes are board feet Scribner to an 8 inch top dib, cubic feet excluding bark to a 0.6 inch top dib, and cords to a 4 inch top dob. Volumes for Gevorkiantz and Scholz are board feet International 1/4-inch to an 8 inch top dib, cubic feet excluding bark to a 0.6 inch top dib, and cords to a 4 inch top dob. Volumes for Gingrich are board feet International 1/4-inch to an 8.5 inch top dob, cubic feet of entire stem including bark, and cords to a 4 inch top dob.

546

Annual survival rate (%)

Cords/acre

0

98 96 94 92 90

15

25

35 45 55 65 75 Stand age (years)

Site index (ft) 60 (Schnur)

85

95

65 (Gingrich)

Fig. 15.16.  Annual survival rates of trees by stand age classes for normally stocked even-aged upland oak stands in the eastern USA. (Adapted from Schnur, 1937; and Gingrich, 1971a.)

2002; Pillsbury et  al., 2002). The most common objective of thinning is therefore to reduce stand density below 100% stocking in order to reallocate growing space to fewer but more desirable trees, and thereby accelerate their growth. Normal yield tables do not apply to thinned stands because normal yield

Chapter 15

tables represent growth relations occurring at or near maximum stand density (i.e. 100% stocking) in unthinned stands. Numerical algorithms can be used to predict the rate at which any stand below 100% stocking (thinned or otherwise) approaches normality through time. Yield estimates for thinned stands accordingly can be related to normal yield tables (Davis, 1966). However, a more direct approach is to use yield tables or yield models that are directly applicable to thinned stands such as Gingrich’s (1971a) yield tables for oak stands in the Central Hardwood Region. These tables are also compatible with the silvicultural recommendations presented by Roach and Gingrich (1962, 1968) and include projections of stand development for several thinning regimes (Fig. 15.17). The usual objective of thinning is to accelerate the diameter and merchantable volume growth of trees in the residual stand. In general, the greater the intensity of thinning (down to the minimum

mu Cu

12,000

l

ua

sid

Re 8,000

ed

hinn

Unt

4,000 Cut

0

30

40

50 60 70 Stand age (years)

80

Fig. 15.17.  Expected board foot yields of thinned and unthinned upland oak stands in the Central Hardwood Region, oak site index 65. (From Gingrich, 1971a.) Total yield for the unthinned stand is shown by the line labelled ‘Unthinned’. The thinned stand was thinned to 60% stocking every 10 years beginning at age 30. Total volume yield for the thinned stand (labelled ‘Cumulative’) is the sum of two components: (i) the residual growing stock after periodic thinnings (labelled ‘Residual’); and (ii) the volume of material harvested during the thinnings (labelled ‘Cut’). Yields for thinned stands are nearly identical to unthinned stands through to age 50, but by age 80 the cumulative yield for thinned stands is nearly double that of unthinned stands.

Growth and Yield

Cumulative 80-year yield (board ft/acre)

Stand yield (board ft/acre)

lat

ive

16,000

stocking required for full utilization of growing space), the greater the diameter growth of the remaining trees. Thinning also provides the opportunity to harvest and utilize trees that would otherwise be lost through mortality. Thinning can be applied by area-wide thinning (i.e. thinning the stand as a whole) or by crop-tree thinning (i.e. thinning around selected trees) (Rogers and Johnson, 1985; Mitchell et  al., 1988; Lamson et  al., 1990). Both methods accelerate the growth of the retained trees. Compared with unthinned oak stands, thinning can increase yields by 25–80% over a rotation. For eastern oak forests in the USA, the greatest yield increases occur when thinning is begun early in the life of the stand and when stocking is reduced to about 60% at each entry (Fig. 15.18) (Gingrich, 1971a, b). Although Gingrich-style stocking guides or stand density index charts have not been developed for oaks in the western USA, thinning studies in coast live oak (Pillsbury et  al., 2002) and California black oak stands (McDonald and Vaughn, 2007) have shown that periodic cubic volume growth was highest in stands thinned from > 150 ft2 of basal area/acre to 75–100 ft2/acre. The gross cubic foot volume and basal area growth of white oak stands in the Midwestern USA 25,000 20,000 15,000 10,000 5,000 0

55

75 65 Oak site index (ft)

Age when thinned (years) 40 50 30

60

Unthinned

Fig. 15.18.  Effects of site quality and age at first thinning on cumulative board foot yields (cut volume plus residual stand volume) of Central Hardwood oak stands. (Data from Gingrich, 1971a.) In this example, stands were thinned every 10 years through to age 80. Cumulative yields can be increased by starting periodic thinnings early in the rotation.

547

remains relatively constant at residual stand densities ranging from 50 to 120% stocking (Dale, 1968). This is consistent with the Langsaeter principle (Smith, 1962), which posits that, other factors being equal, the gross volume growth of all trees per unit area (including growth on trees that ultimately die due to competition) remains nearly constant over a wide range of stocking levels (e.g. between A- and B-levels of stocking on the Gingrich guide as defined in Figs 6.9 and 6.11, this volume). However, more recent re-examination of this principle indicates that, in at least in some cases, gross volume growth continually increases with increasing stand density (see Smith et al., 1997; Zeide, 2001; Nyland, 2002). In either case, thinning directs the volume growth per acre to a smaller number of faster growing trees, while allowing the harvest and utilization of trees that would otherwise die from inter-tree competition. Consequently, controlling density in oak stands by thinning can increase net merchantable volume production (board feet or cubic feet) and value even though total gross cubic foot volume growth may remain constant or even decrease compared to unthinned stands. Oak stands in Kentucky and Iowa attained a maximum net growth of 2.9 ft2 of basal area/acre/ year when they were thinned to a residual stocking of 50–60% based on Gingrich’s stocking chart (Dale, 1968). In comparison, the basal area of unthinned stands at 100% stocking grew at an annual rate of only 1.9 ft2, or about 65% of maximum. Net cubic foot volume growth (total growth less mortality) reached a maximum at 70–80 ft3/acre/year when residual stocking was maintained at 60%. At 100% stocking, net volume growth was reduced to 50–60 ft3/acre/year (Dale, 1968). Thinning oak stands to 40% stocking may further accelerate the growth of the residual trees and the net volume growth of the stand (Leak, 1981; Hilt and Dale, 1989), but sawtimber quality may suffer from increased epicormic branching. Below 40% stocking, net volume growth per acre decreases because growing space is not fully utilized (Hilt and Dale, 1989). Fertilization in combination with thinning can further accelerate basal area and volume growth of oak stands. In the Boston Mountains of northwestern Arkansas, nitrogen fertilization increased basal area and volume growth (ft3 in the bole from ground to tip) by 16–20% relative to unfertilized stands (Graney and Murphy, 1993). This response occurred at residual densities from 40 to 100 ft2/ acre and at site indices ranging from 50 to 80 ft.

548

Effects of fertilization persisted for up to 10 years after treatment. Historically, most thinning research and the resulting silvicultural recommendations were based on the implicit objective of maximizing net volume or value production. Maximization of one measure of productivity (e.g. cubic foot volume) may not maximize another (e.g. financial return). Also, economic yields may not show the smooth response curve characteristic of volume or basal area production. This is especially true for high-value hardwoods. Diameterbased product classes (such as pulpwood versus sawtimber) are characterized by financial yield curves with sharp increases in value whenever a substantial proportion of trees in a stand grow into high-value product classes. Accounting for the timevalue of money (i.e. interest rates and inflation) further complicates assessment of value growth. Analyses of financial returns from thinning (e.g. Utz and Sims, 1981) are dependent on assumptions about costs and revenues. Most recommendations for thinning oak stands are based on yield tables and are intended to produce an economically valuable mix of products including cordwood, sawlogs and, where possible, veneer logs (Roach and Gingrich, 1962, 1968; Gingrich, 1971a; Sander, 1977). Although those recommendations are often appropriate, they may not suit all management objectives. Consequently, a thorough prognosis for a specific stand may require a detailed stand inventory and a growth and yield analysis using one of the models described later in this chapter. Yield tables are best suited to characterizing the expected development of an ‘average’ even-aged stand in the absence of major disturbance. Growth and yield equations overcome some of the limitations of yield tables by their ability to account for factors such as variation in species mixtures, variation in tree size distributions, and variable rates or types of harvest. Growth and yield in uneven-aged stands Yield estimation for uneven-aged stands under single-­ tree or group selection silviculture presents problems separate from those for even-aged stands. Unevenaged stands are periodically thinned by methods that create and sustain three or more age classes. Stands are typically harvested at 10- to 20-year intervals called cutting cycles; there is no specified rotation age. Stand structure goals are usually defined by the use of q values or other ­criteria that define a target

Chapter 15

diameter distribution. If not already in place, the desired uneven-aged stand structure must be created using combinations of practices such as singletree and group selection methods (see Chapter 9, this volume). Yield information for uneven-aged oak stands is largely anecdotal. In an 18-year study of a mixedoak woodlot in central Indiana, annual growth averaged 306 board ft (Doyle log rule)/acre/year of which 176 board ft were cut (Bramble and Fix, 1980). Corresponding net cubic foot volume growth was 76 ft3/acre/year of which 41 ft3 were cut. In a 16-year study of oak–hickory stands on poor sites in southern Illinois managed by singletree selection, periodic annual net board foot volume growth averaged 133 board ft/acre (Schlesinger, 1976). Of that growth, about half (64 board ft) was harvested. Good sites in the same study produced 218 board ft/acre/year of which 149 board ft were harvested. Annual net growth in two West Virginia oak stands managed by the selection method for 20 years averaged 308 board ft/acre of which 190 board ft were harvested (International 1/4-inch log rule). The growth on individual tracts ranged from 228 to 400 board ft/acre/year. Net cubic foot volume growth (trees between 5 and 11 inches dbh) averaged 55 ft3/ acre/year of which 15 ft3 were harvested. Basal area growth averaged 2.2 ft2/year (Trimble, 1970). Appalachian hardwood stands in West Virginia that were managed for 30 years using single-tree selection grew at annual rates between 330 and 505 board ft (International 1/4-inch log rule). Annual basal area growth ranged from 2.1 to 2.7 ft2/acre. Northern red oak was a major component of these stands and red oak site index ranged from 60 to 80 ft. These reported periodic growth rates for managed uneven-aged stands are as high or higher than those for unmanaged even-aged stands when the evenaged yields are expressed as the mean annual increment over a full rotation. The mean annual increment of managed even-aged stands may be higher or lower than that of managed uneven-aged stands, depending on site quality, management intensity and rotation length. However, such comparisons are confounded by several factors. First, reports of growth and yield for uneven-aged stands cover relatively short intervals, usually 20 years or less for stands that were mature when uneven-aged management began. Secondly, the actual harvest from uneven-aged stands is typically half to three-fourths of periodic growth. Over the long term, cumulative

Growth and Yield

periodic harvest, rather than cumulative growth, is a better measure of yield for stands under unevenaged management. Accordingly, long periods of observation would be needed to adequately compare expected yields of even- and uneven-aged stands. Thirdly, uneven-aged stands are usually comprised of a large proportion of oaks in overtopped and intermediate crown classes. Many of those trees do not respond to periodic reductions in stand density. Finally, information related to changes over time in species composition and tree quality is limited for uneven-aged oak forests in many ecoregions (see Chapter 9, this volume).

Growth and Yield Models Modelling methods Any technique for forecasting forest growth is a growth and yield model. This includes yield tables and the educated guesses of experienced foresters. As computing technology has become more sophisticated and accessible, yield tables and educated guesses have been largely replaced by analytical models that can quickly provide estimates of periodic growth or cumulative yield from a given set of initial conditions. General modelling theory and objectives are discussed in conjunction with regeneration models in Chapter 3 of this volume. Most growth and yield models are classified as statistical models as defined in that chapter. Statistical growth and yield models are usually developed by fitting regression curves to numerous observations of tree or stand growth and yield. The resulting models provide the following advantages over other methods of yield estimation: (i) rapid implementation via computers; (ii) incorporation of multiple independent (predictor) variables; (iii) simultaneous consideration of the effects of various species mixtures and thinning regimes; (iv) ability to interface with forest inventory databases; and (v) flexibility to summarize and report growth and yield estimates. Most statistical growth and yield models fall into one of four broad classes: ●● ●● ●● ●●

stand-level models; stand table projection models; individual-tree-level models; and diameter distribution models.

Stand-level models directly predict growth and yield (per acre) for an entire stand. Some stand-level

549

models do this by separately estimating and then combining predictions for the various components of stand growth including survivor growth, ingrowth, mortality and harvest (Beers, 1962). Stand-level models have modest data and computational requirements compared with other types of models. However, flexibility in handling species mixtures and options for summarizing results are limited in this class of models. Stand table projection models use projection formulae to forecast forest change based on expected diameter growth rates and tree survival rates (e.g. Kershaw et  al., 2016). Those rates are applied to a current stand table (with numbers of trees per acre area by species and dbh classes) to estimate what proportion of trees will move into larger diameter classes and what proportion will die. Stand table projection has long been used to project short-term growth based on local field observations of diameter growth and survival. Individual-tree models predict the growth and survival of a representative sample of individual trees in a stand (e.g. from an inventory plot) and then combine those predicted changes to arrive at an estimate of stand change. The implementation of this class of models requires more data input from the user than other modelling approaches. It  also offers greater flexibility for predicting the growth of mixed-species stands, modelling detailed harvest prescriptions and summarizing results. Diameter distribution models define a stand’s diameter frequency distribution (i.e. numbers of trees by dbh classes) using a probability density function such as the Weibull (Bailey and Dell, 1973). These models predict changes in the shape of a diameter distribution over time. Those changes are then translated into changes in numbers of trees by diameter class. Characteristics such as basal area or volume can be estimated for the entire stand or by diameter class using the predicted shape of the diameter frequency distribution. This category of models has shown greatest promise for application to species that grow in relatively pure stands (e.g. southern pines). Their application to oak forests has been limited. With any model, the appropriate use and interpretation of model output requires knowledge of the class of models to which it belongs. Within each class of models there are similarities in model assumptions, data required as model input, the way data are processed, and the details of model output. Every model has limits to its capabilities and applicability; model predictions reflect the range of conditions included in the data used to calibrate the model.

550

Stand-level models for oaks One of the earliest statistical growth and yield models for oaks was the stand-level model, groak (Dale, 1972, 1973). It is based on a system of five equations that predicts annual basal area increment, mean stand diameter, gross cubic foot volume, merchantable cubic foot volume and gross board foot volume. Applying the model requires user-provided information on stand age, site index, stand basal area and number of trees per acre. The model is relatively easy to understand and its component equations are presented below (Dale, 1972): ∆B = 3.68521B ⋅ A−0.75 − B ⋅ A−0.8 ln(B) + 0.011383B ⋅ S ⋅ A−1.05 

[15.10]

CubicVol = exp [3.09094 + 0.00930176S

+1.03909 ln(B) − 20.11035A−1 

[15.11] Dq = 1.1341 + 0.0019876 × A × S

[15.12]



CFratio = −0.052676 + 0.7876045 ⋅ exp  −(1.2987 − 0.08117 Dq )10  [15.13] BFratio = −0.88414 + 3.63827 ⋅ exp  −(2.00 − 0.125Dq )4 



[15.14]

where: ΔB = n  et annual basal area growth (ft2/acre) for trees ≥ 2.6 inches dbh Dq = diameter of the tree of average basal area for trees ≥ 2.6 inches dbh CubicVol = total volume (ft3/acre) for trees ≥ 2.6 inches dbh including bark, stump and tip, but not branchwood A = stand age (years) B = basal area of live trees ≥ 2.6 inches dbh S = site index (feet at 50 years) CFratio = ratio of merchantable cubic foot volume (ft3/acre) to total cubic foot volume. Merchantable volume is for trees ≥ 4.6 inches dbh to a top dob of 4.5 inches; excludes stump, bark and branches. CFratio = 0 when Dq < 2.3, and CFratio = 0.735 when Dq > 16 BFratio = ratio of merchantable board foot volume (ft/acre) to total cubic foot volume. Board foot volumes are in International 1/4-inch rule for trees ≥ 8.6

Chapter 15

3

2

20

1

40 Initial

95

60

basa

l area

da ge (

70 an

0 20

ye ar s)

45

80 cre)

(ft 2/a

100

120

St

Annual basal area

2 growth (ft /acre)

4

Fig. 15.19.  Annual basal area growth (ft2/acre) of upland oak stands in the Central Hardwood Region by age and initial basal area for oak site index 70 (based on Equation 15.10). (From Dale, 1972.)

inches dbh to an 8.5 inches top dob. BFratio = 0 when Dq < 4.8, and BFratio = 3.55 when Dq > 16. Equation 15.10, which predicts basal area growth, is of key importance in this model (Fig. 15.19). Other equations define other stand characteristics in terms of basal area. By adding the predicted basal area growth to the initial basal area and incrementing stand age by 1 year, it is possible to carry the system of equations forward through time, predicting change in basal area and other stand characteristics year by year. Reducing stand basal area simulates the effect of thinning. Initially released in a computer program with the acronym groak, the above model has application to estimating yields of even-aged upland oak stands in the eastern USA. An extensive set of tables derived from the model is available in print (Dale, 1972) and can be used to estimate growth and yield under a wide range of thinning regimes. However, the model also can be implemented using spreadsheet software.

Equations 15.10 through to 15.14 are based on growth measurements on permanent plots in Ohio, Kentucky, Missouri and Iowa. However, Dale (1972) cautions that the predicted maximum cubic foot volume growth (trees ≥ 2.5 inches dbh) occurs at stand densities between 30 and 60% stocking, which are below densities at which maximum growth reportedly can occur (Dale, 1968; Gingrich 1971a). A test of the groak model with data from thinned plots in the Boston Mountains of north-western Arkansas produced acceptable results, although accuracy was improved by using equation coefficients derived from local growth data (Graney and Murphy, 1991). Equations for the model modified for stands in the Boston Mountains (Graney and Murphy, 1994) are listed below: (see equation 15.15 at bottom of the page). 37.920 ö æ T = B0.94 × exp ç 3.1427 + 0.015209S ÷ A ø è [15.16] 1

−0.71754(2.4884) −0.71754   A   B2 = 0.02590 − 0.02590 − B1−0.71754  2   A1    

{

Growth and Yield

}

[15.15]

551

M = 0.92588T [1 − exp{0.028105

1

(−24.422)Dq } 0.028105 Dq = 2.0672 + 0.0024004 ⋅ A ⋅ S − 0.027340 ⋅ B

[15.17]



[15.18]



V = 0.91634 ⋅ M [1 − exp{ 0.12375

1

× (−0.026524) ⋅S ⋅ Dq } 0.12375 [15.19]  FD = 3.4528 × V + 0.00044738 × V 2



FS = 5.1686 × V + 0.00026988 × V 2



FI = 6.0194 × V + 0.00032819 × V 2



[15.20] [15.21] [15.22]

where: A1 = current stand age (associated with B1) A2 = future stand age (associated with B2) A = any specified age B1 = b  asal area (ft2/acre) at age A1, trees ≥ 2.6 inches dbh B2 =  projected basal area (ft2/acre) at age A2, trees ≥ 2.6 inches dbh

B = stand basal area (ft2/acre), trees ≥ 2.6 inches dbh S = site index (feet at age 50) Dq = quadratic mean stand dbh (inches) T = total cubic foot volume (ft3/acre inside bark from 0.2 ft stump height to top of tree) M= merchantable cubic foot volume (ft3/acre inside bark from 0.5 ft stump to 5 inch top dob for trees ≥ 6 inches dbh) V = sawtimber cubic foot volume (ft3/acre inside bark from 1 ft stump to 10 inch top dob for trees ≥ 12 inches dbh) FD = board foot volume (/acre, Doyle rule) FS = board foot volume (/acre, Scribner rule) FI =  board foot volume (/acre, International 1/4-inch log rule). This system of equations predicts basal area and volume growth of even-aged, upland forests in the Boston Mountains of Arkansas that are dominated by black, northern red and white oaks. Based on Equation 15.15, greatest annual basal area growth occurs in young stands with initial basal areas from 50 and 80 ft2/acre (Fig. 15.20). However, for stands on average sites that are older than 55 years, board foot volume growth is great­ est when basal area is 80 ft2/acre or greater. These equations can be implemented using spreadsheet software.

1.5

1.0

20

0.5

70

l area

60 (ft 2/a cre)

ls

basa

tan

95

40 80

120

tia

Initial

Ini

20

da

0.0

(ye ars )

45

ge

2 growth (ft /acre) Annual basal area

2.0

Fig. 15.20.  Annual basal area growth of upland oak stands in Arkansas in relation to initial stand age and initial basal area when site index is 70 (based on Equation 15.15). (Adapted from Graney and Murphy, 1994.)

552

Chapter 15

A system of stand-level yield equations developed for oak–hickory forests in Minnesota can be used to predict yields by stand age, site index and stand density (Walters and Ek, 1993). It was developed from more than 600 permanent sample plots from a statewide forest inventory. These equations are part of a larger set of yield models for the 14 major forest types in Minnesota, and they can be conveniently used to make combined regional estimates for several forest types. Because sample plots were not screened for uniformity of stocking or stand history, the equations estimate the average, statewide forest conditions and estimated yields are not equivalent to values expected for stands at 100% stocking. The Walters and Ek models nevertheless can be used to produce yield tables by site and age classes that are similar in format to the normal yield tables of Schnur (1937) and Gevorkiantz and Scholz (1948), and they also can be used to predict yields at variable stand densities. For oak stands at 100% stocking that are ≤ 30 years old and growing on site index 70, estimates of basal area and total cubic foot volume yields (trees ≥ 5 inches dbh) from the Walters and Ek equations are higher than those reported by Schnur’s (1937) normal yield tables. In older stands, estimated basal area yields are within 80–90% of the values reported by Schnur although estimated total cubic foot volumes are only within 65% of Schnur’s reported yields. Up to stand age 50, board foot volumes are at least 33% greater than those predicted by Schnur, but at older ages, board foot yields are 40% lower than Schnur’s tables. A few stand-level models have been developed for mixed-species forests where oaks are a major component, but do not necessarily comprise most of the stocking. Such models include a red oak– sweetgum model applicable to stands in minor stream bottoms in central Mississippi (Sullivan et al., 1983), and a model for Appalachian mixed hardwoods in North Carolina and Georgia (Bowling et al., 1989). The latter model, in addition to estimating basal area and number of trees per acre, uses the Weibull distribution to generate information on tree diameter distributions. These models are well suited for growth and yield estimation within the region and the range of forest conditions for which they were calibrated. Stand table projection models Stand table projection is a mensurational technique for projecting growth by manipulating a stand

Growth and Yield

table containing numbers of trees by species and dbh classes. Stand tables are usually constructed from a forest inventory. Diameter growth rates and survival rates for the same forest can be derived from repeated measurements of permanent forest inventory plots and/or from increment cores. The estimated diameter growth and survival rates are applied to the stand table to forecast the proportion of trees that will move to a larger diameter class, the proportion of trees that will die, and number of ingrowth trees over time (Kershaw et al., 2016). For short projection periods (e.g. 10 or 20 years), stand table projections based on sitespecific dbh growth and survival data are often accurate if management practices are not expected to greatly change. The growth rates are typically estimated as a simple proportion (movement ratio) of trees in one diameter class moving to a larger diameter class. However, in more complex variations of the stand table projection method, movement ratios also can be expressed as mathematical functions of stand and site variables. A matrix growth and yield model such as one for upland oaks (Lootens et  al., 1999) represents a sophisticated extension of the stand table projection method. Stand table projection algorithms can be implemented using spreadsheet software. Individual-tree-level models for oaks Individual-tree growth and survival models can provide detailed information not available from standlevel models. However, they do so at the cost of greater complexity in application and greater input data requirements. This class of models requires a user-provided list of trees comprising a representative sample of trees from each stand for which future yield estimates are desired. The information required for each sample tree includes its species, dbh and the number of trees per acre it represents. The tree list is usually compiled from a stand inventory. Any of the common sampling procedures can be used to obtain the representative sample of trees. All values are expanded to a per acre basis. Individual-tree models operate directly on the tree list, using estimated periodic tree diameter growth, height growth and survival to update the tree list through time. At any point during the simulation period, the current state of the tree list can be viewed or summarized by species, size classes and product classes. These models thus can account for species’ differences in growth and survival. Because

553

the number of species occurring in oak forests may be large, species are usually grouped according to their silvical characteristics. To simplify model development, the same algebraic equation form is used to predict the growth of each species group. However, each group has a unique set of coefficients that result in different predicted growth rates. Survival models are developed similarly. Software for model implementation may be provided by the model developer or may be available elsewhere. Despite the differences among software programs available for implementing individualtree models, they all follow a similar computational scheme (Fig. 15.21).

Set options Specify input/output

Read tree list

Summarize and display tree and stand conditions

Simulate harvest and other disturbances as required

Compute cost/revenue for harvest Analyse financial parameters

Of the widely available and used individual-tree models, oaksim (Hilt, 1985a), the forest vegetation simulator (FVS) (USDA Forest Service, 2018) and twigs (Miner et  al., 1988; Hilt and Teck, 1989; Teck, 1990) are applicable to oak forests of the eastern USA and are discussed below. There are other individual-tree models that can be applied to oak forests, but their applications are more limited geographically or to specific objectives. Included in this category are models for: ●● northern red oak and other species in Vermont (Hughes and Sendak, 1985); ●● the economic analysis of different harvesting regimes for red oak in New England (Hibbs and Bentley, 1984); ●● mixed oak and other Appalachian hardwoods immediately after thinning (Harrison et  al., 1986); ●● canyon live oak and Oregon white oak in southwestern Oregon (Hann and Larsen, 1991); ●● blue oak in California (Standiford, 1997); ●● oak stands of sprout origin in Missouri (Lowell and Mitchell, 1987; Lowell et al., 1987); ●● English oak, sessile oak, European turkey oak and associated species in Austria (Monserud and Sterba, 1996); ●● sessile oak in mixed stands in Germany (Pretzsch et al., 2002); ●● pine–oak forests in Korea (Lee et al., 2004); ●● cork oak in Spain (Sánchez-Gonzáles et  al., 2006) – this model predicts diameter under the cork, and is used to estimate the time required for a tree to become large enough for cork to be extracted; ●● rebollo oak grown in coppices in Spain (Adame et al., 2008); and ●● Oregon white oak (Gould et al., 2008). oaksim

Grow the trees Increment diameters Compute tree mortality

Final summary and exit

Fig. 15.21.  Simplified flowchart for a typical individualtree-based growth and yield simulation model.

554

oaksim is an individual-tree-based model designed to estimate the growth and yield of thinned and unthinned mixed-oak stands in southern Ohio and south-eastern Kentucky (Hilt, 1983, 1985a, b). The model is applicable to the following conditions (Hilt, 1985b): ●● even-aged stands on upland sites; ●● trees ≥ 2.6 inches dbh; ●● oaks comprising at least 75% of the stand basal area; ●● stand ages 30–120 years;

Chapter 15

[15.25] 

[15.26]

where: ΔB = a verage 5-year basal area growth per tree (ft2) ΔD =  periodic 5-year tree diameter growth (inches, computed from ΔB and D) D = initial tree dbh (inches) S = site index (feet at 50 years) Dq = quadratic mean stand diameter (trees ≥ 2.6 inches dbh) P = stocking per cent (based on Gingrich, 1967) PS = 5-year probability of survival H = tree height (ft) bi, ci =  parameter estimates for species groups (see Hilt, 1985a). The software for model application incorporates an algorithm that adds random variation to the predicted basal area growth. It also includes constraints that ensure the combined growth and survival of all individual trees is approximately equal to the per acre growth predicted by Dale’s (1972) equations (Equations 15.10–15.14) for a comparable stand. oaksim computes yields in cubic and board feet (International 1/4-inch log rule) to user-specified top diameters. Additional details on model implementation, volume computations and thinning algorithms are given in Hilt (1985a). The general shape of the growth and survival models (Fig. 15.22) is consistent with the empirical observations presented earlier. oaksim is implemented as a component of the Northeast Decision Model (ned) (Simpson et  al., 1995), which provides the easiest way to apply this system of models.

Growth and Yield

(% )

g

kin

oc

8 in.)

2

St

14 dbh (

(B) 1.0 0.8 0.6 0.4 0.2

26 20 14

.)

PS = 1 − 1 + exp (b0 + b1 D + b2 ∆D)  −1  b3 + b4 S  H = 4.5 + c0 éë1 - exp ( c1D) ùû

20 Initial

[15.24]

20 40 60 80 100 120

(in



0.05

0.09 0.07 8 0.05 0.03 2 Annua 0.01 l dbh growth (in.)

itia

−D

0.10

In

)

0.15

26

Probability of survival

(

∆D = D + ∆B / 0.005454 2

0.5

0.20

bh

6.96762087 ⋅ 10−6 ⋅ S1.5731724 ⋅   2  ∆B = D ⋅   −0.11839854  exp     ⋅ Dq − 0.01198244 ⋅ P  [15.23] 

0.25

ld

The following equations define the component models that describe tree growth and survival:

(A) Annual dbh growth (in.)

●● a maximum projection period of 50 years; ●● black oak site index 50–85 ft (age 50); and ●● stocking from 20 to 120% based on Gingrich’s (1967) chart.

Fig. 15.22.  Response surfaces for growth and survival of white oak based on the oaksim model. (A) Annual diameter growth for trees growing on site index 70 in relation to initial tree diameter (dbh) and stand density. (Based on Gingrich’s (1967) stocking equation.) The plotted values assume that initial dbh is equal to quadratic mean stand diameter. (B) Annual survival probabilities for white oak in relation to initial dbh and annual diameter growth rate. (From Hilt, 1985a.)

forest vegetation simulator

The forest vegetation simulator (FVS) is an individual-tree-based modelling system that includes growth, survival and regeneration models applicable to the major forest regions of North America (Dixon, 2015; USDA Forest Service, 2018). FVS is an individual-tree based modelling system designed to estimate growth and yield for established stands. The FVS model has 22 regional variants, each with equations that predict annual diameter growth, survival, height growth and crown size for the common tree species in that region. Fifteen of the FVS variants provide growth and yield projections for oaks

555

(Table 15.5). Within a given variant, the same regression model form is used to estimate diameter growth for all tree species. Growth differences among species are accounted for by species-specific regression coefficients. The same approach is used for tree survival equations, height equations, crown equations and volume equations. FVS evolved out of the Prognosis modelling software (Stage, 1973) and was gradually expanded to encompass other individual-tree-based models such as regional variants of twigs (Miner et al., 1988; Hilt and Teck, 1989; Teck, 1990). Over time the modelling procedures and equation forms have become more standardized among the regional variants, but within each region the models of tree growth and survival are calibrated separately for each of the 11–90 species groups tracked in that variant. The approach accommodates a wide range of species mixtures including stands where oaks predominate to those where oaks are a minor component or where oaks are absent. Table 15.5.  Regional variants of the Forest Service, 2018.)

The FVS software facilitates applying the models to predict growth and survival of individual trees on inventory plots. Stand change is computed from the composite change for the sample of individual trees. At its core, the basic modelling process resembles Fig. 15.21. However, the FVS software provides options that incorporate additional capabilities. These include: ●● making projections with forest inventory information stored in Forest Service databases (e.g. Forest Inventory and Analysis data); ●● testing and recalibrating the models with local growth observations; ●● calibrating and modelling insect and disease impacts; ●● modelling fire and its effects; ●● visualizing stand conditions in a virtual threedimensional forest image (Plate 17); ●● marking individual trees for harvest interactively in the virtual mode;

forest vegetation simulator

(FVS) that include models for oaks. (From USDA

Regional varianta

Region of applicability

Lake States Central States Northeast

Michigan, Minnesota, Wisconsin Illinois, Indiana, Iowa, Missouri Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, West Virginia Alabama, Arkansas, Georgia, Florida, southern Illinois, Kentucky, Louisiana, Mississippi, southern Missouri, North Carolina, eastern Oklahoma, South Carolina, Tennessee, eastern Texas, Virginia Arizona, south-eastern California, Colorado, western Kansas, eastern Montana, New Mexico, Nebraska, Oklahoma, South Dakota, Texas Central Oregon, Washington Klamath Mountains in western California and Oregon Nevada, Utah, south-west Wyoming Coastal Oregon, Washington Western Cascade Mountains in Oregon and Washington Western Sierra Nevada Mountains in southern California and western Nevada South-west Oregon Western Oregon, Washington Central California, south-west Oregon

Southern

Central Rockies

East Cascades Klamath Mountains Utah Pacific North West Coast Westside Cascades Westside Sierra Nevada Southwest Pacific Northwest Inland California, Southern Cascades South Central Oregon, North-east California

organon organon

South-central Oregon, north-east California

Number of oak species groups included 3 9 5

17

1

1 1 1 1 1 1 3 1 8 1

a

Current information, documentation and support for FVS and all variants is maintained at: https://www.fs.fed.us/fmsc/fvs/index.shtml (accessed 1 July 2018).

556

Chapter 15

●● modelling complex forest harvesting scenarios for large forest inventories; ●● comparing the consequences of alternative management treatments; ●● modelling a wide range of harvesting practices and silvicultural systems; ●● implementing spatial constraints (e.g. no harvest on adjacent stands within a decade); ●● modelling climate change impacts on forest change for some variants; ●● performing economic analyses for modelled scenarios; and ●● reporting carbon stocks and carbon change for modelled scenarios. Although the complexity of the component equations and differences among the model’s regional variants prohibits detailed presentation here, the equations can be applied to graphically illustrate representative growth and survival relations produced by the model (Fig. 15.23). Detailed descriptions of the entire FVS system and each of the regional variants is available online and regularly updated (USDA Forest Service, 2018). FVS variants for the Lake States, Midwest and Northeast USA were initially derived from tree growth and survival models developed and implemented using the twigs modelling system. Most of those models have been revised and recalibrated in FVS based on more data. Nevertheless the twigs models and documentation (Belcher et  al., 1982; Brand et al., 1987; Shifley, 1987; Miner et al., 1988; Hilt and Teck, 1989; Teck, 1990) provide additional background on derivation and testing of the core growth, survival and volume models applied to oaks in the eastern USA. Additional information on the accuracy and precision of growth estimates produced by twigs and related FVS variants can be found in Brand and Holdaway (1983), Crow (1986), Holdaway and Brand (1986), Kowalski and Gertner (1989) and Schuler et  al. (1993). Schuler and others (1993) compared predictions from twigs, oaksim and other models for north-eastern USA. The Northeast variant of twigs is implemented and maintained in the NED (Simpson et al., 1995; USDA Forest Service, 2016). The twigs software for other regions is still available and useful for some purposes. However, the FVS modelling system is vastly more capable and provides a flexible and powerful tool for predicting and comparing growth, yield and harvesting options for multiple stands managed under different scenarios.

Growth and Yield

Forest landscape models Stand-scale silvicultural decisions increasingly rely on landscape-scale information, particularly when managing for non-timber objectives. For example, stand-scale harvest prescriptions consider locations of wildlife corridors across the landscape. Standscale prescribed fire treatments are often ignited within the context of a landscape-scale burn plan. Stand-scale silvicultural prescriptions intended to adapt a stand to a changing climate must anticipate rates and patterns of climate-driven tree species migration across the landscape. Although forest ‘stands … are the objects of silviculture’ (see Chapter 1, this volume), landscapes reveal the cumulative effects of silvicultural actions or inaction. Thus, joint consideration of stand-scale and landscape-scale factors is essential, and that occurs at the nexus of the disciplines of silviculture and forest management (Introduction, Fig. A, this volume). Forest landscape models (FLMs) are tools that help set the landscape context for silvicultural decisions. FLMs are a class of models that simulate forest change over time on all the forested sites on a given landscape (Mladenoff, 2004, 2005; He, 2008). In contemporary applications those landscapes can range from thousands to millions of acres in extent. FLMs excel at quantifying the long-term, cumulative effects of forest growth, harvest, fire, insect and disease scenarios. FLMs are less capable than stand-scale models like the FVS (described earlier in this chapter) at simulating detailed changes in the species, number and sizes of trees associated with silvicultural prescriptions for a given stand. Nevertheless, FLMs can model cumulative, landscape-scale changes over time in tree species composition and age structure for alternative scenarios that incorporate forest growth and species succession, harvesting practices based on even-aged or uneven-aged silviculture regimes, prescribed fire, wildfire, insects and diseases. The landis model (Mladenoff, 2004; Scheller et al., 2007; He et al., 2012; Wang et al., 2014a, b; He, 2017) is an example of an FLM that has been widely applied to landscapes dominated by oak forests (Larson et  al., 2004; Shifley et  al., 2006; Rittenhouse et  al., 2011; Brandt et  al., 2014). In essence, the model starts with a digital map of current conditions on all forest sites (or pixels) on a landscape, and periodically updates the species, number and age class of trees on each site over

557

(A)

.) wth (in dbh gro l a u n n A

0.16

0.12

0.08

100

26

sa tre l a es rea (ft 2 of /a lar cr ge e) r

80

0.04

60

18

40

10

(in.

Ba

Dbh

)

2

20

1.00 0.99 100 80

0.97 0.96

sa tre l are es a o (ft 2 f la /ac rge re) r

0.98

60 26

40

18 Dbh

(in.)

10 2

Ba

Annual

survival

p

y robabilit

(B)

20

Fig. 15.23.  Estimated annual diameter growth and survival of white oak based on the Central States twigs model. Although shapes of response surfaces vary for other species, all are based on the same mathematical form. (A) Estimated annual dbh growth of a white oak in relation to its dbh and the basal area of surrounding trees with a dbh of the same size or larger. (B) Estimated annual probability of survival of a white oak in relation to its dbh and the basal area of surrounding trees with a dbh of the same size or larger. (Adapted from Shifley, 1987; Miner et al., 1988.)

decades or centuries in response to estimated patterns of: (i) tree growth; (ii) survival; (iii) regeneration; and (iv) disturbance by harvest, fire, biotic agents or climate change. landis applications have been integral to regional assessments of climate

558

vulnerability for oaks and other forest types (e.g. Brandt et al., 2014; Butler et al., 2015; Wang et al., 2016; also see Chapter 14, this volume) and for modelling effects of alternative landscape-scale fire regimes (Yang et al., 2008; Sturtevant et al., 2009).

Chapter 15

Growth and Yield

Estimating ingrowth Ingrowth is the number or volume of trees that periodically grow into the smallest measured tree size class. It is not usually accounted for in oak growth and yield models. For well-stocked, even-aged stands that are pole-size or larger, ingrowth can often be disregarded. In those stands, ingrowth will contribute little to stocking or merchantable volume through to the end of a rotation. But for young stands, understocked stands or stands under unevenaged management, ingrowth can be important. Ingrowth is defined in relation to a specified threshold diameter. Trees below the threshold are not included in stand inventories. Common ingrowth thresholds range from 1 to 9 inches dbh. Across that range, ingrowth can vary greatly. Typically, the average annual number of ingrowth trees declines rapidly as the threshold diameter increases (Shifley, 1990; Shifley et  al., 1993) (Fig. 15.24). However, there is great variation around these averages. Coefficients of variation for the number of ingrowth trees per acre typically range from 100 to 300%. There are few models for estimating ingrowth in oak stands. In thinned oak stands in the Boston Mountains of Arkansas, the average annual number of ingrowth trees per acre for a 2.6 inch dbh threshold was 1.4% of the total number of live trees. These stands covered a wide range of initial ages 40 Ingrowth (trees/acre/year)

Other FLM applications have explored the interaction of forest vegetation and wildlife. Most such wildlife applications have quantified expected cumulative impacts of silvicultural practices on landscape-scale wildlife habitat suitability (Larson et  al., 2004; Shifley et  al., 2006; Dijak and Rittenhouse, 2009; Rittenhouse et  al., 2011; Pauli et  al., 2015; LeBrun et  al., 2016) (Plate 18). However, recent analyses also have added feedback loops that allow FLMs to estimate wildlife effects on tree reproduction in the form of white-tailed deer herbivory constraining tree species regeneration success (De Jager et al., 2016). At this time, traditional growth and yield modelling systems such as the FVS are the best way to model detailed, stand-scale responses to silvicultural actions or inaction over several decades. FLMs such as landis are currently the best way to model landscape-scale shifts in tree species composition, forest age structure and wildlife habitat suitability for alternative scenarios of harvesting, wildfire and climate change. FLM applications typically cover a century or longer and are best used to consider cumulative change for a representative landscape, not detailed change for individual stands on the landscape. With anticipated improvements in forest inventory methods, remote sensing and computing, the capabilities of traditional growth and yield models and of FLMs will expand and begin to overlap (Shifley et al., 2017). In the future it is likely that a growth and yield model like the FVS will be incorporated to model the stand-scale dynamics within an FLM that models and maps the outcomes of alternative landscape disturbance scenarios. Moreover, the tools available to visualize landscape change scenarios will improve (e.g. Gustafson et al., 2016). Silviculturists should stay abreast of the capabilities of applicable FLMs, and they should understand how modelled scenarios of landscape change can help guide stand-scale silvicultural decisions. Given the complexity of FLMs, it is unlikely that a silviculturist would personally apply an FLM to examine alternative landscape-scale scenarios as one might do with a model like FVS. However, silviculturists can benefit from working with a forest management team that utilizes FLMs for scenario analyses that estimate future spatial and temporal patterns of landscape change, cumulative effects of silvicultural treatments, anticipated changes in wildlife habitat suitability, and associated economic activity.

30

20

10

0

1

3 5 7 Ingrowth threshold dbh (in.)

9

Oak–hickory Oak–pine Oak–gum–cypress Fig. 15.24.  Average number of trees per acre per year that grow into a given or threshold dbh class (ingrowth) for three oak forest types; based on data from Indiana, Illinois and Missouri. (From Shifley, 1990; Shifley et al., 1993.)

559

For oak–hickory forests (Fig. 15.25):

(Graney and Murphy, 1991). In contrast, the annual rate of ingrowth in thinned stands ranging from 22 to 90 years old in Ohio, Kentucky, Missouri and Iowa was only 0.6% of live trees ≥ 2.6 inches dbh (Dale, 1973). The following ingrowth model based on the latter study accounted for nearly half of the observed variation in ingrowth (Dale, 1973):

IT = 0.1032MaxT − 0.000004211T (MaxT )2 − 0.0000022T 2 (MaxT )2

[15.28]



For oak–pine forests:

I2.6 = 0.09264 + 0.00000113A − 0.015674 ln(B) − 0.07618Dq A−0.8 2

IT = 0.09132MaxT − 0.0000003867T (MaxT )2 − 0.000001296T 2 (MaxT )2

+ 0.001019NA−0.8 − 0.00000083SN [15.27] where: I2.6 = r atio of annual number of ingrowth trees per acre for a 2.6 inches dbh threshold to total number of trees per acre ≥ 2.6 inches dbh A = stand age in years B = basal area of trees ≥ 2.6 inches dbh (ft2/acre) Dq = quadratic mean stand diameter (inches, for trees ≥ 2.6 inches dbh) S = site index (feet at 50 years) N = number of trees ≥ 2.6 inches dbh (number per acre). For applications requiring a threshold dbh other than 2.6 inches, the following model can be used to estimate ingrowth for any threshold diameter between 1 and 13 inches dbh (Shifley et al., 1993). The model is based on data from three oak forest types in Indiana, Illinois and Missouri; the model equations are as follows:

[15.29]



For oak–gum–cypress forests: IT = 0.04949MaxT + 0.00001457T (MaxT )2 − 0.0000007506T 2 (MaxT )2

[15.30]



where: IT = number of ingrowth trees per acre per decade at threshold T T = ingrowth threshold size between 1 and 13 inches dbh MaxT = an expression for the maximum possible number of ingrowth trees at threshold T, computed as:

(287 − 18.5T − 0.0703T ) − CCF 2

MaxT =

0.00189303 (3.12 + 1.829T )

2

300

200 100

)

1 5

560

20

13

Th

80 n fac tor

petitio

re s

9

140 n com

Crow

ho

200

ld

db h

0

(in .

Ingrowth (trees/a

cre/decade)

400

Fig. 15.25.  Number of trees per acre per decade that grow into a given or threshold dbh class (ingrowth) in oak–hickory forests. (From Shifley et al., 1993, with permission from the Society of American Foresters, Bethesda, Maryland. Not for further reproduction.) Crown competition factor is a measure of stand density based on the maximum growing space a tree of a given diameter can utilize (see Chapter 6, this volume).

Chapter 15

CCF = crown competition factor expressed as a percentage of an acre (see Krajicek et al., 1961 and Chapter 6, this volume). Equations 15.28–15.30 should be selected to match forest type. The ingrowth threshold diameter is specified as T in the right-hand side of the equation. CCF can be calculated using species-­ specific formulae as described by Krajicek and others (1961) or Shifley (1990). However, for oak–­hickory forests, CCF can be approximated by: CCF = 0.1755N + 0.02058ΣD + 0.006032ΣD2 [15.31] where: CCF = crown competition factor expressed as a percentage of an acre N = the number of trees per acre larger than the threshold ΣD = the sum of tree diameters per acre larger than the threshold dbh ΣD2 = sum of tree diameters squared per acre for trees larger than the threshold dbh. (This value can be computed as the sum of basal areas of trees larger than the threshold dbh divided by 0.005454.) Model evaluation Using a computer model or a yield table to obtain a growth or yield estimate does not ensure that the estimate will be accurate. Variation in weather, genetics, tree spacing, and the many unmeasured aboveand below-ground structural characteristics of trees and stands can all affect the accuracy of predictions. All model predictions are erroneous to some degree. A model’s validity is therefore relative and its utility should be judged in relation to an educated guess or another model that would be used in its place (Forrester, 1968). The central questions in evaluating a growth and yield model are whether or not it: (i) is suitable for one’s intended application; (ii) provides a better estimate than alternative methods; and (iii) is practical to apply given limitations on time, data and money (Rykiel, 1996). Although model suitability depends on many factors, they can be grouped into three categories: (i) application environment; (ii) model performance; and (iii) model design (Buchman and Shifley, 1983). Shortcomings in any one category may justify eliminating a model from consideration for a given use. A model’s application environment includes quality of user support, computer requirements, data requirements, cost and outputs. Consideration

Growth and Yield

of these practical matters is usually the first step in model evaluation. Major incompatibilities with a potential user’s needs will usually eliminate a model from further consideration. Model performance evaluation includes quantitative analysis of accuracy and precision associated with the predictions made by the model or its various components. Quantitative evaluations of model performance are not always easy, but there are some simple procedures that can be used to assure that performance is acceptable before relying on a model. These include: (i) comparing model results to other published sources of growth and yield information, to other models, or to educated guesses by local experts; (ii) applying the model to a wide range of conditions and noting any unreasonable predictions that result from initial conditions within the models range of applicability; and (iii) checking for realistic model responses to simulated silvicultural practices such as thinning. The best evaluations of model performance are those that compare model predictions with observed forest change within the locality where the model is to be applied. This is usually not too difficult for traditional growth and yield models (e.g. Reynolds et al., 1981) and model descriptions often include estimates of prediction accuracy. Quantitative evaluations of model performance for FLMs are far more complicated (Oreskes et al., 1994; Wang et al., 2014b). Model design considerations include the ability of the model to simulate silvicultural practices, the ease with which it can be adjusted to meet local conditions, and its ability to accommodate a variety of timber and non-timber objectives. Whereas the design characteristics of yield tables are fixed, computer models are more flexible. Some allow customized output tables that help users compare management alternatives. However, such flexibility often carries with it the cost of added complexity. Among the existing growth and yield models for oaks, it is difficult to generalize about the superiority of one versus another. Direct comparisons of models are few, and rigorous quantitative evaluations of models using independent data are even fewer. Before applying any model it would therefore be prudent to: (i) compare conditions for the stands of interest to the conditions reportedly used to develop the model; (ii) study the information in the user’s guide about how the model was tested at the time of its development and what known limitations apply to the model; and (iii) conduct local evaluations of model performance.

561

When a model prediction fails to reproduce local observations, some remedial measures are possible. Simple ratio adjustments to predicted growth or survival rates can be used to adjust individual-treebased models for individual sites (Stage, 1982; Smith, 1983). The software for the FVS includes self-calibration options that facilitate adjusting model estimates to match growth trends observed locally (USDA Forest Service, 2018). For forest managers, model evaluation should be an ongoing, iterative process. Formal and informal evaluations then can be used to document the model’s strengths and weaknesses to determine if and when adjustments are needed (Starfield and Bleloch, 1991; Rykiel, 1996).

Volume Equations The literature is replete with volume tables and equations applicable to the oaks. The selection of a volume estimation table or equation is often determined by local or regional custom, or by the similarity between stands of interest and those used to develop a given equation or table. Most volume estimation tables and equations for oaks have not been quantitatively compared with one another. Volume estimation for oak trees and stands is complicated by volume differences among species and inconsistent merchantability standards. Available volume tables and equations often differ in minimum top diameters and units (e.g. board feet, cubic feet, green weight, dry weight). Some tables pertain to individual species, while others (e.g. Gevorkiantz and Olsen, 1955) apply to groups of species. The simplest and most direct volume estimates are obtained by observing merchantable tree height and diameter for individual oak trees and looking up the corresponding volume in a species-specific or a composite volume table. For large inventories it is often more convenient to use equations that analytically reproduce the entries in the volume tables (e.g. Beers, 1964). Composite volume tables or equations that are averaged across many species are convenient to use during large inventories. Although information on merchantable tree heights is required in the application of most volume tables and equations, measuring heights of individual trees is usually time-consuming. When merchantable tree height is not known, local volume tables based on diameter and site index can be used. However, there is likely to be some loss of accuracy with such tables compared with volume

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estimates based on measured tree heights. Examples of local volume equations include those of Hahn and Hansen (1991), Raile et al. (1982) and Smith and Weist (1982). The most versatile volume estimation equations are those based on taper equations that allow the user to set and/or alter the merchantable top diameter. Taper-based volume estimation equations for oaks include those of Hilt (1980), Martin (1981) and Williams and Wiant (1994). Application of these equations requires an estimate (measured or modelled) of total tree height and a computer. The growth and yield models described earlier in this chapter provide volume estimates as part of their standard output. Versatile growth and yield software such as the FVS allows users to modify volume equations for local conventions or special conditions (Dixon, 2015; USDA Forest Service, 2018). Although biomass equations for oaks have been available for decades, there is renewed interest in them due to the recent emphasis on utilization of woody biomass for energy and on carbon sequestration by forests (Jenkins et al., 2003; Chojnacky et al., 2014). The quantity of carbon sequestered by trees is closely linked to tree biomass, and stored carbon is typically computed as a percentage of biomass (about 50% of dry weight) (see Chapter 13, this volume). Biomass and sequestered carbon are now routinely reported in USDA Forest Service inventories and can be summarized by state, tree species, size classes and other variables (USDA Forest Service, 2015). Additional information on estimation of biomass and carbon in trees and stands is found in Chapter 13, this volume (see section on ‘Managing Stands for Biomass Production and Carbon Sequestration’).

Regional Patterns in Yield and Productivity Oak growth and yield can be observed at scales ranging from individual trees to stands to regions. Similar to the regional patterns in the distribution of oak species and oak forests (see Chapter 1, this volume, and Plate 1) there are regional patterns in the distribution of the volume of oak timber. In the USA, the total standing volume of oak growing stock on timberland in 2017 was 138 billion ft3. That is double the oak volume reported in 1963, but the annual rate of oak volume increase has gradually declined since 1963 and was essentially zero for the most recent reporting period (Fig. 15.26)

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Oak volume (billion ft3)

160 120 80 40 0 1960

1980

2000

2020

Year Fig. 15.26.  Total oak growing stock volume on USA timberland, 1963–2017. (From Oswalt et al., 2014; Miles, 2017.)

(Oswalt et al., 2014; Miles, 2017). Contributing to this pattern are: (i) conversion of oak timberland to urban or agricultural uses; (ii) increasing mean age of oak forests with a normal slowing of the annual rate of growth; and (iii) successional replacement of oaks by maples and other tree species. The volume of all USA oaks at least 5 inches dbh on forest land, regardless of merchantability, is 181 billion ft3. Although the south central and eastern states contain the highest oak volumes, the relatively droughty western oak forests contain nearly 14 billion ft3, mostly in California. Total oak volumes by state are influenced by a state’s size and the acreage of forest within it (Fig. 15.27). However, summaries of the average oak volume per acre of timberland provide a different perspective. For example, Rhode Island – the smallest state – has

(A)

9

(B)

< 200 201–400 401–600 601–800 > 800

Fig. 15.27.  Oak cubic foot volume on forest land by state. (A) Total volume per state (billion ft3); (B) average oak cubic volume (ft3) per acre of timberland. (From Miles, 2017.)

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only 370,000 acres of forest land, but it has an average of 1080 ft3 of oak volume/acre of forest land, more than any other state. Connecticut and Missouri, respectively, follow at 900 and 800 ft3of oak/acre of forest land (Miles, 2017). Maps from other sources provide additional details on the geographic distribution of major oak species (e.g. USDA Forest Service, 2017). Customized forest resource summaries by species, size, class, ecoregion, state or county can be readily produced from online tools that query, tabulate and display past and current Forest Inventory and Analysis databases for the USA (Miles, 2017).

Note 1

 The terms ‘normal stocking’ and ‘full stocking’ are sometimes used interchangeably. This is potentially confusing because ‘full stocking’ as defined by the Gingrich (1967) stocking chart refers to the range between 58% and 100% stocking (see Chapter 6, this volume, and Fig. 6.9). Any stand density within this range represents full utilization of growing space by trees (assuming trees are well distributed). As used herein, ‘normal’ stand density or ‘normal’ stocking is synonymous with 100% stocking as shown on Gingrich’s or similar stocking charts.

References Adame, P., Hynynen, J., Cañellas, I. and del Río, M. (2008) Individual-tree diameter growth model for rebollo oak (Quercus pyrenaica Willd.) coppices. Forest Ecology and Management 255, 1101–1022. https://doi.org/10.1016/j.foreco.2007.10.019 Allen, R.H. and Marquis, D.A. (1970) Effect of thinning on height and diameter growth of oak and yellowpoplar saplings. USDA Forest Service Research Paper NE-173. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/23782 (accessed 1 July 2018). Ammon, V., Nebeker, T.E., Filer, T.H., McCracken, F.I., Solomon, J.D. and Kennedy, H.E., Jr (1989) Oak decline. Mississippi Agricultural and Forestry Experiment Station Technical Bulletin 151. Missis­ sippi Agricultural and Forestry Experiment Station, Mississippi State, Mississippi, 15 pp. Bailey, R.L. and Dell, T.R. (1973) Quantifying diameter distributions with the Weibull function. Forest Science 19, 97–104. https://doi.org/10.1093/forestscience/ 19.2.97 Beers, T.W. (1962) Components of forest growth. Journal of Forestry 60, 245–248. https://doi.org/ 10.1093/jof/60.4.245

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Beers, T.W. (1964) Composite hardwood volume tables. Purdue University Agricultural Experiment Station Research Bulletin 787. Purdue University, West Lafayette, Indiana. Belcher, D.M., Holdaway, M.R. and Brand, G.J. (1982) A description of stems – the stand and tree evaluation and modeling system. USDA Forest Service General Technical Report NC-79. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10143 (accessed 1 July 2018). Bowling, E.H., Burkhart, H.E., Burk, T.E. and Beck, D.E. (1989) A stand-level multispecies growth model for Appalachian hardwoods. Canadian Journal of For­ est Research 19, 405–412. https://doi.org/10.1139/ x89-064 Bramble, W.C. and Fix, W.L. (1980) Productivity of a managed woodland in the central states. Journal of Forestry 78, 553–556. https://doi.org/10.1093/jof/78.9.553 Brand, G.J. and Holdaway, M.R. (1983) Users need performance information to evaluate models. Journal of Forestry 81, 235–237. https://doi.org/10.1093/jof/81.4.235 Brand, G.J., Holdaway, M.R. and Shifley, S.R. (1987) A description of the twigs and stems individual-treebased growth simulation models and their applications. USDA Forest Service General Technical Report NC-120, Vol. 2. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 950–960. Available at: https://www.fs.usda.gov/treesearch/pubs/ 10184 (accessed 1 July 2018). Brandt, L., He, H., Iverson, L., Thompson, F.R. III, Butler, P. and 29 others (2014) Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods Climate Change Re­sponse Framework project. USDA Forest Service General Technical Report NRS-124. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NRS-GTR-124 Buchanan, W.D., Liming, F.D. and Harrison, W. (1962) Diameter growth patterns of black oak trees. Journal of Forestry 60, 342–343. https://doi.org/10.1093/ jof/60.5.340 Buchman, R.G. and Shifley, S.R. (1983) Guide to evaluating forest growth projection systems. Journal of Forestry 81, 232–235. https://doi.org/10.1093/jof/81. 4.232 Buech, R. (1976) Tree shoot elongation in northern Wisconsin and relationships with temperature and precipitation. Canadian Journal of Forest Research 6, 487–498. https://doi.org/10.1139/x76-067 Bull, H. (1945) Diameter growth of southern bottomland hardwoods. Journal of Forestry 43, 326–327. https:// doi.org/10.1093/jof/43.5.326 Buongiorno, J. and Hseu, J.-S. (1993) Volume and value growth of hardwood trees in Wisconsin. Northern Journal of Applied Forestry 10, 63–69. https://doi. org/10.1093/njaf/10.2.63

Chapter 15

Butler, P.R., Iverson, L., Thompson, F.R. III, Brandt, L. and 43 more (2015) Central Appalachians forest ecosystem vulnerability assessment and synthesis: a report from the Central Appalachians Climate Change Response Framework project. USDA Forest Service General Technical Report NRS-146. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NRS-GTR-146 Carmean, W.H. and Boyce, S.G. (1973) Hardwood log quality in relation to site quality. USDA Forest Service Research Paper NC-103. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/ 10624 (accessed 1 July 2018). Chojnacky, D.C., Heath, L.S. and Jenkins, J.C. (2014) Updated generalized biomass equations for North American tree species. Forestry 87, 129–151. https:// doi.org/10.1093/forestry/cpt053 Clatterbuck, W.K. (1993) Are overtopped white oak good candidates for management? USDA Forest Service General Technical Report SO-93. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 497–500. https://doi.org/ 10.2737/SO-GTR-93 Clatterbuck, W.K. (2002) Growth of a 30-year cherrybark oak plantation 6 years after thinning. USDA Forest Service General Technical Report SRS-48. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 189–192. Available at: https:// www.fs.usda.gov/treesearch/pubs/4775 (accessed 1 July 2018). Cobb, S.W., Miller, A.E. and Zahner, R. (1985) Recurrent shoot flushes in scarlet oak stump sprouts. Forest Science 31, 725–730. https://doi.org/10.1093/ forestscience/31.3.725 Colbert, K.C., Larsen, D.R. and Lootens, J.R. (2002) Height-diameter equations for thirteen midwestern bottomland hardwood species. Northern Journal of Applied Forestry 19, 171–176. https://doi.org/10.1093/ njaf/19.4.171 Crow, T.R. (1986) Comparing the performance of two northern hardwood growth projection systems. Northern Journal of Applied Forestry 3, 28–32. https://doi.org/10.1093/njaf/3.1.28 Dale, M.E. (1968) Growth response from thinning young even-aged white oak stands. USDA Forest Service Research Paper NE-112. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/3930 (accessed 1 July 2018). Dale, M.E. (1972) Growth and yield predictions for upland oak stands 10 years after initial thinning. USDA Forest Service Research Paper NE-241. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/23656 (accessed 1 July 2018).

Growth and Yield

Dale, M.E. (1973) Growth and yield functions for thinned upland oak stands. PhD dissertation, Iowa State University, Ames, Iowa. Available at: https://lib.dr. iastate.edu/rtd/6144 (accessed 1 February 2019). Dale, M.E. (1975) Effect of removing understory on growth of upland oak. USDA Forest Service Research Paper NE-321. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 15449 (accessed 1 July 2018). Dale, M.E. and Sonderman, D.L. (1984) Effect of thinning on growth and potential quality of young white oak crop trees. USDA Forest Service Research Paper NE-539. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Penn­ sylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/15090 (accessed 1 July 2018). Davis, K.P. (1966) Forest Management, 2nd edn. McGraw-Hill, New York. De Jager, N.R., Drohan, P.J., Miranda, B.M., Sturtevant, B.R., Stout, S.L., Royo, A.A., Gustafson, E.J. and Romanski, M.C. (2016) Simulating ungulate herbivory across forest landscapes: an ungulate browsing extension for landis-ii. Ecological Modelling 350, 11–29. https://doi.org/10.1016/j.ecolmodel.2017.01.014 Dijak, W.D. and Rittenhouse, C.D. (2009) Development and application of habitat suitability models to large landscapes. In: Millspaugh, J.J. and Thompson, F.R. III (eds) Models for Planning Wildlife Conservation in Large Landscapes. Academic Press, Burlington, Massachusetts, pp. 367–390. Dixon, G.E. (comp.) (2015) Essential FVS: a User’s Guide to the forest vegetation simulator. USDA Forest Service Forest Management Service Center Internal Report. USDA Forest Service, Washington Office Service Center, Ft Collins, Colorado. Available at: https://www.fs.fed.us/fmsc/ftp/fvs/docs/gtr/ EssentialFVS.pdf (accessed 1 July 2018). Doolittle, W.T. (1958) Site index comparisons for several forest species in the southern Appalachians. Soil Science Society of America Proceedings 22, 455– 458. https://doi.org/10.2136/sssaj1958.0361599500 2200050023x Dunlap, F. (1921) Growth of oak in the Ozarks. Missouri Agricultural Experiment Station Research Bulletin 41. University of Missouri Agricultural Experiment Station, Columbia, Missouri. Ek, A.R., Birdsall, E.T. and Spears, R.J. (1984) A simple model for estimating total and merchantable tree heights. USDA Forest Service Research Note NC309. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11214 (accessed 1 July 2018). Fischer, B.C., Standiford, R.B. and Callahan, J.C. (1980) Value growth rates from overwood trees in the irregular shelterwood method in southern Indiana upland

565

hardwoods. In: Proceedings of the Central Hardwood Forest Conference III. University of Missouri, Columbia, Missouri, pp. 401–407. Forrester, J.W. (1968) Principles of Systems: Text and Workbook, 2nd preliminary edn. Wright-Allen Press, Cambridge. Franklin, J.F., Shugart, H.H. and Harmon, M.E. (1987) Tree death as an ecological process. BioScience 37, 550–556. https://doi.org/10.2307/1310665 Fuller, L.G., Reed, D.D. and Holmes, M.J. (1988) Modeling northern hardwood diameter growth using weekly climatic factors in northern Michigan. USDA Forest Service General Technical Report NC-120, Vol 1. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota, pp. 467–474. Available at: https://www.fs.usda.gov/treesearch/pubs/ 10184 (accessed 1 July 2018). Gansner, D.A., Arner, S.L. and Birch, T.W. (1990) Timber value growth rates in New England. USDA Forest Service Research Paper NE-632. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi. org/10.2737/NE-RP-632 Gemmill, B. (1980) Radial growth of California black oak in the San Bernardino Mountains. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 128– 135. Available at: https://www.fs.fed.us/psw/publications/documents/psw_gtr044/index.shtml (accessed 1 July 2018). Gevorkiantz, S.R. and Olsen, L.P. (1955) Composite volume tables for timber and their application in the Lake States. USDA Technical Bulletin 1104. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc.nal.usda. gov/download/CAT86201093/PDF (accessed 1 July 2018). Gevorkiantz, S.R. and Scholz, H.F. (1948) Timber yields and possible returns from the mixed-oak farmwoods of southwestern Wisconsin. USDA Forest Service Lake States Forest Experiment Station Publication 521. USDA Forest Service, Lake States Forest Experiment Station, St Paul, Minnesota. Gingrich, S.F. (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the Central States. Forest Science 13, 38–53. https://doi. org/10.1093/forestscience/13.1.38 Gingrich, S.F. (1971a) Management of young and intermediate stands of upland hardwoods. USDA Forest Service Research Paper NE-195. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/23703 (accessed 1 July 2018). Gingrich, S.F. (1971b) Stocking, growth and yield of oak stands. In: Oak Symposium Proceedings. USDA

566

Forest Service Northeastern Forest Experiment Station, Upper Darby, Pennsylvania, pp. 65–73. Available at: https://www.fs.usda.gov/treesearch/pubs/ 14517 (accessed 1 July 2018). Gould, P.J., Marshall, D.D. and Harrington, C.A. (2008) Prediction of growth and mortality of Oregon white oak in the Pacific Northwest. Western Journal of Applied Forestry 23, 26–33. https://doi.org/10.1093/wjaf/23.1.26 Graney, D.L. (1987) Ten-year growth of red and white oak crop trees following thinning and fertilization in the Boston Mountains of Arkansas. USDA Forest Service General Technical Report SO-42. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 445–449. Graney, D.L. and Murphy, P.A. (1991) Growth and yield comparisons for upland oak stands in the Boston Mountains of Arkansas. USDA Forest Service Research General Technical Report SE-70. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 229–239. https://doi.org/10.2737/SE-GTR-70 Graney, D.L. and Murphy, P.A. (1993) Growth response of upland oak stands in the Boston Mountains to nitrogen fertilization. USDA Forest Service General Technical Report SO-70. USDA Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, pp. 485–495. Graney, D.L. and Murphy, P.A. (1994) Growth and yield of thinned upland oak stands in the Boston Mountains of Arkansas. Southern Journal of Applied Forestry 18, 10–14. https://doi.org/10.1093/sjaf/18.1.10 Gustafson, E.G., Lucash, M.S., Liem, J., Jenny, H., Scheller, R.M., Barrett, K. and Sturtevant, B.R. (2016) Seeing the future impacts of climate change and forest management: a landscape visualization system for forest managers. USDA Forest Service General Technical Report NC-164. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/53235 (accessed 1 July 2018). Hahn, J.T. and Hansen, M.H. (1991) Cubic and board foot volume models for the Central States. Northern Journal of Applied Forestry 8, 47–57. https://doi. org/10.1093/njaf/8.2.47 Haney, G.P. (1962) Growth of thinned white oak sprouts. Journal of Forestry 60, 644–645. https://academic. oup.com/jof/article/60/9/644/4671938 Hann, D.W. and Larsen, D.R. (1991) Diameter growth equations for fourteen tree species in southwest Oregon. Oregon State University Forest Research Laboratory Research Bulletin 69. Oregon State University, Corvallis, Oregon. Harrison, W.C., Burk, T.E. and Beck, D.E. (1986) Individual tree basal area increment and total height equations for Appalachian mixed hardwoods after thinning. Southern Journal of Applied Forestry 10, 99–104. https://doi.org/10.1093/sjaf/10.2.99

Chapter 15

He, H.S. (2008) Forest landscape models, definition, characterization, and classification. Forest Ecology and Management 254, 484–498. https://doi.org/10. 1016/j.foreco.2007.08.022 He, H.S. (2017) landis pro 7.0. Available at: http://landis. missouri.edu/landispro70.php (accessed 1 April 2018). He, H.S., Wang, W.J., Fraser, J.S, Shifley, S.R. and Larsen, D.R. (2012) landis a Spatially Explicit Model of Forest Landscape Disturbance, Management, and Succession: landis pro 7.0 User’s Guide. Available at: http://landis.missouri.edu/files/LANDIS_PRO_70_ UserGuide.pdf (accessed 5 February 2017). Herrick, O.W. and Gansner, D.A. (1985) Forest-tree value growth rates. Northern Journal of Applied Forestry 2, 11–13. https://doi.org/10.1093/njaf/2.1.11 Hibbs, D.E. and Bentley, W.R. (1984) A growth model for red oak in New England. Canadian Journal of Forest Research 14, 250–254. https://doi.org/10.1139/x84-048 Hilt, D.E. (1979) Diameter growth of upland oaks after thinning. USDA Forest Service Research Paper NE-437. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 14971 (accessed 1 July 2018). Hilt, D.E. (1980) Taper-based system for estimating stem volumes of upland oaks. USDA Forest Service Research Paper NE-458. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/15007 (accessed 1 July 2018). Hilt, D.E. (1983) Individual-tree diameter growth model for managed, even-aged, upland oak stands. USDA Forest Service Research Paper NE-533. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/15084 (accessed 1 July 2018). Hilt, D.E. (1985a) oaksim: an individual-tree growth and yield simulator for managed, even-aged, upland oak stands. USDA Forest Service Research Paper NE562. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 21734 (accessed 1 July 2018). Hilt, D.E. (1985b) User’s guide to oaksim: an individualtree growth and yield simulator for managed, evenaged, upland oak stands. USDA Forest Service General Technical Report NE-104. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/4144 (accessed 1 July 2018). Hilt, D.E. (1987) Using oaksim: an individual-tree growth and yield simulator for managed, even-aged, upland oak stands. In: Proceedings of the 15th Annual Hardwood Symposium of the Hardwood Research Council. Memphis, Tennessee, pp. 67–86.

Growth and Yield

Hilt, D.E. and Dale, M.E. (1982) Height prediction equations for even-aged upland oak stands. USDA Forest Service Research Paper NE-493. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/15047 (accessed 1 July 2018). Hilt, D.E. and Dale, M.E. (1989) Thinning even-aged upland oak stands. USDA Forest Service, North Central Forest Experiment Station Central Hardwood Notes Note 6.06. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/ 11650 (accessed 1 July 2018). Hilt, D.E. and Teck, R.M. (1989) ne-twigs: an individualtree growth and yield projections system for the northeastern United States. The Compiler 7(2), 10–16. Holdaway, M.R. and Brand, G.J. (1986) An evaluation of Lake States stems85. USDA Forest Service Research Paper NC-269. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10056 (accessed 1 July 2018). Hughes, G.A. and Sendak, P.E. (1985) Key algorithms used in gro2: a computer simulation model for predicting tree and stand growth. USDA Forest Service Research Paper NE-570. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs. usda.gov/treesearch/pubs/21726 (accessed 1 July 2018). Jenkins, J.C., Chojnacky, D.C., Heath, L.S. and Birdsey, R.A. (2003) National-scale biomass estimation for United States tree species. Forest Science 49, 12– 15. https://doi.org/10.1093/forestscience/49.1.12 Johnson, P.S. (1975) Growth and structural development of red oak sprout clumps. Forest Science 21, 413–418. https://doi.org/10.1093/forestscience/ 21.4.413 Johnson, P.S. (1979) Shoot elongation of black oak and white oak sprouts. Canadian Journal of Forest Research 9, 489–494. https://doi.org/10.1139/x79-079 Johnson, P.S. and Rogers, R. (1980) Predicting growth of individual stems within northern red oak sprout clumps. In: Proceedings of the Central Hardwood Forest Conference III. University of Missouri, Columbia, Missouri, pp. 420–439. Johnston, J.P. (1941) Height-growth periods of oak and pine reproduction in the Missouri Ozarks. Journal of Forestry 39, 67–68. https://doi.org/10.1093/jof/39.1.66 Karnig, J.J. (1972) Nitrogen fertilizer increases diameter growth of northern red oak. Journal of Forestry 70, 629. Karnig, J.J. and Stout, B.B. (1969) Diameter growth of northern red oak following understory control. Harvard Black Rock Forest, Black Rock Forest Papers No. 30. Cornwall Press, Cornwall, New York.

567

Kershaw, J.A. and Fischer, B.C. (1985) Diameter growth of sawtimber size trees in Indiana. Purdue University Department of Forestry and Natural Resources Bulletin 181, pp. 21–24. Purdue University, West Lafayette, Indiana. Kershaw, J.A., Jr, Ducey, M.J., Beers, T.W. and Husch, B. (2016) Forest Mensuration, 5th edn. Wiley, Chichester, UK. Available at: https://onlinelibrary.wiley.com/ doi/book/10.1002/9781118902028 (accessed 1 July 2018). Kowalski, D.G. and Gertner, G.Z. (1989) A validation of twigs for Illinois forests. Northern Journal of Applied Forestry 6, 154–156. https://doi.org/10.1093/njaf/ 6.4.154 Krajicek, J.E., Brinkman, K.A. and Gingrich, S.F. (1961) Crown competition – a measure of density. Forest Science 7, 35–42. https://doi.org/10.1093/ forestscience/7.1.35 Lamson, N.I. (1978) Fertilization increases growth of sawlog-size yellow-poplar and red oak in West Virginia. USDA Forest Service Research Paper NE403. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/14656 (accessed 1 July 2018). Lamson, N.I. and Smith, H.C. (1978) Response to crop tree release: sugar maple, red oak, black cherry and yellow poplar saplings in a 9-year-old stand. USDA Forest Service Research Paper NE-394. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/14647 (accessed 1 July 2018). Lamson, N.I., Smith, H.C., Perkey, A.W. and Brock, S.M. (1990) Crown release increases growth of crop trees. USDA Forest Service Research Paper NE-635. USDA Forest Service, Northeastern Forest Exper­ iment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-635 Larsen, D.R. and Hann, D.W. (1987) Height-diameter equations for seventeen tree species in southwest Oregon. Oregon State University Forestry Research Laboratory Research Paper 49. Oregon State University, Corvallis, Oregon. Larson, M.A., Thompson, F.R. III, Millspaugh, J.J., Dijak, W.D. and Shifley, S.R. (2004) Linking population viability, habitat suitability, and landscape simulation models for conservation planning. Ecological Modelling 180, 103– 118. https://doi.org/10.1016/j.ecolmodel.2003.12.054 Leak, W.B. (1981) Do stocking guides in the eastern United States relate to stand growth? Journal of Forestry 79, 661–664. https://doi.org/10.1093/jof/ 79.10.661 LeBrun, J.J., Schneiderman, J.E., Thompson, F.R. III, Dijak, W.D., Fraser, J.S., He, H.S. and Millspaugh, J.J. (2016) Bird response to future climate and forest management focused on mitigating climate change.

568

Landscape Ecology 32, 1433–1446. https://doi. org/10.1007/s10980-016-0463-x Lee, W.K., von Gadow, K., Chung, D.J., Lee, J.L. and Shin, M.Y. (2004) DBH growth model for Pinus densi­ flora and Quercus variabilis mixed forests in central Korea. Forest Ecology and Management 176, 187– 200. https://doi.org/10.1016/j.ecolmodel.2003.11.012 Lee, Y.J. (1971) Predicting mortality for even-aged stands of lodgepole pine. Forestry Chronicle 47, 29– 32. https://doi.org/10.5558/tfc47029-1 Longman, K.A. and Coutts, M.P. (1974) Physiology of the oak tree. In: Morris, M.G. and Perring F.H. (eds) The British Oak. E.W. Classey, Faringdon, UK, pp. 192–221. Loomis, R.M. (1973) Estimating fire-caused mortality and injury in oak–hickory forests. USDA Forest Service Research Paper NC-94. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10615 (accessed 1 July 2018). Lootens, J.R., Larsen, D.R. and Loewenstein, E.F. (1999) A matrix transition model for an uneven-aged, oak–hickory forest in the Missouri Ozark highlands. USDA Forest Service General Technical Report SRS-30. USDA Forest Service, Southern Research Station, Asheville, North Carolina, pp. 536–538. Available at: https://www.fs.usda.gov/treesearch/ pubs/3768 (accessed 1 July 2018). Lootens, J.R., Larsen, D.R. and Shifley, S.R. (2007) Height-diameter equations for 12 upland species in the Missouri Ozark Highlands. Northern Journal of Applied Forestry 24, 149–152. https://doi.org/10. 1093/njaf/24.2.149 Lowell, K.E. and Mitchell, R.J. (1987) Stand growth projection: simultaneous estimation of growth and mortality using a single probabilistic function. Canadian Journal of Forest Research 17, 1466–1470. https:// doi.org/10.1139/x87-225 Lowell, K.E., Mitchell, R.J., Johnson, P.S., Garrett, H.E. and Cox, G.S. (1987) Predicting growth and ‘success’ of coppice-regenerated oak stems. Forest Science 33, 740–749. https://doi.org/10.1093/forestscience/ 33.3.740 Lynch, T.B., Holley, A.G. and Stevenson, D.J. (2005) A random-parameter height-dbh model for cherrybark oak. Southern Journal of Applied Forestry 29, 22–26. https://doi.org/10.1093/sjaf/29.1.22 Martin, A.J. (1981) Taper and volume equations for selected Appalachian hardwood species. USDA Forest Ser­ vice Research Paper NE-490. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/15044 (accessed 1 July 2018). McDonald, P.M. (1980) Growth of thinned and unthinned hardwood stands in the northern Sierra Nevada – preliminary findings. In: Plumb, T. (tech. coord.) Proceedings of the Symposium on the Ecology,

Chapter 15

Management, and Utilization of California Oaks. USDA Forest Service General Technical Report PSW-44. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 119–127. Available at: https://www. fs.fed.us/psw/publications/documents/psw_gtr044/ index.shtml (accessed 1 July 2018). McDonald, P.M. and Vaughn, N.R. (2007) Growth of thinned and unthinned hardwood stands on a good site in northern California. USDA Forest Service General Technical Report PSW-204. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California. https://doi. org/10.2737/PSW-GTR-204 McGee, C.E. (1981) Response of overtopped white oak to release. USDA Forest Service Research Note SO273. USDA Forest Service, Southern Forest Exper­ iment Station, New Orleans, Louisiana. https://doi. org/10.2737/SO-RN-273 McGee, C.E. and Bivens, D.L. (1984) A billion overtopped white oak – assets or liabilities? Southern Journal of Applied Forestry 8, 216–220. https://doi. org/10.1093/sjaf/8.4.216 McIntyre, A.C. (1933) Comparative radial growth of various oaks. Journal of Forestry 31, 341–342. https:// doi.org/10.1093/jof/31.3.340 McKnight, J.S. and Johnson, R.L. (1980) Hardwood management in southern bottomlands. Forest Farmer March, 31–35. McQuilkin, R.A. (1974) Site index prediction table for black, scarlet and white oaks in southeastern Missouri. USDA Forest Service Research Paper NC-108. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10629 (accessed 1 July 2018). McQuilkin, R.A. (1982) Fertilization of oaks in the Missouri Ozarks. In: Proceedings of the Central Hardwood Forest Conference IV. University of Kentucky, Lexington, Kentucky, pp. 79–92. Meadows, J.S. and Goelz, J.C.G. (2001) Fifth-year response to thinning in a water oak plantation in north Louisiana. Southern Journal of Applied Forestry 25, 31–39. https://doi.org/10.1093/sjaf/25.1.31 Miles, P.D. (2017) Forest Inventory evalidator Webapplication Version 1.6.0.03. USDA Forest Service. Available at: http://apps.fs.fed.us/Evalidator/evalidator.jsp (accessed 15 February 2017). Miller, G.W. (2000) Effect of crown growing space on the development of young hardwood crop trees. Northern Journal of Applied Forestry 17, 25–35. https://doi. org/10.1093/njaf/17.1.25 Minckler, L.S. (1957) Response of pole-sized white oak trees to release. Journal of Forestry 55, 814–815. https://doi.org/10.1093/jof/55.11.814 Minckler, L.S. (1967) Release and pruning can improve growth and quality of white oak. Journal of Forestry 65, 654–655. https://doi.org/10.1093/jof/65.9.654

Growth and Yield

Miner, C.L., Walters, N.R. and Belli, M.L. (1988) A guide to the twigs program for the North Central United States. USDA Forest Service Research General Technical Report NC-125. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10189 (accessed 1 July 2018). Mitchell, R.J., Dwyer, J.P., Musbach, R.A., Garrett, H.E., Cox, G.S. and Kurtz, W.B. (1988) Crop tree release of a scarlet–black oak stand. Northern Journal of Applied Forestry 5, 96–99. https://doi.org/10.1093/njaf/5.2.96 Mladenoff, D.J. (2004) landis and forest landscape models. Ecological Modelling 180, 7–19. https://doi.org/ 10.1016/j.ecolmodel.2004.03.016 Mladenoff, D.J. (2005) The promise of landscape modeling: successes, failures, and evolution. In: Weins, J.A. and Moss, M.R. (eds) Issues and Perspectives in Landscape Ecology. Cambridge University Press, Cambridge, pp. 90–100. Monserud, R.A. and Sterba, H. (1996) A basal area increment model for individual trees growing in evenand uneven-aged forest stands in Austria. Forest Ecology and Management 80, 262–270. https://doi. org/10.1016/0378-1127(95)03638-5 Myers, C. and Belcher, D.M. (1981) Estimating total-tree height for upland oaks and hickories in southern Illinois. USDA Forest Service Research Note NC272. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/11178 (accessed 1 July 2018). Nyland, R.D. (2002) Silviculture. Concepts and Applica­ tions, 2nd edn. McGraw-Hill, New York, 682 pp. Oreskes, N., Shrader-Frechette, K. and Belitz, K. (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science 263(5147), 641–646. https://doi.org/10.1126/science.263.5147.641 Oswalt, S.N., Smith, W.B., Miles, P.D. and Pugh, S.A. (2014) Forest resources of the United States, 2012: a technical document supporting the Forest Service 2015 update of the RPA Assessment. USDA Forest Service General Technical Report WO-91. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/47322 (accessed 1 July 2018). Patton, R.T. (1922) Red oak and white ash, a study of growth and yield. Harvard Forestry Bulletin No. 4. Harvard University Press, Cambridge, Massachusetts. https://doi.org/10.5962/bhl.title.56500 Pauli, B.P., Zollner, P.A., Haulton, G.S., Shao, G. and Shao, G. (2015) The simulated effects of timber harvest on suitable habitat for Indiana and northern longeared bats. Ecosphere 6(4), 58. http://dx.doi. org/10.1890/ES14-00336.1 Perkey, A.W. and Wilkins, B.L. (2001) Crop tree field guide. USDA Forest Service NA-TP-10-01. Available at: https://www.fs.usda.gov/naspf/sites/default/files/

569

publications/crop_tree_field_guide.pdf (accessed 13 February 2018). Pillsbury, N.H., Bonner, L.E. and Thompson, R.P. (2002) Coast live oak long-term thinning study – twelve-year results. USDA Forest Service General Technical Report PSW-184. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 681–692. Available at: https://www.fs.usda. gov/treesearch/pubs/26164 (accessed 1 July 2018). Pretzsch, H., Biber, P. and Ďurský, B. (2002) The single tree-based stand simulator silva: construction, application and evaluation. Forest Ecology and Manage­ ment 162, 3–21. https://doi.org/10.1016/S0378-1127 (02)00047-6 Raile, G.K., Smith, W.B. and Weist, C.A. (1982) A net volume equation for Michigan’s upper and lower peninsulas. USDA Forest Service General Technical Report NC-80. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/ 10144 (accessed 1 July 2018). Regelbrugge, J.C. and Smith, D.W. (1994) Postfire tree mortality in relation to wildfire severity in mixed oak forests in the Blue Ridge of Virginia. Northern Journal of Applied Forestry 11, 90–97. https://doi.org/ 10.1093/njaf/11.3.90 Reich, P.B., Teskey, R.O., Johnson, P.S. and Hinckley, T.M. (1980) Periodic root and shoot growth in oak. Forest Science 4, 590–598. https://doi.org/10.1093/ forestscience/26.4.590 Reynolds, M.R., Jr, Burkhart, H.E. and Daniels, R.F. (1981) Procedures for statistical validation of stochastic simulation models. Forest Science 27, 349– 364. https://doi.org/10.1093/forestscience/27.2.349 Ritchie, M.W. and Hann, D.W. (1987) Equations for predicting height to crown base for fourteen tree species in southwest Oregon. Oregon State University Forestry Research Laboratory Research Paper 50. Oregon State University, Corvallis, Oregon. Rittenhouse, C.D., Shifley, S.R., Dijak, W.D., Fan, Z., Thompson, F.R., Millspaugh, J.J., Perez, J.A. and Sandeno, C.M. (2011) Application of landscape and habitat suitability models to conservation: the Hoosier National Forest land-management plan. In: Li, C., Lafortezza, R. and Chen, J. (eds) Landscape Ecology in Forest Management and Conservation: Challenges and Solutions for Global Change. Higher Education Press, Beijing and Springer-Verlag, Berlin, Heidelberg, pp. 299–328. https://doi.org/10.1007/ 978-3-642-12754-0_13 Roach, B.A. and Gingrich, S.F. (1962) Timber Management Guide for Upland Central Hardwoods. USDA Forest Service Central States Forest Experiment Station, Columbus, Ohio. Roach, B.A. and Gingrich, S.F. (1968) Even-aged Silviculture for Upland Central Hardwoods. USDA

570

Forest Service Agriculture Handbook 355. USDA Forest Service, Washington, DC. Available at: https:// www.fs.usda.gov/treesearch/pubs/4422 (accessed 1 July 2018). Rogers, R. and Johnson, P.S. (1985) Rule thinning: a field method for meeting stocking goals in oak stands. In: Proceedings of the Central Hardwood Forest Conference V. University of Illinois, Urbana-Champaign, Illinois, pp. 106–110. Rykiel, E.J., Jr (1996) Testing ecological models: the meaning of validation. Ecological Modelling 90, 229–244. Sánchez-Gonzáles, M., del Río, M., Cañellas, I. and Montero, G. (2006) Distance independent tree diameter growth model for cork oak stands. Forest Ecology and Management 225, 57–80. https://doi. org/10.1016/0304-3800(95)00152-2 Sander, I.L. (1977) Manager’s handbook for oaks in the north central states. USDA Forest Service General Technical Report NC-37. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/ treesearch/pubs/10102 (accessed 1 July 2018). Scheller, R.M., Domingo, J.B., Sturtevant, B.R., Williams, J.S., Rudy, A., Gustafson, E.J. and Mladenoff, D.J. (2007) Design, development, and application of landis-ii, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecological Modelling 201, 409–419. https://doi.org/10.1016/j. ecolmodel.2006.10.009 Schlesinger, R.C. (1976) Sixteen years of selection silviculture in upland hardwood stands. USDA Forest Service Research Paper NC-125. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10646 (accessed 1 July 2018). Schlesinger, R.C. (1978) Increased growth of released white oak poles continues through two decades. Journal of Forestry 76, 726–727. https://doi.org/10. 1093/jof/76.11.726 Schnur, G.L. (1937) Yield, stand and volume tables of even-aged upland oak forests. USDA Technical Bulletin 560. United States Department of Agriculture (USDA), Washington, DC. Available at: https://naldc. nal.usda.gov/download/CAT86200555/PDF (accessed 1 July 2018). Schuler, T.M., Marquis, D.A., Ernst, R.L. and Simpson, B.T. (1993) Test of four stand growth simulators for the Northeastern United States. USDA Forest Service Research Paper NE-676. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP676 Shifley, S.R. (1987) A generalized system of models forecasting central states tree growth. USDA Forest Service Research Paper NC-279. USDA Forest Service, North Central Forest Experiment Station,

Chapter 15

St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/10065 (accessed 1 July 2018). Shifley, S.R. (1990) Analysis and modeling of patterns of forest ingrowth in the north central United States. PhD dissertation, University of Minnesota, St Paul, Minnesota. Shifley, S.R. and Smith, W.B. (1982) Diameter growth, survival and volume estimates for Missouri trees. USDA Forest Service Research Note NC-292. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda. gov/treesearch/pubs/11197 (accessed 1 July 2018). Shifley, S.R., Ek, A.R. and Burk, T.E. (1993) A generalized methodology for estimating forest ingrowth at multiple threshold diameters. Forest Science 39, 776–798. https://doi.org/10.1093/forestscience/39.4.776 Shifley, S.R., Thompson, F.R. III, Dijak, W.D., Larson, M.A. and Millspaugh, J.J. (2006) Simulated effects of forest management alternatives on landscape structure and habitat suitability in the Midwestern United States. Forest Ecology and Management 229, 361– 377. https://doi.org/10.1016/j.foreco.2006.04.030 Shifley, S.R., He, H.S., Lischke, H., Wang, W.J., Jin, W., Gustafson, E.J., Thompson, J.R., Thompson, F.R. III, Dijak, W.D. and Yang, J. (2017) The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models. Landscape Ecology 32, 1307–1325. https://doi.org/10.1007/s10980017-0540-9 Simpson, B.T., Kollasch, R.P., Twery, M.J. and Schuler, T.M. (1995) ned/sips user’s manual: Northeast Decision Model stand inventory processor and simulator version 1.00. USDA Forest Service General Technical Report NE-205. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 4322 (accessed 1 July 2018). Smith, D.M. (1962) The Practice of Silviculture, 7th edn. Wiley, New York. Smith, D.M., Larson, B.C., Kelty, M.J. and Ashton, P.M.S. (1997) The Practice of Silviculture: Applied Forest Ecology. Wiley, New York. Smith, W.B. (1983) Adjusting the stems regional forest growth model to improve local predictions. USDA Forest Service Research Note NC-297. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https:// www.fs.usda.gov/treesearch/pubs/11202 (accessed 1 July 2018). Smith, W.B. and Shifley, S.R. (1984) Diameter growth, survival and volume estimates for trees in Indiana and Illinois. USDA Forest Service Research Paper NC-257. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10044 (accessed 1 July 2018).

Growth and Yield

Smith, W.B. and Weist, C.A. (1982) A net volume equation for Indiana. USDA Forest Service Research Bulletin NC-63. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. https://doi.org/10.5962/bhl.title.80907 Sonderman, D.L. (1984) Quality response of even-aged 80-year-old white oak trees after thinning. USDA Forest Service Research Paper NE-543. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/15094 (accessed 1 July 2018). Sonderman, D.L. and Rast, E.D. (1988) Effect of thinning on mixed-oak stem quality. USDA Forest Service Research Paper NE-618. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https:// www.fs.usda.gov/treesearch/pubs/21803 (accessed 1 July 2018). Stage, A.R. (1973) Prognosis model for stand development. USDA Forest Service General Technical Report INT-137. Intermountain Forest and Range Experiment Station, Odgen, Utah. Available at: https:// www.fs.usda.gov/treesearch/pubs/7511 (accessed 1 July 2018). Stage, A.R. (1982) Use of self-calibration procedures to adjust general regional yield models to local conditions. In: Proceedings of Forest Resource Inventory, Growth Models, Management Planning and Remote Sensing, IUFRO World Congress S.4.01. International Union of Forestry Research Organizations (IUFRO), Niigata, Japan, pp. 365–375. Standiford, R.B. (1997) Growth of blue oak on California’s hardwood rangelands. USDA Forest Service General Technical Report PSW-160. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, California, pp. 169–176. Available at: https://www.fs.usda.gov/treesearch/pubs/28171 (accessed 1 July 2018). Starfield, A.M. and Bleloch, A.L. (1991) Building Models for Conservation and Wildlife Management. Burgess International Group, Edina, Minnesota. Starkey, D.A., Oak, S.W., Ryan, G.W., Taintner, F.H., Redmond, C. and Brown, H.D. (1989) Evaluation of oak decline areas in the South. USDA Forest Service Southern Region Protection Report R8-PR 17. USDA Forest Service Southern Region, Atlanta, Georgia. Stout, B.B. and Shumway, D.L. (1982) Site quality estimation using height and diameter. Forest Science 28, 639–645. https://doi.org/10.1093/forestscience/ 28.3.639 Sturtevant, B.R., Scheller, R.M., Miranda, B.R., Shinneman, D. and Syphard, A.D. (2009) Simu­ lating dynamic and mixed-severity fire regimes: a

571

process-based fire extension for landis-ii. Ecological Modelling 220, 3380–3393. https://doi.org/10.1016/j. ecolmodel.2009.07.030 Sullivan, A.D., Matney, T.G. and Hodges, J.D. (1983) Variable density yield tables for red oak–sweetgum stands. USDA Forest Service General Technical Report SE-24. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, pp. 298–301. Available at: https://www.srs.fs.usda. gov/pubs/gtr/gtr_se024.pdf (accessed 1 July 2018). Teck, R.M. (1990) ne-twigs 3.0: an individual-tree growth and yield projection system for the northeastern United States. The Compiler 8(1), 25–27. Teskey, R. and Hinckley, T.M. (1981) Influence of temperature and water potential on root growth of white oak. Physiologia Plantarum 52, 363–369. https://doi. org/10.1111/j.1399-3054.1981.tb06055.x Thomas, C.E. and Parresol, B.R. (1991) Simple, flexible trigonometric taper equations. Canadian Journal of Forest Research 21, 1132–1137. https://doi.org/ 10.1139/x91-157 Trimble, G.R., Jr (1960) Relative diameter growth rates of five upland oaks in West Virginia. Journal of Forestry 58, 111–115. https://doi.org/10.1093/jof/58.2.111 Trimble, G.R., Jr (1969) Diameter growth of individual hardwood trees. USDA Forest Service Research Note NE-145. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Penn­ sylvania. Available at: https://www.fs.usda.gov/ treesearch/pubs/23739 (accessed 1 July 2018). Trimble, G.R., Jr (1970) Twenty years of intensive uneven-aged management: effect on growth, yield and species composition in two hardwood stands in West Virginia. USDA Forest Service Research Paper NE154. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 23764 (accessed 1 July 2018). Trimble, G.R., Jr (1974) Response to crop-tree release by 7-year-old stems of red maple stump sprouts and northern red oak advance reproduction. USDA Forest Service Research Paper NE-303. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda. gov/treesearch/pubs/15410 (accessed 1 July 2018). Trimble, G.R., Jr and Mendel, J.J. (1969) The rate of value increase for northern red oak, white oak and chestnut oak. USDA Forest Service Research Paper NE-129. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/ pubs/7962 (accessed 1 July 2018). USDA Forest Service (2015) Data and Tools, Forest Inventory and Analysis Program. USDA Forest Service. Available at: http://fia.fs.fed.us/tools-data/ default.asp (accessed 18 April 2015).

572

USDA Forest Service (2016) NED – Forest Ecosystem Decision Support Software. USDA Forest Service. Available at: http://nrs.fs.fed.us/tools/ned/ (accessed 14 May 2016). USDA Forest Service (2017) Forest Volume and Species Distribution Maps. USDA Forest Service. Available at: https://www.nrs.fs.fed.us/fia/maps/volume-species/ default.asp (accessed 15 February 2017). USDA Forest Service (2018) forest vegetation simulator (FVS). USDA Forest Service. Available at: https:// www.fs.fed.us/fvs/ (accessed 14 February 2018). Utz, K.A. and Sims, D.H. (1981) Investment analysis of upland oak stands. USDA Forest Service Southeastern Area State and Private Forestry Publication SA-FR12. USDA Forest Service, Atlanta, Georgia. Walters, D.K. and Ek, A.R. (1993) Whole stand yield and density equations for fourteen forest types in Minnesota. Northern Journal of Applied Forestry 10, 75–85. https://doi.org/10.1093/njaf/10.2.75 Wang, C. and Hann, D.W. (1988) Height-diameter equations for sixteen tree species in the central western Willamette Valley of Oregon. Oregon State University Forestry Research Laboratory Research Paper 51. Oregon State University, Corvallis, Oregon. Wang, W.J., He, H.S., Fraser, J.S., Thompson, F.R., Shifley, S.R. and Spetich, M.A. (2014a) landis pro: a landscape model that predicts forest composition and structure changes at regional scales. Ecography 37(3), 225–229. https://doi.org/10.1111/j.1600-0587. 2013.00495.x Wang, W.J., He, H.S., Spetich, M.A., Shifley, S.R., Thompson, F.R., Dijak, W.D. and Wang, Q. (2014b) A framework for evaluating forest landscape model predictions using empirical data and knowledge. Environmental Modelling & Software 62, 230–239. https://doi.org/10.1016/j.envsoft.2014.09.003 Wang, W.J., He, H.S., Thompson, F.R., Fraser, J.S. and Dijak, W.D. (2016) Changes in forest biomass and tree species distribution under climate change in the northeastern United States. Landscape Ecology 32, 1399– 1413. https://doi.org/10.1007/s10980-016-0429-z Ward, J.S. (1995) Intensity of precommercial crop tree release increases diameter and crown growth in upland hardwoods. USDA Forest Service Research General Technical Report NE-197. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania, pp. 388–398. Available at: https://www.fs.usda.gov/treesearch/pubs/12806 (accessed 1 July 2018). Ward, W.W. and Bowersox, T.W. (1970) Upland oak response to fertilization with nitrogen, phosphorous and calcium. Forest Science 16, 113–120. https:// doi.org/10.1093/forestscience/16.1.113 Wargo, P.M. and Haack, R.A. (1991) Understanding the physiology of dieback and decline diseases and its management implications for oak. In: Proceedings

Chapter 15

of The Oak Resource in the Upper Midwest Confer­ ence. University of Minnesota, St Paul, Minnesota, pp. 147–158. Wendel, G.W. and Lamson, N.I. (1987) Effects of herbicide release on the growth of 8- to 12-year-old hardwood crop trees. USDA Forest Service Research Paper NE-598. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/ 21772 (accessed 1 July 2018). Williams, T.B. and Wiant, H.V., Jr (1994) Evaluation of nine taper systems for four Appalachian hardwoods. Northern Journal of Applied Forestry 11, 24–25. https://doi.org/10.1093/njaf/11.1.24 Yang, J., He, H.S. and Shifley, S.R. (2008) Spatial controls of occurrence and spread of wildfires in

Growth and Yield

the Missouri Ozark Highlands. Ecological Appli­ cations 18(5), 1212–1225. https://doi.org/10.1890/070825.1 Yaussy, D.A. and Dale, M.E. (1991) Merchantable sawlog and bole-length equations for the northeastern United States. USDA Forest Service Research Paper NE-650. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. https://doi.org/10.2737/NE-RP-650 Zasada, J.C. and Zahner, R. (1969) Vessel element development in the earlywood of red oak (Quercus rubra). Canadian Journal of Botany 47, 1965–1971. https://doi.org/10.1139/b69-288 Zeide, B. (2001) Thinning and growth: a full turnaround. Journal of Forestry 99, 20–25. https://doi.org/10.1093/ jof/99.1.20

573

Appendix 1 Common and Scientific Names of ­Species Mentioned

Common names of trees generally follow Little (1979)1 whereas scientific names of all vascular plants follow the Flora of North America Editorial

Committee (1993,2 19973). Where a name differs from that cited in the first or second editions, the older name is shown in parentheses.

Plants  

Common name

Scientific name

Ajo (mountain scrub) oak alternate-leaf dogwood American basswood American beech American chestnut American elder American elm American hazel American holly American hornbeam American sycamore apple Arizona white oak Arkansas oak ash autumn olive

Quercus ajoensis C.H. Muller (Quercus turbinella var. ajoensis (C.H. Muller) Little) Cornus alternifolia Linnaeus Tilia americana Linnaeus Fagus grandifolia Ehrhart Castanea dentata (Marshall) Borkhausen Sambucus canadensis Linnaeus Ulmus americana Linnaeus Corylus americana Walter Ilex opaca Aiton Carpinus caroliniana Walter Platanus occidentalis Linnaeus Malus spp. Quercus arizonica Sargent Quercus arkansana Sargent Fraxinus spp. Elaeagnus umbellata Thunb.

bald cypress balsam fir beaked hazel bear oak (scrub oak) bigleaf magnolia bigleaf maple bigtooth aspen bishop pine bitternut hickory black ash black birch black cherry black hickory black locust black oak black walnut blackgum, black tupelo blackhaw

Taxodium distichum (Linnaeus) Richard Abies balsamea (Linnaeus) Miller Corylus cornuta Marshall Quercus ilicifolia Wangenheim Magnolia macrophylla Michaux Acer macrophyllum Pursh Populus grandidentata Michaux Pinus muricata D. Don Carya cordiformis (Wangenheim) K. Koch Fraxinus nigra Marshall see sweet birch Prunus serotina Ehrhart Carya texana Buckley Robinia pseudoacacia Linnaeus Quercus velutina Lamark Juglans nigra Linnaeus Nyssa sylvatica Marshall Viburnum prunifolium Linnaeus

Continued

575

Continued. Common name

Scientific name

blackjack oak blue oak blueberry bluejack oak boxelder Boynton oak bracken fern Brewer oak briar bristlecone fir bur oak butternut

Quercus marilandica Münchhausen Quercus douglasii Hooker & Arnott Vaccinium spp. Quercus incana Bartram Acer negundo Linnaeus Quercus boyntonii Beadle Pteridium aquilinum see Oregon white oak Rubus spp. Abies bracteata D. Don Quercus macrocarpa Michaux Juglans cinerea Linnaeus

California bay laurel California black oak California buckeye California (coast) live oak California (coastal sage) scrub oak California live oak California red fir California white fir California white oak (valley oak) Canada thistle canyon live oak Chapman oak cherrybark oak chestnut (mountain) oak chinkapin oak Chisos oak chokecherry coast live oak coast redwood common juniper common persimmon cork oak Coulter pine crown vetch cucumber tree

Umbellularia californica (Hook and Arn) Nuttall Quercus kelloggii Newberry Aesculus californica (Spach) Nuttall see California live oak Quercus dumosa Nuttall

deciduous holly delta (swamp) post oak diamondleaf oak digger pine Douglas-fir downy oak Dunn oak (Palmer oak) Durand oak dwarf chinkapin oak dwarf live oak dwarf post oak

see possumhaw Quercus similis Ashe (Quercus stellata var. paludosa Sargent) see laurel oak Pinus sabiniana Douglas ex D. Don Pseudotsuga menziesii (Mirb.) Franco Quercus pubescens Willd. Quercus palmeri Engelmann (Quercus dunnii Kellogg) Quercus sinuata Walter (Quercus durandii Buckley) Quercus prinoides Willdenow Quercus minima (Sarg.) Small see sand post oak

eastern bracken fern eastern cottonwood eastern hayscented fern

Pteridium aquilinum (L.) Kuhn var. latiusculum (Desv.) Underw.] Populus deltoides Bartram ex Marshall Dennstaedtia punctilobula Michx.

Quercus agrifolia Née Abies magnifica J.A. Murray Abies concolor var. Iowiana (Gordon) Lemmon Quercus lobata Née Cirsium arvense (L.) Scop. Quercus chrysolepis Liebmann Quercus chapmanii Sargent Quercus pagoda Rafinesque (Quercus falcata var. pagodaefolia Elliot) Quercus montana Willdenow (Quercus prinus Linnaeus) Quercus muehlenbergii Engelmann Quercus graciliformis C.H. Muller Prunus virginiana Linnaeus see California live oak Sequoia sempevirens (D. Don) Endl. Juniperus communis Linnaeus Diospyros virginiana Linnaeus Quercus suber Linnaeus Pinus coulteri D. Don Coronilla varia Magnolia acuminata Linnaeus

Continued

576

Appendix 1

Continued. Common name

Scientific name

eastern hemlock eastern hophornbeam eastern redcedar eastern white pine elder Emory oak Engelmann oak English oak (pedunculate oak) European buckthorn European turkey oak

Tsuga canadensis (Linnaeus) Carriere Ostrya virginiana (Miller) K. Koch Juniperus virginiana Linnaeus Pinus strobus Linnaeus Sambucus spp. Quercus emoryi Torrey Quercus engelmannii Greene Quercus robur Linnaeus Rhamnus cathartica L. Quercus cerris Linnaeus

Florida oak flowering dogwood Fraser fir

Quercus inopina Ashe Cornus florida Linnaeus Abies fraseri (Pursh) Poiret

Gambel oak Georgia oak giant (golden) chinkapin goldenrod grape Graves oak grey birch grey dogwood grey oak green ash greenbriar

Quercus gambelii Nuttall Quercus georgiana M.A. Curtis Chrysolepis chrysophylla (Douglas ex Hooker) Hjelmquist (Castanopsis chrysophylla (Douglas) A. de Candole) Solidago spp. Vitis spp. Quercus gravesii Sudw. Betula populifolia Marshall Cornus racemosa Lamarck Quercus grisea Liebmann Fraxinus pennsylvanica Marshall Smilax spp.

hackberry Havard oak hawthorn hayscented fern hazel hickory Hinckley oak holm oak honeylocust horse chestnut huckleberry oak

Celtis occidentalis Linnaeus Quercus havardii Rydberg Crataegus spp. Dennstaedtia punctilobula Corylus spp. Carya spp. Quercus hinckleyi C.H. Muller Quercus ilex Linnaeus Gleditsia triacanthos Linnaeus Aesculus hippocastanum Linnaeus Quercus vaccinifolia Kellogg

incense cedar interior live oak interrupted fern ironwood island live oak (Channel Island oak)

Calocedrus decurrens (Torrey) Florin (Libocedrus decurrens Torrey) Quercus wislizenii A. de Candolle Osmunda claytoniana Linnaeus see American hornbeam Quercus tomentella Engelmann

jack pine Japanese honeysuckle Japanese stiltgrass Jeffrey pine juniper

Pinus banksiana Lambert Lonicera japonica Thunb. Microstegium vimineum (Trin.) Camus Pinus jeffreyi Greville & Balfour Juniperus

Kentucky coffee tree Kermes oak Knobcone pine

Gymnocladus dioicus (Linnaeus) K. Koch Quercus coccifera Linnaeus Pinus attenuata Lemmon

Lacey oak lateleaf oak

Quercus laceyi Small (Quercus glaucoides M. Martens & Galeotti) Quercus tardifolia C.H. Muller Continued

Common and Scientific Names of Species

577

Continued. Common name a

Scientific name

laurel oak (swamp laurel oak) live oak (southern live oak) loblolly pine longleaf pine

Quercus laurifolia Michaux Quercus virginiana Miller Pinus taeda Linnaeus Pinus palustris Miller

McDonald oak Mexican (Sonoran) blue oak Mexican white oak mockernut hickory Mohr oak Monterey pine mountain ash mountain laurel mountain maple mulberry musk thistle multiflora rose myrtle oak

Quercus × macdonaldii Greene Quercus oblongifolia Torrey Quercus polymorpha Schltdl. & Cham. Carya tomentosa (Poiret) Nuttall Quercus mohriana Buckley Pinus radiata D. Don Sorbus spp. Kalmia latifolia L. Acer spicatum Lamarck Morus spp. Carduus nutans L. Rosa multiflora Thunb. Quercus myrtifolia Willdenow

nannyberry netleaf oak New York fern northern catalpa northern pin oak northern red oak Norway maple nutmeg hickory Nuttall oak (Texas oak)

Viburnum lentago Linnaeus Quercus rugosa Née Thelypteris noveboracensis (L.) Nieuwl. Catalpa speciosa Warder ex Englemann Quercus ellipsoidalis E.J. Hill Quercus rubra Linnaeus Acer platanoides Linnaeus Carya myristiciformis (Michx. f.) Nutt. Quercus texana Buckley (Quercus nuttallii Palmer) (Quercus shumardii var. texana (Buckley) Ashe)

oaks Oglethorpe oak Ohio buckeye Oregon ash Oregon white oak overcup oak

Quercus spp. Quercus oglethorpensis Duncan Aesculus glabra Willdenow Fraxinus latifolia Bentham Quercus garryana Douglas ex Hooker Quercus lyrata Walter

Pacific dogwood Pacific madrone Pacific yew paper birch pawpaw pear pecan pignut hickory pin cherry pin oak pine pinyon (= pinyon pine) pitch pine ponderosa pine possumhaw post oak

Cornus nuttallii Audubon Arbutus menziesii Pursh Taxus brevifolia Nutt. Betula papyrifera Marshall Asimina triloba (Linnaeus) Dunal Pyrus spp. Carya illinoinensis (Wangenheim) K. Koch Carya glabra (Miller) Sweet var. glabra Prunus pensylvanica Linne Quercus palustris Münchhausen Pinus spp. Pinus subsection Cembroides Pinus rigida Miller Pinus ponderosa Douglas ex Lawson Ilex decidua Walter Quercus stellata Wangenheim

quaking aspen

Populus tremuloides Michaux

rebollo oak red maple

Quercus pyrenaica Willdenow Acer rubrum Linnaeus Continued

578

Appendix 1

Continued. Common name

Scientific name

red mulberry red pine redbud red-osier dogwood redtop grass rhododendron river birch roundleaf dogwood

Morus rubra Linnaeus Pinus resinosa Aiton Cercis canadensis Linnaeus Cornus stolonifera Michaux Agrostis gigantea Roth Rhododendron spp. Betula nigra Linnaeus Cornus rugosa Lamarck

sand hickory sand live oak sand post oak sandpaper oak (pungent oak) sarsaparilla sassafras sawtooth oak scarlet oak scrub oak Sebastian bush sedges sericea lespedeza serrata oak serviceberry sessile oak shagbark hickory sheep-laurel shellbark hickory shin oak shingle oak shortleaf pine Shreve oak Shumard oak silver maple silverleaf oak slash pine slender oak slippery elm smooth brome grass sourwood southern California walnut southern magnolia southern red oak speckled alder

Carya pallida (Ashe) Engl. & Graebn. Quercus geminata Small (Quercus virginiana var. geminata (Small) Sargent) Quercus margaretta Ashe (Quercus stellata var. margaretta (Ashe) Sargent) Quercus pungens Liebmann Aralia spp. Sassafras albidum (Nuttall) Nees Quercus accutissima Carruthers Quercus coccinea Münchhausen Quercus ilicifolia Wangenh. Sebastiana fruiticosa (Bartram) Fernald Carex spp. Lespedeza cuneata (Dum. Cours.) G. Don Quercus serrata Thunb. bao li Amelanchier spp. Quercus petraea Mattuschka Carya ovata (Miller) K. Koch Kalmia angustifolia Linnaeus Carya laciniosa (Michx. f.) Lould. see Mohr oak Quercus imbricaria Michaux Pinus echinata Miller Quercus parvula E. Greene var. shrevei (C.H. Muller) Quercus shumardii Buckley Acer saccharinum Linnaeus Quercus hypoleucoides A. Camus Pinus elliottii Engelmann Quercus graciliformis C.H. Mull. Ulmus rubra Muhlenberg Bromus inermis Leyss. Oxydendrum arboreum (Linnaeus) DeCandolle Juglans californica S. Watson Magnolia grandiflora Linnaeus Quercus falcata Michaux Alnus incana (Linnaeus) Moench subsp. rugosa (Du Roi) R.T. Clausen (Alnus rugosa (Du Roi) Sprengel) Lindera benzoin (Linnaeus) Blume Centaurea biebersteinii Dc. Picea spp. Pinus glabra Walter Acer pensylvanicum Linnaeus Acer saccharum Marshall Pinus lambertiana Douglas Celtis laevigata Willdenow Quercus michauxii Nuttall see bitternut hickory Nyssa biflora Walter (Nyssa sylvatica var. biflora (Walter) Sargent) Continued

spicebush spotted knapweed spruce spruce pine striped maple sugar maple sugar pine sugarberry swamp chestnut oak swamp hickory swamp tupelo

Common and Scientific Names of Species

579

Continued. Common name

Scientific name

swamp white oak sweet birch sweet cherry sweetfern sweetgum sycamore

Quercus bicolor Willdenow Betula lenta Linnaeus Prunus avium (Linnaeus) Linnaeus Comptonia peregrina (Linnaeus) J.M. Coulter Liquidambar styraciflua Linnaeus see American sycamore

Table Mountain pine tamarack tanoak teasel Texas live oak Toumey oak turbinella oak turkey oak

Pinus pungens Lambert Larix laricina (Du Roi) K. Koch Lithocarpus densiflorus (Hooker & Arnott) Rehder Dipsacus spp. Quercus fusiformis Small (Quercus virginiana var. fusiformis (Small) Sargent) Quercus toumeyi Sargent Quercus turbinella Greene Quercus laevis Walter

umbrella magnolia

Magnolia tripetala Linnaeus

valley oak Vasey oak viburnum Virginia pine

see California white oak Quercus vaseyana Buckley (Quercus pungens var. vaseyana (Buckley) C.H. Muller) Viburnum spp. Pinus virginiana Miller

water hickory water oak water tupelo waterlocust wavyleaf oak white ash white oak white sweetclover wild grape willow willow oak winged elm witch-hazel

Carya aquatica (F. Michaux) Nuttall Quercus nigra Linnaeus Nyssa aquatica Linnaeus Gleditsia aquatica Marshall Quercus undulata Torrey Fraxinus americana Linnaeus Quercus alba Linnaeus Melilotus alba Vitis spp. Salix spp. Quercus phellos Linnaeus Ulmus alata Michaux Hamamelis virginiana Linnaeus

yaupon yellow birch yellow buckeye yellow-poplar yellow sweetclover

Ilex vomitoria Aiton Betula alleghaniensis Britton Aesculus flava Soland (Aesculus octandra Marshall) Liriodendron tulipifera Linnaeus Melilotus officinalis (L.) Lam.

a

Most authorities consider Q. laurifolia and Q. hemisphaerica Bartram ex Willdenow as synonomous. The Flora of North America reports the common name of Q. laurifolia as swamp laurel oak and Q. hemisphaerica as laurel oak.

Mammals  

Common name

Scientific name

Beechey ground squirrel bear bison black bear black-tailed deer Botta’s pocket gopher

Otospermophilus beecheyi Family Ursidae Bison spp. Ursus americanus Odocoileus hemionus columbianus Thomomys bottae Continued

580

Appendix 1

Continued. Common name

Scientific name

chipmunk (eastern) Columbian ground squirrel deer deer mouse eastern cottontail rabbit elk flying squirrel foxes fox squirrel grey squirrel Indiana bat mantled ground squirrel Mexican grey squirrel mouse mountain sheep mule deer New England cottontail rabbit opossum peccary pocket gopher rabbit raccoon red squirrel shrews squirrels voles western chipmunk western fox squirrel western grey squirrel white-footed mouse white-tailed deer wild boar wood rat

Tamias striatus Urocitellus columbianus Odocoileus spp. Peromyscus maniculatus Sylvilagus floridanus Cervus spp. Glaucomys spp. Vulpes vulpes and Urocyon cinereoargenteus Sciurus niger Sciurus carolinensis Myotis sodalis Callospermophilus lateralis Sciurus aureogaster families Muridae and Cricetidae Ovis canadensis Odocoileus hemionus Sylvilagus transitionalis Didelphis virginiana Pecari tajacu Family Geomyidae Sylvilagus spp., Lepus spp. Procyron lotor Tamiasciurus hudsonicus Sorex spp. Sciurus spp. and Tamiasciurus spp. Microtus spp. Neotamias spp. Sciurus niger rufiventer Sciurus griseus Peromyscus leucopus Odocoileus virginianus Sus scrofa Neotoma spp.

Birds  

Common name

Scientific name

Acadian flycatcher acorn woodpecker American crow ant-eating woodpecker Arizona (Strickland’s) woodpecker band-tailed pigeon black-and-white warbler black-billed cuckoo blue-grey gnatcatcher blue jay bobwhite quail brown-headed cowbird brown thrasher California horned lark California jay

Empidonax virescens Melanerpes formicivorus Corvus brachyrhynchos Colaptes auratus Picoides stricklandii Columba fasciata Mniotilta varia Coccyzus erythropthalmus Polioptila caerulea Cyanocitta cristata Colinus virginianus Molothrus ater Toxostoma rufum Eremophila alpestris ssp. actia Aphelocoma californica Continued

Common and Scientific Names of Species

581

Continued. Common name

Scientific name

California quail California thrasher Carolina wren cerulean warbler Clark’s nutcracker common flicker common grackle eastern crow eastern towhee eastern wood-pewee Euroasian jay field sparrow Florida jay golden-fronted woodpecker great-crested flycatcher greater prairie chicken hairy woodpecker hooded merganser indigo bunting lesser prairie chicken Lewis’s woodpecker mallard Mearns quail Meriam’s turkey mountain quail mourning dove northern cardinal northern (common) bobwhite northern (common) flicker Nuttall’s woodpecker ovenbird pileated woodpecker pine warbler plain titmouse prairie warbler purple grackle quail red-bellied woodpecker red-eyed vireo red-headed woodpecker red-shafted flicker ring-necked pheasant ruffed grouse rufous-sided towhee scrub jay sharp-tailed grouse spotted towhee Steller’s jay tufted titmouse turkey turkey vulture valley quail varied thrush white-breasted nuthatch

Callipepla californica Toxostoma redivivum Thryothorus ludovicianus Setophaga cerulea Nucifraga columbiana Colaptes auratus Quiscalus quisula Corvus sp. Pipilo erythrophthalmus Contopus virens Garrulus glandarius Spizella pusilla Aphelocoma coerulescens Melanerpes aurifrons Myiarchus crinitus Tympanuchus cupido Picoides villosus Lophodytes cucullatus Passerina cyanea Tympanuchus pallidicinctus Melanerpes lewis Anas platyrhynchos Cyrtonyx montezumae Meleagris gallopavo Oreortyx pictus Zenaida macroura Cardinalis cardinalis Colinus virginianus Colaptes auratus Picoides nuttallii Seiurus aurocapilla Dryocopus pileatus Setophaga pinus Parus inornatus Setophaga discolor Quiscalus quiscula Family Phasianidae Melanerpes carolinus Vireo olivaceus Melanerpes erythrocephalus Colaptes auratus cafer Phasianus colchicus Bonasa umbellus Piplo erythrophthalmus Aphelocoma coerulescens Pedioecetes phasianellus Pipilo maculatus Cyanocitta stelleri Parus bicolor Family Meleagrididae Cathartes aura Callipepla californica Ixoreus naevius Sitta carolinensis Continued

582

Appendix 1

Continued. Common name

Scientific name

wild turkey wood duck Woodhouse’s scrub jay woodpeckers wood thrush worm-eating warbler yellow-billed magpie yellow-breasted chat yellow-throated vireo

Meleagris gallopavo Aix sponsa Aphelocoma woodhouseii Family Picidae Hylocichla mustelina Helmitheros vermivorum Pica nuttalli Icteria virens Vireo flavifrons

Insects  

Common name

Scientific name

acorn gall wasps acorn moth acorn weevils canker worm elm spanworm filbert weevil filbertworm moth gypsy moth Karner blue butterfly nitidulid beetles nitidulid sap beetles (on acorns) oak bark beetles red oak borer tachinid flies twolined chestnut borer

Cynips spp. and Callirhytis spp. Valentinia glandulella Curculio spp. and Conotrachelus spp. Alsophila pometaria, Paleacrita vernata Ennomos subsignarius Curculio occidentis Cydia latiferreana (formerly Melissopus latiferreanus) Lymantria dispar Melissa samuelis Family Nitidulidae Stelidota octomaculata and Stelidota ferruginea Pseudopityophthorus spp. Enaphalodes rufulus Family Tachinidae Agrilus bilineatus

Fungi (tree pathogens)  

Common name

Scientific name

Armillaria root disease Biscogniauxia canker chestnut blight Phytophthora root rot sudden oak death

Armillaria spp. Biscogniauxia atropunctatum Cryphonectria parasitica Phytophthora cinnamomi Phytophthora ramorum

Notes 1

 Little, E.L., Jr (1979) Checklist of United States Trees (Native and Naturalized). USDA Forest Service Agriculture Handbook 541. USDA Forest Service, Washington, DC. 2  Flora of North America Editorial Committee (1993) Flora of North America North of Mexico, Vol. 2. Oxford

Common and Scientific Names of Species

University Press, New York. Available at: http://beta. floranorthamerica.org/wiki/Volume_2 (accessed 1 February 2019). 3  Flora of North America Editorial Committee (1997) Flora of North America North of Mexico, Vol. 3. Oxford University Press, New York. Available at: http://beta. floranorthamerica.org/wiki/Volume_3 (accessed 1 February 2019).

583

Appendix 2 Forest Cover Types of Eastern USA Dominated by Oaks or Oaks Mixed with Other Speciesa  

Region

Type group

Northern

Pine and hemlock types

Central

584

Type name (number)

Type species/common associatesb

White pine– Eastern white pine, northern northern red oak, red maple/white red oak–red ash, eastern hemlock, maple (20) birches, black cherry, American basswood, sugar maple, American beech White pine– Eastern white pine, chestnut chestnut oak/scarlet, white, post oak (51) and black oaks; hickories, blackgum; sourwood; red maple; pitch, Table Mountain, Virginia and shortleaf pines; yellowpoplar; black cherry Other Northern pin Northern pin, white, black, northern oak (14) bur and northern red types oaks; jack pine/red pine, eastern white pine, quaking and bigtooth aspens, red maple, black cherry Upland Post oak– Post and blackjack oaks/ oaks type blackjack hickories; black, scarlet, oak (40) bluejack, southern red, shingle, white and turkey oaks; shortleaf and Virginia pines; eastern redcedar; and others Bur oak (42) Bur oak/Uplands: northern pin, black, chinkapin and white oaks; shagbark hickory. Bottomlands: hickories, black walnut, eastern cottonwood, white ash, American elm, green ash and others

Geographic distribution [ecoregion Ecological relations province number]cd Southern New England, central NY, PA, Lake States, southern ON [211, 221a, M221]

Moderately dry to mesic sites; early to mid-successional more persistent on dry sites

Appalachian region Dry to mesic sites; from WV to GA, precipitation: 35–80 south-west VA, east inches; mostly TN, west NC [221a, at 1200–3600 M221, 232] ft elevation; physiographic climax on dry sites Central and northern Lower MI, central WI, northern WI, east and central MN [211, 221b]

Extremely dry sites with acid sandy soils; northern pin oak may be successional to associated oaks when present Primarily dry sites where it forms an edaphic climax; subclimax elsewhere

Eastern KS southwards to TX and eastwards to Atlantic Coastal Plain [221a, 221b, M221, M222, 231, 232, 251, 252] Sporadic in the Dry uplands and prairie–forest moist benches transition zone from on river bottoms; southern Canada successional to southwards to associated species northern MO; in bottomlands; westwards to SD persistence in and WY along uplands depends rivers [251, 331, on fire or other 332] disturbance Continued

Continued.

Region

Type group

Type name (number) Bear oak (43)

Type species/common associatesb

Geographic distribution [ecoregion province number]cd Ecological relations

Bear oak/scarlet, chestnut, Coastal Plain from white, black, blackjack, New England post and northern red southwards to NJ; oaks; eastern white, sporadic in western Virginia and shortleaf VA and eastern WV pines; quaking and [211, 221a, M221] bigtooth aspens; sassafras; red maple; and others Chestnut oak Chestnut oak/bear, northern AL and GA (44) red, southern red, northwards to NJ, black, post, scarlet and NY and southern white oaks; hickories; New England [211, yellow-poplar; blackgum; M211b, 221a, sweetgum; black cherry; 231, 232] red and sugar maples; and others White oak– White oak, black oak and Great Plains black oak– northern red oak/northern eastwards to the northern pin, scarlet, southern Atlantic Coast; Lake red oak (52) red, chinkapin, post and States, southern blackjack oaks; hickories; ON and southern yellow-poplar; blackgum; New England sugar and red maples; southwards to white and green ashes; Coastal Plain [211, and others M221, M222, 231, M231, 232, M211b] White oak (53) White oak/northern red, Across the range of scarlet, black, chestnut white oaks from and bur oaks; hickories; MN southwards to blackgum; yellow-poplar; TX, eastwards to white ash; maples; and the Atlantic Coastal others Plain, and north to New England [211, M221, M222, 231, M231, 232, M211b, 252] Black oak Black oak/white, post, Central States, Ozark (110) blackjack, scarlet, Highlands, northern northern red and chestnut IN, Lower MI [221a, oaks; hickories; blackgum; 221b, 251] shortleaf and loblolly pines; yellow-poplar; and others Northern red Northern red oak/yellowMN, WI and MI; oak (55) poplar, black cherry, sporadic in New sugar maple, white ash, England and white oak, American Appalachians from beech and others PA southwards [211, 221a, 221b, M221]

Tall shrub–dwarf tree type restricted to poor, dry sites; successional to associated species, depending on fire frequency

Most on dry Appalachian uplands from 1500 to 4600 ft where it may form a physiographic climax Dry to mesic sites; ranges from subclimax to climax depending on site factors

Dry to mesic sites; ranges from subclimax to climax depending on site factors

Dry to mesic sites; successional to associated oaks on dry sites and other species on mesic sites Moderately dry to mesic sites; usually successional to associated species

Continued

Forest Cover Types of Eastern USA

585

Continued.

Region

Type group Other central types

Southern

Oak–pine types

Type name (number)

Type species/common associatesb

Geographic distribution [ecoregion province number]cd Ecological relations

Yellow-poplar– Yellow-poplar, white oak, white oak– northern red oak/maples, northern red white ash, black cherry, oak (59) American beech, eastern hemlock, black walnut and others

From 500 to 4500 ft; successional to mixed oak or shade-tolerant non-oaks depending on site quality

Pin oak– sweetgum (65)

Bottomlands; successional to associated species

Longleaf pine–scrub oak (71)

Shortleaf pine–oak (76)

Virginia pine–oak (78)

Southern NY, PA and south-west New England southwards along the Appalachians; also Cumberland and Allegheny Mountains [211, 221a, M221] Pin oak, sweetgum/red Ohio River Valley from maple, American elm, WV through OH, blackgum, swamp white south IN, southern oak, willow oak, overcup IL, KY, west TN; oak, swamp chestnut oak, southwards from green ash, hickories and south-east MO to others central AR [221a, 221b, 232] Shortleaf pine with some Piedmont, combination of white, Cumberland southern red, black, Plateau, southern post, blackjack and Appalachians scarlet oaks/blackgum, below 2000 ft, red maple, hickories and north-east and others north central MS, LA, AR, south MO, north-east TX, eastern and central OK [221b, M222, M231, 232, 252] Shortleaf pine with some Piedmont, combination of white, Cumberland southern red, black, Plateau, southern post, blackjack and Appalachians scarlet oaks/blackgum, below 2000 ft, red maple, hickories and north-east and others north central MS, LA, AR, south MO, north-east TX, east and central OK [221b, M222, M231, 232, 252] Virginia pine and some Southern PA combination of southern southwards to GA red, scarlet, black, and AL along the chestnut, white, post and foothills of southern blackjack oaks/shortleaf Appalachian Mts and pitch pines; dogwood; and Piedmont yellow-poplar; blackgum; [221a, M221, 232] red maple; hickories; and others

Subclimax type maintained by fire

Dry to moderately dry sites; potentially successional to associated oaks depending on frequency and intensity of disturbance

Primarily on old fields of low to moderate site quality; successional to oaks or shadetolerant hardwoods depending on site quality Continued

586

Appendix 2

Continued.

Region

Type group Bottomland types

Other southern types

Type name (number)

Type species/common associatesb

Geographic distribution [ecoregion province number]cd Ecological relations

Willow oak– Willow, water and Coastal Plain from Most common water oak– diamondleaf oaks, south-east VA to on flat, poorly diamondleaf Nuttall and cherrybark west FL westwards drained alluvial (laurel) oak oaks; red maple; green to eastern TX [231] floodplains; may (88) ash; sweetgum; swamp be a topographic/ hickory; spruce and edaphic climax on loblolly pines; and others some sites Live oak (89) Live oak/water oak, Southern LA and Narrow belts < 1 mile southern magnolia, south-west MS wide in lowest sugarberry, American elm [231] bottoms; may be a and green ash topographic/edaphic climax on some sites Swamp Swamp chestnut and Sporadic over small Highest first-bottom chestnut cherrybark oaks/green areas in southern ridges; may be oak– ash, hickories, white oak, floodplains of major climax on older cherrybark Delta post oak, Shumard rivers [231, 232] alluvium oak (91) oak and blackgum Sweetgum– Sweetgum and willow oak/ Alluvial floodplains of Self-perpetuating willow oak sugarberry, green ash, major rivers in AR, LA, on first-bottom (92) America elm and Nuttall MS, AL, eastern MO, ridges of alluvial oak eastern TX [231, 232] floodplains Overcup Overcup oak and water Floodplains of the Floodplains where oak–water hickory/green ash, Gulf and southern water stands into the hickory (96) sugarberry, America elm, Atlantic states; also growing season; clay waterlocust, red maple TN and southern IL or silty clay soils; and Nuttall oak [221b, 231, 232] poor site quality; permanence depends on poor drainage Mohr (shin) Mohr (shin) oak in mixture West central TX to Primarily a shrub type oak (67) with other west Texas south-west OK; on calcareous soils; oaks sporadic in Mexico the type is best [313, 314, 321] represented where precipitation is 20–25 inches/year Southern scrub Mixtures of turkey, bluejack, South-eastern Coastal Dry, infertile, sandy oak (72) blackjack, sand post, sand Plain [231] soils formerly live, live and myrtle oaks occupied by longleaf pine or longleaf pine–scrub oak types; may be self-perpetuating with periodic fire

a

Adapted from Eyre, F.H. (ed.) (1980) Forest Cover Types of the United States and Canada. Society of American Foresters, Bethesda, Maryland. Many other cover types not listed also include oaks as associated species. b The species for which this type is named comprise 50% or more of stand basal area or crown cover; importance of associated species is often highly variable. c States are as follows: AL, Alabama; AR, Arkansas; FL, Florida; GA, Georgia; IL, Illinois; IN, Indiana; KS, Kansas; KY, Kentucky; LA, Louisiana; MI, Michigan; MN, Minnesota; MO, Missouri; MS, Mississippi; NC, North Carolina; NJ, New Jersey; NY, New York; OH, Ohio; OK, Oklahoma; ON, Ontario; PA, Pennsylvania; SD, South Dakota; TN, Tennessee; TX, Texas; VA, Virginia; WI, Wisconsin; WV, West Virginia; WY, Wyoming. d Numbers in brackets are ecoregion provinces shown in Plate 1.

Forest Cover Types of Eastern USA

587

Appendix 3 Forest Cover Types of Western USA Dominated by Oaks or Oaks Mixed with Other Speciesa  

Region North Pacific

Type name (number)

Type species/common associatesb

Oregon white oak (233)

Oregon white oak/Douglas South-east Vancouver fir, bigleaf maple, Island in BC Oregon ash, ponderosa southwards through pine, incense cedar, west WA, west OR, California black oak, north CA to Santa Cruz Pacific madrone, Mts [241] canyon live oak and tanoak Douglas fir, tanoak, Coast Ranges of northern Pacific madrone/canyon and central CA and live oak, sugar pine, south-west OR at ponderosa pine, giant 500–4000 ft [M261, chinkapin, California 262, M262, 263] black oak, Oregon white oak and others Similar to eastern type Along streams in NE, 42 except associates in SD, ND and adjacent western edge of range Canada; extends include ponderosa pine westwards into Black Hills of SD [331, 332] California white fir, West side of Sierra ponderosa pine, sugar Nevada range from pine, incense cedar, 3000 to 6000 ft; locally California black oak, on east side of Sierra Douglas fir/California Nevada Range [M261] red fir, Pacific madrone, tanoak and Pacific dogwood California black oak/ Sporadically in small ponderosa pine, areas from central OR Douglas fir, incense southwards through cedar, knobcone pine, Cascade, Sierra Pacific madrone, Nevada and Coast tanoak, Oregon white ranges to near Mexican oak, canyon live oak border [M261, 262, and others M262] Canyon live oak/Douglas Northern Cascade, Sierra fir; ponderosa, Jeffrey, Nevada and Coast sugar and Coulter Ranges southwards pines; bristlecone fir to extreme south of CA [M241, M261, 262, M262, 263, M263]

Douglas fir– tanoak–Pacific madrone (234)

Low Elevations, Interior

Bur oak (236)

South Pacific

Sierra Nevada mixed conifer (243)

California black oak (246)

Canyon live oak (249)

588

Geographic distribution [ecoregion province]cd

Ecological relations Primarily lower slopes between the Coast and Cascade or Sierra Nevada ranges; forms open savannahs and pure closed-canopy stands as well as mixtures; successional to conifers Best developed in mild, moist climate; complex successional relations determined by disturbance, species composition and site factors Primarily stream bottoms; successional to associated hardwoods

Successional status depends on stand composition, associated species, site and disturbance regime

Persistent subclimax in its optimal range of 1500– 3000 ft and 30–50 inches precipitation; elsewhere successional to associated species where it needs fire or other disturbance to hold its own From sea level to 9000 ft on steep canyon slopes and drier canyon bottoms; forms relatively stable communities of mixed to pure stands Continued

Continued. Region

Type name (number)

Type species/common associatesb

Geographic distribution [ecoregion province]cd

Blue oak–digger pine (250)

Blue oak and digger pine/interior live, canyon live, California black and California scrub, coast live and valley oaks; California buckeye

California coast live oak (255)

Forms a nearly Climax type of the oak continuous belt woodland or foothill around the Central woodland of California; Valley of CA between commonly forms valley grasslands and pure stands of blue montane forests at oak especially at low 3000–5000 ft [M261, elevations; stands M262] variable in density Primarily on the Tends to form climax western side of the stands that form Coastal Range in CA; dense, closed-canopy occasionally on eastern stands resembling a slopes; up to 3000 ft in forest; becomes more the north to 5000 ft in savannah-like to the the south [M261, 262, south M262, 263]

California coast live oak/ bishop, Monterey and knobcone pines; Engelmann, interior live, valley, canyon live and blue oaks; tanoak; southern California walnut, Pacific madrone, bigleaf maple and others Emory, Arizona white, Found at 4000–6000+ ft Climax type but can be Mexican blue and elevation in southern replaced by chaparral silverleaf oaks NM and south-east and on drier sites. Typically (evergreen); may include central AZ [311, M311, open woodland with some deciduous oaks 321] dry-tropical shrubs, succulents, cacti and grasses. Makes best development on mesic semi-arid soils

Desert Western live oak South-west (241)

Ecological relations

a

Adapted from Eyre, F.H. (ed.) (1980) Forest Cover Types of the United States and Canada. Society of American Foresters, Bethesda, Maryland. Many other cover types not listed also include oaks as associated species. b The species for which this type is named comprise 50% or more of stand basal area or crown cover; importance of associated species is often highly variable. c States are as follows: AZ, Arizona; BC, British Columbia; CA, California; ND, North Dakota; NE, Nebraska; NM, New Mexico; OR, Oregon; SD, South Dakota; WA, Washington. d Numbers in brackets are ecoregion provinces shown in Plate 1.

Forest Cover Types of Western USA

589

Appendix 4 Formulae for Converting Site Index (in feet at base age 50) of One Species to Another in Unglaciated Regions of Indiana, Kentucky, Ohio and West Virginiaa Equations can be solved forwards or backwards because each was graphically derived to represent the average of the two equations representing each

Convert the site index (SI) of the species below

species pair (e.g. the equations for estimating northern red oak from yellow-poplar, and estimating yellow-poplar from northern red oak).

To Black oak (BO)

White oak (WO)

Scarlet oak (SO)

Chestnut oak (CO)

Northern red oak (NRO)

SIBO = 7.772 + 0.865SIYP

SIWO = 5.826 + 0.831SIYP

–

–

SINRO = 1.310 + 0.954SIYP

Northern red oak (NRO)

SIBO = –0.181 + 1.018SINRO

SIWO = 1.233 + 0.924SINRO

SISO = 3.455 + 0.976SINRO

SICO = –0.928 + 0.968SINRO

Chestnut oak (CO)

SIBO = –2.118 + 1.064SICO

SIWO = –2.752 + 1.036SICO

SISO = 0.858 + 1.037SICO

Scarlet oak (SO)

SIBO = –0.473 + 0.988SISO

SIWO = –1.032 + 0.941SISO

White oak (WO)

SIBO = –2.706 + 1.106SIWO

Yellow-poplar (YP)

a

From: Carmean, W.H. and Hahn, J.T. (1983) Site comparisons for upland oaks and yellow-poplar in the Central States. Journal of Forestry 81, 736–739. https://doi.org/10.1093/jof/81.11.736

590

Appendix 5 Formulae for Converting Site Indexes (in feet at base age 50) for Oaks and Associated Species from One Species to Another in Three Regions Equations can be solved forwards or backwards.  To Convert the site index (SI) of the species below White oak (WO) Scarlet oak (SO) Black oak (BO) Chestnut oak (CO) Yellow-poplar (YP)e Shortleaf/pitch pine (SP) American basswood (AB) American elm (AE) Aspens (A) Black ash (BA) Paper birch (PB) White ash (WA) Yellow birch (YB)

Northern red oak (NRO) in the northern Appalachiansa SINRO SINRO SINRO SINRO

= = = =

1.05SIWO 0.97SISO 1.03SIBO 1.02SICO – –

Scarlet/black/chestnut northern red oak group (SO) in the southern Appalachiansb

Black oak (BO) in south-eastern Missouric

SISO = 1.171 + 1.076SIWO SIBO = 4 + SIWO – SIBO = SISO – 3 – – – – SISO = 27.674 + 0.586SIYP – SISO = 6.251 + 1.001SISP –

Northern red oak (NRO) in northern Wisconsin and northern Michigand – – – – – –

–

–

–

SINRO = –26.909 + 1.406SIAB

– – – – – –

– – – – – –

– – – – – –

SINRO = 17.252 + 0.713SIAE SINRO = 1.756 + 0.872SIA SINRO = 23.568 + 0.633SIBA SINRO = –16.401 + 1.255SIPB SINRO = 22.442 + 0.646SIWA SINRO = 6.247 + 1.008SIYB

a

From: Trimble, G.R., Jr and Weitzman, S. (1956) Site index studies of upland oaks in the northern Appalachians. Forest Science 2, 162–173. https://doi.org/10.1093/forestscience/2.3.162 From: Doolittle, W.T. (1958) Site index comparisons for several forest species in the southern Appalachians. Soil Science Society of America Proceedings 22, 455–458. https://doi.org/10.2136/sssaj1958.03615995002200050023x c From: McQuilkin, R.A. (1976) The necessity of independent testing of soil-site equations. Soil Science Society of America Journal 40, 783–785. https://doi.org/10.2136/sssaj1976.03615995004000050044x d From: Carmean, W.H., Clark, F.B., Williams, R.D. and Hannah, P.R. (1976) Hardwoods planted in old fields favored by prior tree cover. USDA Forest Service Research Paper NC-134. USDA Forest Service, North Central Forest Experiment Station, St Paul, Minnesota. Available at: https://www.fs.usda.gov/treesearch/pubs/10655 (accessed 1 July 2018). e See also Appendix 6, this volume. b

591

Appendix 6 Formulae for Converting Yellow-poplar Site Index to Oak Site Indexes in the Virginia-Carolina Piedmonta  

To convert yellow-poplar (YP) site index (SI) to that of the species below Black oak (BO) White and southern red oak group (WO) Scarlet and northern red oak group (SO) a

Use this equation (where SI is in feet at base age 50) SIBO = 39.7 + 0.45SIYP SIWO = 36.7 + 0.45SIYP SISO = 44.5 + 0.45SIYP

From: Olson, D.F., Jr and Della-Bianca, L. (1959) Site index comparisons for several tree species in the Virginia-Carolina Piedmont. USDA Forest Service Southeastern Forest Experiment Station Paper SE-104. USDA Forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina. Available at: https://www.fs.usda.gov/treesearch/pubs/7372 (accessed 1 July 2018).

592

Appendix 7 Parameter Estimates for Site Index Asymptotes (S) and Species Coefficients (b) for Deriving Height/dbh Site Index Curves from Equation 4.1a  

Species

Site index class

S

b1

White oakb

40 50 60 70

66.68 75.53 82.57 92.88

0.123 0.123 0.123 0.123

Northern red oakb

50 60 70 80 90

74.28 81.00 89.09 95.50 102.20

0.12 0.12 0.12 0.12 0.12

Black oakb

50 60 70 80 90

84.72 90.10 97.53 100.9 108.3

0.086 0.086 0.086 0.086 0.086

Northern red oakc

60 70 80

79.45 93.331 111.848

0.137 0.109 0.098

a

Based on Equation 4.1: H = 4.5 + S(1 - e−bD) where H = total tree height (ft), D = dbh (inches), S = coefficient for the site-specific asymptote, b = coefficient for the species-specific rate coefficient, e = base of the natural logarithm, 4.5 = correction for D measured at breast height. b From: Stout, B.B. and Shumway, D.L. (1982) Site quality estimation using height and diameter. Forest Science 28, 639–645. https://doi.org/10.1093/forestscience/28.3.639 c From: Lamson, N.I. (1987) Estimating northern red oak site-index class from total height and diameter of dominant and codominant trees in central Appalachian hardwood stands. USDA Forest Service Research Paper NE-605. USDA Forest Service, Northeastern Forest Experiment Station, Newtown Square, Pennsylvania. Available at: https://www.fs.usda.gov/treesearch/pubs/21778 (accessed 1 July 2018).

593

Appendix 8 Common Conversions

Length

  Multiply inches (in.) inches (in.) inches (in.) inches (in.) inches (in.) feet (ft) feet (ft) feet (ft) feet (ft) yards (yd) yards (yd) mile (mi) mile (mi) mile (mi)

By 25.4 2.54 0.0254 0.08333 0.02778 304.8 30.48 0.3048 0.3333 0.9144 3 1.609 5280 1760

To obtain

Multiply

millimetres (mm) centimetres (cm) metres (m) feet (ft) yards (yd) millimetres (mm) centimetres (cm) metres (m) yards (yd) metres (m) feet (ft) kilometres (km) feet (ft) yards (yd)

cubic inches (in.3)

Area

  Multiply square inches (in.2) square inches (in.2) square inches (in.2) square feet (ft2) square feet (ft2) square feet (ft2) square yards (yd2) square yards (yd2) acres (ac) acres (ac) acres (ac) square miles (mi2)

594

By 645.2 6.452 0.006944 929 0.0929 0.1111 0.8361 9 4.047 0.4047 43.560 2.59

Volume

 

cubic cubic cubic cubic

By

feet (ft3) feet (ft3) yards (yd3) yards (yd3)

To obtain

16.39 0.02832 0.03704 0.7646 27

cubic centimetres (cm3) cubic metres (m3) cubic yards (yd3) cubic metres (m3) cubic feet (ft3)

Mass

  Multiply

By

pounds (lb) pounds (lb) pounds (lb) tons tons tons

To obtain

0.4536 0.0004536 0.0005 907.2 0.9072 2000

kilograms (kg) metric tons (t) ton kilograms (kg) metric tons (t) pounds (lb)

To obtain square millimetre (mm2) square centimetre (cm2) square feet (ft2) square centimetres (cm2) square metres (m2) square yards (yd2) square metres (m2) square feet (ft2) square metres (m2) hectares (ha) square feet (ft2) square kilometres (km2)

 

Other Useful Conversions

Multiply square feet per acre (ft2/ac) cubic feet per acre (ft3/ac) pounds per cubic foot (lb/ft3) pounds per acre (lb/ac) tons per acre (ton/ac) number per acre (n/ac)

By 0.2296 0.06997 16.0185

1.121 2.242 2.471

To obtain square metres per hectare (m2/ha) cubic metres per hectare (m3/ha) kilograms per cubic metre (kg/m3) kilograms per hectare (kg/ha) metric tons per hectare (t/ha) number per hectare (n/ha)

Board Foot Conversions1 One board foot is the amount of wood in an unfinished board 1 inch thick, 12 inches wide and 12 inches long. A board foot is a measure of merchantability rather than volume. The board foot product yield per cubic foot of wood volume varies depending on tree (or log) diameter, taper, amount of slab and size of saw kerf. Board foot estimates for trees (or logs) of given dimensions also vary depending on the board foot log scale that is used (e.g. Scribner, Doyle or International 1/4 inch). Although 12 board feet of cut lumber is equivalent to 1 ft3 of cut lumber, 1 ft3 of stumpage in a standing sawlog-size

Common Conversions

oak tree will yield roughly 5 board feet when sawn into lumber. Likewise, 1 m3 of stumpage in a standing sawlog-size oak tree will yield roughly 177 board feet when sawn into lumber. An oak tree of sawlog size in the USA is typically at least 9 inches dbh (often at least 11 inches dbh) and has a least one 8 ft log, but standards vary slightly with regional markets.

Note 1

 See: Helms, J.A. (ed.) (1998) The Dictionary of Forestry. Society of American Foresters, Bethesda, Maryland.

595

Index

Page numbers in bold type refer to figures and tables. –3/2 (self-thinning) rule  226–230, 227

abscission, premature  60–61, 61 absolute density  232, 244 accumulation role in regeneration  124–125, 349 survival rate simulation models  145–147 types, ecosystem classification  129–132, 133, 143 variation, influencing factors  125–129 accuracy, in models  155, 156, 159n4, 561 acorn moth  67, 69, 72 acorn predation/dispersal agents birds 79–83, 80, 81, 302 insects 66–73, 68, 69 protection of planted acorns from  382, 383 rodents 73–79, 75 acorn production annual variation  59, 62–64, 143–144, 461–462 comparison of species  64, 65, 455–457, 456, 457 and crown size  208, 460, 461 genetic control  59, 61–62, 64 in green tree reservoirs  463–466, 465 irregular shelterwood systems  314 management actions  263, 462–463 predictive models  456–459, 461–462 seed tree method  314, 462 and site quality  460–461 spatial factors  65–66, 459–460, 460 weather impacts  60, 60, 461 ACORn regeneration model  307–309, 308 acorns cluster size  456–457 development 57–59, 58, 59 direct-seeding pros and cons  380–381 heat resistance  140 maturation period  10 multi-seeded 57 Native American food use  8, 9, 42 premature abscission  60–61 seeding methods  381–382 size variation  84–85, 382, 456 adaptation to climate change management strategies  513–514, 515–518, 516, 523–525

species’ capacity groupings  519, 519 species-specific adaptability scores  520–522, 521 advance reproduction  124–125, 260, 266–267 active measures to encourage seedlings  301–302 inventories, uses  297, 298, 307, 310–311, 355 adventitious buds  96, 97, 265 ADVREGEN regeneration model  305–307, 306 aesthetic quality human experience variation  494–495 irregular shelterwood  326 landscape scale  496, 498–499 during savannah restoration  447 scenic beauty ratings  495, 495–496, 498 afforestation  377, 379–380, 525 age of trees distributions, correlation with diameter  343–346, 346 effect on stump sprouting probability  97, 98 in old-growth forests  489, 490 and probability of cavities  477 related to wood value/quality  536–537, 537 upper age limit, oaks  448 used in site index determination  179, 179, 184 agriculture abandoned land, reversion to forest  23, 32, 260 forest clearance for  22, 31, 43 agronomic model, silviculture  2, 26, 175 Allegheny hardwood forests  236, 239, 309–310 deer browsing  388, 426–427 thinning and carbon stocks  485, 487, 515 ambivalent accumulation  130, 133 American chestnut  23, 24, 25, 26, 260, 412, 575 anamorphic curves (site index)  181, 182 annual net primary production (ANPP)  170, 171, 188 anthropocentric viewpoint  2 apical dominance  353 Appalachian Mountains  19, 25, 175, 275–276 area control  296, 335, 366, 368, 448 Armillaria root rot disease  100, 411, 418 artificial regeneration enrichment planting  390–399, 463 factors in success  377–378 of ground flora, in degraded sites  272, 447 oak seeding and planting  314, 364, 379–385 plantation establishment  385–390 suitable sites and oak species  378–379, 517, 518

597

aspect, effect on site quality  182–183, 183 average maximum density  236–237, 340 avian habitats see birds

bareroot seedlings  383–384, 384, 390, 426 bark biomass contribution  481, 481, 482 thickness and bud emergence  96, 99 and fire resistance  135, 136, 137, 138, 276–277 bark beetles  421 basal area changes, in stand development  204–206, 205, 210, 211 related to acorn production  458, 458 relationship to q, and guiding curves  354, 354, 356–357, 357 relative change, as biomass indicator  481, 482, 514 relative (RBA), coppice crop stems  320 in relative stand density measures  238, 243 bears, black  472, 475 bifurcation rate, tree branching  458–459 biocentric viewpoint  2, 3, 6 biodiversity and forest resilience  79, 517 role of oak artificial regeneration  377 savannah/woodland  273, 274, 432, 438, 439 value of dead wood  314 biofuels  483–484, 515 biological control  417 biomass accumulation, carbon accounting  485, 486, 514, 562 components, equations and conversions  480–483, 482 estimation, for roots  90, 170, 172 in forest floor woody debris  474 net annual production, acorns  62 for oak bioenergy products  483–485 related to stand density (–3/2 rule)  226–228, 227 use in ecosystem production dynamics  170, 171, 479–480 biomass increment  483, 484, 486, 514 biophysical methods, site evaluation  188 birds acorn consumption and dispersal  79–83, 80, 81, 302, 472 diversity reduced by succession to forest  438 habitat management for  380, 463–464, 466, 467, 492 predators of acorn insect pests  68 species and forest structure  467–469, 468, 469, 470 tree cavities as nesting sites  475, 475 black oak fire effects  276, 279–280 recruitment rate  127–128, 128 reproduction and overstorey density  132, 134, 134

blue jays, acorn dispersal  79, 81, 84, 302, 472 blue oak, stump sprouting  99 blue oak–digger pine forest type  44, 46 board foot volume  186, 553, 600 boars, acorn consumption  474–475 boles of trees fire scarring  254, 277–280, 278 impacts of thinning  318–319 low branching/forking  322–323 wood quality, influencing factors  536–537, 537 Boston Mountains, Arkansas enrichment planting  398–399 stand growth and yield model  551–552, 552 bottomland hardwood forests green tree reservoirs  463, 463–466 regeneration after clearcutting  302–303, 303, 310–311, 311 species and sites for planting  378–379, 385–386 species composition  34, 34–35, 35, 38, 39 tree regeneration strategies  122–123, 123 Brender’s formula  342, 369n3 browsing, animals  97, 388, 426–427, 439 buds adventitious  96, 97, 265 bud bank (dormant) on stems  89, 96, 99 desiccation and mortality  92–93, 104n5 dormancy and flower initiation  54, 57 formation in growth resting phases  85 spring budbreak, timing  89 vegetative/flowering, positions on tree  54 buffer zones, old-growth stands  493–494 bur oak, distribution  37, 39, 378–379 burial of acorns effects on seedling survival  76–77, 77, 78, 264, 264 protection from insect pests  72 recommended planting depths  382 burning see fire

caching of acorns  66, 73–78, 79–83, 81, 472 California chaparral shrub-oaks, fire adaptation  122 forest cover types  44–45, 45 oak woodland reduction  42, 43, 433–434, 435 savannah vegetation  434 scarcity of woody debris in woodlands  474 sudden oak death incidence  424, 425 callous tissue  96, 277, 477 cambial growth  531, 531 canker worm  210, 211 cankers girdling stump sprouts  101 side effect of soil fumigants  421–422 sudden oak death symptoms  424

598Index

canopy closure thresholds, woodlands  433, 467 cover at different stocking levels  238, 238, 244 structure  150–151, 195–196, 196, 490 three-tiered, for uneven-aged stands  358 vertical stratification  204, 213, 460 see also gaps, canopy canyon live oak  44 carbon sequestration accounting, estimates using biomass  479–480, 482–483, 514, 562 forest management for  485–487, 487, 513, 515 longevity in forest products  480, 514–515, 515 catastrophic regeneration mode  150 catkins (male flowers)  54–57, 55, 56, 61 cavities, tree distribution and conservation in stands  476, 476–479, 478 formation/excavation 475–476 wildlife use of  475, 475, 477 Central Hardwood region acorn pests  72 application of single-tree selection  347–348 clearcutting, introduction and outcomes  297, 297–300, 298, 324 climate and topography  20, 25 climate change impact prediction  522, 525–526 forest history  25–26 geographic extent  19, 24–25 oaks as forest components  26–31, 27, 30 old-growth forest extent  488 site classification system  188 thinning guidelines  319 chaparral communities  45, 122, 135, 218 charcoal domestic uses  483 industrial use  22, 260 paleo-historical evidence of fire  255, 257 chestnut blight, impacts  23, 24, 25–26 chipmunks  75, 76, 77 chromosome number  54 cleaning 316–318, 317, 484 clearcutting acceptance and public attitudes  324–326, 495, 496, 497 enrichment planting  390 habitat associations of birds  469 predictive regeneration models  304–311 regeneration outcomes, site variation  297–303 replacement of oaks by other species  263, 299–301, 300 with reserves  298, 298, 314 stands affected by oak decline  420 vegetation flush response  176, 298 climate and fire incidence  250, 255

climate change adaptation strategies  515–518, 516 causes and trends  45–46, 511–512 expected geographic range shifts  197, 512–513 forest management implications  378, 511, 513–514, 523–525 impacts on vegetation  46, 486, 523, 525–526 mitigation by carbon sequestration  480, 514–515 prehistoric 255–256 vulnerability assessment  518–523, 519 climatic ecoregions, US  14, 19, 20, 40 clonal variation, acorn yield  61 CO2 emissions offset by forest carbon sequestration  480, 513, 514–515 projected climate impacts  511, 512 coarse woody debris  473–475, 474 cohort  124–125, 126, 129, 144, 145, 252, 296, 345 compartmentalization of injuries  279, 321, 476 compatibility scores  520–522, 521 compensation point  91, 104n4, 312 competition in clumps of stump sprouts  100–101, 104, 319 effect of tree spacing  303 impacts on range expansion  523 influence of fire on oak dominance  260, 265 seedlings and grass, oak savannahs  77, 78, 124 in stem exclusion stage  203 weed vegetation in plantations  386–387 competitive capacity, planted trees  391–392, 397 competitive sorting (regeneration)  124, 154, 155, 300–301, 304 complex stage, even-aged stands  200, 202, 213–215 Connecticut, stand development study  210, 211, 212 Conotrachelus acorn weevils  68, 68, 71, 72–73 container-grown seedlings  384–385, 390 continuous regeneration mode  150–151 contributing factors, oak decline  418 conversion even-aged to uneven-aged  358, 360–361, 361 stand species composition  416, 417 coppice for charcoal production  260 clump thinning effects on growth  319, 319–321, 320, 322, 535 selection of stems  323–324 defects  319, 321–323, 322 site index curves  180, 180 cork oak, Spain  554 cover crop plants (mulch)  386, 387 cover types, US forests definition and geographic ranges  11–12, 15, 585–590 ecological status and presence of oaks  13–14

Index599

Covington curve  177 crop trees management for sawlog quality  315–316, 317–318 risks from fire  263, 270 thinning, effect on diameter growth  534, 547 Cross Timbers region  37 crown closure rates, canopy gaps  148–150, 149, 198, 362 crown competition factor (CCF)  232, 234–235, 243, 561 crown sprouts  96, 104n6 crowns (of trees) canopy position classes  195, 196, 270, 271, 460 condition, and gypsy moth susceptibility  413–414, 414 crown class related to diameter growth  532, 533 crown fires  253, 313 geometry in stands  227–228, 228, 230–231, 231 maximum/minimum growing space  233–237, 235 relative cover charts  441–443, 442 spatial distribution of acorns  65–66, 457 thinning  315, 534 Cumberland Plateau  188, 309 Curculio acorn weevils  67, 68, 68–69, 69, 71 cutting cycle calculations  344–345, 356, 357, 366

dbh see diameter, trees dead wood in crown dieback  314, 418 effect on fuel load  446 quantity, in stand initiation stage  201 release of sequestered carbon  480, 486, 514 salvage thinning  416 wildlife benefits  473–475, 493 decay compartmentalization in living trees  476 in fire-scarred trees  279, 280 role in nutrient cycling  177 secondary infection in diseased trees  424 of snags and debris  473 in stumps, effect on sprouting  99–100, 321 deer browsing  275, 388, 426–427, 473 defoliation imminence categories  412 den sites, wildlife  475, 479, 479 dendrochronology  257, 259, 532 density, nursery seedlings  383, 385 density of oak wood  483 density, stand –3/2 rule  226–230, 227 absolute and relative measures  231–232 coefficients, range and calculation  341, 342 deviations from theoretical models  229, 229–231, 230 diagrams 237–245, 245 reduction, for community restoration  271–273 Reineke’s model  225–226, 226, 228

stocking measures  232–233, 439–441, 459 see also thinning depletion rate, k 339 diameter distribution curves balanced and unbalanced  337, 337–338 correlation with tree age  343–346, 346 differences between species  350, 350 guiding curve specification  341, 341–343, 344, 355–356 negative exponential (reverse J-shape) 204, 215, 338–340, 340 predictive models of change  550, 553 self-limiting instability  352, 352–353 in stand development  204, 204, 206, 210, 215 diameter-limit cutting, flexible  357–358 diameter, trees effect on stump sprouting  97–99, 98, 101, 103 and estimation of crown cover  441–443, 443 and fire survival  136, 138, 270, 276 growth rates effects of thinning  533–536, 534 measurement 531 variation 531–533, 532 in historical and contemporary forests  440, 441 mean (average), for tree stands  225, 239, 441 in old-growth forests  215, 215–217, 216, 493 and probability of tree cavities  476, 476–477, 478 related to acorn production  460, 460 relationship to biomass  482, 483 relationship to tree height  538, 538–540, 540 used in site quality estimation  184–185, 185 dieback crown, in sudden oak death  424 and decline, older trees  91, 417–420 shoots, and resprouting  89–90, 90, 91–94, 93 digital landscape viewing  498–499 direct seeding  301, 380–382 discing, seeding/planting sites  380, 382, 386, 387 diseases introduction and spread  260, 410, 420–421, 424 symptoms  418, 421, 423–424, 424–425 transmission  321, 421, 425 treatment/prevention  421–423, 424, 425–426 dispersal acorns  66, 73, 74–83, 80, 81 limitations on rate and success  518 gypsy moth  409 invasive plant species  274 pollen  55–57, 518 weevil pests, after fire  264 distribution of oaks see geographic distribution disturbance climate change impacts  511 ecological adaptation, savannahs  432, 434–435 effects on forest species composition  13, 260–261 and gypsy moth susceptibility  413 levels in old-growth forest  488–489

600Index

low-disturbance regimes  262, 262–263 regeneration modes  148–151 responses to  132, 134–143, 135, 155, 198–199 size and frequency  197–198, 199, 253 sprout regrowth responses  90, 91, 96 types 196–197 understanding, as basis of silviculture  2, 121, 196 see also fire regimes disturbance–recovery cycles  217–220, 218 DNA fingerprinting  75 dominance probability estimation for regeneration models  306, 306, 307, 307 planted seedlings in shelterwoods  392–397, 393, 394, 396 red oak seedlings  313 species compared  304, 305 stump sprouts  101–102, 104, 105 dormancy broken after fire  252, 273, 275 buds  54, 99 detection by squirrels (acorns)  74 epicotyl and embryo types  83 drippy nut disease  70 drought inciting factor for oak decline  418 tolerance  13, 88–89, 249 dry weights  481, 481, 482, 483

eastern cottonwood  380, 386 Eastern oak forests  17 Central Hardwood region  24–31 Forest–Prairie Transition region  35–39 Northern Hardwood region  17–24 Southern Pine–Hardwood region  31–35 eastern redcedar  31, 37, 253 ecological amplitude  12, 13 ecological classification of sites  172–174, 303, 355 ecological model, silviculture  2, 26, 174 ecology definition 1 interactions in oak forests  78–79 mimicking, approach to restoration  445–446 production dynamics  170 see also successions, ecological economics artificial regeneration costs  385, 390, 399 deer exclosure fencing  426–427 productivity measures  170, 172 return on investment, oak timber  174–175 selection silviculture costs  367 use of residues for biomass energy  484 value growth, oak trees  536–537, 537, 548 viability of silvicultural practices  4–5, 297, 317, 324 wildlife and timber management links  466

ecoregions and distribution of oaks  14, 16–17, 19 hierarchical classification  14, 16 ecosystem services  2, 4, 455, 477 ecosystems accumulation characteristics  129–132, 133, 143 maintenance of diversity  121, 249, 466 modelling, silvicultural use  154, 156 natural, biocentric view  2, 3 primary succession  201 stability/instability  346, 494 sustainable management  6 edge effects  364, 365, 367, 493–494 ELTPs (ecological landtype phases)  14, 16, 21, 28, 29 embryo dormancy  83 endangered species (wildlife)  445, 474, 516 energy, biomass sources  483, 515, 524 enrichment planting objectives  377, 390 prescriptions and methods  389, 397–399, 398 in shelterwood, principles  390–397 environmental activism  325 epicentres, disease  421–423, 423 epicormic branching after oak decline dieback  418 effect of light intensity/shading  99, 318, 318 effect on economic value  536, 548 related to stand density  244, 323, 535 as response to fire damage  97 in uneven-aged stands  353, 357, 364 epicotyl dormancy  83 eradication, pests/disease  417, 425 Euroasian jays, acorn caching  79 evaluation growth and yield models  561–562 site quality methods not based on tree growth  185–188 site index determination  178–185 even-aged stands complex (mature) stage  213–215 definition 199–200 developmental stages  200, 200–201, 202 growth and yield estimation  544–548 management methods  296, 448 stand initiation  201–203, 217 stem exclusion stage  203–208 understorey reinitiation  208–213 evolution, oaks  8–9, 10–11 exclusive space geometry  227–228, 228 exposure classes (climate change)  518–519, 519 extreme events  512, 526

Fagaceae (beech family)  8, 9 female flowers  57–59, 58 premature abscission  60–61, 61 fencing, for deer exclusion  426–427, 447

Index601

ferns 275 fertilization of ovules  57, 58, 61 fertilizer application  177–178, 388, 532–533, 548 filbertworm moth  66, 69, 69–70 fire damage acorns  255, 264, 264, 266 sprouting responses  97, 265 wounds and scarring  277–280, 278, 446 ecosystem effects  134–135, 136, 249, 254–255 effects on oak accumulation  138–140, 141, 143, 271 fuel models  258 geographical extent  253, 258–259 herbaceous plants  268, 272–274, 280 intensity and species interactions  141–143, 142, 250 litter  250, 253, 257, 258, 263, 264, 264, 269, 271, 272, 273 overstorey mortality  260, 276–277 persistence, oaks’ competitive advantage  137–138, 260–261, 265–267, 266 prescribed fire  249, 250, 254, 255, 263, 264, 265, 266, 267, 268, 268, 269, 270, 272, 274, 275, 277, 280, 386–387 recruitment into the overstorey  270 site preparation  269 stand structure  266 suppression and oak regeneration  262, 262–263 survival rates  543, 543–544 survival related to bark thickness  135, 136, 137, 138, 276–277 used to control insect pests  72–73, 263–264 used in shelterwood regeneration method  313–314 fire regimes attributes  249–255, 257 considerations for savannah restoration  446 ecosystem effects  434–435 effects of wildfire frequency reduction  37, 39, 199, 253, 259–260 fire frequency  250–252, 259–260, 267, 436 intensity 250 seasonality 252–253 size  253, 257, 258–259 type 253 ground flora community management  270–276 historical changes  251, 256–260, 436 in old-growth forests  489, 492 pre-settlement, North America  3–4, 20, 22, 42, 255–256 role in sustaining savannah  36, 134, 256, 273–274, 438–439 FLMs (forest landscape models)  557–559 flooding tolerance  123, 302, 378, 464, 464–465 winter, artificial, in GTRs  463, 463–464, 465–466

flowering age of onset (tree maturity)  53, 104n1 impacts of fire in flowering period  263–264 influencing factors, modelling  461–462 position of flowers on trees  54 see also female flowers; male flowers (catkins) FORCAT regeneration model  309 forest cover type see cover types, US forests forest landscape models (FLMs)  557–559 forest resilience  219, 473, 516, 516–517 forest resources/products biomass products  483, 515 historical uses  1, 8, 9 sustainable management  6, 484–485 thinning products  315 FOREST VEGETATION SIMULATOR (FVS) model  415, 526, 555–557, 556, 559 Forest–Prairie Transition region environmental conditions  20, 36, 432 forest history  36–37, 134, 217, 433–434 geographic extent  19, 35–36 oaks as forest components  37–39, 38 fossil fuel use  480, 485, 511, 515 free thinning  315, 366, 368 frost acorn/seedling survival  83 avoidance by budbreak timing  89 crown damage and acorn production  60, 60 effects of using tree shelters  389 impacts on pollen production  56–57 overstorey protection of oak reproduction  299, 312 shoot dieback and regrowth  92 fuel loading, woodlands  254, 257, 277, 446 fuelwood, domestic  22 fumigants, soil  421 fungal infections  100, 278–279, 420–423 fusion, stems  319, 321 FVS growth and survival model  415, 526, 555–557, 556, 559

gall wasps (cynipids)  67, 68, 69, 70–71 gallery forest  37 Gambel oak, below-ground structures  96, 122 gaps, canopy capture by advance reproduction  125, 128–129, 150, 152 creation, by tree deadening  492 crown closure  148–150, 149, 198, 231 gap-scale disturbances  148–150, 151, 197–198 size group selection method  361–364, 363, 364 single-tree selection method  336 genetics acorn production  59, 61–62, 64 spatial diversity of oaks  75, 517, 518 geographic distribution

602Index

climate change impacts  197, 512–513, 526 ecoregions  14, 16–17, 19 oak species and forest types  11–14, 12, 15, 28, 564 prehistoric spread of oaks  255 Quercus taxonomic groups  9–10 germination acorn desiccation  83, 383 effects of acorn burial  76–77, 77, 264, 264 forest floor characteristics  83, 84 from forest floor seed bank  124, 252, 265 hypogeal and epigeal types  266 stages  82, 83, 84 stratification requirement  381 see also seedlings Gingrich diagram  232, 237, 237–241, 245, 439–440 girdling, stems  387, 388, 465 glaciation landforms and soils  19, 421 post-glacial vegetation change  79 vegetation refugia  255 gophers, acorn/seedling predation  77 gradients, environmental elevation, and flood tolerance  464 moisture, regional to micro scales  378–379 species segregation  12, 13, 121 ‘granary’ trees (woodpecker stores)  79–81, 81 green tree reservoirs (GTRs)  377, 463, 463–466, 465 green weights  481, 481, 482 Greenberg–Parresol method  458, 458 GROAK growth and yield model  550–551, 551 modified for Boston Mountains, Arkansas  551–552, 552 gross primary productivity (GPP)  170 ground flora community restoration  444–445 use of fire  270–272, 277, 440, 446 invasive species  274–276 native species diversity  272–274, 273 group selection method edge effects  364, 365, 367 opening size  361–364, 363, 364 practical application alternatives  365–367, 368 principles 361, 362, 497 growth after shoot dieback  90–91, 91 annual, phenology  530–531, 531 effect of tree shelters  388–389 rate and injury recovery  279 rate responses to thinning  318, 320, 533–536, 534 regulators, translocation  92, 99 responses to canopy gaps  198–199 seedlings 84–89 of shoots, flush patterns  85, 86, 89, 531 stump sprouts  100–101, 102, 103 variation between trees  345, 531–533, 532, 533

growth and yield models accuracy and usefulness  156, 157, 530, 561–562 display of changes through time  243–244, 245 individual-tree-based scale  553–557, 554 ingrowth estimation  559, 559–561, 560 stand-level models  550–553 types and applications  549–550 guiding curves  340–343, 341, 344, 344–345, 355–356 gypsy moth distribution 409, 410 effects on stand development  210, 211, 411–412 life cycle and tree damage  409, 411, 411 management guidelines  412–417 population control by rodents  78, 413, 417 risk rating  413 susceptible and resistant species  411, 412, 416

hail damage  61 hardwood chips  32 harvesting best management practices  177, 477, 485 cutting cycle, uneven-aged stands  335, 337, 345, 357 effect on age:diameter correlation  345–346 fire-scarred trees  280 impacts on site productivity  175–177, 484–485 residual coarse woody debris  473, 483, 484 retention of cavity tree clumps  479 for stands affected by gypsy moth  415, 417 strategies for regenerating oak stands  262–263 use of guiding curves  340, 341, 347, 357 visual/aesthetic impacts  496, 496, 497–498 health and safety, human  255, 272 heartwood, decay resistance  279 height of trees equations/curves for estimation  538–540, 539 merchantable heights  540–542, 541, 542 growth patterns influence of thinning  538 species compared  181, 181–182, 539–540, 540 measurement difficulty  542 and opening size for regeneration  362, 364 used in site index determination  178–181, 179, 184–185, 185, 537–538 herbaceous vegetation  174, 201, 273, 432 herbicides invasive species control  275, 445 to reduce oak competitors  302, 313, 397–398 in root-graft barrier construction  422 subcanopy thinning, shelterwood method  312 treatment for oak wilt  423 weed control, plantations  386, 388 herbivore damage  387, 388, 426, 439, 473 Holocene period  255–256

Index603

human culture emotional responses to forests  494–495 impacts on fire frequency  251, 252, 256, 259–260, 436 social elements and forestry  325–326 uses of oaks  8 humidity, effects on pollination  56, 60, 461

importance value predicted climate change impacts  520, 521, 522, 523 range, along environmental gradients  13 improvement cuttings  316, 319, 419 inciting factors, oak decline  418–419 incomplete stand-scale disturbances  198, 217 indicator species  16 indigenous people see Native Americans individual-tree growth and survival models  550, 553–557, 554 ingrowth modelling  559, 559–561, 560 inhibition model of succession  154 initial floristics models of regeneration  151–152, 153, 157 insects disease vectors  420–421 forest floor pests, control by fire  72–73, 140, 263–264 infestation of acorns  66–73, 68, 69 parasites of oak/acorn pests  68, 71 spatial preferences in tree crowns  66 see also gypsy moth intensity, fire  250, 269, 276, 436, 448 Intergovernmental Panel on Climate Change (IPCC)  512 intermediate cuttings aesthetic consequences  495, 498 coppice management  319, 319–324 objectives and types  297, 315–316 thinning practices  316–319, 317 intrinsic accumulation ecosystem characteristics  129, 133, 155, 447 recruitment dynamics  348 regeneration potential  297–298 use of single-tree selection method  350–353, 351, 355 invasive species control of exotics in old-growth forests  489 native 275–276 non-native  274–275, 445 invertebrates  466, 470, 474 ironworks, use of charcoal  22 irregular shelterwood method  314, 326 isometry (tree proportions)  228, 230–231

juvenile trees, definition  53

knowledge disciplines, position of silviculture  5, 5–6

lag times, acorn production  63–64, 64 LANDIS forest landscape model  557–559 landscape-scale considerations aesthetics  496, 498–499 buffer zones for old-growth stands  493–494 simulation models of change  557–559 wildlife habitat  466–467 Langsaeter principle  548 larder-hoarding 76 latent periodicity  64 lateral roots development and function  84, 383 mass and surface area  85, 88 leaf litter flammability  250, 253 impacts on seedling establishment  83–84 maintenance of soil quality  175 reduction by burning  140, 264, 440 leaves area development in seedlings  85 defoliation by gypsy moth larvae  409, 411 expansion related to pollen shedding  55, 56 legislation, forest reserve establishment  1 liberation cuttings  316 lightning, fire ignition  250, 252, 256 lignotubers  96, 170 linear stages, shoot growth  85 de Liocourt’s q see q values Lithocarpus (tanoak genus)  9 livestock grazing impacts  260, 435, 447 Lobatae (red oak taxonomic group)  9–11, 10, 46n1 logging industry damage during felling  364, 367 historical development  22, 31–32, 42–43, 260 practices and soil erosion risk  175–176 low-severity fires, ecological effects  257, 447 low thinning  315, 443, 444, 485

male flowers (catkins)  54–57, 55, 56, 61 management of forests compared with savannahs/woodlands  439, 439 diversity of goals  487, 514, 524 effects on site productivity  177 requirement in protected areas  3 scientific approach, historical origins  1–2 social acceptability  4, 324–326 timber harvesting decisions  26, 262–263 use of predictive models  156, 415 maps anticipated species range shifts  512–513, 520, 522, 526 geographic distribution of species  564 landscape modelling capabilities  557, 559 pests and diseases  410 soil survey/site quality  172, 173, 419

604Index

marking difficulties in group selection method  365–366 guidelines for single-tree selection  356–357 root-graft barriers  422 for thinning, oak decline sites  419 masting cycles  62, 63 mathematical models  156, 530 mats, fungal (oak wilt)  422, 423 maximum crown widths  233, 234, 441–443, 442, 443 mechanical thinning  315, 535 Mediterranean climates  42, 89, 170, 438 meiosis  54, 57, 61 merchantable height estimation  540–542, 541, 542, 562 mice acorn predation  76, 77 role in gypsy moth control  78, 413, 417 use of tree cavities  475 midstorey 195–196, 196, 253, 268 migration assisted, methods  524–525 corridors, and ecosystem stability  494, 517 migrant birds, habitat needs  467 of oaks, rate limitations  517–518, 522–523, 525 millipedes, acorn damage  72 minimum tree area  235, 241 mitigation strategies, climate change  480, 513, 514–515 mixed severity fires  254, 257 mixed-species plantings  379–380, 381, 524 mixed-stage stands  217, 218 modelling theory and uses  154–156, 549, 561–562 models, predictive acorn production  461–462 climate change impacts  512, 513, 517, 522–523, 526 fire probability/behaviour  255, 258 forest landscape models (FLMs)  557–559 growth and yield  156, 157, 526, 530, 549–557 mortality from gypsy moth defoliation  414, 414–415 oak wilt transmission  422, 422 regeneration after clearcutting  304–311 succession/regeneration potential  151–154, 156–158, 158, 365 Mohr oak forest cover type  11, 41 moisture content critical levels for acorn viability  83, 264, 383 flammability of fuels  250, 252, 253 related to biomass  481, 481 soils  175, 188, 424 monitoring early detection of invasive species  275, 445 long-term climate and forest change  524 of management outcomes  441, 445, 447 for savannah community maintenance  444, 448–449 stand inventories, for single-tree selection  358 sudden oak death detection  425

Monogahela Decision (1970) 325 mortality buds 92–93 due to fires  254, 255, 265, 276–277 in natural and managed forests  225, 338 rate estimation  542 seedlings 77, 78, 92, 94 stump sprouts  100–101, 101, 323 trees, natural causes  148, 225, 409 regular and irregular  542–543 see also diseases moths, acorn-infesting  67, 68, 68, 72 primary invaders  69, 69–70 secondary invaders  69, 72 mulches  386, 387–388

names, common and scientific  11, 575–583 National Cooperative Soil Survey  172 National (public) Forests climate change impact predictions  520 development of old-growth characteristics  492 management decisions  325 Native Americans acorns used as food  8, 9, 42 nomadic/permanent prairie communities 36 role in oak forest fire regimes  3–4, 20, 22, 256, 435–436 natural regeneration clearcutting 297–303 seed tree method  314 shelterwood method  311–314, 312 silvicultural management methods  296–297 in single/group selection methods  351, 353, 364–365 net ecosystem production (NEP)  170 net primary productivity (NPP)  170 niche, ecological individual species  12 regeneration  88, 121–122, 148 nitidulid sap beetles  67, 72–73, 421 NNIS (non-native invasive species)  274–275, 445 normal stocking  233, 544–545, 545, 546, 564n1 normes (French relative density)  232, 244 Northeast Decision Model (NED)  555, 557 Northern Hardwood region carbon loss and recovery study  177 climate, topography and soils  19–20, 20, 21 forest history  20, 22–23 geographic extent  17–19, 19 oaks as forest components  23, 23–24 notching (embryo excision)  73–74, 472 nurse crops  386 nursery practices  383 nursery stock  382–385, 384, 385, 425, 525

Index605

nutrients cycling in forest ecosystems  169 deficiencies and fertilization  177–178, 388 net losses after harvesting  176–177, 484 Nuttall oaks, seeding strategy  123

oak decline  91, 417–420, 425 oak scrub communities  217, 436 oak wilt geographical incidence  410, 420, 421 symptoms  421, 425 transmission and control  321, 420–423, 422, 423 oak–gum–cypress forest cover type  15, 34, 34–35 oak–hickory forest cover type  14, 15, 26–27, 37, 468 oak–pine forest cover type  15, 28, 30–31, 33, 33 OAKSIM growth and yield model  538, 554–555, 555 OAKUS regeneration model  399 old-growth forests biodiversity 488 cumulative carbon sequestration  486 definition and significance  214–215, 487–488 diameter distributions  339–340 extent and characteristics  488, 488–489, 490–491, 493 landscape role, and buffer zones  493–494 maintenance management  489, 492–493 wildlife habitat value  468–469 Oregon white oak cover type  43–44 organic matter, forest floor  177, 486, 525 ‘overstocked’ stands  232, 238, 240–241 overstorey crown classes  195, 196 density reduction effects  311, 312, 3192 disturbance, and regeneration  148–154 inventory, for single-tree selection  354–355 management for ground flora maintenance  273 mortality due to fire  276–277 species composition  203 ovules 57, 58 ownership, small-scale private tracts  26 Ozark Highlands disturbance–recovery study  219, 220, 221 forest cover types  26, 28, 33 oak regeneration  122, 129 predictive regeneration models  305–309, 306, 308 regeneration after clearcutting  298–299, 299, 304, 305 savannah/woodland restoration  433, 438 shelterwood enrichment planting  397–398, 398, 399 single-tree selection feasibility  350–353, 351, 352, 359 site productivity  176–177 stand development  208–210, 209, 216, 216–217

Pacific Mediterranean–Marine region climate, topography and soils  40, 42 forest history  42–43 geographic extent  19, 41–42 oaks as forest components  43–45, 45, 46 packing constant  228 pellets, wood  483 periodicity acorn production  62–64, 64, 65 root and shoot growth  85 persistence of oaks  203–204, 210, 304 phenology, oak growth  530–531, 531 philosophies, environmental  1–4 photosynthate translocation  85, 87, 91, 100 photosynthetically active radiation (PAR) 391, 443, 444 Phytophthora cinnamomi 424 Phytophthora ramorum 424–425 pin cherry  124, 149–150 pin oaks  421, 464–465 Pinchot, Gifford  1 pine-to-oak successions  302 pip galls  70 plant growth regulators  92, 99 plantation establishment  385–390, 525 planting see artificial regeneration ploughing, for severing root grafts  422 pollen 54–57, 55, 255–256, 518 pollen tubes  57, 58, 61 polymorphic curves (site index)  181 population density fluctuation  143–147, 147 prairies, vegetation  36–37, 274 precipitation patterns  512 precommercial thinning  316–317, 317, 361, 525 coppice clumps  321, 323 predators (natural enemies)  68, 382, 387, 417 predisposing factors, oak decline  418, 419 presalvage cuttings  316, 415 prescribed burning see fire regimes primary invaders of acorns  68–71, 69 private forests, acceptable management  325, 367 process models  156, 188 productivity in carbon budgeting  480 ecological components  170, 171 fuels and fire potential  250 ground flora  272 measures of yield  548 regional patterns  562–564, 563 see also acorn production; site productivity Protobalanus group (Quercus section)  9–10, 46n3 pruning injuries, disease transmission risk  423 roots, nursery seedlings  384, 384, 398 seedlings, survival and resprouting  94 self-pruning (branch shedding)  380

606Index

public opinion objections to clearcutting  324–325 perceptions of forest management  2–3, 368–369, 496, 496

q values calculation 339–340, 340, 343, 343 estimation from basal area curves  354, 354 maintenance of desired range  347, 353, 356, 493, 548–549 self-limiting instability  352, 352–353 Quercus (oak genus) evolution  8–9, 255 species, taxonomic groups  9–11, 46n, 276

radial shakes (seams)  321, 322 rapid white oak mortality (RWOM)  423–424 rate-of-spread, fire  250, 253 rating systems, acorn crop  456–458, 457, 458, 462, 462 recalcitrant accumulation ecosystem characteristics  129, 132, 133, 155 oak successional decline  349–350 recreational areas aesthetic quality  498 appropriate forest management  367 hazards from trees  446 recruitment dynamics in uneven-aged stands  348 fire-free period requirement  270, 446, 447, 448 rate, quantitative models  125–128, 128, 146, 146 related to disturbance frequency  143, 260 red-headed woodpecker  82–83 red maple  265–266, 266, 520, 522 red oak borers  418, 425 red oaks acorn preferences of mammals  74, 472 acorn production variation  63 germination patterns  83 growth rates  532 Lobatae section (taxonomic group)  9–11, 10, 46n1 northern, thinning regimes  318–319 oak decline/dieback  417–418 pests of germinating acorns  72 seedling/sprout regeneration  122 reforestation  377, 486, 525 REGEN regeneration model  311 regeneration acorn consumption/dispersal balance  73 causes of failure  66, 123, 268, 299, 448 ecological definition  53 even-aged management  296 long-term decline  76, 261–263 responses to disturbance  132, 134–143, 135, 260–261

strategies, factors in success  72, 122–124, 130 uneven-aged (selection) management  335–336, 337, 347, 361 see also artificial regeneration; natural regeneration regeneration niche  88, 121–122, 148 regeneration period  53, 157, 465 regeneration potential contribution of stump sprouts  104 decline, with fire suppression  263 predictive succession models  151–154, 156–158, 158, 304–311 and scale of disturbance (mode)  148–151 total, of a stand  125, 147–148, 157 regeneration window  130, 132, 143, 150 Reineke’s model  225–226, 226, 228, 241 relative density measures  232, 244, 439–443 relative light intensity  443–444, 444 remote sensing  254, 559 reproduction accumulation 124–132 definitions  53, 195 mechanisms of regeneration  122–124, 124 strategies of invasive species  274–275 reserves (forest), managed use  1–2, 3 residual stand density  342, 347, 356 resilience, forest  219, 473, 516, 516–517 resistance strategies, climate change  516, 516 restoration, savannahs/woodlands  444, 445–447 reverse J-shape distributions see diameter distribution curves rhizomatous oaks  96, 97, 122 riparian zones ecosystem services  477 forests in prairie regions  36, 37, 38 oak growth  17 selection management  367 rodents acorn consumption/dispersal  73–75, 75 caching behaviour  73, 74, 76–78 deterrence, methods  382 populations, ecology  78–79, 472 root collar diameter  90, 125 root-graft barriers  421–423, 423 root production method (RPM®) 384–385 root sprouts  96, 124 roots age, in oak reproduction  125, 126, 127, 159n3 elongation rates  85, 86, 530–531 grafts, and disease transmission  421 growth and light levels  267, 269, 269, 391 injury and shedding  92 pathogenic rots  424 radicle emergence and growth  82, 83, 84 surface area and water absorption  85, 87 undercutting, nursery seedlings  383–384, 384

Index607

root:shoot ratio decline with age of stump sprouts  100 increase, with recurrent shoot dieback  90–91 related to site quality  170 seedling development  85, 87, 87 variation between species  390 rotation coppicing systems  260 definition 104n3 length, and carbon sequestration  485, 514 minimized by thinning  316 related to stand development  208, 296, 337 retention of cavity trees  477 retention of good acorn producers  263, 314, 463 rule thinning  321 runoff  175, 324 RWOM (rapid white oak mortality)  423–424

salvage cuttings  316, 361, 416–417 sampling, for site index determination  180 sanitation cuttings  316, 416 sanitation treatments  422–423, 423, 425–426 saplings, definition  316 savannahs, oak characteristics and extent  432–434, 433, 434, 435 historical creation by recurrent fire  134–135, 256, 260, 434–436 light intensity estimation methods  439–444 maintenance management  439, 439, 445, 447–449 restoration strategies  272, 273–274, 444–447 stand development  208, 436, 436–439, 437 ‘scaffold’ branches  97 scarification, soil  301–302, 313 scarlet oak  356 scatter-hoarding 76 scenic beauty impacts of silvicultural systems  496, 497–498 ratings  495, 495–496, 498 Schnur’s stand and yield tables  485, 544, 546, 553 science-based models  156 scrub jays, acorn dispersal  79 seams (radial shakes)  321, 322 seasonal variation of cutting/killing, effect on sprouting  99 fire incidence changes over time  257–258 ecological impacts  143, 252–253, 274 growth rates  530–531, 531 secondary invaders of acorns  69, 71–73 secondary succession  157, 201, 301 management in buffer zones  494 transition to old-growth  492–493 sedges 299 seed stored in forest floor  124, 135, 135, 297 seed tree regeneration method  314, 326, 497

seeding rates, acorns  381 seedling sprouts accumulation  124, 125 as factor in oak dominance  122, 261, 261 growth characteristics  89–94, 90, 91 root age  125 vulnerability to browsing  426 seedlings for artificial regeneration planting  382–385, 384 cohorts, advance reproduction  124–125 drought and shade tolerance  88–89 early growth patterns  84–85, 89 effects of pest infestation  71, 72 establishment, numbers and densities  84, 123, 426 fire risks, seasonal timing  264–265 physiology 85–88 planting methods  387 survival and mortality  77, 78, 92, 94, 265 survival probability curves  144–145, 145 selection thinning  315 selective cutting  347 self-pruning (branch shedding)  380 self-thinning lines, in stand density models  226, 226–227, 227, 230 natural mortality in oak stands  147–148, 197, 542–543 rate, quantitative expression  339 related to stand density and age  225, 544–545, 546 stump sprouts  100–101, 323 sensitivity classes (climate change)  519, 519 severity, fires  254–255, 257, 280 shading effect on stump sprouting  99 impacts on ground flora  272–273, 273 related to acorn production  459 tolerance, variation between species  13, 89, 148, 390 Sharp’s acorn production index  456, 457, 459, 499n1 shelterwood systems aims and methods  311–314, 312, 420 cutting in stands with gypsy moth  417 cutting in stands with gypsy moth risks  415 enrichment planting  389, 390–399, 395 harvesting with prescribed burning  267–270 irregular removal, aesthetic appeal  326, 497 shipbuilding, use of oak  1, 8 shoot clipping, seedlings  384, 392, 394 shoot dieback  89–90, 90, 91–94, 93 shrews, as predators of insect pests  68, 78, 413, 417 shrubs, competition with oaks  275–276 Shumard oak  520, 522 SILVAH regeneration model  310 silvicultural stability  346–347 silviculture definition and historical origins  1–2, 8 practical and disciplinary contexts  4–6, 5

608Index

skills required  497–498, 526 systems, aesthetic impacts  496, 497–498 utilitarian/ecological viewpoints  2–4, 174–175, 325 simulation models aesthetic quality of alternative regimes  495–496 forest floor solar radiation  361–362, 363 forest landscape models (FLMs)  557–559 management options, FVS model  415, 526, 555–557, 559 regeneration 158, 158, 309 seedling recruitment/survival  146–147, 147 stand growth and density  243–244 tree size distributions (ACORn)  307–309, 308 single-tree selection system application areas Central Hardwood region  347–348 Northern Hardwood region  23–24 Ozark Highlands  350–353, 351, 359 diameter distribution properties  338–346 factors in success/failure  348–350, 349, 353 management principles  335–338, 337, 347 practical implementation  354–358, 359, 497 stand structure development  219, 220, 221 site evaluation  185–188, 378–379 site index definition and uses  178, 178 direct determination  178–181, 179, 180 estimation from tree height/diameter  184–185, 185 indirect estimation from soils/topography  172, 182–184, 183 mapping 172, 173 species comparisons  181, 181–182, 182, 213 site productivity biomass components  474 effect of fertilizers  177–178 as factor in acorn production  59 and fuel availability  250 harvesting impacts  175–177 levels of competition for oaks  269, 270, 542 measures and components  169–172 site quality definition 169 ecological classification systems  172–174 effect on oak regeneration potential  300–301, 301 effects on savannah/woodland composition 437–438 effects on stump sprouting  97, 101, 104, 104 evaluation by site index  178–185 evaluation for artificial regeneration  378–379 influence on diameter growth rate  532 microsite variation  129–130, 131 predisposing factors in oak decline  418 preparation for planting  386–387 soil and topographic factors  182–184, 185–188, 186 slope gradient, and site index  182–183, 183 position and shape  183

Slow the Spread (STS) project  417 snags (standing dead trees)  446, 473, 474, 474, 475 snow snowfall distribution in USA  19, 36 winter cover, protective effects  83, 264 social acceptability, silvicultural practice  4, 324–326 soil base saturation and pH  183 carbon sequestration  480 compaction impacts  176, 379, 447 development processes  183 erosion  175–176, 298, 324, 446 fertility 177–178 pH, and site selection for planting  379 scarification, for seedbed enhancement  301–302, 313 site quality ratings  183–184, 186 surveys and mapping  172, 173, 182 soil fumigants  421 solar radiation available in subcanopy  150–151 in closing canopy gaps  148–149, 150 at forest edges  494 forest floor, simulation model  361–362, 363 growth responses in shelterwoods  390–391, 391 topographic factors  182, 183 Southern Pine–Hardwood region environmental characteristics  20, 31 forest history  31–32 geographic extent  19, 31 oaks as forest components  32–35, 33, 34 stand development study  204–206, 205, 207 see also bottomland hardwood forests Southwestern Desert–Steppe region environmental conditions  39, 40 forest history  39–40 geographic extent  19, 39 oak adaptations to aridity  96, 122 oaks as forest components  40–41, 41 speciation  8, 11 species niche, definition  12 species richness and acorn woodpecker populations  81–82 American oaks  10, 11, 12 determinants in oak forests  143, 144, 271, 273 enhanced by prescribed fire regimes  274 in old-growth forests  490 species, taxonomic groups  9–11, 46n, 53 specific gravity, wood  243 spring budbreak patterns  89 root growth, effects of shoot loss  93–94 sprouting probability, stumps  97–100, 98 squirrels 73–76, 75, 472, 475, 476 stand density index (SDI) diagrams 241–243, 242, 245 relative density measure (Reineke)  232

Index609

stand-initiating disturbances  198 stand initiation stage  200, 201–203, 202, 217, 317 stand-level growth and yield models  549–553 stand table projection models  550, 553 standing dead trees (snags)  446, 473, 474, 474, 475 stands characteristics considered in harvesting  336 comparisons with historical data  440, 441 composition changes after clearcutting  300, 300–301, 304 definition and boundaries  195, 296 growth and yield patterns  544–549 inventory records  354, 358, 553 landscape contexts and wildlife  466–467 management, and bird diversity  467–469, 468, 469, 470 species composition  201, 202–203, 206, 210 stages of development  200, 200–201, 202 structure attributes  195–196, 200 susceptibility to gypsy moth  412–413, 413 uneven-aged, structure control  335, 366, 367, 368 see also density, stand statistical models  156, 157, 549 Steller’s jays, acorn dispersal  79 stem exclusion stage  200, 202, 203–208, 205, 535 stewardship concept  6 stocking adequacy, predictive models  305–307, 310 charts (Gingrich diagram)  206, 237, 237–241, 310, 439–440 in growth and yield estimations  544, 548 as tool for managing aesthetic appeal  496, 499 used as guide for wildlife habitat  469 distribution curves  310, 342–343, 343 levels related to acorn production  459 thresholds for savannahs/woodlands  440, 440 stocking per cent  232, 235–236, 236, 239, 439 stone galls  70 stool sprouts  96 storage, acorns  381, 383 stump sprouts definition and origins  94–96, 95 dominance probabilities  101–102, 104, 105, 524 growth and mortality  100–101, 101, 102, 103 quality after clearcutting  298, 298–299 sprouting probability  97–100, 98, 152 see also coppice subcanopy species  150–151, 152, 199 successions, ecological acceleration by clearcutting  300 autogenic/allogenic influencing factors  13 control, in silviculture  2, 201, 345, 346 displacement of oaks by other trees  27, 129, 199, 208, 348–350 in mature (complex stage) forests  215, 469, 492 mesophication and fire resistance  253 number of pathways in mesic/xeric sites  301

predictive models after disturbance  151–154, 155 woodland to forest  432, 435, 438 sudden oak death  410, 424–426 sugar maple  213, 391, 437, 492 surface fires  253, 257 survival rates  542–544, 543, 545, 546 susceptibility (defoliation potential)  412–413, 413 sustainability, forest management  296, 325–326, 346–347 sweep (stem curvature) 319, 321 sweetgum, competition with oak  206, 213, 214 sycamore 213, 214

tannin content, acorns  73, 74, 472 tanning industry  42 taper functions  540–541, 562 taproots age, in seedling sprouts  125 depth advantages  88, 123, 302 early development and function  84, 85, 88 severed by undercutting  384 taxonomy 8–11 thinning diameter growth responses  533–536, 534 effect on community diversity  272 forests and savannahs, aims  439, 439 impacts on carbon sequestration  485, 487, 515 intensity and stump sprouting  99 related to acorn yields  459–460, 462 as resistance strategy, for stand maintenance  516 self-thinning of stump sprouts  100–101 shelterwood cutting regimes  311, 312, 312 stand yield responses  545–548, 547 stands at risk of oak decline  419–420 for stands susceptible to gypsy moth  415, 416 stump sprout clumps (coppice) 320, 320–321, 535 timing choices  206, 208, 315–316, 535 types and methods  315 thinning radius  320–321, 324 timber high-quality, mixed-species plantings  379 historical production and uses  22, 32, 42 products as sequestered carbon  480, 515, 515, 524 stock volume of oak  14, 23, 44 yield and value annual value growth  536, 537 fire impacts  254, 255, 277, 279–280 related to site quality  169, 536–537 sawtimber compared with fuel/ pulpwood 484, 541, 542 top-kill fire resistance species compared  137–138, 139, 140, 254–255, 265 time taken to develop  270, 276, 447 promotion of oak regeneration  261, 261 removal of oak competitors  267–268, 268, 387

610Index

topographic site coefficient (TSC)  186–187, 187 topography effect on competitive sorting  304 effects on productivity  173, 182–183 influence on fire attributes  253, 259, 437, 437 micro-level variation, planting sites  379 ‘trainer’ trees  379, 380 transitions secondary to old-growth  492–493 strategies for climate change adaptation  516, 517 transpiration rate related to wood production  188 seedlings 85, 88, 94 traps acorn collection  67, 71, 457–458 insect  67, 417 travel corridors  498 tree-area equations  231, 231, 233, 237 tree-area ratio (TAR) measures  233–235, 236, 239 tree shelters  388–390, 427 turkey oak, fire survival  141–143, 142 TWIGS growth and yield model  554, 556, 557, 558 two-aged stands  296, 298, 314, 497 tyloses  10, 92

undercutting 383–384, 384 understorey definition and species  195 encroachment by shade-tolerant species  263 light availability  208, 267, 391 light intensity estimation methods  439–444 oak growth responses to thinning  535 understorey reinitiation stage  202, 208–213 uneven-aged stands conversion from even-aged stands  358, 360–361, 361 economic and environmental issues  346–347, 367–369 growth and yield estimation  548–549 management goals  335, 337, 420, 493 structure and development  214, 215–217, 219–220, 335, 336 see also single-tree selection system ungulates, grazing  36, 256, 446–447 uniform shelterwood method  311, 312, 312–313, 326 upland oak forests sites for artificial regeneration  378 variation and adaptations of oaks  89, 91, 93 USDA Forest Service  1, 10, 562

value growth, oak trees  536–537, 537 vectors of disease  420–421 vegetation, ecological classifications  174 vertical stratification  204, 213, 460 viability testing, acorns  382–383

views, landscape management  498 virtual viewing, landscapes  498–499 visual counts, acorn production  456–457 visual impacts of management  364, 494, 496, 496 volume of coarse woody debris  473–474, 474 estimation tables and equations  562 measures related to total tree biomass  229, 481, 482 units of measure  170, 189n1, 562 US timber stocks  14, 23, 44, 562–564, 563 see also yield volume index, site quality  184, 185 vulnerability assessment, climate change broad scale, species rankings  518–520, 519 regional/local, compatibility scores  518, 520–523, 521 vulnerability index (gypsy moth)  415

Walters and Ek yield models  553 water oak, flooding adaptation  123, 302 water stress biophysical basis  188 recurrent shoot dieback responses  91–92 water-use efficiency  87–88 waterfowl  463–464, 466, 475 weather effects on acorn production  60, 60, 62 and fire severity  254 impacts on pollen dispersal  56–57 weed control, plantations  386–387 weeding (young stand thinning)  534–535 weevils, acorn-infesting  66, 67–69, 68, 69, 71 pre-planting treatments  383 western live oak cover type  40–41, 41 Western oak forests  18 Pacific Mediterranean–Marine region  41–45 Southwestern Desert–Steppe region  39–41 white oak predicted responses to climate change  520, 522 recruitment rate  128, 128 reproduction and overstorey density  132, 134, 134 white oaks (group) acorn production periodicity  62–63 germination patterns  83 Quercus section (taxonomic group)  9–11, 10, 46n2 rapid mortality disease (RWOM)  423–424 weevil infestation of acorns  71 wildfires  37, 217, 252–253, 524 wildlife benefits of clearcutting with reserves  298 decline after European settlement  260 food value of acorns  66, 455, 456, 470, 472–473 habitat management  377, 466–467, 494 impact simulation models  559

Index611

wildlife (continued) related to stand structure  467–469, 468 species using oaks in diet  470, 471 use of tree cavities  475, 475–479 value of snags and woody debris  473–475, 479 wilt see oak wilt wind frequency of damage to trees  199 and pollen shedding  56 protection of old-growth core area  494 windthrow following harvesting  479, 536 windrowing 176 winter seedling dieback and mortality  92, 93 viable acorn survival  83 witness tree data  440, 449n1 wood, composition  480, 483 see also timber wood ducks  464, 466, 475 woodlands, definition  433, 440 see also savannahs, oak

woodpeckers acorn caching  79–83, 80, 81 cavity excavation and nesting  476, 477 habitat preferences  469 wounds caused by fire  254, 278, 279–280 and tree cavity formation  475–476, 479

yellow-poplar 181–182, 182, 199, 266, 395–396 yield benefits of thinning  317, 545–548, 547 definition  169, 530 estimates for normal even-aged stands  544–545, 545 uneven-aged stands  548–549 yield tables applicability to thinned stands  546–547, 548 normal stocking  233, 544, 546 use in carbon accounting  485, 514 use in self-thinning models  229, 229–230

612Index

M262

262

261

263

241

M241

M261 341

323

M341

342

332

311

M334

314

M311 313

331

252

232 M231

M222 |

232

251

100th meridian

M333

321

M334

332

M341

341

M311

M341 342

M332

M331

R

211

232

221b

231

M221

221a

211

411

M211b

221a

Plate 1.  The distribution of oaks in the conterminous USA. The coloured areas indicate the estimated oak basal area for each site. The 100th meridian demarcates the approximate division between eastern and western oaks. See Tables 1.2 and 1.3 for an index to numbered ecoregions and oak species found in each one. (The map was compiled by Kevin McCullough, USDA Forest Service, with data from the USDA Forest Service Forest Health Technology Enterprise Team. Maps available at: http://foresthealth.fs.usda.gov/host/ (accessed 8 January 2018).)

Over 80

61–80

41–60

21–40

1–20

Basal area (ft2/acre)

1

211

2 (A)

(B)

3

Woodland/Forest 16

Oak & Pine 48 25 29 17

Sa

va

Sa

Cu rre nt oo Ri dla nd ver Pine

nn

a

n van

10

24

a/W

Post Oak

Forest Mesic Mized Oak Riparian 9.8 9.6

Oak

7.0 4.3 5.2

Post & Black Oak Ja

cks

11 6.4

24

F ork R iv e r 9.0

2.8

5.1

N

6. 4.1 3.

0

5

3.5 5.0 10 km

Plate 2.  Most prescribed fires in oak forests are low intensity and conducted in the dormant season (November–March in most places). (A) Average flame lengths may vary from < 1 ft in northern hardwood litter, 1–3 ft in oak leaf litter, 6–12 ft in oak forests with evergreen heath shrub understories of mountain laurel and rhododendron, and 10–15 ft in logging slash (Brose, 2009). Typical rates of spread are 2–5 ft/min in oak leaf litter and 5–10 ft/min in understories dominated by mountain laurel and rhododendron. Average fire temperatures just above the ground may range from 200°F in northern hardwood litter, 550°F in oak leaf litter or deciduous shrub understories, to > 1000°F in evergreen heath shrubs or logging slash. (B) Fire intensity can be locally intense when fire burns in cured slash from ice storms, blowdown or silvicultural thinning and harvesting. (Photographs courtesy of USDA Forest Service, Northern Research Station.) Plate 3.  Topography had a strong influence on fire frequency and size before the fire suppression era. In plains and relatively flat terrain, fires were more frequent and larger in size resulting in prairie, or oak–pine savannah and open woodlands. In more severely dissected terrain, fire frequency and size were reduced due to the increase in natural fire breaks in the form of streams and north-east aspects that were less conducive to burning. Oak–pine woodlands were a dominant type on moderately dissected terrain. On the leeward side of major waterways, fires burned relatively infrequently, and mesic forests were able to develop. This example from the upper Current River watershed in the Missouri Ozarks illustrates the interaction of fire and topography that resulted in distinct spatially explicit patterns in tree composition and structure (based on Batek et al., 1999). Numeric values indicate mean fire intervals at fire history sites; black dots indicate prehistoric sites with human habitation; filled triangles indicate historic Native American sites; open squares indicate Euro-American farm sites (1812–1860); and the circle indicates a town location (1812–1860).

< 2.01 2.01 – 4

Mean Fire Interval years 10.1 – 12

4.01 – 6 6.01 – 8 8.01 – 10

Historical estimates of mean fire intervals before 1850 using the physical chemistry of climate 20.1 – 30 30.1 – 35 35.1 – 40 40.1 – 45

20.1 – 22 22.1 – 24 24.1 – 26 26.1 – 28

12.1 – 14 14.1 – 16 16.1 – 18 18.1 – 20

101 – 125

50.1 – 75 75.1 – 100

45.1 – 50

126 –150 176 – 200 201 – 6,360

151 – 175

v. 9.0 MFIAR10f

Plate 4.  Estimated mean fire interval before 1850 using the Physical Chemistry Fire Frequency Model, which was parameterized with 170 tree-ring-dated fire histories collected throughout the USA (Guyette et al., 2012b).

4

Guyette, R.P., M.C. Stambaugh, D.C. Dey, and R.M. Muzika. (2012). Ecosystems15: 322–335.

5 (A)

6 (A)

(B)

(B)

Plate 5.  Small patches or colonies of invasive species in or near management areas are a serious threat to sustaining or restoring quality, diverse oak ecosystems. Some species such as Japanese stiltgrass are able to survive as remnant individuals or populations in shaded forests (A), but proliferate to dominance following disturbances that reduce overstorey density and reduce the depth of litter (B). Some non-native invasive species (NNIS) are adapted also to fire, which is increasingly used to promote oak regeneration and is essential in restoring oak savannahs and woodlands. (Photographs courtesy of USDA Forest Service, Northern Research Station.) Plate 6.  With the change in historical disturbance regimes, especially with the suppression of fire, certain native species such as (A) mountain laurel and (B) eastern redcedar have significantly expanded in distribution and abundance in oak ecosystems. They act as invasive species as they arise in dominance by degrading the quality, diversity and productivity of natural oak communities. Both species are able to reduce the likelihood of fire ignition and spread of low intensity fires, but in years of severe drought they add to the probability of catastrophic wildfire. They also interfere with the oak regeneration and recruitment process necessary to sustain oak ecosystems. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

7 (A)

(B)

(C)

Plate 7.  (A) Prescribed fires generally burn with lower intensity than wildfires, and they typically leave the overstory trees alive. Herbaceous vegetation responded vigorously after a fire in this savannah ecosystem. (B) On rare occasions prescribed fires can cause complete mortality of the overstorey at the stand scale or larger, as in this example of restoring a post oak woodland where eastern redcedars were felled before the area was burned, thus contributing to high fuel loads and high fire severity. (C) More likely is the occurrence of fire-caused mortality in small groups of overstorey trees due to fire burning into ‘jackpots’ (i.e. concentrated piles) of slash from natural disturbances or management activity. The most common fire effect on overstorey trees is the death of individual trees during low- to moderate-intensity burns conducted in the dormant season. (Photographs A and C courtesy of USDA Forest Service, Northern Research Station; photograph B courtesy of Michael Stambaugh, used with permission.)

8 (A)

(D)

(B)

(C)

(E)

Plate 8.  Variation in oak seedling roots. (A) Naturally occurring oak seedlings grown under a dense maple–beech– oak forest (left: a 1-year-old seedling with acorn attached; right: a 3-year-old seedling). (B) A natural oak seedling-sprout grown in the open. (C) Typical 1-year-old nursery-grown (1+0) northern red oak bareroot seedlings. (D) 1-year-old nursery-grown (1+0) white oak bareroot seedlings produced using the Kormanik protocol (Kormanik et al., 2004). (E) A 2-year-old (2+0) container-grown seedling using the root production method (RPM®); background grid lines are 2 cm apart. (Photographs (A) and (B) courtesy of USDA Forest Service, Northern Research Station; photographs (C) and (D) by Stacy Clark; photograph (E) by Ben Grossman; used with permission.)

RPM

(B)

(D)

RPM

Plate 9.  Planted swamp white oak growing in a cover crop of redtop grass in the Lower Missouri River floodplain. (A) Redtop is used here to control competing vegetation and to improve seedling survival and growth (seedlings were produced by the root production method (RPM®)) (Dey et al., 2004). Redtop forms a continuous ground cover during the first summer and limits the growth of annual forbs that typically dominate abandoned crop fields. (B) Redtop persists with no or little maintenance for many years. This aerial view (C) illustrates cover crop condition 7 years after plantation establishment when trees are approximately 10 feet tall, and this ground-level view shows the condition at 9 years when trees are approximately 12 feet tall. (Photographs courtesy of USDA Forest Service, Northern Research Station.)

(C)

9 (A)

10 (A)

(B)

(D)

(C)

(E)

(F)

Plate 10.  Progression of interplanting Nuttall oak in eastern cottonwood plantations. (A) Eastern cottonwood cuttings are planted after site preparation. Herbicides and discing are used to control competing vegetation for 2 years. (B) One-year old eastern cottonwood cuttings. (C) After two growing seasons, 1-0 bareroot Nuttall oak seedlings are interplanted in between every other eastern cottonwood row. (D) Nuttall oak grows well in the eastern cottonwood understory. (E) Nuttall oak attains large sapling sizes after 7 years in the understory. (F) After 10 years, the eastern cottonwood is harvested for pulpwood, releasing the Nuttall oak. (Photographs courtesy of Emile Gardiner, used with permission.)

35°0'0"N

40°0'0"N

45°0'0"N

50°0'0"N

105°0'0"W

100°0'0"W

100°0'0"W

95°0'0"W

51–60

31–40

11–20

150

0

80°0'0"W

71–80

61–70

90°0'0"W

85°0'0"W

Probability of occurrence (%)

41–50

300

85°0'0"W

21–31

90°0'0"W

0–10

95°0'0"W

80°0'0"W

300 km

75°0'0"W

91–100

81–90

70°0'0"W

75°0'0"W

65°0'0"W

Plate 11.  Estimated probability of browse damage to trees by white-tailed deer and other native ungulates. (Reprinted from McWilliams et al., 2018.)

11

30°0'0"N

35°0'0"N

40°0'0"N

45°0'0"N

50°0'0"N

12 Year 0

Year 20

Age 20

Harvest

Harvest

No burn

No burn

Free to burn

Free to burn

Age 20 Map Scale = 1:8880 709ft 0

Map Scale = 1:8880 0 709ft

Year 40

Age 40

Year 60

Age 60

Harvest

Harvest

Age 20

Age 40

Age 20 No burn

No burn

Free to burn

Age 20

Age 40

Age 20

Free to burn

Age 60

Age 20 Map Scale = 1:8880 0 709ft

Age 40

Year 80 Age 20

Age 80

Age 60

Age 100

Harvest Age 60

Year 100 Age 40

Map Scale = 1:8880 0 709ft

Harvest Age 80

No burn

No burn Age 20

Age 60

Age 40

Free to burn

Age 80

Age 60

Free to burn

Age 20

Age 20

Map Scale = 1:8880 0 709ft

Age 40

Age 80

Age 100

Map Scale = 1:8880 0 709ft

Plate 12.  Area regulation can be used to manage a mosaic of structural states and age classes across an administrative compartment or large woodland tract. In this example, a 200-acre area representing an administrative compartment is subdivided into ten equally-sized fire management units. The boundaries follow old skid trails to facilitate timber removal and fire-line maintenance; fire lines elsewhere can be created and maintained with a leaf blower. Regeneration harvests, which can include clearcutting with reserves, seed tree method with reserves or irregular shelterwoods, occur in selected fire management units summing to 20% of the compartment land area during each 20-year re-entry. Fire is excluded from the regenerated areas for at least 30 years to allow for recruitment and to minimize fire damage to a future timber crop. Other configurations can be generated by changing the rotation age, the re-entry period, and assumptions about the duration of the fire-free period required for recruitment. (From Kabrick et al., 2014a.)

13 (A)

White oak Quercus alba

Importance value No data 0 1–3 4–8 9–15 16–100 Lake Habitat suitability 1981–2010 (B)

Habitat suitability 2070–2099 Low emissions scenario

(C)

0

750

Habitat suitability 2070–2099 High emissions scenario

Kilometres Plate 13.  Estimated importance values for white oak under (A) observed climate for the period 1981–2010; (B) modelled for 2070–2099 with results averaged for three alternative climate models under a low greenhouse gas emissions scenario; and (C) modelled for 2070–2099 with results averaged for three alternative climate models under a high greenhouse gas emissions scenario. Importance value is an index of a species’ abundance relative to other species. In this plate, importance value serves as an indicator of habitat suitability for a tree species. Values near 0 (zero) indicate unsuitable habitat, and values > 15 indicate highly suitable habitat, although the break point for highly suitable habitat may vary for other species depending on how abundantly distributed those species are. (Based on Prasad et al. (2007–ongoing) with graphics by USDA Forest Service courtesy of Matthew Peters.)

14

Northern red oak Quercus rubra

(A)

Importance value No data 0 1–3 4–8 9–15 16–100 Lake Habitat suitability 1981–2010

(B)

Habitat suitability 2070–2099 Low emissions scenario (C)

0

750

Habitat suitability 2070–2099 High emissions scenario

Kilometres Plate 14.  Estimated importance values for northern red oak under (A) observed climate for the period 1981–2010; (B) modelled for 2070–2099 with results averaged for three alternative climate models under a low greenhouse gas emissions scenario; and (C) modelled for 2070–2099 with results averaged for three alternative climate models under a high greenhouse gas emissions scenario. Importance value is an index of a species’ abundance relative to other species. In this plate, importance value serves as an indicator of habitat suitability for a tree species. Values near 0 (zero) indicate unsuitable habitat, and values > 15 indicate highly suitable habitat, although the break point for highly suitable habitat may vary for other species depending on how abundantly distributed those species are. (Based on Prasad et al. (2007–ongoing) with graphics by USDA Forest Service courtesy of Matthew Peters.)

15

Red maple Acer rubrum

(A)

Importance value No data 0 1–3 4–8 9–15 16–100 Lake Habitat suitability 1981–2010

(B)

Habitat suitability 2070–2099 Low emissions scenario (C)

0

750 Kilometres

Habitat suitability 2070–2099 High emissions scenario

Plate 15.  Estimated importance values for red maple under (A) observed climate for the period 1981–2010; (B) modelled for 2070–2099 with results averaged for three alternative climate models under a low greenhouse gas emissions scenario; and (C) modelled for 2070–2099 with results averaged for three alternative climate models under a high greenhouse gas emissions scenario. Importance value is an index of a species’ abundance relative to other species. In this plate, importance value serves as an indicator of habitat suitability for a tree species. Values near 0 (zero) indicate unsuitable habitat, and values > 15 indicate highly suitable habitat, although the break point for highly suitable habitat may vary for other species depending on how abundantly distributed those species are. (Based on Prasad et al. (2007–ongoing) with graphics by USDA Forest Service courtesy of Matthew Peters.)

16

Colonization likelihood (A) ( )

White oak (Quercus alba)

((B))

Chestnut oak (Quercus prinus)

(C)

Post oak (Quercus stellata)

(D)

Black oak (Quercus velutina)

Probability (%) 1–5 6–20 21–40 41–60 0 61–100

500

1000 Kilometres

Plate 16.  Current range (bold black boundary line) and projected future probability of new habitat occupied by 2100 for white oak, chestnut oak, post oak and black oak in the eastern USA for a climate change scenario that continues current trends in fossil fuel use with as much as a 7°F (4°C) increase in mean temperature by 2100. The rate of movement is based on the current spatial pattern of a species’ dominance within its range, the anticipated location of future suitable habitat under the assumed climate change scenario, and a mean historical migration rate of 30 miles per century. The vast majority of sites with potentially suitable future climate conditions are expected to have less than a 20% probability of being colonized. (Based on Prasad et al. (2013) with graphics by USDA Forest Service courtesy of Matthew Peters.)

17

Plate 17.  Modelled views of stand structure from the FOREST VEGETATION SIMULATOR (FVS) for an oak–pine stand in the stem exclusion stage of development. The tree species, diameters and heights are based on the stand inventory tree list that FVS projects forward in time. The two- and three-dimensional images show representative stand structure for a 1-acre plot within the stand. This image also shows down trees and the effects of a prescribed fire. Images created before and after a simulated harvest operation or other disturbance graphically illustrate the expected changes in stand structure. (For more on the FVS see https://www.fs.fed.us/fvs/ (accessed 30 May 2018).)

No harvest

Mixed 10%

UAM 10%

EAM 10%

Stand size class

Ovenbird habitat

Hooded warbler habitat

Maple 0

200

Pine 25 Year

Red oak 50

0

White oak

Species group

76–100

51–75

26–50

0–25

Habitat suitability

Sawlog

Pole

Sapling

Seedling

Size class

2 miles

75

100

0

25

50

75

100

0

25

50

75

100

0

25

50

75

100

Tree species

Plate 18.  Examples of outputs from the LANDIS (He, 2017) forest landscape model applied to a 176,000 acre oak-dominated landscape in the Missouri Ozarks. Scenarios compare four harvest alternatives over 200 years for: (i) even-aged management (EAM) regenerating 10% of the landscape per decade; (ii) uneven-aged management (UAM) regenerating 10% of the landscape per decade in group openings; (iii) a mixture of UAM and EAM; and (iv) no harvest. Map panels illustrate spatial patterning on a 7000 acre subset of the landscape at the end of 200 years for forest size (age) class and for habitat suitability index for two avian species of conservation concern. Estimates of habitat suitability (0 = unsuitable, 100 = optimal) are grouped into four classes. The line graphs indicate change by year in the relative abundance of major tree species groups on the landscape. (Based on Shifley et al. (2006) which presents additional metrics and additional scenarios.)

18

Proportion of sites where tree species group occurs

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