Biogeochemical Cycles: Ecological Drivers and Environmental Impact (Geophysical Monograph Series) [1 ed.] 1119413303, 9781119413301

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Geophysical Monograph 251

Biogeochemical Cycles

Ecological Drivers and Environmental Impact Katerina Dontsova Zsuzsanna Balogh‐Brunstad Gaël Le Roux Editors

This Work is a co‐publication of the American Geophysical Union and John Wiley and Sons, Inc.

This Work is a co‐publication between the American Geophysical Union and John Wiley & Sons, Inc. This edition first published 2020 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and the American Geophysical Union, 2000 Florida Avenue, N.W., Washington, D.C. 20009 © 2020 American Geophysical Union All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions

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CONTENTS Contributors..........................................................................................................................................................vii Preface...................................................................................................................................................................xi Acknowledgments................................................................................................................................................xiii

Part I: Biological Weathering 1. Biological Weathering in the Terrestrial System: An Evolutionary Perspective Dragos G. Zaharescu, Carmen I. Burghelea, Katerina Dontsova, Christopher T. Reinhard, Jon Chorover, and Rebecca Lybrand...............................................................................................................3 2. Plants as Drivers of Rock Weathering Katerina Dontsova, Zsuzsanna Balogh‐Brunstad, and Jon Chorover...............................................................33 3. Microbial Weathering of Minerals and Rocks in Natural Environments Toby Samuels, Casey Bryce, Hanna Landenmark, Claire Marie-Loudon, Natasha Nicholson, Adam H. Stevens, and Charles Cockell..........................................................................................................59 4. Micro‐ and Nanoscale Techniques to Explore Bacteria and Fungi Interactions with Silicate Minerals Zsuzsanna Balogh‐Brunstad, Kyle Smart, Alice Dohnalkova, Loredana Saccone, and Mark M. Smits..............81 5. Modeling Microbial Dynamics and Heterotrophic Soil Respiration: Effect of Climate Change Elsa Abs and Régis Ferrière..........................................................................................................................103

Part II: Elemental Cycles 6. Critical Zone Biogeochemistry: Linking Structure and Function Bryan Moravec and Jon Chorover................................................................................................................133 7. Tracking the Fate of Plagioclase Weathering Products: Pedogenic and Human Influences Scott W. Bailey.............................................................................................................................................151 8. Small Catchment Scale Molybdenum Isotope Balance and its Implications for Global Molybdenum Isotope Cycling Thomas Nägler, Marie‐Claire Pierret, Andrea Voegelin, Thomas Pettke, Lucas Aschwanden, and Igor Villa................................................................................................................163 9. Trace Metal Legacy in Mountain Environments: A View from the Pyrenees Mountains Gaël Le Roux, Sophia V. Hansson, Adrien Claustres, Stéphane Binet, François De Vleeschouwer, Laure Gandois, Florence Mazier, Anaelle Simonneau, Roman Teisserenc, Deonie Allen, Thomas Rosset, Marilen Haver, Luca Da Ros, Didier Galop, Pilar Durantez, Anne Probst, Jose Miguel Sánchez-Pérez, Sabine Sauvage, Pascal Laffaille, Séverine Jean, Dirk S. Schmeller, Lluis Camarero, Laurent Marquer, and Stephen Lofts....................................................................................191 10. Poised to Hindcast and Earthcast the Effect of Climate on the Critical Zone: Shale Hills as a Model Pamela L. Sullivan, Li Li, Yves Goddéris, and Susan L. Brantley......................................................................207 v

vi CONTENTS

Part III: Frontier and Managed Ecosystems 11. Importance of the Collection of Abundant Ground‐Truth Data for Accurate Detection of Spatial and Temporal Variability of Vegetation by Satellite Remote Sensing Shin Nagai, Kenlo Nishida Nasahara, Tomoko Kawaguchi Akitsu, Taku M. Saitoh, and Hiroyuki Muraoka................................................................................................................................225 12. Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems Elizabeth Herndon, Lauren Kinsman‐Costello, and Sarah Godsey.................................................................245 13. Anthropogenic Interactions with Rock Varnish Ronald I. Dorn.............................................................................................................................................267 14. Cycling of Natural Sources of Phosphorus and Potassium for Environmental Sustainability Biraj B. Basak, Ashis Maity, and Dipak R. Biswas..........................................................................................285 15. Ecological Drivers and Environmental Impacts of Biogeochemical Cycles: Challenges and Opportunities Katerina Dontsova, Zsuzsanna Balogh‐Brunstad, and Gaël Le Roux.............................................................301 Index...................................................................................................................................................................307

CONTRIBUTORS Elsa Abs Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA; and Institute of Biology of Ecole Normale Superieure (IBENS), National Center for Scientific Research (CNRS), INSERM, PSL University, Paris, France

Casey Bryce Geomicrobiology Group, Centre for Applied Geoscience, University of Tübingen, Tübingen, Germany Carmen I. Burghelea Biosphere 2, University of Arizona, Tucson, Arizona, USA

Deonie Allen Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Lluis Camarero Center for Advanced Studies of Blanes, CSIC, Blanes, Girona, Spain

Tomoko Kawaguchi Akitsu Faculty of Life and Environmental Sciences, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan

Jon Chorover Department of Environmental Science, University of Arizona, Tucson, Arizona, USA

Lucas Aschwanden Institute of Geological Sciences, University of Bern, Bern, Switzerland

Adrien Claustres Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Scott W. Bailey US Forest Service, Northern Research Station, North Woodstock, New Hampshire, USA

Charles Cockell UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK

Zsuzsanna Balogh‐Brunstad Department of Geology and Environmental Sciences, Hartwick College, Oneonta, New York, USA

Luca Da Ros Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Biraj B. Basak ICAR‐Directorate of Medicinal and Aromatic Plants Research (DMAPR), Anand, India Stéphane Binet Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France; and Institute of Earth Sciences, ISTO, University of Orléans, BRGM, Orléans, France

François De Vleeschouwer Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France; and Franco-Argentine Institute for the Study of Climate and its Impacts, University of Buenos Aires, Argentina

Dipak R. Biswas Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute (IARI), New Delhi, India

Alice Dohnalkova Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA

Susan L. Brantley Earth and Environmental Systems Institute and Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania, USA

Katerina Dontsova Department of Environmental Science, and Biosphere 2, University of Arizona, Tucson, Arizona, USA

vii

viii CONTRIBUTORS

Ronald I. Dorn School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA

Séverine Jean Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Pilar Durantez Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Lauren Kinsman‐Costello Department of Biological Sciences, Kent State University, Kent, Ohio, USA

Régis Ferrière Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA; and Institute of Biology of Ecole Normale Superieure (IBENS), National Center for Scientific Research (CNRS), INSERM, PSL University, Paris, France; and International Center for Interdisciplinary and Global Environmental Studies (iGLOBES), CNRS, ENS, University of Arizona, Tucson, Arizona, USA Didier Galop GEODE, Geography of the Environment, University of Jean‐Jaurès Toulouse, France, and LabEx DRIIHM (ANR-11-LABX-0010), INEE-CNRS,Paris, France Laure Gandois Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Yves Goddéris Environmental Geosciences Toulouse, CNRS— Midi-Pyrénées Observatory, Toulouse, France Sarah Godsey Department of Geosciences, Idaho State University, Pocatello, Idaho, USA Sophia V. Hansson Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France; and Department of Bioscience – Arctic Research Centre, Aarhus University, Aarhus, Denmark

Pascal Laffaille Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Hanna Landenmark UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK Gaël Le Roux Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Li Li Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA Stephen Lofts Centre for Ecology and Hydrology, Lancaster University, Lancaster, UK Claire Marie-Loudon UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK Rebecca Lybrand Oregon State University, Corvallis, Oregon, USA Ashis Maity ICAR‐National Research Center for Pomegranate, Solapur, India

Marilen Haver Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Laurent Marquer Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France; and GEODE, Geography of the Environment, CNRS, University of Jean‐Jaurès Toulouse, France

Elizabeth Herndon Department of Geology, Kent State University, Kent, Ohio, USA

Florence Mazier GEODE, Geography of the Environment, University of Jean‐Jaurès Toulouse, France

CONTRIBUTORS  ix

Bryan Moravec Department of Environmental Science, University of Arizona, Tucson, Arizona, USA Hiroyuki Muraoka River Basin Research Center, Gifu University, Yanagido, Gifu, Japan Shin Nagai Research Institute for Global Change, Japan Agency for Marine‐Earth Science and Technology, Showamachi, Kanazawa‐ku, Yokohama, Kanagawa, Japan; and Institute of Arctic Climate and Environment Research, Japan Agency for Marine‐Earth Science and Technology, Showamachi, Kanazawa‐ku, Yokohama, Kanagawa, Japan Thomas Nägler Institute of Geological Sciences, University of Bern, Bern, Switzerland Kenlo Nishida Nasahara Faculty of Life and Environmental Sciences, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan Natasha Nicholson UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK

Thomas Rosset Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Loredana Saccone Department of Architecture and Civil Engineering, University of Bath, Bath, UK Taku M. Saitoh River Basin Research Center, Gifu University, Yanagido, Gifu, Japan Toby Samuels UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK; and Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK Sabine Sauvage Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Dirk S. Schmeller Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Jose Miguel Sánchez-Pérez Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France

Anaelle Simonneau Institute of Earth Sciences, ISTO, University of Orléans, BRGM, Orléans, France

Thomas Pettke Institute of Geological Sciences, University of Bern, Bern, Switzerland

Kyle Smart Department of Geology and Environmental Sciences, Hartwick College, Oneonta, New York, USA

Marie‐Claire Pierret Laboratory of Hydrology and Geochemistry of Strasbourg, EOST, Strasbourg University, CNRS, Strasbourg, France

Mark M. Smits Applied Biology, HAS University of Applied Sciences, Hertogenbosch, the Netherlands

Anne Probst Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Christopher T. Reinhard School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA

Adam H. Stevens UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK Pamela L. Sullivan College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA

x CONTRIBUTORS

Roman Teisserenc Laboratory of Functional Ecology and Environment, University of Toulouse, CNRS, INPT, UPS, Toulouse, France Igor Villa Institute of Geological Sciences, University of Bern, Bern, Switzerland; and University Center for Dating and Archaeometry, University of Milan Bicocca, Milano, Italy

Andrea Voegelin Institute of Geological Sciences, University of Bern, Bern, Switzerland Dragos G. Zaharescu School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA

PREFACE Biogeochemical cycles describe the flow of various elements through Earth’s critical zone. These cycles are interconnected and strongly influenced by water and energy fluxes, including chemical energy preserved in organic compounds, which influence and are influenced by ecological processes and climate shifts. This book provides an overview of the current state of knowledge regarding many aspects of biogeochemical cycles in the context of global change. The book also highlights areas of need for forming collaborations and method development to gain a better understanding of the cause and effect relationships between biogeochemical cycling of elements, climate shifts, human impacts and disturbances, and ecological responses. In addition, it is important to place an emphasis on further investigations of the interconnections between traditionally studied natural ecosystems, frontier ecosystems, and managed (agricultural) systems, because they are all part of global cycles and subjected to global changes that affect the biogeochemical cycling of elements. Most of the current publications in the area of biogeochemical cycles focus exclusively on carbon and how it is influenced by climate change, as well as feedbacks between climate change and biogeochemical processes linked to the fate of carbon. However, other element cycles are equally affected by climate change and other human activities, even if they do not provide direct feedback to the atmospheric concentrations of greenhouse gases and therefore climate change. In the past decade, many research groups around the globe invested in further examination of Earth’s critical zone in order to evaluate the effect of the rapidly increasing population and industrialization of developing nations on ecosystems and geochemical cycles. The results showed that Earth undergoes rapid changes in response to human activities and some subsystems are extremely vulnerable to ongoing changes; for example, permafrost, mountain, and desert ecosystems. The warming and drying of these ecosystems causes an increase in carbon release into the atmosphere in the form of CO2 and methane, which provides positive feedback to global warming and triggers changes in other elemental cycles. This book is organized into three sections, starting with  a summary of all biological drivers of weathering

and  carbon sequestration (Chapter  1), detailed descriptions of plant‐induced rock weathering (Chapter  2) and microbial weathering (Chapter  3), available analytical techniques to study the impact of biological weathering on small‐scales (Chapter  4), and modeling approaches to examine changes in CO2 flux due to respiration as climate changes (Chapter 5). The second section focuses on relationships between structure and function of the critical zone with respect to biogeochemical processes (Chapter 6), on plagioclase weathering and soil formation in ecosystems historically affected by anthropogenic acid deposition (Chapter 7), on molybdenum (Chapter 8) and other trace metal cycling in mountain environments (Chapter 9), and prediction of future changes in the critical zone (Chapter 10). The third section provides some insights into how spatial and  temporal variability of vegetation in a changing environment can be quantified (Chapter  11), how permafrost ecosystems respond to changes in climate (Chapter 12), how rock varnish responds to anthropogenic disturbances (Chapter  13), and how natural sources of phosphorus and potassium can improve the sustainability of managed systems (Chapter  14). Lastly, the book ­summarizes challenges and opportunities of studying the biogeochemical cycles under changing environments (Chapter 15). This book grew out of the Goldschmidt conference session titled “Ecological Drivers of Biogeochemical Cycles under Changing Environment” held in Yokohama, Japan in 2016. Original research was presented during the conference. However, for the purpose of this book, the editors encouraged the contributors to provide a more inclusive overview and summarize the current state of knowledge in the areas of their expertise. Katerina Dontsova University of Arizona, USA Zsuzsanna Balogh‐Brunstad Hartwick College, USA Gaël Le Roux University of Toulouse, France

xi

ACKNOWLEDGMENTS The editors would like to acknowledge the following reviewers: Deonie Allen, Megan Andrews, Keith A. Brunstad, Dawn Cardace, Anthony Chappaz, Salvatore Gazze, David H. Griffing, Kate Heckman, Peter Hooda, Thomas Houet, Nina Koele, Yizhang Liu,

Carmen Nezat, Oluyinka Oyewumi, Julia Perdrial, Julie Pett‐Ridge, Viktor Polyakov, Olivier Pourret, Frank Ramos, Jennifer Reeve, Toby Samuels, Marjorie Schulz, Debjani Sihi, Benjamin Sulman, Roman Teisserenc, and Kimberly Wickland.

xiii

Part I Biological Weathering

1 Biological Weathering in the Terrestrial System: An Evolutionary Perspective Dragos G. Zaharescu1, Carmen I. Burghelea2, Katerina Dontsova2,3, Christopher T. Reinhard1, Jon Chorover3, and Rebecca Lybrand4

ABSTRACT Weathering is the process by which a solid breaks up into its building blocks when in thermodynamic disequilibrium with the surrounding environment. Weathering plays an important role in the formation of environments that can support life, including human life. It provides long‐term control on nutrient availability in natural and agricultural ecosystems through release of lithogenic elements and formation of secondary minerals that allow storage of nutrients in soils. Life itself, however, has a profound effect on weathering processes. Absence of oxidants characterized the weathering environment on early Earth (4.6–2.4 Ga), when CO2 released during volcanic activity was the principal driver of weathering processes. The advent of photosynthesis in the Archean and resulting biogenic flux of O2 to the atmosphere, ultimately shifted weathering towards oxidation, influencing the mineral landscape and the cycles of nutrients that supported an evolving biosphere. Land colonization by vascular plants in the early Phanerozoic and evolution of mycorrhizal symbiosis enhanced weathering by selectively mining minerals and redistributing nutrients across plant and fungi in the ecosystem. Development of complex human societies and the ever‐increasing influence people exert on the environment further impact weathering and nutrient cycling, both directly and indirectly.

1.1. INTRODUCTION

chemical denudation, influencing soil formation, soil ­fertility, landscape evolution, and long‐term productivity of terrestrial ecosystems. The fine balance between abiotic and biotic factors driving rock weathering is modulated by both planetary‐scale forces (solar radiation, gravity, plate tectonics) and molecular‐scale interactions, and is fundamental to the evolution of the terrestrial critical zone and its capacity for supporting life.

Modern‐day silicate weathering is strongly influenced by abundant organic and inorganic forms of carbon linked to biological activity. Dissolution of the rock releases nutri­ ents and creates ecological niches for microorganisms and plants, while microorganisms and plant roots in symbiosis with mycorrhizal fungi create hot spots where intense gra­ dients in carbon and water affect mineral dissolution and

1.2. WEATHERING  School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA 2  Biosphere 2, University of Arizona, Tucson, Arizona, USA 3   Department of Environmental Science, University of Arizona, Tucson, Arizona, USA 4  Oregon State University, Corvallis, Oregon, USA 1

Weathering is the process of physical and chemical breaking up of a solid, such as rock, into its elementary building blocks due to the thermodynamic disequilibrium with the surrounding environment (Figure 1.1). This simple but ubiquitous process in nature is a direct consequence of

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

3

4  BIOGEOCHEMICAL CYCLES

Low entropy

High entropy

Time Work (kinetic, electromagnetic)

High potential energy

Low potential energy

Input energy (mass) = Output energy (mass)

Dissolution Mineral

(irreversible) Water

Weathering products

Figure 1.1 The principle of entropy in a theoretical, closed system, and how it applies to open‐system natural processes, such as weathering. Initial conditions are characterized by low entropy (e.g., ordered mineral structures, water crystals) and high potential energy. As electromagnetic energy is applied over time, a portion of the initial potential energy irreversibly changes the system to a new, higher entropic state, e.g., breaking of mineral structures and binding of elements with liquid water molecules. Removal of destabilizing energy causes the system to move to a new configuration state, different from the initial one.

the universal Second Law of Thermo­dynamics, which con­ nects energy and work (e.g., heat, chemical, mechanical) along the dimension of time. The law postulates that in an isolated physical system, entropy (a thermodynamic mea­ sure of unavailable energy) increases irreversibly over time (e.g., energy dissipates) when the system is out of equilibrium, or it remains constant when the system is at equilibrium (Bailyn, 1994). Open, out of equilibrium sys­ tems, such as natural environments, spontaneously evolve to reach a thermodynamic equilibrium with the outside environment, dissipating the available free energy to main­ tain existing gradients, unless electromagnetic radiation, kinetic/chemical, and gravitational sources of external energy are introduced. As a result, comets disintegrate over time, oceans mix, and exposed rock weathers irreversibly. Thermodynamics is a unifying principle in Earth sciences, and can predict energy and mass transfer ­ processes among Earth’s various solid, fluid, and gas­ eous reservoirs, from weather, to crustal renewal and weathering. These processes can be quantified in terms of mass and energy balance between input and output com­ ponents. For instance, in the present‐day terrestrial environ­ ment, rock weathering can be expressed as the sum of its products (equation 1.1) (Zaharescu et al., 2017):

Weathering = secondary solids, (1.1) dissolved solutes, volatiles,, biota

1.3. THE EARLY ANOXIC EARTH Earth is subject to one of the largest thermodynamic disequilibria in the inner solar system, with large frac­ tions of matter and energy mixing in surface and sub­ surface portions of global cycles (Kleidon, 2010a). Despite a considerable decrease in the available energy from its formation, but with an evolving biosphere, Earth surface processes have maintained strong environ­ mental gradients counteracting entropy. One impor­ tant gradient is the surface redox state. The planetary surface has experienced a drastic change in its redox environment, from greatly reducing in the Hadean and Archean geological eons (4.6–2.4 Ga; Holland, 1984; Sverjensky & Lee, 2010), to one characterized by a sharp disequilibrium gradient between an oxygen‐rich atmosphere–hydrosphere system and a reduced crust (2.4 Ga to present). The capacity of life to indepen­ dently produce chemical‐free energy (generally by using the energy transfer at the redox boundary), which counteracts entropy, further enhances this gradient and largely explains the cycles of matter we see today (Kleidon, 2010b). During the first half of Earth’s history (4.6–2.4 Ga), a lack of free oxidants such as O2 at Earth’s surface, but abundant CO2 due to volcanic outgassing (Brimblecombe, 2013), governed mineral dissolution,

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  5

Electromagnetic

energy

Gravitational energy

H2O Electromagnetic energy H2CO3

CO2

Chemical kinetics (anoxic weathering) Silicates

Carbonates

Gr

av i kin tatio eti nal cs We ath e reg r olit ed h

Abiotic anoxic weathering

Fe2+ Mn2+

Water body Sediment

Figure 1.2  Simplified schematic of carbon and energy flows during the Archean Eon (3.5 Ga). Volcanic degassing releases CO2 (together with other gases and aerosols) to the atmosphere, which reacts with water vapor to produce carbonic acid. In an anoxic atmosphere, silicate rocks exposed through tectonic forces or volcanism react with carbonic acid from precipitation and release chemical elements as dissolved ions. If supersaturating conditions prevail, carbonates of different reduced ions (e.g., Fe2+, Mn2+) form. Gravitational forces transport and deposit weathered products to lakes, rivers, or marine sediments, where they are solidified over geologic time through diagenesis. Sedimentary rocks resulting from diagenesis can thus record the initial conditions of the weathering environment (e.g., redox variability in Proterozoic Banded Iron Formations).

the formation of secondary minerals (Hazen, 2013), and niche and habitat development on the vacant land (when first life emerged), ultimately shaping the distributions of protoecosystems in the landscape (Figure  1.2). It is still not entirely clear when life on Earth first emerged (4.2–3.8Ga; Bell et  al., 2015; Battistuzzi et  al., 2004). In a late Hadean to early Archean environment, however, with an abundance of carbon, both highly oxidized (CO2, carbonates, bicar­ bonates) and highly reduced (CH4 and various hydro­ carbon complexes; Arndt, 2013; Zerkle et  al., 2012), biota–mineral interactions would have been very ­modest (Hazen, 2013; Hazen et al., 2008). Such inter­ actions were likely chemolithotrophic, limited to epil­ ithic and endolithic surfaces under a highly erosive environment (Sleep, 2010). The carbon cycle, while perhaps not strongly mediated by life on earliest Earth, was a significant driver of silicate rock weathering through the acid‐generating capacity of rainwater‐dissolved CO2 (Ushikubo et  al., 2008).

Carbon release (crustal CO2 outgassing) and capture (aqueous ­carbonate formation during H2CO3 –mineral reactions) is t­emperature dependent; and this would have created a primordial planetary thermostat, stabi­ lizing the early climate and pH of surface waters (Berner, 2004; Walker et al., 1981). Ocean‐floor volca­ nism and weathering provided complementary carbon feedbacks to terrestrial weathering, but their relative contributions are not entirely understood (Coogan & Dosso, 2015). Various planetary models have highlighted the criti­ cal importance of the early carbon cycle for silicate weathering budgets and the global climate. The most recent estimates suggest that the young anoxic Earth fea­ tured a temperate climate and a circumneutral ocean pH around 6.6 (compared to 8.2 in modern times) due to sta­ bilizing feedbacks from both terrestrial and ocean floor weathering (Krissansen‐Totton et  al., 2018). Methane should also be expected for an anoxic Archean atmosphere (3.8–2.4 Ga), derived from serpentinization—the anaerobic

6  BIOGEOCHEMICAL CYCLES

oxidation and hydrolysis of hot, low‐silica ferromagne­ sian minerals (Kasting, 2014; Preiner et al., 2018)—and methanogenesis, when it evolved in Archean microbes (Catling & Kasting, 2017). Recent studies of modern biological soil crusts (with N2 fixation qualities linking to primordial element cycles) advance the idea that in the pre‐oxygenic world, early land‐colonizing diazotrophic microbes were the first to endow the biosphere with the capacity to capture free nitrogen gas (N2) from the atmosphere into usable forms (e.g., NH3; Thomazo et al., 2018). By developing the nitro­ genase enzymatic system, an oxygen‐sensitive Fe–Mo protein, these communities would have been able to trans­ form N2 into bioavailable forms, either using hydrogen to reduce it to ammonia, or using oxygen to oxidize it to nitrites and nitrate in soil and water (Thomazo et  al., 2018). Most of the biosphere would have relied on incipient N2 fixation. The Archean signatures of such transformations have been recently dated to more than 3.2 Ga in South Africa fluvial deposits (Homann et al., 2018). By linking rock‐derived nutrients with nitrogen from the atmosphere, these microbes, together with sulfur reducers that appeared earlier (3.47 Ga; Shen & Buick, 2004), are thought to have established the first nutrient links among the biosphere, atmosphere, geosphere, and hydrosphere, or the earliest biogeochemical cycles. This also would have helped fertilize the early oceans and con­ nect marine and continental biogeochemical cycles before the Great Oxidation Event (GOE; Thomazo et al., 2018). The mineral diversity of the upper continental crust likely increased modestly during the emergence of a young biosphere, most likely in localized carbonate and sulfate hot spots (e.g., biogenic pyrite) with little effect on the depositionary (soil and sediment) environment (Hazen et al., 2008; Shen & Buick, 2004). Remnants of early Earth biogeochemical cycles can be found in modern anoxic analogs such as the deep bio­ sphere—several kilometers under terrestrial and marine floors (Ijiri et al., 2018; Lever et al., 2013), where endo­ lithic cyanobacteria were recently discovered (Puente‐ Sánchez et  al., 2018)—some marine and lacustrine sediments (Bowles et  al., 2014; Wallmann et  al., 2008), and pelagic areas of anoxic lakes and seas, e.g. Lake Matano (SE Asia), Black Sea (eastern Europe), and Cariaco Basin (NE South America; Crowe, 2008; Reinhard et al., 2014; Wright et al., 2012). 1.4. THE GREAT OXIDATION EVENT The revolutionary “invention” of photosynthesis and nitrogen fixation by Cyanobacteria at some point in the Archean (Olson, 2006; Schirrmeister et  al., 2015; Shih, 2015) triggered a cascade of events in the weathering environment, the mineral landscape, and the cycles of

nutrients that supported an evolving and more complex biosphere. Oxygen enrichment by photosynthetic biota slowly consumed the available pool of redox‐sensitive elements (e.g., Fe, Mn, Cu, Mo, Cr) from surface envi­ ronments in the late Archean, followed by their depletion in the deep oceans at the end of Proterozoic (Scott et al., 2008). This shifted the redox balance of most of Earth’s surface towards an oxidative state, increasing the surface thermodynamic disequilibrium gradient, and providing a major biological conduit for nutrient flows between continental crust, atmosphere, and hydrosphere (Figure 1.3). Microbial methane production likely further increased Earth’s oxygen reservoir, and its role in surface chemistry, by facilitating hydrogen (from water) to escape from the atmosphere to space by methane photolysis (Catling et al., 2001; Fixen et al., 2016). Oxidation of terrestrial landscapes was not a one‐time event (Figure  1.3). Episodic (few million years span) increases in continental oxidative weathering prior to the GOE have been indicated by Se spikes in rock forma­ tions of Western Australia, resulting from oxidation of sulfide minerals on land about 2.66 Ga (Koehler et  al., 2018). Other traces of oxidative weathering “oases” (likely due to stromatolithic photosynthesis) have been dated using sulphur isotopes in Archean sedimentary pyrites as far back as 3 and 2.97 Ga in the Pangola Supergroup, South Africa (Crowe et al., 2013; Eickmann et al., 2018), and using radiogenic Os to 2.5 Ga (late Archean) in Mount McRae Shale, Western Australia (Kendall et al., 2015; Reinhard et al., 2009; Stüeken et al., 2012). Possible pathways for the first biological oxidative weathering and biological organic matter stabilization in soil/ sediment by cyanobacteria–archaea–fungi consortia there­ fore may have occurred in soil and aquatic ecosystems on land during early Archean times (Lalonde & Konhauser, 2015), as well as in cryptoendolithic ecosystems in silicate rock crust as found in present day East Antarctica (Mergelov et  al., 2018). Hints for the existence of such endolithic ­ecosystems, likely aquatic, have been preserved in both Archean and Proterozoic mineral deposits (Golubic & Seong‐Joo, 1999; McLoughlin et al., 2007). The GOE, a planetary scale photosynthesis‐driven shift in the redox state of Earth’s surface occurring in the late Archean (Catling, 2013; Kump, 2008; Lyons et al., 2014), irreversibly set the reduced crust on an oxidative weathering path that has remained stable up to the pre­ sent. Abundant “biological oxygen” amounted to major changes in the interaction of geosphere, atmosphere, hydrosphere, and biosphere. One of the consequences was a diversification boost in the mineral world, with the incorporation of a large number of novel life‐promoted oxide species, particularly minerals of different (oxidized) species of As, Co, Cu, Fe, Mn, Ni, S, U, and Zn, and other trace elements (Hazen, Sverjensky, et  al., 2013;

3.4

Archean

1. Lyons T.W. et al. (2014). Nature, 506, 307–315. 2. Srinivasan, P. et al. (2018). Nature Communications, 3036. 3. Ushikubo, T. et al. (2008). Earth and Planetary Science Letters, 272, 666–676. 4. Sleep, N.H. (2010). Cold Spring Harbor Perspectives in Biology, 2(6), a00252. 5. Shen, Y. & Buick, R. (2004). Earth-Science Reviews, 64, 243–272. 6. Homann, M. et al. (2018). Nature Geoscience,11, 665. 7. Crowe, S.A. et al. (2013). Nature, 501, 535–539.

Proterozoic

10–3

Phanerozoic

10–4 10–5 10–6

Oxygen (Log PO2 atm)1

Grassland weathering

Modern weathering

Vascular plant weathering Wood-wide web redistribute weathering products

Deep oceans oxygenate10

Fungi – cyanobacteria symbiosis Epi/endolithic weathering

Planetary-scale biological oxidative weathering Oxygen oases

Epilithic oxidative weathering

Microbial-driven redox landscapes Terrestrial, aquatic microbial mats Biogenic N-reduction

3 Ga7

0.45 Ga11 0.12 Ga12

Oxic modern Earth

3.8

Ga6

Great Oxidation Event

Hadean

Ga5 3.2

1.4 – 0.4

Ga9

Proterozoic Oxidation Event

4.57 Ga

Ga3,4

Microbial sulfate reduction (biogenic pyrites) Anoxic terrestrial weathering

Likely continental Fe-based photosynthesis and weathering by microbes (S-based in oceans)

Hazy anoxic Earth

Solar system forms Oldest silica-rich volcanism (meteorite)

4.56 Ga2

2.47 Ga8

1

–1 0 10 Ga13 10–2

8. Partin, C.A. et al. (2013). Chemical Geology, 362, 82–90. 9. Heckman, D.S. et al. (2001). Science, 293, 1129–1133. 10. Sperling, E.A. et al. (2015). Nature, 523, 451–454. 11. Morris, J.L. et al. (2018). Proceedings of the US National Academy of Sciences, 115, E2274–E2283. 12. Prasad, V. et al. (2011). Nature Communications, 2, 480. 13. Zaharescu, D.G. et al. (2017). Nature Scientific Reports, 7, 43208.

Figure 1.3  Timeline of major events in the geosphere–atmosphere–biosphere interactions and how they shaped Earth system evolution, including a fundamental shift in its surface thermodynamic disequilibrium attained during The Great Oxidation Event.

8  BIOGEOCHEMICAL CYCLES

Sverjensky & Lee, 2010), phosphates, and new carbon‐ based biominerals such as organic biominerals and bio­ carbonates (Hazen, Downs, et al., 2013). It is estimated that about 4000 of the total of about 5500 minerals found on Earth today emerged during this major environmental redox shift (Hazen & Ferry, 2010; Pasero, 2018). Biogenic atmospheric oxygenation also freed an unprecedented amount of potential energy at the redox boundary, which stimulated the emergence of oxygen‐breathing eukaryotic life. This, in turn, would have further stabilized the planetary surface to a new biogeochemical state (Lenton et al., 2018; Lovelock, 1995). Land colonization by vascular plants in the early Phanerozoic (Middle to Late Ordovician, 0.45 Ga), and the almost concomitant evolution of glomeromycota symbiosis, to which arbuscular mycorrhiza belongs (Morris et al., 2018; Strullu‐Derrien et al., 2018), would have introduced the first network of plant roots and fungal mycelia we now recognize as the “Wood Wide Web” (Simard et al., 1997). They enhanced weathering by selectively mining minerals and redistributing nutrients and information across plant and fungi individuals and species in the ecosystem (Klein et al., 2016). This increased ecosystem resilience allowed the emergence of a more complex terrestrial biosphere, including diverse forests and grassland ecosystems, which further captured and fixed C and N from the atmosphere into biomass and stabilized the global cycles of rock‐derived nutrients. Biosphere diversification also shifted biomass distribu­ tion from predominantly a subsurface biosphere in a microbial world, to above‐ground ecosystems after photosynthetic plants colonized the land (McMahon & Parnell, 2018). It is estimated that as much as 80% of current planetary biomass is hosted in land plants (Bar‐On et al., 2018). The emergence of organic and clay‐ rich soils following the rise of the terrestrial biosphere in the Phanerozoic also meant that plant roots, mycorrhizal fungi, and the rhizosphere microbiome became the main drivers of continental weathering and biogeochemical cycles (Hazen, Sverjensky, et al., 2013). The following sections will provide a comprehensive update on the role of different ecosystem components in modern weathering and the carbon cycle, including the inevitable anthropogenic effect. 1.5. MODERN‐DAY OXIDATIVE WEATHERING Vast nutrient and energy transfers between Earth’s solid, fluid, and gaseous reservoirs support the development of modern terrestrial ecosystems. Under the oxygen‐rich atmosphere, this planetary‐scale bioreaction continu­ ously consumes exposed rock minerals, oxygen, and CO2 to drive the cycling of C, N and rock‐derived elements through oxidative weathering. Bedrock weathering prepares

the terrestrial surface for developing ecosystems by physically and chemically altering rocks, releasing major and micronutrients to pore water, transporting them to rivers, lakes, and seas, integrating them into secondary minerals and organic–mineral aggregates, and delivering them in accessible forms to various biota. There is a very tight coupling between the exposed upper crust and the biosphere, which results in a slow but continuous physical fracturing and chemical alteration of bedrock to secondary minerals in a continuous flow or “river” of clay minerals which progresses upwards, then follows gravity gradients to constantly replenish the biosphere’s nutrient‐rich substrates (Holbrook et  al., 2019; Richter, 2017). The intensity of these processes as well as the nutrient and mineral make‐up of bedrock dictates the functioning of the overlaying ecosystems, and their feedbacks to the wider hydrosphere and atmosphere (Kaspari & Powers, 2016; Zaharescu, Hooda, Burghelea, & Palanca‐Soler, 2016). The transfer of chemical elements between rock and living systems during weathering unfolds over a wide range of scales, from molecules to the entire biosphere, and these transfers have been the focus of a plethora of studies. Particularly noteworthy is the comprehensive effort to understand matter and energy fluxes in the shallow and porous crust harboring life in the interdisci­ plinary framework of Critical Zone science (Richter & Billings, 2015). Recent advances in isotope geochemistry, hydrology, ecology, and remote sensing have made it pos­ sible to better constrain the interactions between differ­ ent components of atmosphere, geosphere, and biosphere at various scales and better understand how they shape the surface of Earth and transform parent rock to soil and sediments that sustain life (Chorover et  al., 2011; Zaharescu, Palanca‐Soler, Hooda, et al., 2016). Incipient stages of mineral weathering, when the first microbes, fungi, and plant roots explore freshly exposed mineral surfaces, are among the most active (Zaharescu et  al., 2017), and they trigger the flow of energy and nutrients feeding the major biogeochemical cycles. Mass‐balance approaches are often used to follow the flow of chemical elements from minerals thorough different ecosystem components during weathering in both natural and experimental settings (Anderson et al., 2002; Burghelea et al., 2018; Yousefifard et al., 2012). The modern‐day silicate‐weathering environment is character­ ized by abundant carbon in oxidized (CO2) and reduced (organic acids, siderophores, and biopolymers) forms, mostly released by the biosphere through respiration, decomposition, and other metabolic activities. Human activity adds an important and increasing fraction of carbon through the fossil‐fuel extractive industry (Figure 1.4). Interactions among abiotic and biotic com­ ponents of the biosphere modulate modern‐day weathering

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  9

energy

Electromagnetic

Gravitational energy

H2O Electromagnetic energy

CO2

H2CO3

O2

C-bioligands Fossil C

Chemical Silicates Chem kinetics ic kinetic al (oxidative s g) in weather

O2

Soi

l

Carbonates (Hydr)oxides Gravita kine tiona

Biological oxidative weathering

tics

l

Microbes Algae

Water body Sediment

Figure 1.4 Carbon and energy flows on the modern, biosphere‐dominated Earth surface. Under modern‐day weathering, CO2 released through mantle degassing (terrestrial and marine), biosphere respiration, or anthropogenic fossil‐fuel extraction and burning reacts with rainwater, producing carbonic acid. The biosphere further converts CO2 to organic acids (through light‐harvesting photosynthesis), which together with the carbonic acid and O2 from the atmosphere react with exposed silicate rock to release chemical elements to flowing water. These elements enter the biosphere and migrate through its different trophic levels as nutrients, are transported to oceans, or precipitate as secondary minerals in soils and sediments.

of the exposed upper crustal environment and the cycles of elements through Earth’s solid, fluid, and gaseous reservoirs. 1.5.1. Abiotic Weathering Disentangling the contribution of various abiotic and biotic factors to weathering in a biosphere‐dominated terrestrial world is challenging. Whether living or non­ living factors are the first agents of weathering has been a persistent “chicken‐and‐egg” question in Earth sciences. Perhaps a good way to approach this problem is by study­ ing incipient weathering and ecosystem colonization of freshly exposed minerals or in recently exposed rock such as volcanic fields, exposed bedrock in the mountains, and landscapes exposed by glacial retreat. Studies carried out in controlled laboratory settings with unreacted rock exposed to incipient weathering under abi­ otic conditions have shown an initial spike in solute (anion and cation) export to pore waters (driven by carbonation reactions), which was significantly affected by microbial

and plant presence (Burghelea et  al., 2018; Zaharescu et  al., 2019). This was consistent with early mineral exposure by fracturing and initial mass loss of elements from freshly exposed mineral lattices due to increased exchange at the water–mineral interface, e.g., cracking developed during oxidative/hydration expansion stresses of reduced mineral surfaces under unsaturated pore fluids. Repulsive forces during water–rock interaction have been demonstrated in laboratory experiments (Levenson & Emmanuel, 2017), and field studies have shown evidence of micron‐scale surface spalling and loss of Na‐containing glass from grain surface to a depth of 250 μm, with minimal secondary mineral deposition in subsurface basalt exposed to subpolar climate (Hausrath et al., 2008). Temperature has a strong effect on incongruent min­ eral weathering due to the different activation energies of mineral dissolution; e.g., between pH ~7 and 9, basaltic glass dissolution is faster than embedded minerals at low temperature (~0°C), while basaltic forsterite dissolves more quickly than glass at higher temperatures (~50°C; Bandstra & Brantley, 2008).

10  BIOGEOCHEMICAL CYCLES

Ice nucleation, pervasive over large swaths of the terres­ trial surface, particularly at high altitudes and latitudes, and during periods of terrestrial history, e.g., glaciations and Snowball Earth events, is also a major driver of physical and, indirectly, chemical weathering. Studies have shown that active sites of ice nucleation on mineral surfaces generally coincide with sites of incipient chemical weathering in field conditions, e.g., lamellar edges in bio­ tite, cracks, and other mineral defects (Lybrand & Rasmussen, 2014; Murray et  al., 2012). Such crystal defects increase the surface area exposed to weathering. Ice nucleation in rock cracks and pores also increases water volume by about 9% (Fahey & Dagesse, 1984), increasing the stresses on minerals making it about three to four times more effective than wetting–drying in ­disintegrating rock (Fahey, 1983). Cycles of water adsorp­ tion on minerals followed by drying, however, have a ­similar or greater effect on mass loss (leaching) compared to freeze–thaw cycles, releasing ~0.2% of basalt mass after 200 cycles (Yesavage et al., 2015) and up to 3–10% after 25 dry–wet cycles on carbonate rocks (Dunn & Hudec, 1972). Wetting–drying effect on physical disag­ gregation is enhanced in clays (Dunn & Hudec, 1972) due to their layered structure, which is exposed to ­repulsive forces when layers adsorb highly polar water molecules in the interspace (Fahey, 1983). Friction/abrasion of mineral surfaces during gravi­ tational kinetics, e.g., rock transport by rivers and streams (Petrovich, 1981), as well as mechanical fracturing of bedrock during exhumation/orogeny (Holbrook et  al., 2019), greatly increase the density of active sites on rock surfaces, and hence the total area available for chemical weathering. A thermodynamic disequilibrium of crustal materials reaching Earth’s surface (degassing spaces, thermal/ pressure fractures; Figure 1.5) therefore sets the stage for abiotic weathering. Oxygen and water percolation in developing fractures, strong short‐range electrostatic forces on grain surfaces, and weak long‐range gravitational gradients further enhance incipient physical and chemical weathering, largely depending on the sub­ strate’s physical and geochemical properties and latitu­ dinal/altitudinal location. Zaharescu et  al. (2019) estimate that the total global denudation rate of terres­ trial surface by abiotic chemistry alone is about 6.1 Tmol year−1 of major bedrock cations (Si, Al, Na, K, Ca, Mg, P, Ti, Mn, and Fe). 1.5.2. Microbial Contribution to Weathering Microbes are a key ecosystem component and one of the most abundant and active biological agents that shape the Earth’s surface through weathering processes and carbon burial. Microbial ecosystems including free‐living

heterotrophic and phototrophic colonizers of bare rock surfaces characterize the first stages of primary succession in terrestrial ecosystems. Rocks and minerals represent an ecological niche, which provides microbes with living habitats and nutrients, while microbes impact primary to secondary mineral weathering rates through their effects on mineral solubility and metal speciation. It is estimated that the microbial parts of terrestrial ecosystems con­ tribute about 11.5% over abiotic rock dissolution glob­ ally, or 6.8 Tmol year−1 (microbes + abiotic; Zaharescu et al., 2019). An increasing number of studies during the past two decades lie at the heart of geomicrobiology, an emerging field that studies mineral–microbe interactions at different scales, in different environments, using a multitude of experimental approaches (electron microscopy, atomic force microscopy, spectroscopy, x‐ray, molecular, and iso­ tope techniques; Balogh‐Brunstad et al., 2020, Chapter 4, this volume; Banfield & Nealson, 1997; Buss et al., 2007; Huang et al., 2014; Miot et al., 2014; Parikh & Chorover, 2005, 2006; Perdrial et al., 2009; Omoike & Chorover, 2004). Microbes’ close association with mineral particles has been reported extensively in the literature as influencing soil genesis, nutrient and lithogenic element cycling, mineral dissolution, CO2 drawdown, and plant nutrition (Ahmed & Holmström, 2015; Balogh‐Brunstad, Keller, Dickinson, et  al., 2008; Barker et  al., 1998; Cockell et al., 2007; Gadd, 2013; Gislason et al., 2009; Gleeson et al., 2006; Hilley & Porder, 2008; Kinzler et al., 2003; Muentz, 1890; Puente et  al., 2009; Uroz et  al., 2009, 2011; Wightman & Fein, 2004; Wu et  al., 2008). Microbial effects on weathering extend from micro‐ to global scale with a wide ecological impact on ecosystem services (biogeochemical cycling and atmospheric CO2 regulation; Bonneville et al., 2009; Hilley & Porder, 2008; Z. Li et  al., 2016). In the critical zone, biogeochemical processes controlled by microbes influence the retention and export of organic matter, nutrients and toxic ele­ ments, affecting soil fertility and water quality (Brantley et al., 2011; Gadd, 2013). Microorganisms, inclusive of bacteria, archaea, and fungi are the first to colonize new substrates, promote physical and chemical weathering, and biotransforma­ tion of minerals (Balogh‐Brunstad, Keller, Dickinson, et  al., 2008; Balogh‐Brunstad, Keller, Gill, et  al., 2008; Balogh‐Brunstad, Keller, Bormann, et al., 2008; Brunner et al, 2011; Burford et al., 2003; Dong et al., 2015; Finlay et al., 2009; Gorbushina & Broughton, 2009; Frey et al., 2010; Leake et al., 2008; Z. Li et al., 2016; Seiffert et al., 2014; L.L. Sun et al., 2013; R.R. Wang et al., 2015; W.I. Wang et al., 2015; B. Xiao et al., 2012; L.L. Xiao et al., 2016). Multiple studies showed the role of bacteria and fungi in both mineral formation (Ehrlich, 1999; Gadd, 2007; Gorshkov et  al., 1992; Kawano & Tomita, 2001)

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  11

Unweathered rhyolite

Unweathered basalt

(a)

(b)

(c)

0.0025 0.002

0.03

0.0015 0.02 0.001 0.01 0.0005 Error bars: +/– 2 SE

0 0

60

Si prefer. leaching (%, cumul. sum)

(d)

0.04

(A)biotic denudation - basalt

120 180 240 300 360 420 480 540 600 Weathering days

REE prefer. leaching (%, cumul. sum)

Abiotic denudation - granite Na prefer. leaching (%, cumul. sum)

Weathered basalt - abiotic

0.001 0.0008 0.0006

(e) A B GB GBM

0.0004 0.0002 0.0001 Error bars: +/– 1 SE 15 45 90 150 210 270 330 390 450 510 570 Weathering days

Figure 1.5  Incipient weathering as driven by abiotic and biotic agents. Internal and peripheral fractures set the stage for physical weathering. Electron probe microanalysis showing internal microfractures within and between minerals in (a) rhyolite and (b) basalt, together with (c) surface microfractures on separated rock grains. (a) Color back‐scattered electron map; (b) gray‐scale back‐scattered electrons map. Leaching experiments showing (d) ­abiotic preferential (normalized to their rock abundances) leaching to pore water of Na (light gray) and Si (dark gray) from granular granite, and (e) rare earth elements (sum) leaching from granular basalt (0.25–0.5mm, Zaharescu et al. 2017, 2019). Treatments were in order of increasing denudation: A, abiotic; B, rock microbes; GB, buffalo grass (Buchloe dactyloides) microbes; GBM, grass–microbes–arbuscular mycorrhiza (Glomus intraradices).

and dissolution (Bennett et  al., 1996; Liermann et  al., 2000; O’Reilly et al., 2006; Perdrial et al., 2009; Rosenberg & Maurice, 2003). In terrestrial ecosystems, microbial controls on weathering have been studied in carbonates and silicate rocks (Bennett et al., 2001; Folk, 1993; Lian, 1998; Lian et  al., 2002, 2005, 2006, 2008; Viles, 1988). Silicate weathering is of global importance due to its role in soil development, nutrient cycling, and carbon sequestration (Beaulieu et  al., 2012; Berner, 1995; Ehrlich, 1998; Shirokova et al., 2012; Schulz et al., 2013; L.L. Sun et al., 2013; White & Brantley, 1995; Wofsy et al., 2001; B. Xiao et al., 2012). Microbes can directly and indirectly impact silicate dissolution and secondary mineral formation

(Finlay et al., 2009) through their attachment to the min­ eral surfaces and their metabolic products, respectively. Some of the microbial strategies that enhance mineral dissolution and disrupt silicate framework are: mineral– water equilibria alteration at the point of contact, proton and hydroxyl production inducing the formation of min­ eral surface ion complexes, catalyzing redox reactions, or mediating the formation of secondary mineral phases (Barker et al., 1998; Bennett et al., 2001; Bonneville et al., 2004; Brown et al., 1999; Drever & Stillings, 1997; Duff et al., 1963; Goldstein, 1986; Huang et al., 2014; Hutchens et al., 2003; Kalinowski et al., 2000; Lapanje et al., 2012; Z. Li et al., 2016; Liermann et al., 2000; Rogers & Bennett, 2004; Rogers et al., 1998; Ullman et al., 1996; Wendling

12  BIOGEOCHEMICAL CYCLES (a)

(b) n

isitio

u Acq

Plants

Silicate minerals

P, K, Ca, Mg, Mn, Fe se

Cell membrane DNA

Acqu isit

Bacteria

ion

Rele a

Interestial H2O equilbrium siderophores, metal-organic complexes

LMWOA, tion

re Sec

Exopolysaccharides, carbonic anhydrase

CO2 + H2O

Metal oxidation or reduction

acidification and complexation

HCO3– + H+

k Attac

Soil formation Soil fertility Element cycling Ecosystem evolution

Release

Ca+2, Mg+2

Secondary mineral formation (clays, crystalline oxides and hydrous oxides of Fe and AI)

Carbon sequestration carbonates formation

CaCo3, MgCO3

Figure 1.6  Microbes–rock interactions during weathering: (a) fungal hyphae prospecting basalt grains (Zaharescu et  al., 2019) and (b) a schematic of mineral weathering by microbes and the affected ecosystem processes. [(a) Zaharescu et al. (2019). Reproduced with permission of Dragos G Zaharescu.]

et  al., 2005; L.L. Xiao & Lian, 2016; L.L. Xiao et  al., 2014; Yao et al., 2013; Zhao et al., 2013). In addition to enhancing dissolution of crystalline silicates, microor­ ganisms can play a significant role in glass dissolution— glasses being less resistant to chemical weathering than their well‐crystallized counterparts (Callot et  al., 1987; White, 1983). From silicate minerals and glasses, microbes derive both macro (e.g., N, P, and S) and trace nutrients (e.g., K, Fe, Ni, V, and Mn; Brantley et al., 2001; Valsami‐ Jones et  al., 1998) for their metabolic use and plant growth (Figure 1.6). Microbial exometabolites (e.g. extracellular polysac­ charides, and metal‐complexing ligands, such as low‐ molecular‐weight organic acids and siderophores) are important agents in promoting mineral dissolution, oxidation, or reduction of metals at mineral surfaces (Berthelin & Belgy, 1979; Buss et al., 2007; Ivarson et al., 1978, 1980, 1981; Malinovskaya et  al., 1990; Neilands, 1995; Welch et al., 1999, 2002). The most common bio­ genic chelators are siderophores and organic acids, which can act independently or together enhancing mineral dis­ solution rate 10 to 100 times (Buss et al., 2007; Cama & Ganor, 2006; Reichard et al., 2007). Organic acids, including heterogeneous condensed compounds of variable charge and solubility, and simple low‐molecular‐weight organic acids, like phenolic acids secreted by soil bacteria and oxalic acid produced by fungi, are particularly significant in enhancing silicate dissolution rates by decreasing pH, forming framework‐ destabilizing surface complexes, or by complexing metals in solution (Bennett & Casey, 1994; Blake & Walter, 1996; Cama & Ganor, 2006; Dontsova et al., 2014; Drever & Stillings, 1997; Drever & Vance, 1994; Goyne

et al., 2006, 2010; Neaman et al., 2005, 2006; Stephens & Hering, 2004; Stillings et al., 1996; Ullman et al., 1996; Welch & Ullman, 1993; Wieland et al., 1988). Microbial impacts on mineral surfaces depend on the substrate type, composition, porosity, surface reactivity, surface aging, and microbial adaptability (Hutchens, 2009; Olsson‐ Francis et al., 2012; Uroz et al., 2012; Wild et al., 2018; Zaharescu et al., 2017). While it is known that minerals control the diversity of bacterial communities in soil (Uroz et al., 2012), questions remain about qualitative and quantitative changes that weathering microbial com­ munities will undergo under global climate change or other human‐induced environmental perturbations. 1.5.3. Vascular Plant and Mycorrhizae Effect on Weathering Plant–soil interactions play a central role in the bio­ geochemical carbon, nitrogen, and hydrological cycles, with feedbacks to the atmosphere, oceans, and climate. Intense biological activity in soil coupled with the hydrological cycle drives progressive weathering of g eological media and affects soil and ecosystem ­ development. Experimental studies combined with field measurements of river nutrient fluxes globally estimate that vascular plants and associated microbial communities together with abiotic leaching add about 6.6 Tmol year−1 to the major element cycles, while add­ ing symbiotic fungi would reduce the contribution to about 6.2 Tmol year−1 (Zaharescu et  al., 2019). This means that plant colonization increases element reten­ tion into soil and biomass while microbes and fungi accelerate denudation.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  13

Plant roots influence mineral dissolution and chemical denudation, with consequences for soil formation, soil fertility, its stability, landscape evolution, and long‐term productivity of terrestrial ecosystems. The rooting zone is a hot spot where intense gradients in carbon and water are superimposed upon low‐temperature geochemical disequilibria. Plants affect weathering through direct contact of roots with mineral surfaces, water redistribu­ tion, rhizosphere production of organic and inorganic acids, root and heterotrophic respiration, biorecycling of cations, and formation of biogenic minerals (Bormann et  al. 1998; Kelly et  al., 1998; Landeweert et  al., 2001; Leyval & Berthelin, 1991; Marschner, 2012). It is acknowledged that vascular plants enhance weathering of phosphates (Grinsted et al., 1982; Hinsinger & Gilkes, 1997), carbonates (Jaillard, 1987), and silicates (Burghelea et  al., 2015, 2018; Drever, 1994; Hinsinger et  al., 2001; Robert & Berthelin, 1986; Zaharescu et al., 2017). Higher plants are efficient rock‐weathering agents due to a high mass of fine roots that produce etching and create vast contact areas with minerals (April & Keller, 1990; Berner & Cochran, 1998; Cochran & Berner, 1996). Moreover, plant growth and storage of rock‐derived elements can also accelerate weathering (Akter & Akagi, 2006; Bashan et  al., 2002, 2006; Berner, 1992, 1995; Drever, 1994; Franklin & Dyness, 1973; Jackson, 1996; Lundström et al., 2000; Pawlik et al., 2016). Rhizosphere processes, including rhizodeposition of low‐molecular‐weight organic acids decrease pH, release gases (e.g. CO2), and enhance availability of cations (e.g. Ca, Mg, and K) in rhizosphere soil solution (Gobran et  al., 1998; Gregory, 2006; Griffiths e al., 1994; Marschner, 2012; Yatsu, 1988). The pH in the rhizosphere microenvironment can be as low as 3, whereas in the bulk soil it commonly varies between 5 and 7 (Arthur & Fahey, 1993; Hinsinger, 1998). Additionally, the decay of organic matter produces organic acids and carbonic acid, which also attack mineral surfaces. When soil resources are limited, plants turn to common symbiotic partners, such as mycorrhizal fungi, to provide them with necessary mineral‐derived nutrients otherwise not available to plants and to ensure plant growth, nutri­ tion, and ecosystem productivity (Aghili et  al., 2014; Smith & Read, 2008; Treseder, 2004, 2013). The mutual­ istic relationship efficacy is substrate‐dependent, since the costs and benefits depend on resource availability and/or imbalance among the symbionts (Burghelea et al., 2015; Grman & Robinson, 2013; N.C. Johnson et  al., 1997, 2010; Rosenstock, 2009). Laboratory and field studies provide compelling evidence that ectomycorrhizal (ECM) fungi, commonly associated with trees, are able to enhance weathering and extract nutrients such as P, K, Ca, Mg, and Fe from apatite, biotite, feldspars, and other silicates (Balogh‐Brunstad, ­

Keller, Gill, et al., 2008; Finlay et  al., 2009; Gadd, 2007; Hoffland et  al., 2004; Jongmans et  al., 1997; Landeweert et  al., 2001; Leyval & Berthelin, 1991; Paris et  al., 1995; Rosling, Lindahl, and Finlay, 2004; Rosling, Lindahl, Taylor, et al., 2004; Smits et al., 2012; van Breemen, Finlay, et  al., 2000; van Breemen, Lundström, et  al., 2000; van Schöll et al., 2008; Wallander et al., 1997). A network of hyphae (mycelium) accessing a higher mineral surface area than roots alone extends around the root tips like a sheath, protruding in between the cortical cells of roots, transport­ ing nutrients and water to the plant in exchange for photo­ synthetically derived carbon (Leake et al., 2004, 2008; Smits et  al., 2008). Other mechanisms by which ECM enhance weathering include secretion of organic acids (oxalic and citric acid) and targeted ligands, like ­siderophores, that form complexes with the metals in solution and on mineral surfaces (Hoffland et al., 2004; Schmalenberger et al., 2009; Y.P. Sun et al., 1999; van Hees et al., 2006). Within fungal mats, the pH of the soil solution is lower by more than 1 pH unit (Cromack et al., 1979) and oxalate concentrations are at least an order of magnitude higher (Griffiths et al., 1994) than in the surrounding soil. Another type of widespread mycorrhizal fungi (80% of plant species) with indirect effects on weathering is ­arbuscular mycorrhiza (AM; Taylor et  al., 2009). Small diameter fungal hyphae (3–4 μm) are able to penetrate between mineral grains (Figure 1.7), bind mineral parti­ cles, extract limiting nutrients (e.g., P and N), and trans­ locate them to the plant through the hyphal invaginations into the root‐cell membrane (Hetrick, 1989; Hetrick et al., 1988; Marschner, 1995; Marschner & Dell, 1994). The AM can enhance weathering through selective ion absorption (Lange Ness & Vlek, 2000), increased ­respiration, alter­ ation of soil pH due to increased uptake of nitrate and ammonium, and stabilization of soil through production of glomalin (Bago et  al., 1996; Burghelea et  al., 2014, 2018; Johansen et al., 1993; Rillig, 2004; Six et al., 2004; Smith & Read, 2008; Tisdall & Oades, 1982; Zaharescu et al., 2017, 2019). 1.5.4. The Animal World While plants are the main conduits of chemical energy input into the Earth biogeochemical cycles through photosynthetic carbon fixation that drives lithogenic ­ ­elements release from the rock during weathering, ­animals can also contribute towards biogeochemical cycling. A  major mechanism of animal effects on weathering involves translocation and mixing of altered minerals and elements released during weathering, as well as incor­ poration and transformation of organic compounds produced by the plants. Translocation can happen within the soil profile through activity of burrowing animals and on the surface through predation.

14  BIOGEOCHEMICAL CYCLES

(b) 1 cm

(a)

Root Vesicles Hyphae

Newly formed spores

20 μm

Figure 1.7  Mycorrhiza symbiosis: (a) root of buffalo grass (Bouteloa dactyloides) infected by arbuscular mycorrhizae symbiont (Glomus intraradices); (b) mycorrhiza reproductive spores (inset).

Bioturbation by both invertebrate and vertebrate a­ nimals is one of most studied contributions of ani­ mals to biogeochemical cycling of lithogenic elements (Meysman et al., 2006). Bioturbation is soil and sedi­ ment mixing by biological agents, such as plants and animals. Invertebrates that contribute to soil mixing include permanent and transient soil inhabitants such as earthworms, nematodes, arachnids (mites), isopods, coleopteran insects (beetles—adults and larvae) that move particles when they borrow, as well as hymenop­ teran insects (termites, ants, wasps, and bees) that engineer structures within soil. Vertebrates include fish, reptiles, amphibians, and fossorial mammals such as moles and gophers, as well as birds (Gabet et al., 2003). Burrowing type influences characteristics of soil mix­ ing: animals that burrow horizontal tunnels on slopes, like pocket gophers and some ground squirrels, result in horizontal movement of soils, while prairie dogs and harvester ants result in vertical mixing (Zaitlin & Hayashi, 2012). Most animals, however, prefer the top layer of the soil and generally do not burrow in sapro­ lite. M.O. Johnson et al. (2014) used optically stimulated luminescence (OSL) dates and isotopes (meteoric 10Be)

to demonstrate that mixing rate decreases nonlinearly with increasing soil depth in soils of Queensland, Australia. In general, bioturbation results in vertical homogenization of the profile by exposing less‐weath­ ered material to weathering, however, vertebrates can increase horizontal soil heterogeneity (patchiness) through burrowing and foraging (Eldridge et al., 2012; Zaitlin & Hayashi, 2012). Earthworm effects on soil properties have been studied extensively (e.g., Hodson et al., 2014; Shipitalo & Le Bayon, 2004; Swaby, 1949). Charles Darwin observed burial of material deposited on the soil sur­ face over time through soil mixing by earthworms, and he dedicated his last book to the earthworms (Darwin, 1881). Earthworms pass soil through their digestive tract while moving through the soil, producing casts covered in mucus. As a result, they leave macropores that allow rapid, preferential flow of water, increasing soil hydraulic conductivity, with indirect effects on weathering processes, and soil aggregation (Shipitalo & Le Bayon, 2004; Shipitalo & Protz, 1989; Pulleman et al., 2005; Ziegler & Zech, 1992b). This has implica­ tions for water holding capacity and potentially organic matter preservation in the soils.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  15

The effect of earthworms on C preservation has been studied extensively (Angst et al., 2017) but no clear trend has been reported. Effect of worms on composition of organic matter is rather limited, plant residues decrease in size as they pass through the digestive tract but do not undergo significant change in chemical composition (Hong et al., 2011; Ziegler & Zech, 1992a;), though there is evidence that earthworms promote formation of organo‐ mineral complexes (Shipitalo & Protz, 1989; Ziegler & Zech, 1992b). It has been reported that C can be preserved through physical protection of organic matter inside cast aggregates (Angst et  al., 2017), but it is not reflected on whole soil C content (Frouz et al., 2014). Similarly, while overall amount of C and N in well‐drained silt loam soil from Ohio and their distribution with depth did not change in the presence of earthworms, an increase in water‐stable aggregates that are enriched in C and N can preserve them (Ketterings et al., 1997). It was also shown for tropical soils that cast aggregates were enriched in C and N compared to control aggregates of similar size (Hong et al., 2011). Earthworms play a significant role in soil mixing. In natural northern forest ecosystems, invasive species of the earthworms can significantly affect distribution of  C over the soil profile, completely destroying the O‐horizon by mixing it with underlying mineral soil and significantly affecting habitat for microorganisms and plants (e.g., Bal et al., 2017; Bohlen et al., 2004; Craven et al., 2017). Vertebral animals that burrow contribute to mixing as well. Gophers, moles, and mountain beavers mix soil while building their burrows or foraging for food (Eldridge et al., 2012). Vertebrate burrows and foraging pits tend to be enriched in organic matter and higher in labile carbon and support greater levels of infiltration. There is limited information regarding earthworm and bioturbation effect on other chemical properties of the soils. It has been shown that earthworms can form calcium carbonate in the gut (Lee et  al., 2008; Robertson, 1936; Versteegh et al., 2014). There also has been some direct evi­ dence of chemical weathering promotion by the earth­ worms (Carpenter et  al., 2007; Hodson et  al., 2014). Earthworms also influence mobility of metals (Chen et al., 2019; Duarte et al., 2012; Sizmur & Hodson, 2009; Sizmur et al., 2011), likely through changes in pH and dissolved organic carbon (DOC), as well as P availability (Ros et al., 2017), and affect transformation of the organic contami­ nants (Chen et al., 2019). Chemistry of hazardous waste sites can be particularly affected by bioturbation, when plants and animals penetrate protective barriers, become exposed to contaminated material themselves. and bring them closer to the surface where other organisms and humans can be exposed (Bowerman & Redente, 1998). Animals that live on top of soils, both herbivores and carnivores, affect the biogeochemical cycles of C, N, P, and

other life essential and nonessential elements through their influence on the plant and animal biomass. Herbivores consume plants affecting ecosystem productivity and carbon inputs into the soil and predators influence herbi­ vore populations (Schmitz et al., 2018; Willoughby, 2018). In general, overall effects of the h ­ erbivores on C storage in the ecosystem is negative and predator is positive (Schmitz et  al., 2018). There are additional effects, related to soil compaction and temperatures, as well as through effect on composition of organic matter during digestion. Indirect evidence of the important role of bioturbation in weathering and soil formation is that incorporation of bioturbation in soil‐formation models successfully repre­ sents soil development. The Model for Integrated Landscape Evolution and Soil Development (MILESD; Vanwalleghem et al., 2013) was successfully applied to a 6.25 km2 area in the Werrikimbe National Park, Australia, simulating soil development over a period of 60,000 years. Temme & Vanwalleghem (2016) incorporated bio­ turbation in LORICA—a new model for linking landscape and soil profile evolution. While it has not been tested on a natural system, in the sensitivity analysis, bioturbation was one of the key factors affecting chemical weathering. Carbon fluxes have also been successfully modeled by inclusion of bioturbation (Yoo et al., 2011). In addition to the role of animals in bioturbation, or mixing of soils and sediments, there are some groups of animals that directly influence geochemical processes. For example, termites and ants have been shown to directly increase weathering through release of organic acids, similar to plants and microorganisms. In fact, effect of ants on weathering has been shown to be much greater than that of plants in some ecosystems (Figure 1.8). Dorn et al. (2014) have shown for six sites in Arizona and Texas that eight different ant species enhanced mineral dissolution by ~50× to 300× over con­ trols. High densities of vesicular‐arbuscular mycorrhizal fungi and microbial enrichment have also been associ­ ated with the harvester ant mounds (Friese & Allen, 1993), potentially providing another mechanism for increased weathering, as discussed above. 1.5.5. Humans Humans are part of the animal world but have a disproportionate effect on their environment compared to other animals. They exert the biggest influence on bio­ geochemical cycles as they not only affect them directly but also influence all other aspects of the environment. Land use, including agriculture and forestry, mining, industry emissions of acid‐producing gasses, and now climate change are some of the largest effects of humans on weathering and biogeochemical cycles.

16  BIOGEOCHEMICAL CYCLES

Median enhancement compared to control

ne iv i

350

Ol

Pla g

ioc

las

e

400

300

Ant Root mat Termite Bare ground

Ants

250

200

150

100

Root mats

50

Termites Bare ground

0

1

5

10

50

100

400

Mean enhancement compared to control

Figure 1.8  Mineral dissolution enhancements during 25‐year experiments at six field settings in Texas and Arizona (USA). Samples of emplaced basalt grains containing plagioclase and olivine were extracted from ant nests, termite nests, root mats of trees, bare‐ground settings, and a control consisting of basalt grains in plastic pipes exposed only to infiltrating precipitation. [Dorn (2014). Reproduced wih permission of Geological Society of America.]

1.5.5.1. Land Use Change in land use through agriculture and animal husbandry has multiple effects on biogeochemical cycles. One of them is use of mineral fertilizer, which signifi­ cantly affects nutrient fluxes. At the same time, removal of the crop biomass depletes nutrient store in the soils, possibly promoting weathering. Other processes involved include change in water fluxes and erosion rates. About a quarter of land used in agricul­ ture is affected by water and wind erosion (Hurni et al., 2008). Generally, intensive agriculture can increase soil erosion removing weathered, productive top layer high in organic matter and clays with high capacity to hold cat­ ions on exchange sites. Increase in erosion has been linked to acceleration of weathering processes (Dixon et al., 2012; Dixon & von Blanckenburg, 2012; Ferrier & Kirchner, 2008; Larsen et  al., 2014; Stallard & Edmond, 1987). Irrigated agriculture can often result in soil degradation due to salt accumulation (Vlek et al., 2008). A number of studies showed differences in weathering fluxes between agricultural and nonagricultural water­ sheds (Barnes & Raymond, 2009; Fortner et  al., 2012; Liu et  al., 2000; Oh & Raymond, 2006; Weller et  al., 2003), with increased weathering in watersheds used as cropland. Fortner at al. (2012) showed evidence that

nitrification of nitrogen added as fertilizer increases soil acidity and promotes weathering. Another explanation of increased weathering in cropland is the increase in  plant productivity and exudation (Raymond and Cole, 2003). Land‐use conversion from forest to cultivation has been shown to impact root density and exudation of organic compounds that promote weathering even 70 years after the area was reforested again (Billings et al., 2018). At the Calhoun Critical Zone Observatory in the USA’s Southern Piedmont, in cultivated plots, root densities approached zero at depths > 70 cm, while in forested plots, root density declined with depth to 200 cm; and below 70 cm, root densities in old‐growth forests averaged 2.1 times those in regenerating forests. This root distribution influenced microbial community composition, as well as relative abundance of root‐associated bacteria, which was greater in old‐growth soils than in regenerating forests. Soil respi­ ration and salt‐extractable organic C, a proxy for organic acids, both factors in biological weathering, were signifi­ cantly greater at 3–5 m depth in old forests relative to regenerating forests and cultivated plots. Forested sites that are used to harvest lumber can also be subject to acidification due to removal of the basic cations with the harvest (McGivney et al., 2019).

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  17

1.5.5.2. Mining Mining results in excavation of the ore material and often deposition of the finer material remaining after ore enrichment on the soil surface, where it is at disequilib­ rium with the atmospheric conditions. Because of this mining strongly promotes weathering processes (Ross et  al., 2018). In pyrite, an iron ore, as well as accessory mineral during coal mining, Fe and S are present in reduced form. When residual pyrite in the ore is exposed to atmospheric levels of O2, it is oxidized resulting in soluble forms of iron and decrease in solution pH. The produced sulfuric acid can then oxidize and dissolve various elements including toxic metals (Blodau, 2006; Cravotta, 1991). Acid mine drainage due to pyrite in coal accounts for 28–40% of total riverine sulfate derived from pyrite oxidation (Raymond & Oh, 2009). The legacy of trace metal contamination as result of mining activities can persist for a very long time after mining has been discon­ tinued (Le Roux et al., 2020). 1.5.5.3. Rain Acidification While a decreasing concern now (Engardt et al., 2017), rain acidification can have a direct effect on weathering and element cycles in the environment. Rainfall at equilibrium with ambient CO2 (410 ppm) would have pH of 5.61. An increase in the partial pressure of CO2 in the atmosphere due to burning of fossil fuels decreases the pH of rainwater. However, burning fossil fuels, particu­ larly coal, can also release N, and particularly S, oxides, which when combined with water in the air form strong acids (Driscoll et al., 2001). In mid‐1970s—at the height of the crisis—rainfall acidity value averaged pH 4 in the industrial NE of the United States (Likens and Bormann, 1974). Passing of The Clean Air Act (42 U.S.C. §7401 et  seq., 1970) and installation of scrubbers in the coal burning power plants and later shift to gas‐burning power plants decreased importance of these processes. Increase in acidity of the rainfall increases leaching of basic cat­ ions from the soils, mobility of Al, and S and N content (Driscoll et al., 2001). Modeling of the effect of acid rain on weathering of the soils was not definitive due to opposite effects of pH and soluble Al on rock weathering (McGivney et  al., 2019), but measurements indicate increased weathering due to atmospheric deposition of S (N.M. Johnson et al., 1972; Lerman et al., 2007; S.‐L. Li et  al., 2008; Xu & Liu, 2007). Bailey (2020) showed increase in Ca export normalized to Na through the 1960s with maximum in the 1970s in Hubbard Brook Experimental Forest, New Hampshire, USA due to acid deposition. 1.5.5.4. Climate Change Climate change caused by combustion of the fossil fuel is affecting every aspect of the environment. The increases in temperature and partial pressure of CO2 in

the atmosphere not only influence biogeochemical cycles directly by affecting weathering reaction rates but they also cause changes in geochemical cycles by affecting ecological processes that influence weathering. Increases in temperature can accelerate weathering by influencing the kinetics of reactions, but weathering reactions are typically exothermic and therefore elevated temperatures can also shift the equilibrium of these reactions towards reactants. Whether dissolution would increase or decrease with temperature increase would depend on whether kinetics or thermodynamics of the reaction con­ trols weathering, which would depend on the residence time of the water relative to time needed to achieve equilibrium (Maher, 2011). Multiple studies demon­ strated an increase in weathering with temperature (Dessert et  al., 2003; G. Li et  al., 2016; Turner et  al., 2010; White & Blum, 1995), indicating net kinetic con­ trol of dissolution processes and that further increase in temperature with climate change will potentially further increase weathering. However, temperature increase is also often accompanied by changes in microbial activity and plant productivity (Phillips et  al., 2011). The net effect can be positive or negative depending on initial conditions (Feeley et al., 2007; Melillo et al., 2017), with increases in both biomass production and soil respira­ tion measured for cold systems and decreases for the tropical ecosystems. As shown in previous sections of this chapter, increase in plant and microbial activity accelerate weathering. Therefore, increase in tempera­ ture can indirectly influence weathering through change in biological activity, providing a feedback loop, as weathering processes sequester more or less carbon in inorganic form either in soils or in the oceans. Elevated CO2 has a direct effect on weathering (Berner, 1992), as a reactant in the carbonation reaction; however, it also can have indirect effect by increasing productivity of autotrophs (CO2 fertilization). A number of greenhouse (Barron‐Gafford et  al., 2005), covered field (Osborne et  al., 1997), and Free Air Carbon‐dioxide Enrichment (FACE) studies (Ainsworth and Long, 2005) confirm increase in plant productivity and soil respiration (King et al., 2004). A change in weathering at elevated CO2 has been demonstrated in laboratory studies (Bruant et  al., 2003; Lagache, 1965; Navarre‐Sitchler & Thyne, 2007; Osthols & Malmstrom, 1995) but has not been demon­ strated conclusively in the field because of inherent soil heterogeneity (Andrews & Schlesinger, 2001; Cheng et  al., 2010). However, modeling of the past climates demonstrated a strong relationship between atmospheric CO2 and biological activity and feedbacks to atmospheric CO2 resulting both from plant uptake during photosyn­ thesis and enhanced weathering driven by plant activity (Taylor et al., 2012). Change in precipitation type, amount, distribution, and intensity (Dore, 2005; Trenberth, 2011) is also a part

18  BIOGEOCHEMICAL CYCLES

of the climate change that influences fluxes of water through the soils and as a result affects biogeochemical cycles. Another effect of the changing climate on biogeo­ chemical cycles is melting of the permafrost that makes C stored there available to heterotrophic microorganisms (Douglas et al., 2014). Along with C, other elemental cycles are also affected by permafrost melting. For example, reduced conditions resulting from water saturation of warmed soils changes Fe oxidation state and behavior (Herndon et al., 2020). Under natural conditions, a number of environmental factors can be changing at the same time. For instance, in a field case study in the Pyrenees mountains, Zaharescu, Hooda, Burghelea, Polyakov, et  al. (2016), showed that climate‐change nested variables, such as increases in temperature and spring freezing altitude, a reducing snow cover (earlier and larger unfrozen sur­ faces), a general increase in the frequency of drier periods, and changes in the frequency of winter freezing days since the early 1980s, accelerated the weathering of naturally metal‐rich mountain topography, with several variables showing a multiannual lagged response (Figure 1.9). Such increased weathering released poten­ tially harmful elements such as As and Ni at levels of concern for ecosystem and human populations in the area and further afield.

(b) 2 3000

Spring 0°C altitude (m)

A complex interplay between abiotic and biotic factors at the Earth surface drive the breakdown of exposed crustal materials, which feeds the geochemical cycles of elements supporting life. This massive thermodynamic process is governed by an energy cascade extending from planetary to molecular scales (Figure 1.10). A gradual loss of gravitationally induced heat left over from the planet formation, together with radioactive decay of isotopes (238U, 232Th, 40K) in the mantle and crust (amounting to ~44.2 Tw, in equal proportion; Stacey & Davis 2008), put in motion plate tectonics through energy dissipating convective cells. Such a process continuously surfaces fresh crust in rift areas, buries bedrock and sediments in subduction zones at an inferred rate of 0.01–22.72 cm year−1 (Zahirovic et  al., 2015), ultimately exposing new rock materials during crustal uplift, e.g., mountain chains and volcanoes. At the same time, the daily external input of electromagnetic radiation from the Sun (about 1.361 kw m−2 of total solar irradiance during a solar minimum, amounting to 173,000 Tw globally; Archer, 2009; Kopp & Lean, 2011), keeps the Earth surface at temperatures suit­ able for liquid water. The transfer of solar energy through Earth’s gaseous and liquid reservoirs stimulates turbulent

0 –2

2500 2000 1500

Trace elements (PCA regr.fact scores)

(a)

1.6. LIFE AND MATTER INTERACTIONS ACROSS SCALES

1970 1975 1980 1985 1990 19952000

Figure 1.9  Climate change effect on weathering. (a) A generally upward trend and step changes in spring freezing level during 1972–2006 period coincided with changes in trace‐metal deposition in a sediment core from Lake Bubal (central Pyrenees, Spain). The lake drains waters from a metal‐rich granitic and metamorphic basin, and the climate effect (Pearson r = 0.6, p < 0.05) is lagged by 4 years. Arsenic, Co, Cr, Cu, Mn, Ni, Pb and Zn concentrations were summarized into one trace element variable (as regression factor [regr. fact.] scores) by principal component analysis (PCA), while the broad variability line in the climate variable was obtained using a locally estimated scatterplot smoothing (LOESS fit line). Horizontal lines are set at variable averages. [Adapted from Zaharescu, Hooda, Burghelea, Polyakov, et al. (2016).] (b) Pyrite mineral in a building granite slab (similar in mineralogical composition to catchment bedrock in (a)) with visible signs of physical (spalling), chemical (reduced Fe oxidation and sulfuric acid staining) and biological (microbial/algae growth) weathering due to exposure to climate agents during the 1999–2009 period.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  19 Gravity–turbulence (a)

Homogeneous redox landscape (b)

(c)

Capillary forces (f)

(e)

(d)

Electrostatic forces (g)

(h)

(i)

Heterogeneous redox landscape Figure 1.10  Weathering across scales. Interaction among Earth’s solid, liquid, gaseous, and living systems from planetary to nanoscale, with changes in the dominant forces and the oxidative landscape included. (a) Earth view from the International Space Station showing the interface between atmosphere, hydrosphere, geosphere (Pyrenees Mountains are highlighted) and anthroposphere. (b) Pyrenees Mountains as seen by NASA mission STS‐51 in 1993. (c) Differential effect of catchment components on littoral macroinvertebrates community composition at different scales. (d) Grass and moss growing on a granite bolder. (e) Arbuscular mycorrhiza connecting basalt grains with seedling of buffalo grass (Bouteloua dactyloides). (f–h) A biotite grain attached to a fungal hyphae as shown by He‐ion microscopy. (i) A transmission electron microscopy (atomic lattice‐resolution) image of a fungal hyphae (Fg) interacting with biotite mineral (Bt), showing 3‐nm‐wide disruption in the mineral sheet stacking. [(c)Adapted from Zaharescu, Burghela, et al. (2016). (i) Bonneville et al. (2009). Reproduced with permission of Geological Society of America.]

20  BIOGEOCHEMICAL CYCLES

mixing of air and water, and results in the patterns of weather and climate we observe (Houze Jr, 2014). Earth’s gravity, on the other hand creates buoyancy gradients for air gases and conduits for the turbulent movement of fluids on the terrestrial surface from continental scales down to Kolmogorov microscales, where turbulent kinetics is lost due to drag, and gives off to viscosity (Kantha & Clayson, 2000). At micro‐ and nanoscales, short‐range interactions, such as capillarity and electrostatic forces, and Brownian kinetics become critical. Such an energy cascade across the planetary surface gives birth to ­erosional and accretion structures along fractal lines of development, from the largest mountains to the smallest soil aggregates (Figure 1.10). Chemical interactions between air, liquid water, biota and freshly exposed mineral surfaces are critical to driving the nutrient cycles along the energy conduit mentioned above. Such biogeochemical reactions spon­ taneously consume reactive minerals, oxygen, and atmo­ spheric acidity (as rainwater‐dissolved CO2) to start the cycle of chemical elements through oxidative weathering (Burghelea et al., 2015; Zaharescu et al., 2019). Surface interactions between microbes, plant roots, fungi, ani­ mals, and mineral grains create structures (pore spaces, mineral–organic aggregates) and micro‐ and macrohabitats within soil and sediment matrices, which in turn dictate redox gradients, and nutrient exchanges with water, minerals, and biota. These stabilize soils on hillslopes, and stimulate the colonization and development of various ecosystems. Species assemblages of such ecosystems depend on the structure and composition of nutrient sources in the bedrock, their position in the landscape, and their connection and feedback relationships with the flows of matter and energy in the wider hydrosphere and atmosphere (Figure 1.10; Zaharescu, Burghela, et al., 2016; Zaharescu et  al., 2017), ultimately directing the coevolution of the biosphere–geosphere system. 1.7. FUTURE DIRECTIONS We presented an integrated overview of the various living and nonliving actors that control the breakdown of upper crustal materials when they become exposed to the thermodynamic disequilibrium at the interface with air, water, and life. We further highlight major steps in the evolution of Earth weathering, and how small‐scale interactions on mineral surfaces connect with planetary‐ scale fluxes of energy and matter. With recent advances in high‐energy spectroscopy, microscopy, molecular biology, remote sensing, and modelling, there are great opportunities for further research that could improve our picture of the coevolution of life and its life‐support system, particularly in the context of sustainable development of human civilization and understanding

its place in the Earth system and beyond. We therefore recommend: 1. Increased transdisciplinary efforts including inputs from fields such as physics, mathematics, computing, material sciences, to better constrain key factors directing Earth biogeochemical evolution. 2. Recognition of turbulence as the major paradigm of complex nonlinear multi‐scale systems, such as natural processes. Understanding the role of turbulence mixing in transporting mass and energy across the terrestrial surface during abiotic and biotic transformation of the upper crust remains an unknown quantity. 3. Working to understand dominant processes at fundamental scales of interactions, e.g., grain/aggregate scales in soils/sediments, and interface processes at nano‐ to atomic scales. 4. Closing critical knowledge gaps regarding weathering in Earth evolution context and throughout the solar system, and the development of new methodologies and space missions to target such questions. 5. Developing improved proxies for individual geo­ chemical transformations to aid in better characterizing weathering. 6. Distinguishing and defining geochemical signatures of biosphere interactions with the geomedia, as well as with the liquid and gaseous reservoirs at the planetary surface. 7. Development of complete biogeochemical cycles of all elements and linking them to the evolution of key life functions. 8. Identifying biosphere’s keystone species capable of large cascade effects in surface biogeochemistry. 9. Better understanding of the role of deep biosphere in crustal evolution. 10. Better constraining the contribution of ocean, lake, and river floor weathering to global element cycles. 11. Developing a more complete understanding of the effects of climate change on biogeochemical cycles, par­ ticularly as it relates to trace‐metal weathering—which can have potentially detrimental consequences for the ecosystem and human health. 12. A better understanding of the relationship between the limits of nutrient replenishment through weathering and limits of human and ecosystem growth. REFERENCES Aghili, F., Jansa, J., Khoshgoftarmanesh, A.H., Afyuni, M., Schulin, R., Frossard, E., & Gamper, H.A. (2014). Wheat plants invest more in mycorrhizae and receive more benefits from them under adverse than favorable soil conditions. Applied Soil Ecology, 84, 93–111. Ahmed, E., & Holmström, S.J.M. (2015). Microbe–mineral interactions: the impact of surface attachment on mineral

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  21 weathering and element selectivity by microorganisms. Chemical Geology, 403, 13–23. Ainsworth, E.A., & 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. Akter, M., & Akagi, T. (2006). Role of fine roots in the plant‐ induced weathering of andesite for several plant species. Geochemical Journal, 40, 57–67. Anderson, S.P., Dietrich, W.E., & Brimhall, G.H.Jr. (2002). Weathering profiles, mass‐balance analysis, and rates of solute loss: Linkages between weathering and erosion in a small, steep catchment. GSA Bulletin, 114(9), 1143–1158. Andrews, J.A., & Schlesinger, W.H. (2001). Soil CO2 dynamics, acidification, and chemical weathering in a temperate forest with experimental CO2 enrichment. Global Biogeochemical Cycles, 15, 149–162. Angst, Š., Mueller, C.W., Cajthaml, T., Angst, G., Lhotáková, Z., Bartuška, M., Špaldoňová, A., & Frouz, J. (2017). Stabilization of soil organic matter by earthworms is connected with physical protection rather than with chemical changes of organic matter. Geoderma, 289, 29–35. April, R., & Keller, D. (1990). Mineralogy of the rhizosphere in forest soils of the eastern United States. Biogeochemistry, 9, 1–18. Archer, D. (2009). Global warming: Understanding the forecast. Chichester, UK: Wiley‐Blackwell. Arndt, N. (2013). Formation and evolution of the continental crust. Geochemical Perspectives, 2(3), 405–533. http://www. geochemicalperspectives.org/online/v2n3 Arthur, M.A., & Fahey, T.J. (1993). Controls on soil solution chemistry in a subalpine forest in north‐central Colorado. Soil Science Society of America Journal, 57, 1123–1130. Bago, B., Vierheilig, H., Piché, Y., & Azcón‐Aguilar, C. (1996). Nitrate depletion and pH changes induced by the extraradi­ cal mycelium of the arbuscular mycorrhizal fungus Glomus intraradices grown in monoxenic culture. New Phytologist, 133, 273–280. Bailey, S.W. (2020). Tracking the fate of plagioclase weathering products: pedogenic and human influences. In K. Dontsova, Z. Balogh‐Brunstad, G. Le Roux (Eds.), Biogeochemical cycles: Ecological drivers and environmental impact, Geophysical Monograph Series (Vol. 251, pp. 151–162). Washington, DC: American Geophysical Union. Hoboken, NJ: John Wiley & Sons. Bailyn, M. (1994). A survey of thermodynamics. New York: American Institute of Physics Press. Bal, T. L., Storer, A.J., & Jurgensen, M.F. (2018). Evidence of damage from exotic invasive earthworm activity was highly correlated to sugar maple dieback in the Upper Great Lakes region. Biological Invasions, 20, 151–164. Balogh‐Brunstad, Z., Smart, K., Dohnalkova, A., Saccone, L., & Smits, M.M. (2020) Micro‐ and Nanoscale Techniques to Explore Bacteria and Fungi Interactions with Silicate Minerals. In K. Dontsova, Z. Balogh‐Brunstad, G. Le Roux (Eds.), Biogeochemical cycles: Ecological drivers and environmental impact, Geophysical Monograph Series (Vol. 251, pp. 81–101). Washington, DC: American Geophysical Union. Hoboken, NJ: John Wiley & Sons.

Balogh‐Brunstad, Z., Keller, C.K., Bormann, B.T., O’Brien, R., Wang, D., Hawley., G. (2008). Chemical weathering and chemical denudation dynamics through ecosystem development and disturbance. Global Biogeochemical Cycles, 22, GB1007. Balogh‐Brunstad, Z., Keller, K.C., Dickinson, T.J., Stevens, F., Li, C., & Bormann, B.T. (2008). Biotite weathering and nutrient uptake by ectomycorrhizal fungus, Suillus tomentosus, in liquid‐culture experiments. Geochimica et Cosmochimica Acta, 72, 2601–2618. Balogh‐Brunstad, Z., Keller, C.K., Gill, R.A., Bormann, B.T., & Li, C.Y. (2008). The effect of bacteria and fungi on chemical weathering and chemical denudation fluxes in pine growth experiments. Biogeochemistry, 88, 153–167. Bandstra, J.Z., & Brantley, S.L. (2008). Data fitting techniques with applications to mineral dissolution kinetics. In S.L. Brantley, J.D. Kubicki, A.F. White (Eds.), Kinetics of water– rock interaction (pp. 211–257). New York: Springer Science & Business Media. Banfield, J.F., & Nealson, K.H. (1997). Geomicrobiology: Interactions between microbes and minerals. Reviews in mineralogy (Vol. 35, 448 pp.).Washington, DC: Mineralogical Society of America. Barker, W.W., Welch, S.A., Chu, S., & Banfield, J.F. (1998). Experimental observations of the effects of bacteria on alumi­ nosilicate weathering. American Mineralologist, 83, 1551–1563. Barnes, R.T., & Raymond, P.A. (2009). The contribution of agri­ cultural and urban activities to inorganic carbon fluxes within temperate watersheds. Chemical Geology, 266, 327–336. Bar‐On, Y.M., Phillips, R., & Milo, R. (2018). The biomass dis­ tribution on Earth. Proceedings of the US National Academy of Sciences, 115(25), 6506–6511. Barron‐Gafford, G., Martens, D., Grieve, K., Biel, K., Kudeyarov, V., Mclain, J.E.T., & Lipson, D., Murthy, R. (2005). Growth of eastern cottonwoods (Populus deltoides) in elevated CO2 stimulates stand‐level respiration and rhizode­ position of carbohydrates, accelerates soil nutrient depletion, yet stimulates above‐ and belowground biomass production. Global Change Biology, 11, 1207–1219. Bashan, Y., Li, C.Y., Lebsky, V.K., Moreno, M., & de‐Bashan, L.E. (2002). Primary colonization of volcanic rocks by plants in arid Baja California, Mexico. Plant Biology, 4, 392–402. Bashan, Y., Vierheilig, H., Salazar, B.G., & de‐Bashan, L.E. (2006). Primary colonization and breakdown of igneous rocks by endemic, succulent elephant trees (Pachycormus discolor) of the deserts in Baja California, Mexico. Naturwissenschaften, 93, 344–347. Battistuzzi, F.U., Feijao, A., & Hedges, S.B. (2004). A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evolutionary Biology, 4, 44. Beaulieu, E., Godderis, Y., Donnadieu, Y., Labat, D., & Roelandt, C. (2012). High sensitivity of the continental‐ weathering carbon dioxide sink to future climate change. Nature Climate Change, 2, 346–349. Bell, E.A., Boehnke, P., Harrison, T.M., & Mao, W.L. (2015). Potentially biogenic carbon preserved in a 4.1 billion‐year‐ old zircon. Proceedings of the US National Academy of Sciences, 112(47), 14518–14521.

22  BIOGEOCHEMICAL CYCLES Bennett, P.C., & Casey, W.H. (1994). Organic acids and the dissolution of silicates. In E.D. Pittman, M. Lewan (Eds.), The role of organic acids in geological processes (pp. 162– 201), New York: Springer‐Verlag. Bennett, P.C., Hiebert, F.K., & Choi, W.J. (1996). Microbial colonization and weathering of silicates in a petroleum‐ contaminated groundwater. Chemical Geology, 132, 45–53. Bennett, P.C., Rogers, J.R., Choi, W.J., Hiebert, F.K. (2001). Silicates, silicate weathering, and microbial ecology. Geomicrobiology Journal, 18, 3–19. Berner, R. A. (1995). Chemical weathering and its effect on atmospheric CO2 and climate. In White, A.F., Brantley, S.L. (Eds.), Chemical weathering rates of silicate minerals, Reviews in mineralogy (Vol. 31, 565–583). Washington, DC: Mineralogical Society of America. Berner, R.A. (1992). Weathering, plants, and the long‐term carbon cycle. Geochimica et Cosmochimica Acta, 56, 3225–3231. Berner, R.A. (2004). The Phanerozoic carbon cycle: CO2 and O2. New York: Oxford University Press. Berner, R.A., & Cochran, M.F. (1998). Plant‐induced weathering of Hawaiian basalts. Journal of Sedimentary Research, 68, 723–726. Berthelin, J., & Belgy, G. (1979). Microbial degradation of phyllosilicates during simulated podzolization. Geoderma, 21(4), 297–310. Billings, S.A., Hirmas, D., Sullivan, P.L., Lehmeier, C., Bagchi, S., Min, K., et  al. (2018). Loss of deep roots limits biogenic agents of soil development that are only partially restored by decades of forest regeneration. Elementa: Science of the Anthropocene, 6, 34. Blake, R.E., & Walter, L.M. (1996). Effects of organic acids on the dissolution of orthoclase at 80 degrees C and pH 6. Chemical Geology, 132, 91–102. Blodau, C. (2006). A review of acidity generation and consump­ tion in acidic coal mine lakes and their watersheds. Science of the Total Environment, 369, 307–332. Bohlen, P.J., Scheu, S., Hale, C.M., McLean, M.A., Migge, S., Groffman, P.M., & Parkinson, D. (2004). Non‐native invasive earthworms as agents of change in northern temperate forests. Frontiers in Ecology and the Environment, 2, 427–435. Bonneville, S., Van Capperlen, P., & Behrends, T. (2004). Microbial reduction effects of mineral solubility of iron (III) oxyhydroxides: Effects of mineral solubility and availability. Chemical Geology, 212, 255–265. Bonneville, S., Smits, M. M., Brown, A., Harrington, J., Leake, J. R., Brydson, R., & Benning, L.G. (2009). Plant‐driven fungal weathering: Early stages of mineral alteration at the nanometer scale. Geology, 37(7), 615–618. Bormann, B.T., Wang, D., Bormann, F.H., Benoit, G., April, R., & Snyder, M.C. (1998). Rapid, plant‐induced weathering in an aggrading experimental ecosystem. Biogeochemistry, 43, 129–155. Bowerman, A.G., & Redente, E.F. (1998). Biointrusion of protective barriers at hazardous waste sites. Journal of Environmental Quality, 27, 625–632. Bowles, M.W., Mogollón, J.M., Kasten, S., Zabel, M., & Hinrichs, K.U. (2014). Global rates of marine sulfate reduction and implications for sub–sea‐floor metabolic activities. Science, 344, 889–891.

Brantley, S., Balogh‐Brunstad, Z., Barnes, B., Bruns, M.A., van Cappelen, P., Dontsova, K., et al. (2011). Thirteen hypotheses to test how biology, weathering and erosion interact within the Critical Zone. Geobiology, 9, 140–165. Brantley, S.L., Liermann, L., Bau, M., & Wu, S. (2001). Uptake of trace metals and rare earth elements from hornblende by a soil bacterium. Geomicrobiology Journal, 18, 37–61. Brimblecombe, P. (2013). The global sulfur cycle, 2nd edn. Elsevier. http://dx.doi.org/10.1016/B978‐0‐08‐095975‐7.00814‐7 Brown, G.E.Jr., Foster, A.L., & Ostergren, J.D. (1999). Mineral surfaces and bioavailability of heavy metals: a molecular‐ scale perspective. Proceedings of the US National Academy of Sciences, 96, 3388–3395. Bruant, R.G.J., Giammar, D.E., Myneni, S.C.B., Peters, C.A., Gale, J., & Kaya, Y. (2003). Effect of pressure, temperature, and aqueous carbon dioxide concentration on mineral weathering as applied to geologic storage of carbon dioxide. Presented at the 6th International Conference on Greenhouse Gas Control Technologies (pp. 1609–1612). Oxford, UK: Pergamon, Brunner, I., Plotze, M., Rieder, S.R., Zumsteg, A., Furrer, G., & Frey, B. (2011). Pioneering fungi from the Damma glacier forefield in the Swiss Alps can promote granite weathering. Geobiology, 9, 266–279. Burford, E.P., Fomina, M., & Gadd, G.M. (2003). Fungal involvement in bioweathering and biotransformation of rocks and minerals. Mineral Magazine, 67, 1127–1155. Burghelea, C., Zaharescu, D.G., Dontsova, K., Maier, R., Huxman, T., & Chorover, J. (2015). Mineral nutrient mobiliza­ tion by plants from rock: influence of rock type and arbuscular mycorrhiza. Biogeochemistry, 124(1–3), 187–203. Burghelea, C.I., Dontsova, K., Zaharescu, D.G., Maier, R.M., Huxman, T., Amistadi, M.K., Hunt, E., & Chorover, J. (2018). Trace element mobilization during incipient bio­ weathering of four rock types. Geochimica et Cosmochimica Acta, 234, 98–114. Buss, H.L., Lüttge, A., & Brantley, S.L. (2007). Etch pit formation on iron silicate surfaces during siderophore‐ promoted dissolution. Chemical Geology, 240, 326–342. Callot, G., Maurette, M., Pottier, L., & Dubois, A. (1987). Biogenic etching of microfractures in the amorphous and crystalline silicates. Nature, 328(6126), 147–149. Cama, J., & Ganor, J. (2006). The effects of organic acids on the dissolution of silicate minerals: a case study of oxalate catal­ ysis of kaolinite dissolution. Geochimica et Cosmochimica Acta, 70, 2191– 2209. Carpenter, D., Hodson, M.E., Eggleton, P., & Kirk, C. (2007). Earthworm induced mineral weathering: Preliminary results. European Journal of Soil Biology, 43, S176–S183. Catling, D.C. (2013). The great oxidation event transition, 2nd edn. Elsevier. http://dx.doi.org/10.1016/B978‐0‐08‐095975‐7.01307‐3 Catling, D.C., & Kasting, J.F. (2017). Atmospheric evolution on inhabited and lifeless worlds. Cambridge, UK: Cambridge University Press. Catling, D.C., Zahnle, K.J., & McKay, C.P. (2001). Biogenic methane, hydrogen escape, and the irreversible oxidation of early Earth. Science, 293(5531), 839–843. Chen, X., Gu, X., Zhao, X., Wang, Y., Pan, Y., Ma, X., Wang, X., & Ji, R. (2019). Species‐dependent effects of

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  23 earthworms on the fates and bioavailability of tetrabromobi­ sphenol A and cadmium coexisted in soils. Science of the Total Environment, 658, 1416–1422. Cheng, L., Zhu, J., Chen, G., Zheng, X., Oh, N.H., Rufty, T.W., Richter, D.D., & Hu, S. (2010). Atmospheric CO2 enrichment facilitates cation release from soil. Ecology Letters, 13, 284–291. Chorover, J., Troch, P.A., Rasmussen, C., Brooks, P.D., Pelletier, J.D., Breshars, D.D., et  al. (2011). How water, carbon, and energy drive critical zone evolution: the Jemez–Santa Catalina critical zone observatory. Vadose Zone Journal, 10(3), 884–899. Cochran, M.F., & Berner, R.A. (1996). Promotion of chemical weathering by higher plants: field observations of Hawaiian basalts. Chemical Geology, 132, 71–77. Cockell, C.S., Kennerley, N., Lindstrom, M., Watson, J., Ragnarsdottir, V., Sturkell, E., Ott, S., & Tindle, A.G. (2007). Geomicrobiology of a weathering crust from an impact crater and a hypothesis for its formation. Geomicrobiology Journal, 24, 425–440. Coogan, L.A., & Dosso, S.E. (2015). Alteration of ocean crust provides a strong temperature dependent feedback on the  geological carbon cycle and is a primary driver of the Sr‐isotopic composition of seawater. Earth and Planetary Science Letters, 415, 38–46. Craven, D., Thakur, M.P., Cameron, E.K., Frelich, L.E., Beauséjour, R., Blair, R.B., et al. (2017). The unseen invaders: introduced earthworms as drivers of change in plant commu­ nities in North American forests (a meta‐analysis). Global Change Biology, 23, 1065–1074. Cravotta, C.A. (1991). Geochemical evolution of acidic ground water at a reclaimed surface coal mine in western Pennsylvania. Proc. 8th Annual Meeting of the ASSMR, Durango, CO, pp. 43–68. Cromack, Jr.K., Sollins, P., Graustein, W.C., Speidel, K., Todd, A.W., Spycher, G., Li, C.Y., & Todd, R.L. (1979). Calcium oxalate accumulation and soil weathering in mats of the hypogeous fungus Hysterangium crassum. Soil Biology and Biochemistry, 11, 463–468. Crowe, S.A. (2008). Biogeochemical cycling in iron‐rich Lake Matano, Indonesia: An early ocean analogue (PhD thesis), McGill University. Crowe, S.A., Døssing, L.N., Beukes, N.J., Bau, M., Kruger, S.J., Frei, R., & Canfield, D.E. (2013). Atmospheric oxygenation three billion years ago. Nature, 501(7468), 535–538. Darwin, C. (1881). The formation of vegetable mould through the action of worms with observation of their habits. London: John Murray. Dessert, C., Dupré, B., Gaillardet, J., François, L.M., & Allègre, C.J. (2003). Basalt weathering laws and the impact of basalt weathering on the global carbon cycle. Chemical Geology, 202, 257–273. Dixon, J.L., Hartshorn, A.S., Heimsath, A.M., DiBiase, R.A., & Whipple, K.X. (2012). Chemical weathering response to tectonic forcing: A soils perspective from the San Gabriel Mountains, California. Earth and Planetary Science Letters, 323–324, 40–49. Dixon, J.L., & von Blanckenburg, F. (2012). Soils as pacemakers and limiters of global silicate weathering. Comptes Rendus Geoscience, 344, 597–609.

Dong, K., Kim, W. S., Tripathi, B. M., & Adams, J. (2015). Generalized soil Thaumarchaeota community in weathering rock and saprolite. Microbial Ecology, 69, 356–360. Dontsova, K., Zaharescu, D., Henderson, W., Verghese, S., Perdrial, N., Hunt, E., Chorover, J., 2014. Impact of organic carbon on weathering and chemical denudation of granular basalt. Geochimica et Cosmochimica Acta 139, 508–526. Dore, M.H.I. (2005). Climate change and changes in global precipitation patterns: What do we know? Environment ­ International, 31, 1167–1181. Dorn, R.I. (2014). Ants as a powerful biotic agent of olivine and plagioclase dissolution. Geology, 42, 771–774. Douglas, T.A., Jones, M.C., Hiemstra, C.A., & Arnold, J.R. (2014). Sources and sinks of carbon in boreal ecosystems of interior Alaska: A review. Elementa: Science of the Anthropocene, 2, 000032. Drever, J.I. (1994). The effect of land plants on weathering rates of silicate minerals. Geochimica et Cosmochimica Acta, 58, 2325–2332. Drever J.I., & Stillings, L.L. (1997). The role of organic acids in mineral weathering. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 120, 167–181. Drever, J.I., & Vance, G. (1994). Role of soil organic acids in mineral weathering process. In E.D. Pittman, M.D. Lewan (Eds.), Organic acids in geological processes (pp. 138–161). New York: Springer‐Verlag. Driscoll, C., Lawrence, G., Bulger, A.J., Butler, T., Cronan, C.S., Eager, C., et al. (2001). Acidic deposition in the northeastern United States: Sources and inputs, ecosystem effects and management strategies. BioScience, 51, 180–198. Duarte, A.P., Melo, V.F., Brown, G.G., & Pauletti, V. (2012). Changes in the forms of lead and manganese in soils by passage through the gut of the tropical endogeic earthworm (Pontoscolex corethrurus). European Journal of Soil Biology, 53, 32–39. Duff, R.B., Webley, D.M., & Scott, R.O. (1963). Solubilization of minerals and related materials by 2‐ketogluconic acid producing bacteria. Soil Science, 95, 105– 114. Dunn, J.R., & Hudec, P.P. (1972). Frost and sorption effects in argillaceous rocks. Highway Research Record, 393, 65–78. Ehrlich, H.L. (1998). Geomicrobiology: its significance for geology. Earth‐Science Reviews, 45, 45–60. Ehrlich, H.L. 1999). Microbes as geologic agents: Their role  in mineral formation. Geomicrobiology Journal, 16, 135–153. Eickmann, B., Hofmann, A., Wille, M., Bui, T.H., Wing, B.A., & Schoenberg, R. (2018). Isotopic evidence for oxygenated Mesoarchaean shallow oceans. Nature Geoscience, 11(2), 133–138. Eldridge, D.J., Koen, T.B., Killgore, A., Huang, N., & Whitford, W.G. (2012). Animal foraging as a mechanism for sediment movement and soil nutrient development: Evidence from the semi‐arid Australian woodlands and the Chihuahuan Desert. Geomorphology, 157–158, 131–141. Engardt, M., Simpson, D., Schwikowski, M., & Granat, L. (2017). Deposition of sulphur and nitrogen in Europe 1900– 2050. Model calculations and comparison to historical observations. Tellus B: Chemical and Physical Meteorology, 69, 1328945.

24  BIOGEOCHEMICAL CYCLES Fahey, B.D. (1983). Frost action and hydration as rock weathering mechanisms on schist: A laboratory study. Earth Surface Processes and Landforms, 8, 535–545. Fahey, B.D., & Dagesse, D.F. (1984). An experimental study of the effect of humidity and temperature variations on the granular disintegration of argillaceous carbonate rocks in cold climates. Arctic and Alpine Research, 16, 291–298. Feeley, K.J., Joseph Wright, S., Nur Supardi, M.N., Kassim, A.R., & Davies, S.J. (2007). Decelerating growth in tropical forest trees. Ecology Letters, 10, 461–469. Ferrier, K.L., & Kirchner, J.W. (2008). Effects of physical erosion on chemical denudation rates: A numerical modeling study of soil‐mantled hillslopes. Earth and Planetary Science Letters, 272, 591–599. Finlay, R., Wallander, H., Smits, M., Holmstrom, S., Van hees, P., Lian, B., & Rosling, A. (2009). The role of fungi in bio­ genic weathering in boreal forest soils. Fungal Biology Reviews, 23, 101–106. Fixen, K.R., Zheng, Y., Harris, D.F., Shaw, S., Yang, Z‐Y., Dean, D.R., Seefeldt, L.C., & Harwood, C.S. (2016). Light‐ driven carbon dioxide reduction to methane by nitrogenase in a photosynthetic bacterium. Proceedings of the US National Academy of Sciences, 113(36), 10163–10167. Folk, R.L. (1993). SEM imaging of bacteria and nannobacteria in carbonate sediments and rocks. Journal of Sedimentary Research, 63(5), 990–999. Fortner, S.K., Lyons, W.B., Carey, A.E., Shipitalo, M.J., Welch, S.A., & Welch, K.A. (2012). Silicate weathering and CO2 con­ sumption within agricultural landscapes, the Ohio–Tennessee River Basin, USA. Biogeosciences, 9, 941–955. Franklin, J.F., & Dyness, C.T. (1973). Natural vegetation of Oregon and Washington (General Technical Report PNW‐8). Corvallis, OR: USDA Forest Service. Frey, B., Rieder, S.R., Brunner, I., Ploetze, M., Koetzsch, S., Lapanje, A., Brandl, H., & Furrer, G. (2010). Weathering‐ associated bacteria from the Damma glacier forefield: physiological capabilities and impact on granite dissolution. Applied and Environmental Microbiology, 76, 4788–4796. Friese, C.F., & Allen, M.F. (1993). The interaction of harvester ants and vesicular‐arbuscular mycorrhizal fungi in a patchy semi‐arid environment: the effects of mound structure on fungal dispersion and establishment. Functional Ecology, 7, 13–20. Frouz, J., Špaldoňová, A., Fričová, K., & Bartuška, M. (2014). The effect of earthworms (Lumbricus rubellus) and simulated tillage on soil organic carbon in a long‐term microcosm experiment. Soil Biology and Biochemistry, 78, 58–64. Gabet, E.J., Reichman, O.J., & Seabloom, E.W. (2003). The effects of bioturbation on soil processes and sediment ­transport. Annual Review of Earth and Planetary Sciences, 31, 249–273. Gadd, G.M. (2007). Geomycology: Biogeochemical transfor­ mations of rocks, minerals, metals, and radionuclides by fungi, bioweathering and bioremediation. Mycology Research, 111, 3–49. Gadd, G.M. (2013). Microbial roles in mineral transformations and metal cycling in the Earth’s Critical Zone. In J. Xu, D.L. Sparks (Eds.), Molecular environmental soil science (pp. 115–165). Dordrecht, Netherlands: Springer‐Verlag.

Gislason, S.R., Oelkers, E.H., Eiriksdottir, E.S., Kardjilov, M.I., Gisladottir, G., Sigfusson, B., et al. (2009). Direct evi­ dence of the feedback between climate and weathering. Earth and Planetery Science Letters, 277, 213–222. Gleeson, D.B., Kennedy, N.M., Clipson, N., Melville, K.G.M., Gadd, & Mc‐Dermott, F.P. (2006). Characterization of bac­ terial community structure on a weathered pegmatitic granite. Microbial Ecology, 51, 526–534. Gobran, G.R., Clegg, S., & Courchesne, F. (1998). Rhizospheric processes influencing the biogeochemistry of forest ecosys­ tems. Biogeochemistry, 42, 107–120. Goldstein, A.H. (1986). Bacterial solubilization of mineral phosphates. American Journal of Alternative Agriculture, 1, 51–57. Golubic, S., & Seong‐Joo, L. (1999). Early cyanobacterial fossil record: Preservation, palaeoenvironments and identification. European Journal of Phycology, 34(4), 339–348. Gorbushina, A.A., & Broughton, W.J. (2009). Microbiology of the atmosphere–rock interface: how biological interactions and physical stresses modulate a sophisticated microbial eco­ system. Annual Reviews in Microbiology, 63, 431–450. Gorshkov, A.I., Drits, V.A., Dubinina, G.A., Bogdanova, O.A., & Sivtsov, A.V. (1992). The role of bacterial activity in the formation of hydrothermal Fe‐Mn‐formations in the northern part of the Lau Basin (south‐western part of the Pacific Ocean). Izvestiya—Akademiya Nauk, Seriya Geologicheskaya, 9, 84–93. Goyne, K.W., Brantley, S.L., & Chorover, J. (2006). Effects of organic acids and dissolved oxygen on apatite and chalcopy­ rite dissolution: implications for using elements as organo­ markers and oxymarkers. Chemical Geology, 234, 28–45. Goyne, K.W., Brantley, S.L., & Chorover, J. (2010). Rare earth element release from phosphate minerals in the presence of organic acids. Chemical Geology, 278, 1–14. Gregory, P.J. (2006). Plant roots. Growth, activity and interaction with soils. Oxford, UK: Blackwell Publishing. Griffiths, R.P., Baham, J.E., & Caldwell, B.A. (1994). Soil solu­ tion chemistry of ectomycorrhizal mats in forest soil. Soil Biology and Biochemistry, 26, 331–337. Grinsted, M.J., Hedley, M.J., White, R.E., & Nye, P.H. (1982) Plant induced changes in the rhizosphere of rape (Brassica napus var. Emerald) seedlings. I. pH change and the increase in P concentration in the soil solution. New Phytologist, 91, 19–29. Grman, E., & Robinson, T.M.P. (2013). Resource availability and imbalance affect plant–mycorrhizal interactions: a field test of three hypotheses. Ecology, 94, 62–71. Hausrath, E.M., Treiman, A.H., Vicenzi, E., Bish, D.L., Blake, D., Sarrazin, P., et  al. (2008). Short‐ and long‐term olivine weathering in Svalbard: implications for Mars. Astrobiology, 8(6), 1079–92. Hazen, R.M. (2013). Paleomineralogy of the Hadean Eon: A preliminary species list. American Journal of Science, 313(9), 807–843. http://www.ajsonline.org/cgi/doi/10.2475/09.2013.01 Hazen, R.M., Downs, R.T., Kah, L., & Sverjensky, D. (2013). Carbon mineral evolution. Reviews in Mineralogy and Geochemistry, 75(1), 79–107. Hazen, R.M., & Ferry, J.M. (2010). Mineral evolution: Mineralogy in the fourth dimension. Elements, 6(1), 9–12.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  25 Hazen, R.M., Papineau, D., Bleeker, W., Downs, R.T., Ferry, J.M., McCoy, T.J., Sverjensky, D.A., & Yang, H. (2008). Mineral evolution. American Mineralogist, 93(11–12), 1693–1720. Hazen, R.M., Sverjensky, D.A., Azzolini, D., Bish, D.L., Elmore, S.C., Hinnov, L., & Milliken, R.E. (2013). Clay min­ eral evolution. American Mineralogist, 98(5), 2007–2029. Herndon, E., Kinsman‐Costello, L., & Godsey, S. (2020). Biogeochemical cycling of redox‐sensitive elements in perma­ frost‐affected ecosystems. In K. Dontsova, Z. Balogh‐ Brunstad, G. Le Roux (Eds.), Biogeochemical cycles: Ecological drivers and environmental impact, Geophysical Monograph Series (Vol. 251, pp. 245–265). Washington, DC: American Geophysical Union. Hoboken, NJ: John Wiley & Sons. Hetrick, B.A.D. (1989). Acquisition of phosphorus by VA mycorrhizal fungi and the growth responses of their host plants. In L. Boddy, R. Marchant, D.J. Reid (Eds.), Nitrogen, phosphorus, and sulphur utilization by fungi (pp. 205–226). New York: Cambridge University Press. Hetrick, B.A.D., Leslie, J.F., Wilson, G.T., & Kitt, D.G. (1988). Physical and topological assessment of effects of a vesicular‐ arbuscular mycorrhizal fungus on root architecture of big bluestem. New Phytologist, 110, 85–96. Hilley, G.E., Porder, S. (2008). A framework for predicting global silicate weathering and CO2 drawdown rates over geologic time‐scales. Proceedings of the US National Academy of Sciences, 105, 16855–16859. Hinsinger, P. (1998). How do plant roots acquire mineral nutrients? Chemical processes involved in the rhizosphere. Advances in Agronomy, 64, 225–265. Hinsinger, P., Barros, O.N.F., Benedetti, M.F., Noack, Y., & Callot, G. (2001). Plant‐induced weathering of a basaltic rock: Experimental evidence. Geochimica et Cosmochimica Acta, 65(1), 137–152. Hinsinger, P., & Gilkes, R. J. (1997). Dissolution of phosphate rock in the rhizosphere of five plant species grown in an acid, P‐fixing mineral substrate. Geoderma, 75, 231–249. Hodson, M.E., Black, S., Brinza, L., Carpenter, D., Lambkin, D.C., Mosselmans, J.F.W., et al. (2014). Biology as an agent of chemical and mineralogical change in soil. Procedia Earth and Planetary Science, 10, 114–117. Hoffland, E., Kuyper, T.W., Wallander, H., Plassard, C., Gorbushina, A.A., Haselwandter, K., et al. (2004). The role of fungi in weathering. Frontiers in Ecology and the Environment, 2, 258–264. Holbrook, W.S., Marcon, V., Bacon, A.R., Brantley, S.L., Carr, B.J., Flinchum, B.A., Richter, D.D., & Riebe, C.S. (2019). Links between physical and chemical weathering inferred from a 65‐m‐deep borehole through Earth’s critical zone. Nature Scientific Reports, 9, 4495. Holland, H.D. (1984). The chemical evolution of the oceans and atmosphere. Princeton, NJ: Princeton University Press. Homann, M., Sansjofre, P., Van Zuilen, M., Heubeck, C., Gong, J., Killingsworth, B., et al. (2018). Microbial life and biogeochemical cycling on land 3,220 million years ago. Nature Geoscience, 11, 665. Hong, H.N., Rumpel, C., Henry des Tureaux, T., Bardoux, G., Billou, D., Tran Duc, T., & Jouquet, P. (2011). How do earth­ worms influence organic matter quantity and quality in tropical soils? Soil Biology and Biochemistry, 43, 223–230.

Houze, Jr, R.A. (Ed.) (2014). Basic cumulus dynamics. International Geophysics, 104, 165–185. Huang, J., Sheng, X‐F., Xi J., He, L‐Y., Huang, Z., Wang, Q., and Zhang Z‐D. (2014). Depth‐related changes in community structure of culturable mineral weathering bacteria and in weathering patterns caused by them along two contrasting soil profiles. Applied and Environmental Microbiology, 80, 29–42. Hurni, H., Herweg, K., Portner, B., Liniger, H. (2008). Soil erosion and conservation in global agriculture. In A.K. Braimoh, P.L.G. Vlek (Eds.), Land use and soil resources (pp. 41–71). Dordrecht, Netherlands: Springer‐Verlag. Hutchens, E., Valsami‐Jones, E., McEldowney, S., Gaze, W., & McLean, J. (2003). The role of heterotrophic bacteria in feldspar dissolution—an experimental approach. Mineral Magazine, 67, 1157–1170. Hutchens, E. (2009). Microbial selectivity on mineral surfaces: possible implications for weathering processes. Fungal Biology Reviews, 23, 115–121. Ijiri, A., Inagaki, F., Kubo, Y., Adhikari, R.R., Hattori, S., Hoshino, T., et al. (2018). Deep‐biosphere methane produc­ tion stimulated by geofluids in the Nankai accretionary com­ plex. Science Advances, 4(6). Ivarson, K.C., Ross, G.J., & Miles, N.M. (1978). Alteration of micas and feldspars during microbial formation of basic ferric sulfates in the laboratory. Soil Science Society of America Journal, 42, 518–524. Ivarson, K.C., Ross, G.J., & Miles, N.M. (1980). The microbio­ logical formation of basic ferric sulfates: 3. Influence of clay minerals on crystallization. Canadian Journal of Soil Science, 60(1), 137– 140. Ivarson, K.C., Ross, G.J., & Miles, N.M. (1981). Formation of rubidium jarosite during the microbiological oxidation of ferrous iron at room temperature. Canadian Mineralogist, 19, 429– 434 (Part 3). Jackson, T.A. (1996). Comment on “The effect of land plants on weathering rates of silicate minerals” by James I. Drever. Geochimica et Cosmochimica Acta, 60, 723–724. Jaillard, B. (1987). Techniques for studying the ionic environ­ ment at the soil/root interface. Proceedings of the 20th International Potash Institute Colloquium, 23–25 June 1987 (pp. 231–245), Baden bei Wien, Austria. Johansen, A., Jakobsen, I., & Jensen, E.S. (1993 Hyphal trans­ port by a vesicular‐arbuscular mycorrhizal fungus of N applied to the soil as ammonium or nitrate. Biology and Fertility of Soils, 16, 66–70. Johnson, M.O., Mudd, S.M., Pillans, B., Spooner, N.A., Keith Fifield, L., Kirkby, M.J., & Gloor, M. (2014). Quantifying the rate and depth dependence of bioturbation based on optically‐stimulated luminescence (OSL) dates and meteoric 10Be. Earth Surface Processes and Landforms, 39, 1188–1196. Johnson, N.C., Graham, J.H., & Smith, F.A. (1997). Functioning of mycorrhizal associations along the mutualism‐parasitism continuum. New Phytologist, 135, 575–586. Johnson, N.C., Wilson, G.W.T., Bowker, M.A., Wilson, J.A., & Miller, R.M. (2010). Resource limitation is a driver of local adaptation in mycorrhizal symbioses. Proceedings of the US National Academy of Sciences, 107, 2093–2098.

26  BIOGEOCHEMICAL CYCLES Johnson, N.M., Reynolds, R.C., & Likens, G.E. (1972). Atmospheric sulfur: its effect on the chemical weathering of New England. Science, 177, 514–516. Jongmans, A.G., van Breemen, N., Lundström, U., van Hees, P.A.W., Finlay, R.D., Srinivasan, M., et  al. (1997). Rock‐ eating fungi. Nature, 389, 682–683. Kalinowski, B.E., Liermann, L.J., Givens, S., & Brantley, S.L. (2000). Rates of bacteria‐promoted solubilization of Fe from minerals: a review of problems and approaches. Chemical Geology, 169, 357–370. Kantha, L.H., & Clayson, C.A. (2000). Small scale processes in geophysical fluid flows. International Geophysics, 67, 1–111. Kaspari, M., & Powers, J.S. (2016). Biogeochemistry and geographical ecology: embracing all twenty‐five elements required to build organisms. The American Naturalist, 188, S62–S73. Kasting, J.F. (2014). Atmospheric composition of Hadean– early Archean Earth: The importance of CO. Geological Society of America Special Papers, 504, 19–28. Kawano, M., & Tomita, K. (2001). Microbial biomineralization in weathered volcanic ash deposit and formation of biogenic minerals by experimental incubation. American Mineralogist, 86, 400–410. Kelly, E.F., Chadwick, O.A., and Hilinski, T.E. (1998). The effect of plants on mineral weathering. Biogeochemistry, 42, 21–53. Kendall, B., Creaser, R.A., Reinhard, C.T., Lyons, T.W., & Anbar, A.D. 2015). Transient episodes of mild environmental oxygenation and oxidative continental weathering during the late Archean. Science Advances, 1(10), 1–7. Ketterings, Q.M., Blair, J.M., & Marinissen, J.C.Y. (1997). Effects of earthworms on soil aggregate stability and carbon and nitrogen storage in a legume cover crop agroecosystem. Soil Biology and Biochemistry, 29, 401–408. King, J.S., Hanson, P.J., Bernhardt, E., DeAngelis, P., Norby, R.J., & Pregitzer, K.S. (2004). A multiyear synthesis of soil respira­ tion responses to elevated atmospheric CO2 from four forest FACE experiments. Global Change Biology, 10, 1027–1042. Kinzler, K., Gehrke, T., Telegdi, J., &Sand, W. (2003). Bioleaching—a result of interfacial processes caused by extracellular polymeric substances (EPS). Hydrometallurgy, 71, 83–88. Kleidon, A. (2010a). A basic introduction to the thermodynamics of the Earth system far from equilibrium and maximum entropy production. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1545), 1303–1315. Kleidon, A. (2010b). Life, hierarchy, and the thermodynamic machinery of planet Earth. Physics of Life Reviews, 7(4), 424–460. http://dx.doi.org/10.1016/j.plrev.2010.10.002 Klein, T., Siegwolf, R.T.W., & Körner, C. (2016). Belowground carbon trade among tall trees in a temperate forest. Science, 352(6283), 342–344. Koehler, M.C., Buick, R., Kipp, M.A., Stüeken, E.E., Zaloumis, J. (2018). Transient surface ocean oxygenation recorded in the ∼2.66‐Ga Jeerinah Formation, Australia. Proceedings of the US National Academy of Sciences, 115(30), 7711–7716. Kopp, G., & Lean, J.L. (2011). A new, lower value of total solar irradiance: Evidence and climate significance. Geophysical Research Letters, 38(1), 1–7.

Krissansen‐Totton, J., Arney, G.N., and Catling, D.C. (2018). Constraining the climate and ocean pH of the early Earth with a geological carbon cycle model. Proceedings of the US National Academy of Sciences, 115(16), 4105–4110. Kump, L.R. (2008). The rise of atmospheric oxygen. Nature, 451(7176), 277–278. Lagache, M. (1965). Contribution a l’etude de l’alteration des feldspaths, dans l’eau, entre 100 et 200 C, sous diverses pressions de CO2, et application a la synthese des mineraux argileux. Bulletin de la Société Française de Minéralogie et de Cristallographie, 88, 223–253. Lalonde, S.V., & Konhauser, K.O. (2015). Benthic perspective on Earth’s oldest evidence for oxygenic photosynthesis. Proceedings of the US National Academy of Sciences, 112(4), 995–1000. Landeweert, R, Hoffland, E., Finlay, R.D., Kuyper, T.W., & van Breemen, N. (2001). Linking plants to rocks: ectomycorrhizal fungi mobilize nutrients from minerals. Trends in Ecology and Evolution, 16, 248–254. Lange Ness, R.L., & Vlek, P.L.G. (2000). Mechanism of calcium and phosphate release from hydroxy‐apatite by mycorrhizal hyphae. Soil Science Society of America Journal, 64, 949–955. Lapanje, A., Wimmersberger, C., Furrer, G., Brunner, I., & Frey, B. (2012). Pattern of elemental release during the granite dissolution can be changed by aerobic heterotrophic bacterial strains isolated from Damma glacier (central Alps) deglaciated granite sand. Microbial Ecology, 63, 865– 882. Larsen, I.J., Almond, P.C., Eger, A., Stone, J.O., Montgomery, D.R., & Malcolm, B. (2014). Rapid soil production and weathering in the Western Alps, New Zealand. Science, 343(6171), 637–640. Le Roux, G., Hansson, S., Claustres, A., Binet, S., De Vleeschouwer, F., & Gandois, L., et  al. (2020). Trace metal legacy in mountain environments: a view from the Pyrenees Mountains. In K. Dontsova, Z. Balogh‐Brunstad, G. Le Roux (Eds.), Biogeochemical cycles: Ecological drivers and environmental impact, Geophysical Monograph Series (Vol. 251, pp. 191–206). Washington, DC: American Geophysical Union. Hoboken, NJ: John Wiley & Sons. Leake, J.R., Duran, A.L., Hardy, K.E., Johnson, I., Beerling, D.J., Banwart, S.A., & Smits, M.M. (2008). Biological weathering in soil: The role of symbiotic root‐associated fungi biosensing minerals and directing photosynthate‐ energy into grain‐scale mineral weathering. Mineralogical Magazine, 72(1), 85—89. Leake, J.R., Johnson, D., Donnelly, D.P., Muckle, G., Boddy, L., & Read, D.J. (2004). Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Canadian Journal of Botany, 82, 1016–1045. Lee, M.R., Langworthy, G., Hodson, M.E. (2008). Earthworms produce granules of intricately zoned calcite. Geology, 36, 943–946. Lenton, T.M., Daines, S.J., Dyke, J.G., Nicholson, A.E., Wilkinson, D.M., Williams, H.T.P. (2018). Selection for Gaia across multiple scales. Trends in Ecology and Evolution, 33(8), 633–645. Lerman, A., Wu, L., & Mackenzie, F.T. (2007). CO2 and H2SO4 consumption in weathering and material transport to the

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  27 ocean, and their role in the global carbon balance. Marine Chemistry, 106, 326–350. Levenson, Y., & Emmanuel, S. (2017). Repulsion between calcite crystals and grain detachment during water–rock interaction. Geochemical Perspectives Letters, 3, 133–141. Lever, M.A., Rouxel, O.J., Alt, J., Shimizu, N., Ono, S., Coggon, R.M., et al. (2013). Evidence for microbial carbon and sulfur cycling in deeply buried ridge flank basalt. Science, 339, 1305–1308. Leyval, C., & Berthelin, J. (1991). Weathering of a mica by roots and rhizospheric microorganisms of pine. Soil Science Society of America Journal, 55, 1009–1016. Li, G., Hartmann, J., Derry, L.A., West, A.J., You, C.‐F., Long, X., et  al. (2016). Temperature dependence of basalt weathering. Earth and Planetary Science Letters, 443, 59–69. Li, S.‐L., Calmels, D., Han, G., Gaillardet, J., Liu, C.‐Q. (2008). Sulfuric acid as an agent of carbonate weathering constrained by δ13CDIC: Examples from Southwest China. Earth and Planetary Science Letters, 270, 189–199. Li, Z., Liu, L, Chen, J., and Teng H.H. (2016). Cellular dissolution at hypha‐ and spore‐mineral interfaces revealing unrecognized mechanisms and scales of fungal weathering. Geology, 44(4), 319–322. Lian, B. (1998). A study on how silicate bacteria GY92 dis­ solves potassium from illite. Acta Mineralogica Sinica, 18(2), 234–238. Lian, B., Chen, J., Fu, P.Q., Liu, C.Q., Chen, Y. (2005). Weathering of silicate minerals by microorganisms in culture experiments. Geological Journal of China Universities, 11(2), 181–186. Lian, B., Fu, P.Q., Mo, D.M., & Liu, C.Q. (2002). A compre­ hensive review of the mechanism of potassium releasing by silicate bacteria. Acta Mineralogica Sinica, 22(2), 179–183. Lian, B., Hu, Q.N., Ji, J.F., & Teng, H.H. (2006). Carbonate biomineralization induced by soil bacteria Bacillus ­megaterium. Geochimica et Cosmochimica Acta, 70(22), 5522–5535. Lian, B., Wang, B., Pan, M., Liu, C., & Teng, H.H. (2008). Microbial release of potassium from K‐bearing minerals by thermophilic fungus Aspergillus fumigatus. Geochimica et Cosmochimica Acta, 72(1), 87–98. Liermann, L.J., Kalinowski, B.E., Brantley, S.L., & Ferry, J.G. (2000). Role of bacterial siderophores in dissolution of horn­ blende. Geochimica et Cosmochimica Acta, 64, 587–602. Likens, G.E., & Bormann, F.H. (1974). Acid rain: A serious regional environmental problem. Science, 184, 1176–1179. Liu, Z.‐J., Weller, D.E., Correll, D.L., & Jordan, T.E. (2000). Effects of land cover and geology on stream chemistry in watersheds of Chesapeake Bay. Journal of the American Water Resources Association, 36, 1349–1365. Lovelock, J. (1995). The ages of Gaia: a biography of our living Earth. Norton. https://books.google.com/books/about/The_Ages_ of_Gaia.html?id=vVLuR8VxMWIC [accessed 18 April 2019]. Lundström, U.S., van Breemen, N., Bain, D.C., van Hees, P.A.W., Giesler, R., Gustafsson, J.P., et al. (2000). Advances in understanding the podzolization process resulting from multidisciplinary study of three coniferous forest soils in the Nordic countries. Geoderma, 94, 335–353.

Lybrand, R.A., & Rasmussen, C. (2014). Linking soil element‐ mass‐transfer to microscale mineral weathering across a semiarid environmental gradient. Chemical Geology, 381, 26–39. Lyons, T.W., Reinhard, C.T., & Planavsky, N.J. (2014). The rise of oxygen in Earth’s early ocean and atmosphere. Nature, 506(7488), 307–315. Maher, K. (2011). The role of fluid residence time and topo­ graphic scales in determining chemical fluxes from landscapes. Earth and Planetary Science Letters, 312, 48–58. Malinovskaya, I.M., Kosenko, L.V., Votselko, S.K., & Podgorskii, V.S. (1990). Role of Bacillus mucilaginosus poly­ saccharide in degradation of silicate minerals. Mikrobiologiya, 59, 49–55. Marschner, H. (1995). Mineral nutrition of higher plants, 2nd edn. New York: Academic Press. Marschner, H. (2012). Mineral nutrition of higher plants, 3rd edn. New York: Academic Press. Marschner, H., & Dell., B. (1994). Nutrient uptake in mycor­ rhizal symbiosis. Plant Soil, 159, 89–102. McGivney, E., Gustafsson, J.P., Belyazid, S., Zetterberg, T., & Löfgren, S. (2019). Assessing the impact of acid rain and forest harvest intensity with the HD‐MINTEQ model—soil chemistry of three Swedish conifer sites from 1880 to 2080. Soil, 5, 63–77. McLoughlin, N., Brasier, M., Wacey, D., Green, O., & Perry, R. (2007). Establishing biogenicity criteria for endolithic microbor­ ings on early Earth and beyond. Astrobiology, 7(1), 110–126. McMahon, S., & Parnell, J. (2018). The deep history of Earth’s biomass. Journal of the Geological Society, 175, 716–720. Melillo, J.M., Frey, S.D., DeAngelis, K.M., Werner, W.J., Bernard, M.J., Bowles, F.P., et al. (2017). Long‐term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358, 101–105. Mergelov, N., Mueller, C.W., Prater, I., Shorkunov, I., Dolgikh, A., Zazovskaya, E., et al. (2018). Alteration of rocks by endo­ lithic organisms is one of the pathways for the beginning of soils on Earth. Nature Scientific Reports, 8, 3367. Meysman, F.J.R., Middelburg, J.J., & Heip, C.H.R. (2006). Bioturbation: a fresh look at Darwin’s last idea. Trends in Ecology and Evolution, 21, 688–695. Miot, J., Benzerara, K., & Kappler, A. (2014). Investigating microbe–mineral interactions: recent advances in X‐ray and electron microscopy and redox‐ sensitive methods. Annual Reviews in Earth Planetary Science, 42, 271–289. Morris, J.L., Puttick, M.N., Clark, J.W., Edwards, D., Kenrick, P., Pressel, S., Wellman, C.H., Yang, Z., Schneider, H., Donoghue, P.C.J. (2018). The timescale of early land plant evolution. Proceedings of the US National Academy of Sciences, 115, E2274–E2283. Muentz, A. (1890). Sur la decomposition des roches et la formation de la terre arable. Comptes rendus hebdomadaires des séances de l’Académie des sciences, 110, 1370–1372. Murray, B.J., O’Sullivan, D., Atkinson, J.D., & Webb, M.E. (2012). Ice nucleation by particles immersed in supercooled cloud droplets. Chemical Society Reviews, 41, 6519–6554. Navarre‐Sitchler, A., & Thyne, G. (2007). Effects of carbon dioxide on mineral weathering rates at earth surface condi­ tions. Chemical Geology, 243, 53–63.

28  BIOGEOCHEMICAL CYCLES Neaman, A., Chorover, J., & Brantley, S.L. (2005). Implications of the evolution of organic acid moieties for basalt weathering over geological time. American Journal of Science, 305, 147–185. Neaman, A., Chorover, J., & Brantley, S.L. (2006). Effects of organic ligands on granite dissolution in batch experiments at pH 6. American Journal of Science, 306, 451–473. Neilands, J.B. (1995). Siderophores: Structure and function of microbial iron transport compounds. Journal of Biology and Chemistry, 270, 26723–26726. Oh, N.‐H., & Raymond, P.A. (2006). Contribution of agricul­ tural liming to riverine bicarbonate export and CO2 sequestra­ tion in the Ohio River basin. Global Biogeochemical Cycles, 20(3), GB3012. Olson, J.M. (2006). Photosynthesis in the Archean era. Photosynthesis Research, 88(2), 109–117. Olsson‐Francis, K., Simpson, A.E., Wolff‐Boenisch, D., & Cockell, C.S. (2012). The effect of rock composition on cyanobacterial weathering of crystalline basalt and rhyolite. Geobiology, 10(5), 434–444. Omoike, A., & Chorover, J. 2004). Spectroscopic study of extra­ cellular polymeric substances from Bacillus subtillis: Aqueous chemistry and adsorption effects. Biomacromolecules, 5, 1219–1230. O’Reilly, S.E., Furukawa, Y., Newell, S. (2006). Dissolution and microbial Fe(III) reduction of nontronite (NAu‐1). Chemical Geology, 235, 1–11. Osborne, C.P., Drake, B.G., LaRoche, J., & Long, S.P. (1997). Does long‐term elevation of CO2 concentration increase photosynthesis in forest floor vegetation? (Indiana strawberry in a Maryland forest). Plant Physiology, 114, 337–344. Osthols, E., & Malmstrom, M. (1995). Dissolution kinetics of ThO2 in acids and carbonate media. Radiochimica Acta, 68, 113–119. Parikh, S.J., & Chorover, J. (2005). FTIR spectroscopic study of biogenic Mn‐oxide formation by Pseudomonas putida GB‐1. Geomicrobiology Journal, 22, 207–218. Parikh, S.J., & Chorover, J. (2006). ATR‐FTIR spectroscopy reveals bond formation during bacterial adhesion to iron oxide. Langmuir, 22, 8492–8500. Paris, F., Bonnaud, P., Ranger, J., & Lapeyrie, F. (1995). In vitro weathering of phlogopite by ectomycorrhizal fungi: I. Effect of K+ and Mg2+ deficiency on phyllosilicate evolution. Plant and Soil, 177, 191– 201. Pasero, M. (2018). The new IMA list of minerals—a work in progress–updated: November 2018. Commission on New Minerals Nomenclature and Classification. Pawlik, L., Phillips, J.D., & Šamonil, P. (2016). Roots, rock, and regolith: Biomechanical and biochemical weathering by trees and its impact on hillslopes—a critical literature review. Earth‐Science Reviews, 159, 142–159. Perdrial, J.N., Warr, L.N., Perdrial, N., Lett, M.‐C., & Elsass F. (2009). Interaction between smectite and bacteria: implica­ tions for bentonite as backfill material in the nuclear waste disposal. Chemical Geology, 264, 281–294. Petrovich, R. (1981). Kinetics of dissolution of mechanically comminuted rock‐forming oxides and siIicates—II. Deformation and dissolution of oxides and silicates in the

taboratory and at the Earth’s surface. Geochimica et Cosmochimica Acta, 45, 1675–1686. Phillips, C.L., Gregg, J.W., & Wilson, J.K. (2011). Reduced diurnal temperature range does not change warming impacts on ecosystem carbon balance of Mediterranean grassland mesocosms. Global Change Biology, 17, 3263–3273. Preiner, M., Xavier, J., Sousa, F., Zimorski, V., Neubeck, A., Lang, S., et  al. (2018). Serpentinization: connecting geo­ chemistry, ancient metabolism and industrial hydrogenation. Life, 8(4), E41. Puente, M.E., Li, C.Y., & Bashan, Y. (2009). Rock‐degrading endophytic bacteria in cacti. Environmental and Experimental Botany, 66, 389–401. Puente‐Sánchez, F., Arce‐Rodríguez, A., Oggerin, M., García‐ Villadangos, M., Moreno‐Paz, M., Blanco, Y., et al. (2018). Viable cyanobacteria in the deep continental subsurface. Proceedings of the US National Academy of Sciences, 115(42), 10702–10707. Pulleman, M.M., Six, J., Uyl, A., Marinissen, J.C.Y., & Jongmans, A.G. (2005). Earthworms and management affect organic matter incorporation and microaggregate formation in agricultural soils. Applied Soil Ecology, 29, 1–15. Raymond, P.A., & Cole, J.J. (2003). Increase in the export of alkalinity from North America’s largest river. Science, 301, 88–91. Raymond, P.A., & Oh, N.H. (2009). Long term changes of chemical weathering products in rivers heavily impacted from acid mine drainage: Insights on the impact of coal mining on regional and global carbon and sulfur budgets. Earth and Planetary Science Letters, 284, 50–56. Reichard, P.U., Kretzschmar, R., Kraemer, S.M. (2007). Dissolution mechanisms of goethite in the presence of sider­ ophores and organic acids. Geochimica et Cosmochimica Acta, 71, 5635–5650. Reinhard, C.T., Raiswell, R., Scott, C., Anbar A.D., & Lyons, T.W. (2009). A late Archean sulfidic sea stimulated by early oxidative weathering of the continents. Science, 326, 713–717. Reinhard, C.T., Planavsky, N.J., Wang, X., Fischer, W.W., Johnson, T.M., & Lyons, T.W. (2014). The isotopic composi­ tion of authigenic chromium in anoxic marine sediments: A case study from the Cariaco Basin. Earth and Planetary Science Letters, 407, 9–18. Richter, D. deB. (2017). Soil production and the soil clay factory. ASA, CSSA, SSSA International Annual Meeting: Managing global resources for a secure future, Tampa, FL. https:// scisoc.confex.com/crops/2017am/webprogram/Paper104731. html 101‐4 Richter, D.D., & Billings, S.A. (2015). One physical system: Tansley’s ecosystem as Earth’s critical zone, Tansley review. New Phytologist, 206(3), 900–912. Rillig, M.C. (2004). Arbuscular mycorrhizae, glomalin and soil aggregation. Canadian Journal of Soil Science, 84, 355–363. Robert, M., & Berthelin, J. (1986). Role of biological and biochemical factors in soil mineral weathering. In P. M. Huang, M., Schnitzer (Eds.), Interactions of soil minerals with natural organics and microbes (SSSA Special Publication 17, pp. 453– 495). Madison, WI: ASA, CSSA, and SSSA. Robertson, J.D. (1936). The function of the calciferous glands of earthworms. Journal of Experimental Biology, 13, 279–297.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  29 Rogers, J.R., & Bennett, P.C. (2004). Mineral stimulation of subsurface microorganisms: release of limiting nutrients from silicates. Chemical Geology, 203, 91– 108. Rogers, J.R., Bennett, P.C., & Choi, W.J. (1998). Feldspars as a  source of nutrients for microorganisms. American Mineralologist, 83, 1532–1540. Ros, M.B.H., Hiemstra, T., van Groenigen, J.W., Chareesri, A., & Koopmans, G.F. (2017). Exploring the pathways of earthworm‐ induced phosphorus availability. Geoderma, 303, 99–109. Rosenberg, D.R., & Maurice, P.A. (2003). Siderophore adsorp­ tion to and dissolution of kaolinite at pH 3 to 7 and 22°C. Geochimica et Cosmochimica Acta, 67, 223–229. Rosenstock, N.P. (2009). Can ectomycorrhizal weathering activity respond to host nutrient demands? Fungal Biology Reviews, 23, 107–114. Rosling, A., Lindahl, B.D., & Finlay, R.D. (2004). Carbon allo­ cation to ectomycorrhizal roots and mycelium colonising dif­ ferent mineral substrates. New Phytologist, 162, 795–802. Rosling A, Lindahl BD, Taylor AFS, & Finlay RD (2004). Mycelial growth and substrate acidification of ectomycor­ rhizal fungi in response to different minerals. FEMS Microbiology Ecology, 47, 31–37. Ross, M.R.V., Nippgen, F., Hassett, B.A., McGlynn, B.L., & Bernhardt, E.S. (2018). Pyrite oxidation drives exceptionally high weathering rates and geologic CO2 release in mountaintop‐mined landscapes. Global Biogeochemical Cycles, 32, 1182–1194. Schirrmeister, B.E., Gugger, M., & Donoghue, P.C.J. (2015). Cyanobacteria and the Great Oxidation Event: Evidence from genes and fossils. Palaeontology, 58(5), 769–785. Schmalenberger, A., Duran, A.L., Leake, J.R., Romero‐ Gonzales, M.E., & Banwart, S.A. (2009). Mineralogy controls oxalic acid release in mycorrhiza weathering. Geochimica et Cosmochimica Acta Supplement, 73, 1177. Schmitz, O.J., Wilmers, C.C., Leroux, S.J., Doughty, C.E., Atwood, T.B., Galetti, M., Davies, A.B., & Goetz, S.J. (2018). Animals and the zoogeochemistry of the carbon cycle. Science, 362, eaar3213. Schulz, S., Brankatschk, R., Dümig, A., Kögel‐Knabner, I., Schloter, M., & Zeyer, J. (2013). The role of microorganisms at different stages of ecosystem development for soil formation. Biogeosciences, 10, 3983–3996. Scott, C., Lyons, T.W., Bekker, A., Shen, Y., Poulton, S.W., Chu, X., & Anbar, A.D. (2008). Tracing the stepwise oxygenation of the Proterozoic ocean. Nature, 452(7186), 456–459. Seiffert, F., Bandow, N., Bouchez, J., von Blanckenburg, F., & Gorbushina, A.A. (2014). Microbial colonization of bare rocks: laboratory biofilm enhances mineral weathering. Procedia of Earth Planetary Sciences, 10, 123–129. Shen, Y., & Buick, R. (2004). The antiquity of microbial sulfate reduction. Earth‐Science Reviews, 64, 243–272. Shih, P.M. (2015). Photosynthesis and early earth. Current Biology, 25(19), R855–R859. Shipitalo, M., & Le Bayon, R.C. (2004). Quantifying the effects of earthworms on soil aggregation and porosity. In C.A. Edwards (Ed.), Earthworm ecology, 2nd edn (pp. 183–200). Atlanta, GA: CRC Press. Shipitalo, M.J., & Protz, R. (1989). Chemistry and micromor­ phology of aggregation in earthworm casts. Geoderma, 45, 357–374.

Shirokova, L.S., Bénézeth, P., Pokrovsky, O.S., Gerard, E., Ménez, B., & Alfredsson, H. (2012). Effect of the heterotro­ phic bacterium Pseudomonas reactans on olivine dissolution kinetics and implications for CO2 storage in basalts. Geochimica et Cosmochimica Acta, 80, 30–50. Simard, S.W., Perry, D.A., Jones, M.D., Myrold, D.D., Durall, D.M., & Molina, R. (1997). Net transfer of carbon between ectomycorrhizal tree species in the field. Nature, 388(6642), 579–582. Six, J., Bossuyt, H., Degryze, S., & Denef, K. (2004). A history of research on the link between (micro) aggregates, soil biota, and soil organic matter dynamics. Soil and Tillage Research, 79, 7–31. Sizmur, T., & Hodson, M.E. (2009). Do earthworms impact metal mobility and availability in soil?—A review. Environmental Pollution, 157, 1981–1989. Sizmur, T., Palumbo‐Roe, B., Hodson, M.E. (2011). Impact of earthworms on trace element solubility in contaminated mine soils amended with green waste compost. Environmental Pollution, 159, 1852–1860. Sleep, N.H. (2010). The Hadean–Archaean environment. Cold Spring Harbor Perspectives in Biology, 2(6), a002527. doi:10.1101/cshperspect.a002527 Smith, S.E., & Read, D.J. (2008). Mycorrhizal symbiosis, 3rd edn. London: Academic Press. Smits, M.M., Bonneville, S., Benning, L.G., Banwart, S.A., & Leake, J.R. (2012). Plant‐driven weathering of apatite–the role of an ectomycorrhizal fungus. Geobiology, 10, 445–456. Smits, M.M., Bonneville, S., Haward, S., & Leake, J.R. (2008). Ectomycorrhizal weathering, a matter of scale? Mineralogical Magazine, 72, 135–138. Stacey, F.D., & Davis, P.M. (2008). Physics of the Earth (pp. 348–360). Cambridge: Cambridge University Press. Stallard, R.F., & Edmond, J.M. (1987). Geochemistry of the Amazon: 3. Weathering chemistry and limits to dissolved inputs. Journal of Geophysical Research: Oceans, 92, 8293–8302. Stephens, J.C., & Hering, J.G. (2004). Factors affecting the dis­ solution kinetics of volcanic ash soils: dependencies on pH, CO2, and oxalate. Applied Geochemistry, 19, 1217–1232. Stillings, L.L., Drever, J.I., Brantley, S.L., Sun, Y., & Oxburgh, R. (1996). Rates of feldspar dissolution at pH 3–7 with 0–8 mM oxalic acid. Chemical Geology, 132, 79– 90. Strullu‐Derrien, C., Selosse, M‐A., Kenrick, P., & Martin, F.M. (2018). The origin and evolution of mycorrhizal symbioses: from palaeomycology to phylogenomics. New Phytologist, 220, 1012–1030. Stüeken, E.E., Catling, D.C., & Buick, R. (2012). Contributions to late Archaean sulphur cycling by life on land. Nature Geoscience, 5(10), 722–725. http://dx.doi.org/10.1038/ngeo1585. Sun, L.L., Xiao, L.L., Xiao, B., Wang, W.Y., Pan, C., Wang, S.J., & Lian, B. (2013). Differences in the gene expressive quantities of carbonic anhydrase and cysteine synthase in the weathering of potassium‐bearing minerals by Aspergillus niger. Science China Earth Sciences, 56(12), 2135–2140. Sun, Y.P., Unestam, T., Lucas, S.D., Johanson, K.J., Kenne, L., & Finlay, R. (1999). Exudation‐reabsorption in a mycorrhizal fungus, the dynamic interface for interaction with soil and soil microorganisms. Mycorrhiza, 9, 137–144.

30  BIOGEOCHEMICAL CYCLES Sverjensky, D.A., & Lee, N. (2010). The great oxidation event and mineral diversification. Elements, 6(1), 31–36. Swaby, R.J. (1949). The influence of earthworms on soil aggregation. European Journal of Soil Sciences, 1(2), 195–196. Taylor, L.L., Leake, J.R., Quirk, J., Hardy, K., Banwart, S.A., & Beerling, D.J. (2009). Biological weathering and the long‐ term carbon cycle: integrating mycorrhizal evolution and function into the current paradigm. Geobiology, 7, 171–191. Taylor, L.L., Banwart, S.A., Valdes, P.J., Leake, J.R., Beerling, D.J. (2012). Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global‐scale process‐based approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 565–582. Temme, A.J.A.M., & Vanwalleghem, T. (2016). LORICA–A new model for linking landscape and soil profile evolution: Development and sensitivity analysis. Computers and Geosciences, 90, 131–143. Thomazo, C., Couradeau, E., & Garcia‐Pichel, F. (2018). Possible nitrogen fertilization of the early Earth Ocean by microbial continental ecosystems. Nature Communications, 9(1), 2530. Tisdall, J.M., & Oades, J.M. (1982). Organic matter and water stable aggregates in soils. Journal of Soil Science, 33, 141–163. Trenberth, K.E. (2011). Changes in precipitation with climate change. Climate Research, 47, 123–138. Treseder, K.K. (2004). A meta‐analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytologist, 164, 347–355. Treseder, K. (2013). The extent of mycorrhizal colonization of roots and its influence on plant growth and phosphorus content. Plant Soil, 371, 1–13. Turner, B.F., White, A.F., & Brantley, S.L. (2010). Effects of temperature on silicate weathering: Solute fluxes and chemical weathering in a temperate rain forest watershed, Jamieson Creek, British Columbia. Chemical Geology, 269, 62–78. Ullman, W.J., Kirchman, D.L., Welch, S.A., and Vandevivere, P. (1996). Laboratory evidence for microbially mediated sili­ cate mineral dissolution in nature: Chemical Geology, 132, 11–17. Uroz, S., Calvaruso, C., Turpault, M.P., Frey‐Klett, P. (2009). Mineral weathering by bacteria: ecology, actors and mecha­ nisms. Trends in Microbiology, 17, 378–386. Uroz, S., Oger, P., Lepleux, C., Collignon, C., Frey‐Klett, P., Turpault, M.P. (2011). Bacterial weathering and its contribu­ tion to nutrient cycling in temperate forest ecosystems. Research in Microbiology, 162, 820–831. Uroz, S., Turpault, M.P., Delaruelle, C., Mareschal, L., Pierrat, J.‐C., & Frey‐Klett, P. (2012). Minerals affect the specific diversity of forest soil bacterial communities. Geomicrobiology Journal, 29, 88–98. Ushikubo, T., Kita, N.T., Cavosie, A.J., Wilde, S.A., Rudnick, R.L., & Valley, J.W. (2008). Lithium in Jack Hills zircons: Evidence for extensive weathering of Earth’s earliest crust. Earth and Planetary Science Letters, 272, 666–676.

Valsami‐Jones, E., McLean, J., McEldowney, S., Hinrichs, H., & Pili, A. (1998). An experimental study of bacterially induced dissolution of K‐feldspar. Mineral Magazine, 62A (3), 1563– 1564. van Breemen, N., Finlay, R., Lundström, U., Jongmans, A.G., Giesler, R., & Olsson, M. (2000). Mycorrhizal weathering: a true case of mineral plant nutrition? Biogeochemistry, 49, 53–67. van Breemen, N., Lundström, U., & Jongmans, A.G. (2000). Do plants drive podzolization via rock‐eating mycorrhizal fungi? Geoderma, 94, 163–171. van Hees, P.A.W., Rosling, A., Essén, S., Godbold, D.L., Jones, D.L., & Finlay, R. (2006). Oxalate and ferricrocin exudation by the extrametrical mycelium of an ectomycorrhizal fungus in symbiosis with Pinus sylvestris. New Phytologist, 169, 367–378. van Schöll, L., Kuyper, T.W., Smits, M.M., Landeweert, R., Hoffland, E., & van Breemen, N. (2008). Rock‐eating mycor­ rhizas: their role in plant nutrition and biogeochemical cycles. Plant and Soil, 303, 35–47. Vanwalleghem, T., Stockmann, U., Minasny, B., & McBratney, A.B. (2013). A quantitative model for integrating landscape evolution and soil formation. Journal of Geophysical Research: Earth Surface, 118, 331–347. Versteegh, E.A.A., Black, S., & Hodson, M.E. (2014). Environmental controls on the production of calcium car­ bonate by earthworms. Soil Biology and Biochemistry, 70, 159–161. Viles, H.A. (1988). Cyanobacterial and other biological influ­ ences on terrestrial limestone weathering on Aldabra: impli­ cations for landform development. Bulletin of the Biological Society of Washington, 8, 5–13. Vlek, P.L.G., Hillel, D., Braimoh, A.K. (2008). Soil degradation under irrigation. In A.K. Braimoh, P.L.G. Vlek (Eds.), Land use and soil resources (pp. 101–119). Dordrecht, Netherlands: Springer‐Verlag. Walker, J.C., Hays, P., & Kasting, J.F. (1981). A negative feedback mechanism for the long‐term stabilization of Earth’s surface temperature. Journal of Geophysical Research Oceans, 86, 9776–9782. Wallander, H., Wickman, T., & Jacks, G. (1997). Apatite as a P source in mycorrhizal and non‐mycorrhizal Pinus sylvestris seedlings. Plant and Soil, 196, 123–131. Wallmann, K., Aloisi, G., Tischenko, P., Pavlova, G., Greinert, J., Kutterolf, S., & Eisenhauer, A. (2008). Silicate weathering in anoxic marine sediments. Geochimica et Cosmochimica Acta, 72(12), 2895–2918. Wang, R.R., Wang, Q., He, L.Y., Qiu, G., & Sheng, X.F. (2015). Isolation and the interaction between a mineral‐weathering Rhizobium tropici Q34 and silicate minerals. World Journal of Microbiology and Biotechnology, 31, 747–753. Wang, W.Y., Lian, B., & Pan, L. (2015). An RNA‐sequencing study of the genes and metabolic pathways involved in Aspergillus niger weathering of potassium feldspar. Geomicrobiology Journal, 32(8), 689–700. Welch, S.A., Barker, W.W., & Banfield, J.F. (1999). Microbial extracellular polysaccharides and plagioclase dissolution. Geochimica et Cosmochimica Acta, 63(9), 1405–1419.

BIOLOGICAL WEATHERING IN THE TERRESTRIAL SYSTEM  31 Welch, S.A., Taunton, A.E., & Banfield, J.F. (2002). Effect of microorganisms and microbial metabolites on apatite disso­ lution. Geomicrobiology Journal, 19, 343–367. Welch, S.A., & Ullman, W.J. (1993). The effect of organic acids on plagioclase dissolution rates and stoichiometry. Geochimica et Cosmochimica Acta, 57, 2725–2736. Weller, D.E., Jordan, T.E., Correll, D.L., & Liu, Z.‐J. (2003). Effects of land‐use change on nutrient discharges from the Patuxent River watershed. Estuaries, 26, 244–266. Wendling, L.A., Harsh, J.B., Ward, T.E., Palmer, C.D., Hamilton, M.A., Boyle, J.S., & Flury., M. (2005). Cesium desorption from illite as affected by exudates from rhizosphere bacteria. Environmental Science and Technology, 39, 4505–4512. White, A.F. (1983). Surface chemistry and dissolution of glassy rocks at 25°C. Geochimica et Cosmochimica Acta, 47, 805– 815. White, A.F., & Blum, A.E. (1995). Effects of climate on chemical weathering in watersheds. Geochimica et Cosmochimica Acta, 59, 1729–1747. White, A.F., & Brantley, S.L. (1995). Chemical weathering rates of silicate minerals: an overview. In A.F. White, S.L. Brantley (Eds.), Chemical weathering rates of silicate minerals, Reviews in mineralogy (Vol. 31, pp. 1–22). Washington, DC: Mineralogical Society of America. Wieland, E., Wehrli, B., & Stumm, W. (1988). The coordination chemistry of weathering: III. A generalization on the dissolu­ tion rates of minerals. Geochimica et Cosmochimica Acta, 52, 1969–1981. Wightman, P.G., & Fein, J.B. (2004). The effect of bacterial cell wall adsorption on mineral solubilities. Chemical Geology, 212, 247–254. Wild, B., Imfeld, G., Guyot, F., & Daval, D. 2018). Early stages of bacterial community adaptation to silicate aging. Geology, 46, 555–558. Willoughby, L. (2018). News feature: Can predators have a big impact on carbon emissions calculations? Proceedings of the US National Academy of Sciences, 115, 2260–2263. Wofsy, S.C. (2001). Climate change: Where has all the carbon gone? Science, 292, 2261–2263. Wright, J.J., Konwar, K.M., & Hallam, S.J. (2012). Microbial ecology of expanding oxygen minimum zones. Nature Reviews Microbiology, 10(6), 381–394. Wu, L., Jacobsen, A.D., & Hausner, M. (2008). Characterization of elemental release during microbe–granite interactions at T = 28°C. Geochimica et Cosmochimica Acta, 72, 1076–1095. Xiao, B., Lian, B., Sun, L.L., & Shao, W.L. (2012). Gene tran­ scription response to weathering of K‐bearing minerals by Aspergillus fumigatus. Chemical Geology, 306–307, 1–9. Xiao, L.L., Hao, J.C., Wang, W.Y., Lian, B., Shang, G.D., Yang, Y.W., Liu, C.Q., & Wang, S.J. (2014). The up‐regulation of carbonic anhydrase genes of Bacillus mucilaginosus under soluble Ca2+ deficiency and the heterologously expressed enzyme promotes calcite dissolution. Geomicrobiology Journal, 31(7), 632–641. Xiao, L.L., & Lian, B. (2016. Heterologously expressed carbonic anhydrase from Bacillus mucilaginosus promoting CaCO3 formation by capturing atmospheric CO2. Carbonates and Evaporites, 31(1), 39–45.

Xiao, L.L., Lian, B., Dong, C.L., & Liu, F.H. (2016. The selective expression of carbonic anhydrase genes of Aspergillus nidulans in response to changes in mineral nutrition and CO2 concentration. Microbiology Open, 5(1), 60–69. Xu, Z., Liu, C.‐Q. (2007). Chemical weathering in the upper reaches of Xijiang River draining the Yunnan–Guizhou Plateau, Southwest China. Chemical Geology, 239, 83–95. Yao, M., Lian, B., Teng, H.H., Tian, Y., & Yang, X. (2013). Serpentine dissolution in the presence of bacteria Bacillus mucilaginosus. Geomicrobiology Journal, 30(1):72–80. Yatsu, E. (1988). Nature of weathering—an introduction. Tokyo: Sozosha. Yesavage, T., Thompson, A., Hausrath, E.M., & Brantley, S.L. (2015). Basalt weathering in an Arctic Mars‐analog site. Icarus, 254, 219–232. Yoo, K., Ji, J., Aufdenkampe, A., & Klaminder, J. (2011). Rates of soil mixing and associated carbon fluxes in a forest versus tilled agricultural field: Implications for modeling the soil carbon cycle. Journal of Geophysical Research: Biogeosciences, 116, G01014. Yousefifard, M., Ayoubi, S., Jalalian, A., Khademi, H., & Makkizadeh, M.A. (2012). Mass balance of major elements in relation to weathering in soils developed on igneous rocks in a semiarid region, northwestern Iran. Journal of Mountain Science, 9(1), 41–58. Zaharescu, D.G., Burghela, C.I., Hooda, P.S., Lester, R.N., & Palanca‐Soler, A. (2016). Small lakes in big landscape: Multi‐ scale drivers of littoral ecosystem in alpine lakes. Science of the Total Environment, 551–552, 496–505. Zaharescu, D.G., Hooda, P.S., Burghelea, C.I., & Palanca‐Soler, A. (2016). A Multiscale framework for deconstructing the ecosystem physical template of high‐altitude lakes. Ecosystems, 19(6), 1064–1079. Zaharescu, D.G., Hooda, P.S., Burghelea, C.I., Polyakov, V., & Palanca‐Soler, A. (2016). Climate change enhances the mobili­ sation of naturally occurring metals in high altitude environ­ ments. Science of the Total Environment, 560–561, 73–81. Zaharescu, D.G., Palanca‐Soler, A., Hooda, P.S., Tanase, C., Burghelea, C.I., & Lester, R.N. (2016). Riparian vegetation in the alpine connectome: Terrestrial–aquatic and terrestrial– terrestrial interactions. Science of the Total Environment, 601–602, 247–259. Zaharescu, D. G., Burghelea C.I., Dontsova K., Presler J.K., Maier R.M., Huxman T., et al. (2017). Ecosystem composi­ tion controls the fate of rare Earth elements during incipient soil genesis, Nature Scientific Reports, 7, 43208. Zaharescu, D.G., Burghelea, C.I., Dontsova, K., Presler, J.K., Maier, R.M., Domanik, K.J., et  al. (2019). Ecosystem‐ bedrock interaction changes nutrient compartmentalization during early oxidative weathering. Nature Scientific Reports, https://doi.org/10.1038/s41598‐019‐51274‐x Zahirovic, S., Muller, R.D., Seton, M., & Flament, N. (2015). Tectonic speed limits from plate kinematic reconstructions. Earth and Planetary Science Letters, 418, 40–52. Zaitlin, B., & Hayashi, M. (2012). Interactions between soil biota and the effects on geomorphological features. Geomorphology, 157–158, 142–152.

32  BIOGEOCHEMICAL CYCLES Zerkle, A.L., Claire, M., Domagal‐Goldman, S.D., Farquhar, J., & Poulton, S.W. (2012). A bistable organic‐rich atmosphere on the Neoarchaean Earth. Nature Geoscience, 5, 359–363. Zhao, F., Qiu, G., Huang, Z., He, L.Y., & Sheng, X.F. (2013). Characterization of Rhizobium sp. Q32 isolated from weathered rocks and its role in silicate mineral weathering. Geomicrobiology Journal, 30, 616–622.

Ziegler, F., & Zech, W. (1992a). Chemical changes of beech litter during passage through the gut of the lumbricid earth­ worm Eisenia fetida (SAV.). Journal of Plant Nutrition and Soil Science, 155, 69–70. Ziegler, F., & Zech, W. (1992b). Formation of water‐stable aggregates through the action of earthworms. Implications from laboratory experiments. Pedobiologia, 362, 91–96.

2 Plants as Drivers of Rock Weathering Katerina Dontsova1,2, Zsuzsanna Balogh‐Brunstad3, and Jon Chorover1

ABSTRACT The transformation of rock to soil affects the habitability of Earth because of its role in regulating climate and nourishing ecosystems. Soil formation has a strong biotic component because plants and associated microbes can influence the rate and trajectory of weathering processes. However, quantifying the effects of biota on weathering is challenging because such effects are interwoven with other biotic and abiotic influences. A need to resolve the role of vegetation in weathering is magnified by ongoing environmental change, which affects vegetation distribution and productivity. The changing environment is influencing plant interactions with rocks and biogeochemical cycles of rock‐derived elements. Weathering processes also result in the removal of carbon dioxide from the atmosphere making plant enhancement of weathering a potential mechanism of carbon sequestration and therefore of interest as one of the mechanisms for climate mitigation. This chapter examines the mechanisms of plant enhancement of weathering and evidence of how it operates on different scales, micro, mesocosm, field, watershed, and global. We also discuss how global environmental change, including elevated temperatures, atmospheric CO2, as well as agricultural practices are affecting plant enhancement of weathering. Finally, we conclude with questions that require further examination and a call for future directions of research.

2.1. INTRODUCTION

biological weathering and differences observed when researchers scale up experimental results from laboratory to field. This chapter discusses biochemical weathering from mechanisms operative at nano‐ and microscales to processes observed in watershed‐scale fluxes and global modeling with a focus specifically on the role of plants. Plants take up CO2 from the atmosphere and water from the soil to produce organic compounds during photosynthesis, whereas they acquire most of the other nutrients from the soil. Soils are able to hold and transmit water and air, contain organic matter that supplies N and S, and a large pool of mineral nutrients held either in primary minerals, which need to be dissolved for nutrients to become available, or in exchangeable form, retained on the surfaces of organic matter and secondary minerals. Formation of the soils and release of the nutrients from rocks through weathering is largely driven by plants themselves (Graham et  al., 2010; Stallard & Edmond, 1987; Vitousek et al., 1997).

Since Jenny (1941) included biota as one of the main soil‐forming factors, strong evidence has been compiled that plants are driving biochemical and biomechanical weathering (reviewed in Pawlik et  al., 2016). However, assessments of the significance of biological weathering, especially on geological time scales, and its contribution to global elemental cycles vary (E.K. Berner et al., 2003; Brantley et al., 2011; Leake & Read, 2017; Schwartzman, 2013; Smits & Wallander, 2017; Smits et  al., 2014; Sverdrup, 2009). Important questions remain open such as how to reconcile different measures used to quantify 1 Department of Environmental Science, University of Arizona, Tucson, Arizona, USA 2 Biosphere 2, University of Arizona, Tucson, Arizona, USA 3 Department of Geology and Environmental Sciences, Hartwick College, Oneonta, New York, USA

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

33

34  BIOGEOCHEMICAL CYCLES (a)

(b)

Figure 2.1  (a) A tree growing on the rock in the Grand Canyon National Park and (b) trees on the about 1400‐ year‐old Bonito Lava Flow (Fenton & Niedermann, 2014) at the Sunset Crater, Arizona (images by Dontsova).

The impacts of biological colonization may be most readily observed in the initial stages of soil development when soils have not yet reached the complexity that usually characterizes them. For example, plant establishment on newly deposited or freshly exposed rock surfaces is clear evidence that plants modify their environment to extract nutrients needed for their growth (Figure  2.1). Early colonization of basalt flows or emergent glacial till (during glacial retreat) and other fresh rock surfaces elsewhere, isotopic evidence that plants source some of their nutrients from weathering of the local rock, and strong evidence of increased weathering when plants are present, all point towards a crucial role of plants in soil formation (R. A. Berner & Cochran, 1998; Cochran & Berner, 1996; Gemma & Koske, 1990; Matson, 1990; Phillips et  al., 2008; Raich et al., 1996; Vitousek et al., 1993). 2.2. MECHANISMS OF WEATHERING BY PLANTS Several mechanisms allow plants to impact their environment in order to directly and indirectly enhance weathering processes. Most of these mechanisms are local, limited to the immediate environment surrounding plant roots, which is termed the rhizosphere. The rhizosphere is not readily defined in terms of physical space or size but can be conceptualized more as a gradient where chemical, biological, and physical processes are driven by the plant’s actions along the root (McNear Jr., 2013). This is the zone where plant‐driven mineral weathering predominantly takes place via several processes described in this section (Figure 2.2). 2.2.1. Carbonic Acid Production Great emphasis has been placed on determining the mechanisms/processes of acquisition and uptake of ele-

ments from rocks and minerals. One of the mechanisms involved in plant enhancement of weathering is the increase in subsurface CO2 linked to plant activity. Plants increase CO2 concentrations in soils via: (a) root respiration; (b) decay of senescent roots, leaf litter, and associated microbial biomass; and (c) release of root exudates that are mineralized to CO2 by bacteria and fungi. Soil CO2 concentrations are 10 to more than 100 times higher than ambient PCO2 (Amundson & Davidson, 1990), but soil PCO2 is highly variable in time and space, depending on precipitation dynamics, season (Terhune & Harden, 1991), diffusivity, organic matter content, biological activity, moisture content, temperature and soil management, among others (Reicosky et al., 2008). Carbon dioxide in the soil air reacts with water to form carbonic acid. Carbonic acid can then dissociate releasing protons and influencing soil pH. R.A. Berner (1992) calculated that for the simplest case where the pH of a solution is determined only by the dissolution of CO2(g) in water, the concentration of protons is proportional to the square root of CO2 partial pressure. The decrease in pH caused by CO2 dissolution in rain and soil pore water can drive the dissolution of primary silicate minerals present in the soils. Therefore, an increase in local CO2 concentration in the rhizosphere increases the proton‐promoted rate of primary silicate weathering. Meteoric pure water in equilibrium with atmospheric CO2 (PCO2 = 4.10 × 10−4, as of 2018) has a pH of 5.61. Based on consideration of local PCO2 variation alone (ignoring all other soil pH buffers that may subsequently consume H+) soil solutions can be orders of magnitude more acidic than rainfall (Reicosky et  al., 2008; Terhune & Harden, 1991). Moving water from the atmosphere to soil air with PCO2 of 3.3% would alone decrease pH from 5.61 to 4.66, i.e., a 10‐fold increase in H+ concentration.

Plants as Drivers of Rock Weathering  35 CO2 photosynthesis

H2O evapotranspiration

CO2 2.2.1 Carbonic acid production Proton promoted dissolution

H+

Carbon flow

Water flow DOC

CO2 + H2O H+

Fe, Mn, P... H2O redistribution H+

H+(pH)

P, K, Mg ... 2.2.3 Mycorrhizal weathering & nutrient acquisition

2.2.2 Plant root exudates Ligand promoted dissolution

K, Ca, Mg ... C O2 – CO2

H 2O uptake

pe

2.2.4 Selective uptake of dissolution products

2.2.6 Regulating water flow

2.2.5 Influencing redox conditions 2.2.7 Mechanical effects

Figure 2.2  Schematic summary of the main mechanisms whereby plants drive weathering in soil environments. Each mechanism is discussed in the text under the corresponding subheading indicated in the figure.

Brantley (2008) based on data on mineral dissolution kinetics summarized in Bandstra et al. (2008) and using an algorithm described in Bandstra and Brantley (2008) developed relationships between dissolution rates and solution pH for a large number of primary minerals, as well as kaolinite and basalt rock. For most minerals, dissolution rates followed a v‐shaped trend as a function of pH, with rates highest at extreme pH values and lower at more neutral ones. Brantley (2008) also concluded that the effect of CO2 on dissolution was indirect, through a decrease in solution pH. Recent modeling by Winnick and Maher (2018) expanded on this by developing a range of coefficients relating silicate weathering flux to PCO2 depending on the stoichiometry of dissolution reaction.

Experimental studies of PCO2 effects on mineral weathering rates generally support these predictions. In studies not controlled for pH, there is a trend for increased dissolution with increased PCO2 (Bruant et  al., 2003; Lagache, 1965; Navarre‐Sitchler & Thyne, 2007; Osthols & Malmstrom, 1995). In studies where pH was controlled, PCO2 had no detectable effect on weathering (Brady & Carroll, 1994; Grandstaff, 1977; Knauss et al., 1993; Stephens & Hering, 2004). Golubev et  al. (2005) evaluated the effect of both pH and PCO2 on silicate weathering and concluded that the direct effect of CO2 on weathering is weak relative to its indirect effect through pH. Conversely, while Wogelius and Walther (1991) did not see a PCO2 effect in pH‐controlled studies at low pH values, they did observe a PCO2 effect at pH 11, where the

36  BIOGEOCHEMICAL CYCLES

presence of atmospheric CO2 reduced the dissolution rate of olivine by over an order of magnitude. Berg and Banwart (2000) also suggested that anorthite dissolution may be promoted via surface complexation of carbonate species leading to destabilization of surface metal–oxygen bonds. They concluded that because of pH effects on the weak‐acid speciation of dissolved carbonate, ligand‐promoted weathering would be especially important at near‐ neutral and basic pH.

relative to ligand‐free solution in indifferent background electrolyte solution at the same pH) decreases as pH decreases, indicating that the ligand‐promoted dissolution mechanism becomes relatively more important as the rate of proton‐promoted dissolution decreases (Welch & Ullman, 1993). While the majority of these studies have been done with the pure organic acids because they provide easier control, mixtures of different exudates and metabolites, such as forest‐floor extracts (Chorover & Amistadi, 2001; Dontsova et al., 2014; Ochs, 1996; Ochs et al., 1993; van Hees et al., 2002) can significantly affect 2.2.2. Plant Root Exudation dissolution of the rocks compared to inorganic solutions. Another mechanism whereby plants drive mineral Many plants also exude phyto‐siderophores (Kraemer weathering is exudation of organic ligands. Plant root et al., 2006), ligands that have an especially high affinity exudates are a complex mixture of high‐ and low‐­ for Fe(III). They are considered important to the dismolecular‐weight organic compounds. High‐molecular‐ solution of Fe‐bearing minerals, particularly in Fe‐ or weight compounds include glycoproteins, polysaccharides, P‐ limited systems because P is often complexed with and ectoenzymes. Low‐molecular‐weight compounds Fe oxides and siderophores dissolve Fe and release P include some of the carbohydrates, organic acids, amino (Römheld, 1991). acids, peptides, and phenolics. Sugars (most commonly Plant root exudates and border cell abscission also proglucose and fructose) and organic acids are predominant mote heterotrophic microbial growth (Lynch, 1990; soluble organic components in rhizosphere soil. Stotzky & Pramer, 1972) and microbial cell exudation of The  relative abundance of organic acids in soils is LMWOAs, and siderophores enhancing the direct effect ­aliphatic > aromatic > amino acids, with the hydroxyl‐, of root exudates on rock weathering. Multiple studies di‐ and tricarboxylic acids (oxalic, malic, succinic, citric, have demonstrated the effects of bacteria on both mintartaric, and malonic acids) being the most common ali- eral growth (Ehrlich, 1999; Gorshkov et al., 1992; Kawano phatic acids present in the rhizosphere (Violante & & Tomita, 2001; Kohler et  al., 1994) and dissolution Glanfreda, 2000). The precise composition of exudates (Bennett et  al., 1996; Liermann et  al., 2000; O’Reilly varies between plants (Naveed et al., 2017) and depends et al., 2006; Perdrial et al., 2009; Rosenberg & Maurice, on many environmental and plant factors. For example, 2003) as reviewed in Samuels et al. (2020). However, it is conifer root exudates contained 0.10–5% sugar, 0.02–0.10% common in experimental studies of heterotrophic bacteamino acid, and 0.01–0.06% organic acids; 0.005–0.012% rial weathering (Kawano & Tomita, 2001; Perdrial et al., N, 0.006–0.018% K, 0.001–0.005% Ca, and 0.001% Mg. 2009) to employ a nutrient solution to sustain bacterial The pH of the exudate solutions ranged from 5.3 to 5. 7 growth. This highlights the bacterial demand for reduced (Ketchie & Lopushinsky, 1981). carbon, usually derived from photosynthesizing plants. Low‐molecular‐weight organic acids (LMWOAs) have In fact, Welch and Ullman (1999) observed that while been shown to enhance mineral dissolution rates through bacteria alone had no effect on the dissolution of bytownbioligand‐promoted detachment of metal centers from ite feldspar, glucose addition to the bacteria–mineral mineral surfaces (Stephens & Hering, 2004; Wieland suspension resulted in bacterial production of gluconic et al., 1988) and complexation of metal cations in solu- acid, which accelerated the dissolution reaction of tion (Bray et al., 2015; Cama & Ganor, 2006; Dontsova feldspar by both proton‐ and ligand‐promoted mechaet al., 2014; Drever & Stillings, 1997; Neaman et al., 2005, nisms. Depending on the temperature, twofold to 20‐fold 2006; Schmalenberger et  al., 2015; Welch & Ullman, increases in dissolution rates compared to the control 1993, 1996). Welch and Ullman (1993) showed that the were observed. Bacterial exudates also have been shown rates of plagioclase dissolution in solutions containing to increase Al dissolution in illite (Wendling et al., 2005). organic acids are up to 10 times greater than rates deter- Of course, chemolithoautotrophic bacteria, which gain mined in solutions containing inorganic acids at the same energy from the oxidation of reduced metal forms, and pH. The polyfunctional acids, oxalate, citrate, succinate, grow by fixation of CO2, do not require photosynthate pyruvate, and 2‐ketoglutarate, are the most effective at for metabolism, but can still promote mineral weathering. promoting dissolution. Acetate and propionate are not as Influence of these bacteria on weathering becomes pareffective as the other organic acids but are more effective ticularly relevant at the bedrock–soil interface, where than solutions containing only inorganic acids. The reduced carbon that plants fix may be limited, but other degree of ligand‐promoted enhancement of dissolution forms of energy are available for chemolithoautotrophic rate (increase in rate in organic‐containing solution/rate bacterial (e.g., Fe oxidizers) growth. Similar to bacteria,

Plants as Drivers of Rock Weathering  37

fungi also promote mineral weathering if provided with an organic nutrient source. Balogh‐Brunstad, Keller, Dickinson, et al. (2008) isolated the weathering function of Suillus tomentosus in liquid‐cultures with biotite micas. Weathering of biotite flakes was about two to three orders of magnitude faster in shaken liquid -cultures with the fungus compared to controls. The nutrients dissolved from biotite, K+, Mg2+, and Fe2+, were preferentially partitioned into fungal biomass. 2.2.3. Carbon Allocation to Symbiotic Associations with Fungi About 90% of plant species live in symbiosis with fungi (Brundrett, 2009; Smith & Read, 2008), and their role in mineral and rock weathering has been documented (Leake & Read, 2017, and references therein). Among the six types of mycorrhiza, arbuscular mycorrhizae (AM) and ectomycorrhizae (EM) are the most dominant, and they have been the subject of most mycorrhizal research on weathering. Arbuscular mycorrhizae evolved with the establishment of vascular land plants about 500 Ma (Beerling, 2007) and they depend exclusively on photosynthate carbon supplied by the host plants forming arbuscules inside the root cells where most of the carbon and nutrient exchange occurs with some external hyphae (van der Heijden et al., 2015). Ectomycorrhizae evolved with the rise of angiosperms and colonized many gymnosperms during the Cretaceous period about 100 Ma (Brundrett, 2002). The EM have the advantage of being able to gain energy from the decomposition of organic matter and can live without the host plants. The carbon and nutrient exchange occurs in the Hartig net where the fungal cells surround the root cells without crossing the cell membranes, and each EM root continues into long hyphal networks extending to large distances in the soil

(a)

(b) 200

Total 13C uptake Cation gain (µmol tree–1)

1000

I (µmol 13C tree–1)

(Smith & Read, 2008). Mycorrhizal fungi essentially serve as an “extension” of the plants’ root system, and have shown great mineral weathering abilities (Leake & Read, 2017). Plants enable mycorrhizal fungal growth and activity by reduced carbon (chemical energy) translocation into the roots. The resulting mycorrhizal extension into soil or rock pores for nutrient acquisition leads to enhancement of weathering processes. An increasing amount of laboratory evidence shows that about 10–20% of plant photosynthate is allocated to AM fungi and up to 50% to EM (van der Heijden et al., 2015). The plant‐provided carbon allows the fungi to build a long hyphal network to explore the soil for nutrients (Leake et al., 2001, 2004). Plants deliver various organic compounds (organic acids, enzymes, sugars, siderophores, etc.) in exchange for lithogenic nutrients (Finlay et  al., 2009). Laboratory 13CO2 pulse‐labeling studies have demonstrated that the EM plants fix a significantly larger amount of carbon into their tissues and accumulate 70% more cations than nonmycorrhizal plants (Figure 2.3). Organic carbon allocation depends on nutrient availability. For example, belowground carbon allocation of mycorrhizal tree seedlings increased and more fungal hyphae grew into patches of K‐feldspar than quartz (Rosling et  al., 2004), and into soil layers with a high amount of weatherable minerals (Heinonsalo et al., 2004) in laboratory experiments. Smits et  al. (2012) observed that proportionally more plant energy was invested into the fungi under P limitations in a 14CO2‐labeled experiment with Scots pine. They also confirmed that plant–fungi association was able to biosense the apatite because preferentially more photosynthate was allocated to the apatite patches and that the weathering rate was enhanced in the plant–fungi system. However, Wallander and Hagerberg (2004) challenged the idea of increased

800 600 400 200 0

160

Sum of Ca, Mg, and K uptake

120 80 40 0

B/P

B/F/P

B/P

B/F/P

Figure 2.3  A 48 h 13CO2 pulse‐labeling experiment on 1‐year‐old red pine (Pinus Resinosa Ait.): (a) 13C uptake by the trees after 48 h; (b) total cation gain after 12 months of growth. B/P, nonmycorrhizal red pine with bacteria; B/F/P, mycorrhizal red pine with bacteria; fungi, Suillus tomentosus. [Balogh‐Brunstad, unpublished.]

38  BIOGEOCHEMICAL CYCLES

weathering by mycorrhizal trees of K‐ and Mg‐containing minerals, because it has been shown that carbon allocation belowground decreased when K and Mg were deficient and increased only when N and P were deficient (Ericsson, 1995). 2.2.4. Plant Uptake of Mineral Dissolution Products Selective plant uptake of the products of mineral weathering such as base cations (Ca2+, K+, Mg2+) and P (all macronutrients) drives weathering by maintaining favorable disequilibrium (high negative ΔG of the dissolution reaction) and, therefore, increasing dissolution rate (Meheruna & Akagi, 2006). Plants can actively acquire nutrients either via fine roots that excrete H+ in exchange for the cationic nutrients (Mg2+, Ca2+, NH4+, and K+), which they take up in order to maintain charge balance (Marschner, 2002) or through mycorrhizal associations. Even nonessential elements are taken up by plants. For example, Markewitz and Richter (1998) showed the importance of biota in the cycling of Si and Al in soils. A significant portion of available Si and Al was in live biomass or litter. In a mass‐balance rock weathering study, Burghelea et  al. (2015) observed more than 10 times greater uptake of Al by rock‐colonizing grass than was released into drainage waters. Dissolved silica taken up by plants can precipitate in the cell walls, cell lumen, and extracellular loci as SiO2·nH2O, or amorphous opal (Meunier et al., 2008), forming microscopic silica particles called phytoliths. According to Lucas (2001), the effect of plant uptake on mineral weathering strongly affects the formation of secondary minerals that are close to equilibrium with soil solutions. Translocation, turnover, and concentration of products of mineral weathering in the top layer of soil also promote the local formation of secondary minerals. Quantifying stable isotope ratios in minerals, plants, and soil solution is particularly effective in identifying the source of nutrients plants take up and the amount that originates from different sources, including dissolution of the minerals (e.g., Uhlig et al., 2017). 2.2.5. Modulation of Redox Environment Plants also influence redox processes in the immediate vicinity of the roots, either by reducing or oxidizing metals. Some plants are capable of root‐zone oxygenation, whereby they provide a biological pathway for O2 transport to deeper layers in water‐saturated soils. Precipitation of Fe(III) oxide crusts, an indication of root‐zone oxygenation, around some wetland plant roots has been observed (Khan et  al., 2016; Levan & Riha, 1986; Schreiner & Reed, 1909; Violante et  al., 2003). Oxide precipitates have also been observed around the roots of plants colonizing oxic soils. In either case, as

primary minerals largely contain iron and manganese in reduced form, precipitation of oxidized forms of these elements would decrease saturation state and promote further dissolution of the primary minerals. Plant roots also produce enzymes, reductases, which reduce less mobile forms of Fe and Mn and promote their uptake. Plants can also use the same mechanism to enhance uptake of P, which can be bound by Fe3+ (Marschner et al., 1986). In addition, the rhizosphere is a highly bioactive environment, enriched in labile organic C that is often more limited in O2 than the surrounding bulk soil. For example, organic root exudates when used by microorganisms as a source of energy consume O2 and decrease the redox potential of the soil solution. 2.2.6. Water Flow Regulation Abiotic weathering is strongly affected by water flow (Steefel et  al., 2005). Plants modify patterns of water movement in soils (e.g., prevent deep percolation due to water uptake) and as a consequence change weathering pathways and patterns (Lucas, 2001). Evapotranspiration intensity was shown to control the pore water saturation with Na‐plagioclase, which determines the depth of chemical weathering (soil vs. saprolite), regulates the local groundwater discharge, and the water residence time in the vadose zone (from about 1 year downslope to 20 years upslope; Riotte et al., 2014). Dissolution can be either surface‐reaction or transport controlled depending on which process is limiting (Steefel, 2008). Both conditions can be present locally. If dissolution is transport controlled (minerals are in local equilibrium with solution), then decreased water flux may diminish weathering rate. If dissolution is surface‐ reaction controlled, an increase in water residence time in the rooting zone should enhance the amount of weathering per unit water flux. An increase in residence time also increases precipitation of secondary minerals. Modeling and large‐scale measurements indicate that vegetation decreases drainage and as a result decreases removal of weathering products from the soils, slowing down dissolution. Modeling by Roelandt et  al. (2010), assuming transport (rather than kinetic dissolution) control on weathering flux, suggested that the complete removal of continental vegetation would lead to an increase in the dissolved fluxes to the ocean by 80% because of the collapse in the evapotranspiration, resulting in a more efficient drainage of weathering profiles. Large watershed measurements (Raymond & Cole, 2003; Raymond & Hamilton, 2018) show increased bicarbonate export when drainage is increased as a result of cropland replacing forest, indicating transport control on weathering. This, however, does not take into account that weathering is not fully reflected in the drainage, as a

Plants as Drivers of Rock Weathering  39

fraction of the weathering products is taken up by the plants and is not transported with the drainage water (Burghelea et al., 2015; Zaharescu et al., 2017). In addition to water uptake, plants have been shown to be capable of hydraulic redistribution within the plant and soil system driven by water‐potential gradients. This increases availability of the water, necessary for weathering reactions, particularly in dry areas of the soil, and therefore may increase weathering and nutrient availability (Brantley et al., 2011). However, at the same time, a decrease in water content in other areas of the soil can promote precipitation of secondary minerals due to an increase in solution saturation state. Mycorrhizal fungi have been shown to enhance plant water uptake through a better exploration of soil, reaching into smaller pores and covering a larger area (Allen, 2007). There is also growing evidence that mycorrhizal networks allow redistribution of water between plants (Brantley et al., 2011; Egerton‐Warburton et al., 2007; Plamboeck et al., 2007; Warren et al., 2008). Hydrophilic exudates prevent drying of the soil in the immediate vicinity of the roots. Root mucilage can hold up to 1000 times water relative to its dry weight (McCully & Boyer, 1997), and even though it loses water easily as water potential decreases, presence of mucilage in soils increases their water retention at any matric potential (Carminati et al., 2016). Exudates can also promote soil aggregation and structure formation (Traoré et  al., 2000). Particle aggregation increases water‐holding capacity and provides a better environment for plant and microorganisms. Finally, upon senescence and decomposition, plant roots can leave behind root channels that exert strong controls over water flow in both saturated and unsaturated soils. Such channels can operate as preferential flow paths, which divert water from the bulk soil into the high conductivity “conduits” that remain following root decomposition. By altering the propagation of the wetting front, root channels can be subjected to a disproportionately high relative volume of water throughflux, while diminishing the amount of throughflux occurring in bulk soil. This can, in turn, influence the local distribution of weathering processes and rates at the scale of root networks. 2.2.7. Mechanical Effects Plants also affect erosion (physical denudation). Multiple experiments demonstrated increase in infiltration and decrease in runoff and erosion, when vegetation is present (Collins et al., 2004; Dunne et al., 1978; Durán Zuazo et al., 2008; Gyssels & Poesen, 2003; Wainwright et  al., 2000); and while not as strong due to covariance between climate and vegetation, there is also evidence of decreased erosion on vegetated areas at the landscape

scale (Torres Acosta et al., 2015). At the same time, there is a link between rates of weathering and soil formation and erosion rates (Dixon & von Blanckenburg, 2012; Dixon et al., 2012; Ferrier & Kirchner, 2008; Stallard & Edmond, 1987). Low erosion results in highly weathered profiles with low current chemical weathering fluxes, and high erosion results in less weathered profiles with high chemical weathering fluxes. In fact, Larsen et al. (2014) have shown that soil chemical denudation rates increase proportionally with erosion rates. Therefore, we can expect that presence of the vegetation can also slow down weathering by preserving the soil in place. However, in addition to preventing erosion because of the shear strength contributed by roots to the soil, there is the opposite effect of roots penetration into rock, which contributes to the expansion of cracking and enhanced “freeze–thaw” types of effects. Also, tree throw, as commonly occurs in forested hillslope environments, is a major factor in mixing less weathered subsoil with lower pH, higher organic matter surface soils, and bringing C‐horizon material into the weathering zone, increasing weathering (Brantley et  al., 2017; Gabet et  al., 2003; Pawlik & Samonil, 2018; Pawlik et al., 2016). 2.3. EXPERIMENTAL AND FIELD EVIDENCE OF BIOLOGICAL WEATHERING BY PLANTS AND SYMBIOTIC FUNGI 2.3.1. Aseptic Microcosm Experiments Plant‐driven weathering has been studied extensively in natural forested ecosystems as they are the largest biomes on the terrestrial landscape and there is a multitude of human resources associated with wood production (Gamfeldt et al., 2013). However, since in natural systems, the trees and their fungal and microbial associates are inseparable and weathering mineral assemblages are complex, the scientific community has been trying to quantify the contribution to weathering and elemental cycles of each biological factor affecting weathering individually. Several studies employed aseptic pine microcosms (Scots pine inoculated with Paxillus involutus; Figure 2.4a) to document mineral dissolution of the minerals, biotite (Bonneville et  al., 2009, 2011, 2016) and hornblende, biotite, and chlorite (Gazzè et  al., 2012, 2013; Saccone et al., 2012). Bonneville et al. (2009, 2011) measured the direct biomechanical and biochemical impacts of a single ectomycorrhizal fungal hypha on a large target mineral piece using a combination of new microscopy techniques, such as scanning electron microscopy (SEM) coupled with focused ion beam (FIB) and high‐resolution transmission electron microscopy (HRTEM) with energy‐dispersive X‐ray spectroscopy (EDX). The early stages of plant‐ driven fungal weathering (after 130 days growth) showed

40  BIOGEOCHEMICAL CYCLES (a)

(b)

(c)

1 cm

Figure 2.4  (a) In the axenic microcosm, P. sylvestris roots are in symbiosis with P. involutus. Two chlorite flakes are positioned in the circular wells, with (b) one flake well colonized while (c) the other is almost devoid of hyphae. [From Gazzè et al. (2012). Reproduced with permission of John Wiley & Sons.]

that fungal hypha strongly attached to the mineral surface, and depleted 50–65% of K, 55–75% of Mg, 80–85% of Fe and 75–85% of Al from the top 40 nm of the biotite lattice structure (Bonneville et  al., 2011). Strong in situ acidification was also measured (pH < 4.6). Biotite alteration kinetics was accelerated by about 0.04 μmol biotite m−2 h−1 compared to abiotic controls through the action of the mycorrhizal fungi. No control with pine but without mycorrhiza was tested. In a similar experiment with the same species combination (Bonneville et  al., 2016), a substantial amount of structural Fe(II) was ­oxidized to about 2 μm depth resulting in an increase in Fe(III) to total Fe ratio to about 0.8. Authors suggested that the biologically induced growth of Fe(III) hydroxide was able to exert enough strain to “­ microcrack” the biotite below the hypha–biotite ­interface, accelerating chemical dissolution. Formation of channel‐like dissolution features on chlorite (Figure  2.5) was shown using atomic force microscopy (AFM) (Gazzè et  al., 2012). Investigation of the same area of the mineral before and after 7 months of exposure to a Pinus sylvestris seedling with mycorrhizal Paxillus involutus showed that primary dissolution channels 50 nm deep and 1 μm wide formed on the basal surfaces of chlorite with morphology indicating fungal origin (Gazzè et al., 2012). In addition, exudation of extracellular

polymeric substances was observed as distinct “halos” surrounding the hyphae (Gazzè et al., 2013) and also as a thin layer (about 10 40 nm thick) on the whole mineral surface (Saccone et al., 2012). These observations at the nanoscale documented alteration of mineral surfaces in situ by plant‐driven ectomycorrhizal fungal weathering. 2.3.2. Controlled Mesocosm Experiments 2.3.2.1. Plant‐only Experiments Laboratory mesocosm studies have focused on growing trees, grasses, and forbs with and without associated fungi and bacteria using granular rock media as the only source of nutrients or under nutrient (P, K, Ca, Mg, Fe, etc.) limitation. In one of the earlier mesocosm studies, Hinsinger et  al. (2001) obtained experimental evidence of the role of crop plants, banana, maize, rape, and lupine, in weathering of basaltic rock. Over 36 days, dilute nutrient solution was pumped through the mesocosms, and plant growth and the effect of the plants on basalt weathering were quantified. In addition to increased release of rock‐derived nutrients, such as K, Ca, and Mg, this study also reported that release of nonplant essential elements, like Si and Na, was increased about one to five times compared to the abiotic control. Ca and Na were preferentially released during plant

Plants as Drivers of Rock Weathering  41 (a)

(b) 3 2 1 2

3

n

tio

wth gro a ph Hy

ec dir

1 500 nm

500 nm

Figure 2.5  Two examples of the herringbone texture associated with some of the hyphal channels formed in chlorite when exposed to P. sylvestris seedling with mycorrhizal P.involutus observed using atomic force microscopy (AFM). (a, b) Secondary channels oriented along the growth direction of the hyphal branch, with various stages of secondary channel evolution present (indicated by numbers) and showing progressive expansion, for example numbers 1, 2 and 3 in (a). Secondary channel number 3 in (b) shows channel enlargement and coalescence with the main channel. Z‐height scales: (a) 0–17 nm, (b) 0–20 nm. [From Gazzè et al. (2012). Reproduced with permission of John Wiley & Sons.]

weathering, indicating dissolution of plagioclase (the main source of Ca and Na in the rock). In addition, the release of Fe, an important micronutrient, was about 100‐ to 500‐fold higher in the banana and maize mesocosms than in the abiotic treatment. It was speculated by the authors that the plant effect was localized to the rhizosphere (root zone), as the bulk pH stayed above 5.5 for the entire experiment. The results of Hinsinger et  al. (2001) supported the earlier suggestion by Bormann et  al. (1998) that the effects of land plants need to be taken into account when analyzing geochemical cycles of elements released during basalt weathering. Other laboratory mesocosm studies have since confirmed that plants enhance dissolution of other rocks and minerals (e.g., Balogh‐Brunstad et  al., 2017; Balogh‐Brunstad, Keller, Bormann, et  al., 2008; Burghelea et  al., 2015; Shi et  al., 2014; Van Schöll, Hoffland, et  al., 2006). Scots pine accompanied by Burkholderia glathei PML1(12), a rhizosphere bacterium, promoted dissolution and uptake of K and Mg from biotite mica, but only when the nutrient solution did not contain these elements, indicating that plants are able to adapt to nutrient‐poor environments and enhance the release of nutrients into solution from the rocks when deficient (Calvaruso et al., 2006). Shi et al. (2014) tested the effect of K and Ca concentrations in nutrient solutions

on growth of nonmycorrhizal red pine (Pinus resinosa Ait.) and utilization of biotite (K) and anorthite (Ca) to supplement the needs of these elements using Ca/Sr and K/Rb ratios. The study showed that while the fraction of K derived from biotite in biomass increased with decreasing potassium supply in nutrient solution, the seedlings were not able to obtain enough K from biotite without symbiont fungi. Surprisingly, calcium uptake by the pine seedlings from anorthite did not depend on Ca in the nutrient solution. Solution pH was lower in planted mesocosm compared to abiotic controls, indicating that organic and inorganic acids resulting from plant activity could have contributed towards weathering of the minerals. Controlled mesocosm experiments where plants were grown in four rock types without any additional nutrient input (Figure  2.6) demonstrated a significant plant‐induced increase in total mobilization (plant uptake and denudation, combined) of lithogenic elements compared to the abiotic controls (Figure 2.7; Burghelea et al., 2015, 2018; Zaharescu et al., 2017, 2019). These examples show that plants respond to nutrient deficiency by stimulating processes that enhance weathering and nutrient mobilization from minerals and rocks. Additional studies have demonstrated that plants are able to survive when the dissolving rock is the only source of all or some of the necessary lithogenic nutrients.

42  BIOGEOCHEMICAL CYCLES

Figure 2.6  Design of the (a) controlled environment modules containing (b) mesocosms to examine the effect of plants and associated microorganisms on rock weathering. [Adapted from Burghelea et  al. (2015, 2018) and Zaharescu et al. (2017, 2019).]

P amount (μg)

Basalt

Rhyolite

300

300

200

200

* 100 0

*

100

*

300

300

200

200

+AM

–AM Control

100

2,000

2,000

1,500

1,500

1,500

1,000

500

500

0

0 +AM –AM Control

* *

*

* *

+AM –AM Control

1,000

100

*

–AM Control

+AM

*

1,500

*

–AM Control

*

2,000

500 0

Plant Solution

0 +AM

–AM Control

2,000

1,000

*

0

0 +AM

K amount (μg)

*

* *

Schist

Granite

*

*

1,000 500

+AM –AM Control

0

+AM –AM Control

Figure 2.7  Mass balance analysis of the column experiment demonstrated that plants (buffalo grass, Bouteloua dactyloides) increased total mobilization (plant uptake + denudation) of P and K. [Burghelea et  al. (2015). Reproduced with permission of Springer Nature.]

However, in the absence of plant‐free controls, weathering by plants and by abiotic processes cannot be separated, and thus their contribution cannot be quantified. For example, Norway spruce (Picea abies) was shown to

access Ca and Mg from both fine and coarse rock fragments (gneiss) when Ca and Mg were omitted from the nutrient solutions (Koele & Hildebrand, 2008; Koele et al., 2010).

Plants as Drivers of Rock Weathering  43

2.3.2.2. Plants with Mycorrhiza (Mycorrhizal Enhancement of Weathering) Presence of mycorrhiza is expected to increase weathering compared to nonmycorrhizal but planted controls, but evidence shows that its effect varies depending on experimental conditions, rocks and minerals used, and species of both plants and their fungal symbionts. In addition, even when rock weathering is increased due to mycorrhiza, it may not be reflected in increased denudation of dissolving elements. Scots pine seedlings colonized by several species of ectomycorrhizal fungi were able to dissolve apatite and take up associated P to a greater extent than nonmycorrhizal seedlings (Wallander, 2000; Wallander et  al., 1997). They also accumulated more biomass (Wallander, 2000). However, the results varied depending on fungal species and percentage of colonization of the tree roots. It was also observed that exudation of oxalic acid was smallest in the nonmycorrhizal treatment and positively correlated with P in solution. Van Schöll, Smits, et  al. (2006) also documented that ectomycorrhizal seedlings were able to weather muscovite (only K source) and hornblende (only Mg source) more efficiently than nonmycorrhizal seedlings. All seedlings increased muscovite weathering compared to the abiotic controls (3.3 times with Paxillus involutus and 1.7 times without fungi). Hornblende weathering increased by a factor of 1.5–2 in pots with trees compared to the pots without trees, but fungal symbionts did not increase the hornblende weathering any further, which indicates a feedback mechanisms from the physical environment in addition to species‐ specific response of the associated fungi. In a red pine (Pinus resinosa Ait.) mesocosm study where the sole sources of Ca, K, and Mg were biotite mica and anorthite (Ca‐feldspar), the trees increased weathering rates and decreased drainage losses (chemical denudation) compared to controls without trees (Balogh‐ Brunstad, Keller, Bormann, et  al., 2008). However, the ratio between weathering and denudation was similar between mycorrhizal and nonmycorrhizal seedlings, 1.5 and 1.3, respectively, indicating that excess nutrients were taken up by the plants. The red pine seedlings were also inoculated with bacteria isolated from mycorrhizosphere, which may have contributed to mineral weathering and nutrient acquisition by the seedlings. Koele et al. (2009)’s experiment with Scots pine, two types of ectomycorrhizal fungi, and two strains of mycorrhizosphere bacteria showed that when bacterial strains alone were added to the seedlings the weathering rates were the same as the pine alone, while fungi increased Mg uptake of the plant without bacterial presence. However, the highest Mg release from biotite was observed in the co‐inoculated treatment, which indicates a unique role of the plant– fungi–bacteria interactions during mineral weathering.

Calvaruso et al. (2010, 2013) independntly confirmed that ectomycorrhizal Scots pine seedlings were always able to assimilate higher amounts of Ca, K and Mg than non‐mycorrhizal seedlings under the same conditions, while both mycorrhizal and non‐mycorrhizal seedlings significantly increased the weathering rate of minerals when compared to no‐tree controls. In addition, P and trace elements accumulated at higher rates in seedlings with mycorrhizal fungi, but the impact on weathering rate seemed to be species‐specific. Balogh‐Brunstad et al. (2017) analyzed together several published studies (Balogh‐Brunstad, Keller, Bormann, et  al., 2008; Shi et  al., 2014) and new experiments and concluded that while both red and Scots pine seedlings increase weathering and the added fungi stimulate silicate dissolution under potassium and calcium limitations, the cation release from biotite and calcium‐rich feldspar was more affected by abiotic environmental conditions such as water applications than by biological factors. Most experiments examining the effect of mycorrhizae on weathering focus on ectomycorrhizal conifer species because: (a) EM species are relatively easy to isolate and grow in pure cultures; (b) maintaining temperate and boreal forest productivity has been a critical motivation for research; and (c) EM fungi are believed to have higher potential to enhance plant weathering than AM fungi due to greater mycelia networks (Leake & Read, 2017; Smith & Read, 2008). However, Burghelea and coauthors (Burghelea et  al., 2015, 2018; Zaharescu et  al., 2017, 2019) demonstrated that buffalo grass, an AM fungi species, was able to accelerate the weathering of four crystalline rock types, and the addition of mycorrhizal fungi further enhanced the biological weathering impact in basalt and rhyolite (less so in granite and schist). As shown earlier for conifers (Koele et al., 2011; Van Schöll, Smits, et al., 2006; Wallander, 2000), the type of rock and its composition greatly impacts the dissolution rates under the exact same growing conditions, providing lithologic feedback to the weathering processes. Iron, Na, Ti, and Al were mobilized at highest rates from basalt, P and Mn from rhyolite, Ca and K from granite, and K from schist. Enrichment (plant uptake normalized by solution concentration) of Mn, Fe, Ti, and Al was the highest in biomass grown on basalt, possibly because the high Al mobility limited Ca and Mg uptake (Burghelea et al., 2015). Furthermore, the plants mobilized about 10 times more rare earth elements (REE) than the abiotic treatment and also accumulated larger amounts of REE than was lost in drainage, indicating selectivity for these elements (Zaharescu et  al., 2017). The added AM fungi generally increased the REE mobilization as well, indicating the AM fungi also play an active role in rock/mineral dissolution. There was a significant positive correlation between total uptake per

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Figure 2.8  Scanning electron microscopy (SEM) images showing evidence of root hair and fungal‐sized etched channels developed on surfaces of (a) muscovite, (b) biotite, (c) K‐feldspar, and (d) garnet under pine from the sandbox experiment of the Hubbard Brook Experimental Forest in New Hampshire. These channels are categorized as formed by a biological activity because of their size, round ends, and curvy and branching nature. [Balogh‐Brunstad, unpublished.]

plant of P, Ca, and Mn and the mycorrhizal infection rate (Burghelea et  al., 2015). However, the distribution of mobilized elements differed and for many elements while their total mobilization increased, denudation in the water was not affected due to plant uptake. These bulk geochemical measurements are supported by microscopic evidence of biological weathering. Examining various silicate mineral grains of the sandbox experiment at Hubbard Brook Experimental Forest in New Hampshire (described in Balogh‐Brunstad, Keller, Bormann, et al., 2008; Bormann et al., 1998) showed that

fungal hyphal and root‐hair‐sized surface dissolution patterns were observed under red pine (Pinus resinosa Ait.) and pitch pine (Pinus rigida) (Figure  2.8), but not under grass cover. Direct physical contact with mycorrhizal roots left evidence of hyphal dissolution of the basal planes on biotite in situ in short‐term laboratory experiments (Balogh‐Brunstad, Keller, Dickinson, et al., 2008; Balogh‐Brunstad, Keller, Gill, et al., 2008). These fungal hyphae‐sized, curvy, and branching channels were very similar to the fungal network patterns attached to the mineral particles (Figure 2.9).

Plants as Drivers of Rock Weathering  45 (a)

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Figure 2.9  Similar patterns of (a) fungal attachment and (b) etching on biotite basal surfaces were observed from column red pine (Pinus resinosa Ait.) growth experiments. [Balogh‐Brunstad, unpublished.]

2.3.3. Field Experiments Mesocosm studies designed to quantify the contribution of plants and mycorrhizal fungi to total weathering fluxes typically employ one or several plant species paired with a single species of fungi with or without added bacteria and well‐defined minerals or rocks. This improves researchers’ ability to identify causes of observed treatment differences but ignores many interactions that occur in natural environments. Direct field‐testing of the contribution of plants and associated microbes to overall weathering processes is more difficult. To resolve the differences in weathering environments and resulting weathering rates in plant‐free zones and under vegetation, scientists often employ a technique involving specific minerals buried in soil in mesh bags (Wallander et al., 2001). The bags enable later retrieval and analysis of the samples and allow mineral access to soil solution and mycorrhizal fungi in situ, but the bags typically exclude plant roots. The advantage of this approach is that well‐characterized, initially homogeneous mineral samples are exposed to realistic field conditions, enabling a quantitative assessment of the field treatment effect. For example, Turpault et al. (2009) tested whether dissolution rates of fluorapatite and Ca‐feldspar could be linked to the activity of plant roots and rhizospheric fungi and bacteria under naturally occurring low‐nutrient availability conditions. After 4 years of incubation, both fluorapatite and Ca‐feldspar filled mesh bags showed about three to four times higher weathering rates in the rhizosphere of beech trees (Fagus sylvatica) without calcium fertilization compared to the root‐free zones. These studies suggest a direct impact of the roots and associated microbes on mineral weathering in soil. Earlier, long‐term field lysimeter (Quideau et al., 1996) and mesh

bag (Augusto et al., 2000) studies at San Dimas Experi­ mental Forest comparing conifers to oak had concluded that plants were regulating mineral weathering by modifying the acidity of the soil, thus, coniferous species promoted higher weathering rates than deciduous species. Carbonate weathering has been investigated to a lesser extent than silicates, but Thorley et al. (2015) provided an excellent review of the key physical and chemical mechanisms that forest trees and their associated EM or AM fungi employ to enhance carbonate rock weathering. They also reported that calcite‐containing rocks weathered the fastest under angiosperms partnered with EM fungi followed by gymnosperms also partnered with EM fungi (Figure 2.10). The higher weathering rate by trees associated with EM fungi is attributed to higher soil acidity and suggests a strong impact of “tree–mycorrhizal plant functional groups,” combinations of plants and mycorrhiza, on weathering of carbonates. When Norway spruce ecosystems in Sweden with P and K limitations were supplied with apatite (as a P source) and biotite (as a K source), apatite bags stimulated fungal growth and the EM colonized tree roots also accumulated 10‐fold higher REE concentrations from the apatite than from biotite or quartz sand controls (Hagerberg et al., 2003). However, the chemical composition of the mesh‐bag contents did not change significantly after 17 months field incubation, and fungal colonization of biotite remained similar to the quartz sand controls. Rosenstock et al. (2016) similarly observed that EM fungi colonized apatite mesh bags preferentially, but there was no difference among other minerals in Norway spruce ecosystems in the Czech Republic. All mesh bags (apatite, hornblende, biotite, and quartz sand) were more colonized by EM fungi at the serpentinite bedrock site (both P and K limited) than at the granite (Mg limited) and

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Figure 2.10  Mass loss due to weathering of rock grains buried in root‐excluding mesh bags after 3 months (May–August 2013) at Bedgebury Pinetum, Kent, UK, under representative trees in established stands. Values are mean ± standard error. Results sorted by (a) tree species (n = 3 replicate trees of each of 13 species) and (b)  tree–mycorrhizal functional group for each calcite (chalk, oolitic limestone, and marble), dolomite, and basalt minerals (n = 3 tree species of each functional group, except arbuscular mycorrhizal (AM) gymnosperm where n = 4). [From Thorley et al. (2015). Licensed Under CC BY SA 4.0.]

Plants as Drivers of Rock Weathering  47

amphibolite (no limitations) sites. Granite and amphibolite sites had no difference in EM colonization rates. Both of these studies concluded that the host‐tree phosphorus demand increased fungal foraging, colonization, and P uptake from apatite, but no such response was detected in cases of K and Mg limitations. Thus, these authors suggest that contrary to laboratory experiments in natural forest ecosystems EM trees only respond to P limitation with increased weathering of P containing minerals, but are not able to increase weathering of other minerals under cation limitations. In 24 paired AM and EM field plots in New Zealand’s South Island with highly weathered, P limited soils, fungal tunneling and etching of feldspars of the native soils were equally observed (Koele et al., 2014). Apatite mesh bags were incubated for about 1 year to test the difference in apatite weathering efficiency of AM and EM plants. The results suggest that mineral weathering is caused by acidification of the rhizosphere by mycorrhizal fungi, saprotrophic fungi, and bacteria since there were no differences between the exclusively AM and EM tree sites (Koele et  al., 2014). This similarity between AM and EM trees was also observed under temperate climate at the Hubbard Brook Experimental Forest in New Hampshire, USA, where mesh bags containing ground rocks (either granite or biotite‐rich tonalite) were incubated for 100 days in sugar maple (AM tree) and birch (EM tree) rhizospheres (Remiszewski et  al., 2016). The results of sequential extractions and isotope pair signatures (87Sr/86Sr, 208Pb/204Pb, 207Pb/204Pb) of the mesh‐bag contents suggested that biotite dissolved first followed by feldspars and apatite as weathering progressed. Elemental fluxes from both sites were similar for P and Mg, but Ca fluxes were higher for sugar maple. This study shows that both EM and AM trees can increase the weathering of silicate minerals in contrast to the findings of Rosenstock et al. (2016). Also, Quirk et al. (2012) and Quirk, Leake, et al. (2014) demonstrated that while both AM and EM fungi colonized and dissolved mesh‐bag minerals, the EM trees induced larger belowground carbon allocation and weathering than the AM trees. However, Smits and Wallander (2017) challenge the magnitude of fungal weathering and the methods of the quantification of the Quirk et al. (2012) and Quirk, Leake, et al. (2014) studies, while acknowledging that trees have a major effect on soil mineral weathering and carbon sequestration. The high variation in the results from laboratory model systems and field mesh‐bag studies highlights the fact that quantifying the plant‐driven mycorrhizal weathering is still challenging because of species‐specific interactions between host‐plants and fungi, differences in plant physiology, climatic variations, bedrock variations, and severity of various non‐nitrogen elemental limitations, in addition to anthropogenic and natural disturbances. There is a

continued need to conduct field studies to understand the interplay between plants, fungi, and bacteria on mineral and rock weathering, nutrient cycling, and soil formation in the face of changing climate, and numerous anthropogenic forcing. 2.3.4. Field Observations in Situ In order to link weathering to individual trees, hillslope locations, or ecosystems without any disturbance to the system, scientists conduct observational studies that examine weathering in situ, from soil‐profile development, solution fluxes in the pedon, and evidence of mineral alteration by mycorrhiza. These studies realistically represent local conditions and processes but allow minimal control or manipulation ability since they are tied to local heterogeneity and site history. Examination of the influence of angiosperms and gymnosperms on mineral weathering under field conditions has produced variable results over the past two decades. Several studies concluded that soil physical and chemical properties (pH), and parent material (e.g., feldspar content) controlled weathering processes more than tree species, while the trees had a slight influence via effects on pH and soil structure (Andrews et  al., 2008; Augusto et  al., 2000; Dijkstra et  al., 2003). However, comparing base cation and silica‐based weathering fluxes in soil profiles under pine and hardwood forests indicated higher weathering fluxes in pine than in hardwood forest (Johnson‐Maynard et  al., 2005), particularly in the top portion of the soil profile (Schroth et al., 2007). Going a step further, soil from the rhizospheres of Norway spruce and oak (Quercus sessiliflora) was compared to bulk soil and the impact of roots on mineral weathering was assessed (Calvaruso et  al., 2009). The results demonstrated that mineral weathering was enhanced in the rhizosphere, shown by the increased amount of illite‐like clay minerals and decrease of the amorphous phases in the rhizosphere of both trees, in addition to lower total Fe and Al in both, and higher Si and K in the oak rhizosphere than in the bulk soil. Alexandre et al. (1997) examined plant impacts on the biogeochemical cycle of silicon and associated weathering processes. They observed that plant uptake of Si increased the chemical weathering rate without increasing the denudation rate. Plant effect on weathering can also be observed from the physical changes to the rock around tree roots (Roering et al., 2010) and from cation depletion in soil profiles under conifers and hardwoods as observed in Hubbard Brook Experimental Forest in New Hampshire (Nezat et al., 2004). Microscopic evidence from samples collected in situ can be used to examine the contribution of mycorrhiza to rock weathering. The “Rock‐eating fungi” provocatively

48  BIOGEOCHEMICAL CYCLES

titled Nature paper by Jongmans and co‐authors (1997) described widespread fungal hypha‐sized channels and tubular pores, sometimes containing fungal hyphae, in feldspars and hornblendes in podzol E soil horizons and granitic bedrock under Scots pine, Norway spruce and ericaceous shrubs in Sweden, Finland, Denmark, the Netherlands, and Switzerland. They were accompanied by detections of micro‐ to millimolar concentrations of LMWOAs, an indication that fungi are able to dissolve silicate minerals. The “fungi-drilled” channels were found in feldspars of all thin sections made from the E  horizon samples of podzols under Scots pine and Norway spruce in European forests, but none or very few were found in samples collected under deciduous trees, thus van Breemen, Lundström, et al. (2000) suggested that the fungi might be linked to the process of podzolization. In addition, the evidence of direct fungal attachment and dissolution of primary minerals suggested that ectomycorrhizal plants can utilize very low solubility mineral sources for their nutrient needs, and challenged the traditional view of plants solely taking up nutrients from bulk soil solution (Landeweert et al., 2001a, 2001b; van Breemen, Finlay, et al., 2000). Field evidence from a Swedish podzol chronosequence showed that “feldspar tunneling” positively correlated with ectomycorrhizal density, it was more intense in nutrient‐ poor sites indicating that the fungi may be contributing to higher potassium and calcium uptake (Hoffland et al., 2003), but it was only predominant in soils older than 2000 years, time that coincided with the disappearance of easily weatherable K‐ and Ca‐containing ­minerals (Hoffland et al., 2002). A Lake Michigan sand dune chronosequence study with soil ages between 450 and 5000 years supported the Swedish observations that feldspar tunneling becomes more important as the soil ages (older than 100, but younger than 100,000 years), however, the contribution of feldspar tunneling to the total weathering budget remained low, less than 1% (Smits et al., 2005). 2.3.5. Evidence of a Plant Effect on Weathering at Watershed Scales Watershed‐scale measurements of weathering fluxes are an efficient way of estimating the integrated effect of plants on weathering. Flux of solutes in the streams and rivers draining watersheds is often used as an indicator of the changes in weathering fluxes as driven by biota, though it has been shown before that weathering fluxes do not equal denudation for many elements due to retention in biomass (Burghelea et al., 2015; Uhlig et al., 2017). For example, Uhlig et al. (2017) compared the dissolved fluxes of K, Ca, Mg, P, and Si in rivers with their solubilization fluxes from rock (rock dissolution flux minus

secondary mineral formation flux) and observed a deficit in the dissolved fluxes, which they attributed to the nutrient uptake by forest trees. Still, the relative ease and integrative character of river measurements to evaluate weathering fluxes ensure their continued use. Classical work by Moulton et  al. (2000) demonstrated that vegetated watersheds resulted in four times higher rate of weathering release of Ca and Mg to streams than bare areas (while mineral mass balance results showed that trees increased pyroxene weathering by as much as a factor of 10). Gislason et al. (1996) observed that the total rate of chemical denudation was not affected by vegetation, however, fluxes of Ca, Mg, and Sr increased with increasing vegetative cover at constant runoff, whereas fluxes of Na and K decreased. The type of vegetation also influences weathering rates in the watersheds. Biomass‐normalized weathering fluxes from the landscape under birch trees were greater than those from the coniferous forest, suggesting that angiosperms enhance weathering to a greater degree than gymnosperms (Moulton et al., 2000) in agreement with the mesh‐bag study results (Thorley et al., 2015). Song et al. (2011) showed that bamboo forested watersheds had higher weathering than mixed and broadleaf forests. Furthermore, Kardjilov et  al. (2006) were able to establish a relationship between net primary production and net ecosystem exchange (NEE) and dissolved inorganic carbon (DIC) flux, as an indicator of weathering. In the streams draining seven catchments in Iceland, they observed that factor 5 variations in NEE result in factor 2.8 variations in river DIC flux. Vegetation effect on weathering can also be observed through seasonal changes in river water chemistry as observed in the Madeira River basin by Lyons and Bird (1995). 2.3.6. Global Estimates for Present, Past, and Future Climates Modeling can be used to evaluate the effect of vegetation on weathering fluxes under changing environmental conditions and help estimate global element fluxes as affected by past, present, or future vegetation. Taylor et  al. (2012) showed that vegetation and mycorrhizal fungi likely doubled climate‐driven weathering in the geological past. Johnson et al. (2014) also demonstrated with the soil evolution model tested using Hawaiian chronosequences the significant role that vegetation plays in accelerating the rate of weathering and hence soil profile development. However, the role of terrestrial plants in limiting atmospheric CO2 decline over the past 24 Ma was somewhat constrained by low CO2 concentrations in the atmosphere, which decreased plant productivity (Pagani et al., 2009).

Plants as Drivers of Rock Weathering  49

2.4. CLIMATE CHANGE AND OTHER ANTHROPOGENIC EFFECTS ON PLANT WEATHERING AND CARBON FLUXES Changes in the temperature and CO2 concentrations that are associated with ongoing climate change are expected to influence plant growth and therefore plant nutrient demand, respiration, and exudation (P. Li et al., 2007; Quirk, Andrews, et al., 2014; Quirk, Leake, et al., 2014). We showed earlier that CO2 increase in the soil atmosphere could have a direct effect on weathering. However, indirect effects, mediated by plant responses to elevated CO2 in the atmosphere, have also been shown to contribute to enhanced weathering. Elevated CO2 concentrations increased the belowground carbon allocation of Sequoia sempervirens (AM) and Scots pine (EM) by two‐ to sevenfold and also enhanced mineral dissolution (Andrews et  al., 2011; Quirk, Andrews, et  al., 2014). Quirk, Leake, et  al. (2014) obtained experimental evidence for negative feedback between CO2 and biological weathering rates by forest trees and associated fungi: with increasing atmospheric CO2 concentrations, biological weathering increased CO2 sequestration in bicarbonate. Mesocosm experimental results suggest that biotic weathering feedbacks may have a stabilizing effect on CO2 concentrations over geologic timescales supplementing abiotic CO2 and climate weathering feedback (Zeebe & Caldeira, 2008). The finding that increases in CO2 have historically coincided with the proliferation of weathering‐enhancing mycorrhizal associations, supports the hypothesis of Taylor et al. (2009), which states that host trees partnering with EM fungi have been playing a significant role in the long‐term drawdown of CO2 concentrations since the Cretaceous. It also helps us to predict the future effect of rhizosphere processes on climate change. Variation in temperature can have a direct abiotic effect on mineral weathering kinetics and equilibria, but it can also affect weathering through biota. Several studies have reported an Arrhenius‐like dependence of weathering on temperature (Dessert et  al., 2003; G. Li et  al., 2016; Turner et  al., 2010; White & Blum, 1995). G. Li and co‐authors (2016) examined the temperature dependence of basalt weathering. They compiled data from basaltic catchments worldwide and demonstrated that the rate of CO2 consumption associated with the weathering of basaltic rocks is strongly correlated with mean annual temperature as predicted by chemical kinetics. Dessert et  al. (2003) measured an exponential increase in mean bicarbonate concentration, mean cationic concentration, and mean total dissolved solid as indicators of chemical weathering of basalts as a function of mean surface ­temperature. On the other hand, Gislason et  al. (2008) observed statistically significant linear positive c­ orrelations

between mean annual temperature and chemical weathering in eight catchments in Iceland. Riebe et  al. (2004) observed a positive correlation between chemical weathering rates and temperature for 42 watersheds mostly in North and Central America, but reported that the temperature sensitivity was two to four times smaller than what would be expected from laboratory measurements of activation energies for feldspar weathering and previous intercomparisons of catchment mass‐balance data from the field. They hypothesized that biology accounted for the difference, both accelerating weathering, and decreasing activation energy and sensitivity to temperature. Plants may have mediated some of the temperature effects observed in these studies. However, such an effect is difficult to deconvolute from field data. Mesocosm experiments conducted at Ecotron Ile‐de‐France with and without plants (velvet mesquite, Prosopis velutina, alfalfa, Medicago sativa, and green sprangletop, Leptochloa dubia) under controlled CO2 and temperature conditions indicated increased electrical conductivity and Si concentrations at elevated temperatures across treatments. Temperature and atmospheric CO2 also affected plant productivity, biomass concentrations, and total uptake of the lithogenic elements by plants (Figure 2.11) (Dontsova, unpublished). While the effect of temperature on weathering has been confirmed over multiple systems, it is more important under conditions of kinetic control on weathering fluxes (high flow rates). If a system is under equilibrium (long residence times of water in contact with the rock) then weathering becomes less sensitive to temperature (Raymond, 2017). Changes in temperature and CO2 concentration associated with ongoing climate change are expected to influence plant growth and, in turn, plant effects on biogeochemical cycling of the elements. In addition to climate effects described above, Brault et al. (2017) estimated that the largest contribution to future changes in weathering rates would derive from the expansion of tropical and mid‐latitude vegetation in grid cells dominated by weathering‐vulnerable rock types, whereas changes in temperature and river runoff had a more modest direct effect. However, not all anthropogenic effects on the geochemical cycles are related to climate, even if they are driven by vegetation. Change in land use associated with agriculture is a powerful driver of biogeochemical change. For example, a large increase in bicarbonate flux has occurred over the past 50 years in the Mississippi River; and annual riverine alkalinity flux for the Mississippi and Ohio rivers correlated positively with the percentage cropland and negatively with present forestland draining into these rivers (Raymond & Cole, 2003). However, these changes in DIC export were shown to be driven by  changes in evapotranspiration resulting from the increase in agriculture in the Mississippi watershed

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Figure 2.11  Total uptake of K per mesocosm for three different plants grown under ambient and elevated CO2 and temperature [Dontsova, unpublished.]

(Raymond et  al., 2008). Increase in agricultural lands decreased evapotranspiration and increased drainage. Since bicar­ bonate flux is transport limited in the Mississippi River watershed, increased drainage increased weathering and bicarbonate flux. Other studies argue that vegetation can also affect biogeochemical cycles through changes in erosion rates (Amundson et  al., 2015; Torres Acosta et  al., 2015). As discussed earlier, vegetated areas generally have lower erosion rates. Weathering and soil production is influenced by soil erosion as erosion exposes fresh rock to weathering; decreased erosion causes a decrease in weathering rates of the underlying rock. However, some minimum soil cover is needed to hold water allowing for weathering to occur. This results in the ‘humped’ soil production function, with fastest weathering happening when soil is removed quickly but not completely by erosion (Amundson et  al., 2015; Heimsath, 2014; Heimsath et al., 2009). Therefore, change in erosion rates, driven by intensive agriculture, can potentially increase weathering. There are also reports of significant positive correlation between the proportion of the cropland and silicate discharge for watersheds draining into the Chesapeake Bay (Liu et al., 2000; Weller et al., 2003) and of three to four times greater export of bicarbonate from watersheds dominated by agriculture compared to nonagricultural ones (Barnes & Raymond, 2009; Oh & Raymond, 2006). Fortner and coauthors (2012) examined five small watershed experimental sites with different land use: a forested site (70+ year stand), mixed agricultural use (corn, forest, pasture), an unimproved pasture, tilled corn, and a recently (< 3 year) converted no‐till cornfield, and the whole Ohio–Tennessee River Basin, and observed a significant positive correlation between the molar ratio of (Ca2+ + Mg2+)/alkalinity to Si weathering flux in the

tilled corn and the forested site, as well as in the Ohio– Tennessee River Basin overall. They suggested that weathering was not driven only by dissolved CO2 but also by acidity released during nitrification that was enhanced by N fertilizer additions in the agricultural watersheds. This they indicated can make weathering estimates using bicarbonate inaccurate for fertilized agricultural watersheds (with as much as 67% difference in estimates for the corn till watershed). Bicarbonate fluxes in rivers draining agricultural watersheds can be affected by lime application (unrelated to changes in silicate weathering; Oh & Raymond, 2006) or by an increase in plant productivity, exudation and biological weathering (Raymond & Cole, 2003). 2.5. SUMMARY AND FUTURE OUTLOOK/ KNOWLEDGE GAPS We discussed mechanisms of biological weathering, their manifestation on different scales—from micro (fungal hyphae) to global, and the anthropogenic effects on vegetation and weathering. On the smallest scale, we have demonstrated that rocks show physical (surface channels and pores) and chemical (depletion of cations) evidence of dissolution in contact with mycorrhizal fungi, supported by carbon flow from the plants. Multiple mesocosm studies showed that the presence of plants results in an increased rate of dissolution of rock, but the extent of their effect depends on the rock composition. Increased rock weathering may result in increased denudation of dissolution products (something that would be observed in streams and rivers) but this is not consistent, as plants often take up the balance of released lithogenic elements. Presence of mycorrhiza often results in increased weathering and plant uptake of

Plants as Drivers of Rock Weathering  51

nutrient, but results are highly dependent on plant species, mycorrhizal fungi, specific pair of plant and fungus, rock type, and nutrient availability in the soil. It was also shown that plant–fungal associations can respond to the presence of rock that contains growth‐ limiting nutrients by increasing carbon flow to the area where rock is present. Reported laboratory and field studies have documented that both AM and EM trees significantly contribute to the belowground carbon allocation and enhanced mineral weathering, especially under elevated carbon dioxide concentrations, and linking the biogeochemical cycling of carbon and base (and other) cations. On watershed and global scales, it becomes more difficult to track the effect of vegetation on weathering, as it is hard to separate the effect of vegetation from other environmental factors. However, we would expect that there would be a dilution of plant effect on weathering from the rhizosphere where conditions are significantly altered by the roots, to the watershed scale where effects are averaged between areas that are affected by the plants and ones where this effect is minimal. However, this is not observed, as biological effect of weathering can be similar or greater on the watershed scale, e.g., 10 times increase in weathering on vegetated watersheds was shown by Moulton et  al. (2000), while three‐ to fourfold increase in weathering was observed in rhizosphere of beech trees compared to soil without tree roots. In addition, intensity of abiotic weathering as a reference point for biological enhancement (e.g., high abiotic flux of elements during the initial dissolution of the basaltic glass), plant biomass as an indicator of biological activity, and even plant growth phase can affect how much plant enhancement of weathering is observed. As a result, the magnitude and scale of plant effects on weathering processes, as well as relative contribution of biological components are still debated (Leake & Read, 2017; Smits & Wallander, 2017). For example, Smits and Wallander (2017) argue that the role of mycorrhizal fungi in weathering is negligible when there is no P limitation. It is apparent that different rocks and different elements in a single rock display varying degrees of biological enhancement (Burghelea et al., 2015, 2018; Zaharescu et al., 2017, 2019). In addition, the variable results from laboratory and field studies comparing AM and EM trees, and gymnosperms and angiosperms, suggest that combined biological, chemical, and physical conditions of each ecosystem ­determine the dominant processes, which underlines the importance of conducting more field‐based comparative studies under natural, but well‐constrained, settings. There are multiple unresolved questions that we still need to answer (Brantley et al., 2011; Schwartzman, 2013). ••What are the impacts of human‐induced land use change on biological weathering? As indicated above, widespread landscape alteration (e.g., forest conversion to

agriculture) can have a dramatic effect not only on plant species composition (and the attendant impacts on biological mechanisms of weathering) but also on hydrologic fluxes, and the partitioning of precipitation into recharge, runoff, and evapotranspiration. How can we best separate these confounding effects on weathering rates? ••What is the impact of changing environment/climate on the “dominant biological weathering processes”? We know that multiple aspects of weathering can be affected by changing climate—but further studies are needed to evaluate which effects will be significant and which, while present, would not translate into impacts that can affect environment or humans. ••What is the impact of extreme events (disturbances) such as the hurricanes, fire, extended drought, etc., on the feedbacks between biological and geological processes? These events are unpredictable, large in spatial scale, and with long potential temporal impact, making them very hard to study. However, we need to examine them as ­climate change is bringing an increase in frequency and severity of such events. ••How can increased knowledge of biogeochemical weathering processes be exploited to sequester atmospheric CO2? Given the known capacity of silicate ­mineral weathering to draw down CO2, are there geo‐ engineering or land management practices that could result in enhanced nutrient availability to ecosystems while also sequestering carbon? Such approaches as amending agricultural fields with kinetically labile silicates are increasingly suggested in the scientific literature (Beerling, 2017; Kantola et al., 2017). It has been shown that potassium and phosphorus uptake by common food crops from mineral and rock amendments can be used to decrease reliance on manufactured fertilizers and help the movement toward sustainable and organic farming practices (Basak et  al., 2017, and references therein). An improved understanding of plant‐mediated weathering processes is required to provide the foundation for such geoengineering approaches. ACKNOWLEDGMENTS Authors gratefully acknowledge financial support of their work by National Science Foundation grants: EAR‐1023215 ETBC—Plant–microbe–mineral interaction as a driver for rock weathering and chemical denudation; EAR‐1331408—Transformative behavior of energy, water and carbon in the Critical Zone II: Interactions between long‐ and short‐term processes that control delivery of Critical Zone services; EAR‐1742941 EAGER—The effects of soil moisture on fungal weathering, diversity, and abundance; as well as French government grants ANR‐10‐ IDEX‐0001‐02 PSL (EXPECTS) (PI R. Ferrière) and ANR‐11‐INBS‐0001 AnaEE France.

52  BIOGEOCHEMICAL CYCLES

REFERENCES Alexandre, A., Meunier, J.‐D., Colin, F., & Koud, J.‐M. (1997). Plant impact on the biogeochemical cycle of silicon and related weathering processes. Geochimica et Cosmochimica Acta, 61, 677–682. Allen, M.F. (2007). Mycorrhizal Fungi: Highways for water and nutrients in arid soils. Vadose Zone Journal, 6, 291–297. Amundson, R., Heimsath, A., Owen, J., Yoo, K., & Dietrich, W.E. (2015). Hillslope soils and vegetation. Geomorphology, 234, 122–132. Amundson, R.G., & Davidson, E.A. (1990). Carbon dioxide and nitrogenous gases in the soil atmosphere. Journal of Geochemical Exploration, 38, 13–41. Andrews, M.Y., Ague, J.J., & Berner, R.A. (2008). Weathering of soil minerals by angiosperm and gymnosperm trees. Mineralogy Magazine, 72, 11–14. Andrews, M.Y., Leake, J.R., Palmer, B.G., Banwart, S.A., & Beerling, D.J. (2011). Plant and mycorrhizal driven silicate weathering: Quantifying carbon flux and mineral weathering processes at the laboratory mesocosm scale. Applied Geochemistry, 26(Supplement), S314–S316. Augusto, L., Turpault, M.‐P., & Ranger, J. (2000). Impact of forest tree species on feldspar weathering rates. Geoderma, 96, 215–237. Balogh‐Brunstad, Z., Keller, C.K., Bormann, B.T., O’Brien, R., Wang, D., &Hawley, G. (2008). Chemical weathering and chemical denudation dynamics through ecosystem development and disturbance. Global Biogeochemical Cycles, 22, GB1007. Balogh‐Brunstad, Z., Keller, C.K., Dickinson, J.T., Stevens, F., Li, C.Y., & Bormann, B.T. (2008). Biotite weathering and nutrient uptake by ectomycorrhizal fungus, Suillus tomentosus, in liquid‐culture experiments. Geochimica et Cosmochimica Acta, 72, 2601–2618. Balogh‐Brunstad, Z., Keller, C.K., Gill, R.A., Bormann, B.T., & Li, C.Y. (2008). The effect of bacteria and fungi on chemical weathering and chemical denudation fluxes in pine growth experiments. Biogeochemistry, 88, 153–167. Balogh‐Brunstad, Z., Keller, C., Shi, Z., Wallander, H., & Stipp, S. (2017). Ectomycorrhizal fungi and mineral interactions in the rhizosphere of Scots and red pine seedlings. Soils, 1, 5. Bandstra, J.Z., Brantley, S.L. (2008). Data fitting techniques with applications to mineral dissolution kinetics. In S.L. Brantley, J.D. Kubicki, A.F. White (Eds.), Kinetics of water– rock interaction (pp. 211–257). New York: Springer Science & Business Media. Bandstra, J.Z., Buss, H.L., Campen, R.K., Liermann, L.J., Moore, J., Hausrath, E.M., et  al. (2008). Appendix: Compilation of mineral dissolution rates. In S.L. Brantley, J.D. Kubicki, A.F. White (Eds.), Kinetics of water–rock interaction. New York: Springer Science & Business Media. Barnes, R.T., & Raymond, P.A. (2009). The contribution of agricultural and urban activities to inorganic carbon fluxes within temperate watersheds. Chemical Geology, 266, 327–336. Basak, B.B., Sarkar, B., Biswas, D.R., Sarkar, S., Sanderson, P., & Naidu, R. (2017). Bio‐intervention of naturally occurring silicate minerals for alternative source of potassium:

Challenges and opportunities. In D.L. Sparks, (Ed.), Advances in agronomy (pp. 115–145). Academic Press. Beerling, D.J. (2017). Enhanced rock weathering: biological climate change mitigation with co‐benefits for food security? Biology Letters, 13, 20170149. Bennett, P.C., Hiebert, F.K., & Choi, W.J. (1996). Microbial colonization and weathering of silicates in a petroleum‐ contaminated groundwater. Chemical Geology, 132, 45–53. Berg, A., & Banwart, S.A. (2000). Carbon dioxide mediated dissolution of Ca‐feldspar: implications for silicate weathering. Chemical Geology, 163, 25–42. Berner, E.K., Berner, R.A., & Moulton, K.L. (2003). 5.06. Plants and mineral weathering: Present and past. In J.I. Drever (Ed.), Surface and ground water, weathering, and soils. Treatise on geochemistry, Vol. 5, pp. 169–188. Elsevier. Berner, R.A. (1992). Weathering, plants, and the long‐term carbon cycle. Geochimica et Cosmochimica Acta, 56, 3225–3231. Berner, R.A., & Cochran, M.F. (1998). Plant‐induced weathering of Hawaiian basalts. Journal of Sedimentary Research, 68, 723–726. Bonneville, S., Bray, A.W., & Benning, L.G. (2016). Structural Fe(II) oxidation in biotite by an ectomycorrhizal fungi drives mechanical forcing. Environmental Science and Technology, 50, 5589–5596. Bonneville, S., Smits, M.M., Brown, A., Harrington, J., Leake, J.R., Brydson, R., & Benning, L.G. (2009). Plant‐driven fungal weathering: Early stages of mineral alteration at the nanometer scale. Geology, 37, 615–618 Bonneville, S.C., Morgan, D., Schmalenberger, A., Bray, A., Banwart, S., & Benning, L. (2011). Tree‐mycorrhiza symbiosis accelerate mineral weathering: Evidences from nanometer‐scale elemental fluxes at the hypha–mineral interface. Geochimica et Cosmochimica Acta, 22, 6988–7005. Bormann, B.T., Wang, D., Bormann, F.H., Benoit, G., April, R., & Snyder, M.C. (1998). Rapid, plant‐induced weathering in an aggrading experimental ecosystem. Biogeochemistry, 43, 129–155. Brady, P.V., & Carroll, S.A. (1994). Direct effects of CO2 and temperature on silicate weathering: Possible implications for climate control. Geochimica et Cosmochimica Acta, 58, 1853–1856. Brantley, S.L. (2008). Kinetics of mineral dissolution. In S.L. Brantley, J.D. Kubicki, A.F. White (Eds.), Kinetics of water– rock interaction (pp. 151–210). New York: Springer Science & Business Media. Brantley, S.L., Eissenstat, D.M., Marshall, J.A., Godsey, S.E., Balogh‐Brunstad, Z., Karwan, D.L., et  al. (2017). Reviews and syntheses: on the roles trees play in building and plumbing the critical zone. Biogeosciences, 14, 5115–5142. Brantley, S.L., Megonigal, J.P., Scatena, F.N., Balogh‐Brunstad, Z., Barnes, R.T., Bruns, M.A., et al. (2011). Twelve testable hypotheses on the geobiology of weathering. Geobiology, 9, 140–165. Brault, M.‐O., Damon Matthews, H., & Mysak, L.A. (2017). The importance of terrestrial weathering changes in multimillennial recovery of the global carbon cycle: a two‐dimensional perspective. Earth System Dynamics, 8, 455–475.

Plants as Drivers of Rock Weathering  53 Bray, A.W., Oelkers, E.H., Bonneville, S., Wolff‐Boenisch, D., Potts, N.J., Fones, G., & Benning, L.G. (2015). The effect of pH, grain size, and organic ligands on biotite weathering rates. Geochimica et Cosmochimica Acta, 164, 127–145. Bruant, R.G.J., Giammar, D.E., Myneni, S.C.B., Peters, C.A., Gale, J., & Kaya, Y. (2003). Effect of pressure, temperature, and aqueous carbon dioxide concentration on mineral weathering as applied to geologic storage of carbon dioxide. Presented at the 6th International Conference on Greenhouse Gas Control Technologies (pp. 1609–1612). Oxford, UK: Pergamon. Brundrett, M.C. (2002). Coevolution of roots and mycorrhizas of land plants. New Phytologist, 154(2), 275–304. Brundrett, M.C. (2009). Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant and Soil, 320, 37–77. Burghelea, C., Zaharescu, D.G., Dontsova, K., Maier, R., Huxman, T., & Chorover, J. (2015). Mineral nutrient mobilization by plants from rock: influence of rock type and arbuscular mycorrhiza. Biogeochemistry, 124, 187–203. Burghelea, C.I., Dontsova, K., Zaharescu, D.G., Maier, R.M., Huxman, T., Amistadi, M.K., Hunt, E., & Chorover, J. (2018). Trace element mobilization during incipient bioweathering of four rock types. Geochimica et Cosmochimica Acta, 234, 98–114. Calvaruso, C., Mareschal, L., Turpault, M.‐P., & Leclerc, E. (2009). Rapid clay weathering in the rhizosphere of norway spruce and oak in an acid forest ecosystem. Soil Science Society of America Journal, 73, 331–338. Calvaruso, C., Turpault, M.‐P., & Frey‐Klett, P. (2006). Root‐ associated bacteria contribute to mineral weathering and to mineral nutrition in trees: a budgeting analysis. Applied and Environmental Microbiology, 72, 1258–1266. Calvaruso, C., Turpault, M.‐P., Frey‐Klett, P., Uroz, S., Pierret, M.‐C., Tosheva, Z., & Kies, A. (2013). Increase of apatite dissolution rate by Scots pine roots associated or not with Burkholderia glathei PML1(12)Rp in open‐system flow microcosms. Geochimica et Cosmochimica Acta, 106, 287–306. Calvaruso, C., Turpault, M.‐P., Leclerc, E., Ranger, J., Garbaye, J., Uroz, S., & Frey‐Klett, P. (2010). Influence of forest trees on the distribution of mineral weathering‐ associated bacterial communities of the Scleroderma citrinum mycorrhizosphere. Applied and Environmental Microbiology, 76, 4780–4787. Cama, J., & Ganor, J. (2006). The effects of organic acids on the dissolution of silicate minerals: A case study of oxalate catalysis of kaolinite dissolution. Geochimica et Cosmochimica Acta, 70, 2191–2209. Carminati, A., Zarebanadkouki, M., Kroener, E., Ahmed, M.A., & Holz, M. (2016). Biophysical rhizosphere processes affecting root water uptake. Annals of Botany, 118, 561–571. Chorover, J., & Amistadi, M.K. (2001). Reaction of forest floor organic matter at goethite, birnessite and smectite surfaces. Geochimica et Cosmochimica Acta, 65, 95–109. Cochran, M.F., & Berner, R.A. (1996). Promotion of chemical weathering by higher plants: field observations on Hawaiian basalts. Chemical Geology, 132, 71–77.

Collins, D.B.G., Bras, R.L., & Tucker, G.E. (2004). Modeling the effects of vegetation–erosion coupling on landscape evolution. Journal of Geophysical Research: Earth Surface, 109. https://doi.org/10.1029/2003JF000028 Dessert, C., Dupré, B., Gaillardet, J., François, L.M., & Allègre, C.J. (2003). Basalt weathering laws and the impact of basalt weathering on the global carbon cycle. Chemical Geology, 202, 257–273. Dijkstra, F.A., Van Breemen, N., Jongmans, A.G., Davies, G.R., & Likens, G.E. (2003). Calcium weathering in forested soils and the effect of different tree species. Biogeochemistry, 62, 253–275. Dixon, J.L., Hartshorn, A.S., Heimsath, A.M., DiBiase, R.A., & Whipple, K.X. (2012). Chemical weathering response to tectonic forcing: A soils perspective from the San Gabriel Mountains, California. Earth and Planetary Science Letters, 323–324, 40–49. Dixon, J.L., & von Blanckenburg, F. (2012). Soils as pacemakers and limiters of global silicate weathering. Comptes Rendus Geoscience, 344, 597–609. Dontsova, K., Zaharescu, D., Henderson, W., Verghese, S., Perdrial, N., Hunt, E., & Chorover, J. (2014). Impact of organic carbon on weathering and chemical denudation of granular basalt. Geochimica et Cosmochimica Acta, 139, 508–526. Drever, J.I., & Stillings, L.L. (1997). The role of organic acids in mineral weathering. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 120, 167–181. Dunne, T., Dietrich, W.E., & Brunengo, M.J. (1978). Recent and past erosion rates in semi‐arid Kenya. Zeitschrift für Geomorphologie, 29, 130–140. Durán Zuazo, V.H., Pleguezuelo, C.R.R., Francia Martínez, J.R., Martínez Raya, A., Arroyo Panadero, L., Càrceles Rodríguez, B., & Navarro Moll, M.C. (2008). Benefits of plant strips for sustainable mountain agriculture. Agronomy for Sustainable Development, 28, 497–505. Egerton‐Warburton, L.M., Querejeta, J.I., & Allen, M.F. (2007). Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants. Journal of Experimental Botany, 58, 1473–1483. Ehrlich, H.L. (1999). Microbes as geologic agents: Their role in mineral formation. Geomicrobiology Journal, 16, 135–153. Ericsson, T. (1995). Growth and shoot: root ratio of seedlings in relation to nutrient availability. Plant and Soil, 168, 205–214. Fenton, C.R., & Niedermann, S. (2014). Surface exposure dating of young basalts (1–200 ka) in the San Francisco volcanic field (Arizona, USA) using cosmogenic 3He and 21 Ne. Quaternary Geochronology, 19, 87–105. Ferrier, K.L., & Kirchner, J.W. (2008). Effects of physical erosion on chemical denudation rates: A numerical modeling study of soil‐mantled hillslopes. Earth and Planetary Science Letters, 272, 591–599. Finlay, R., Wallander, H., Smits, M., Holmstrom, S., van Hees, P., Lian, B., & Rosling, A. (2009). The role of fungi in biogenic weathering in boreal forest soils. Fungal Biology Reviews, 23, 101–106.

54  BIOGEOCHEMICAL CYCLES Fortner, S.K., Lyons, W.B., Carey, A.E., Shipitalo, M.J., Welch, S.A., & Welch, K.A. (2012). Silicate weathering and CO2 ­ consumption within agricultural landscapes, the Ohio–Tennessee River Basin, USA. Biogeosciences, 9, 941–955. Gabet, E.J., Reichman, O.J., & Seabloom, E.W. (2003). The effects of bioturbation on soil processes and sediment transport. Annual Review of Earth and Planetary Sciences, 31, 249–273. Gamfeldt, L., Snäll, T., Bagchi, R., Jonsson, M., Gustafsson, L., Kjellander, P., et al. (2013). Higher levels of multiple ecosystem services are found in forests with more tree species. Nature Communications, 4, 1340. Gazzè, S.A., Saccone, L., Smits, M.M., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdottir, K.V., McMaster, T.J. (2013). Nanoscale observations of extracellular polymeric substances deposition on phyllosilicates by an ectomycorrhizal fungus. Geomicrobiology Journal, 30, 721–730. Gazzè, S.A., Saccone, L., Vala, R.K., Smits, M.M., Duran, A.L., Leake, J.R., Banwart, S.A., McMaster, T.J. (2012). Nanoscale channels on ectomycorrhizal‐colonized chlorite: Evidence for plant‐driven fungal dissolution. Journal of Geophysical Research: Biogeosciences, 117(G3). G00N09. Gemma, J.N., Koske, R.E. (1990). Mycorrhizae in recent volcanic substrates in Hawaii. American Journal of Botany 77, 1193–1200. Gislason, S.R., Arnorsson, S., Armannsson, H. (1996). Chemical weathering of basalt in Southwest Iceland: Effects of runoff, age of rocks and vegetative/glacial cover. Am. J. Sci. 296, 837–907. Gislason, S.R., Oelkers, E.H., Eiriksdottir, E.S., Kardjilov, M.I., Gisladottir, G., Sigfusson, B., et al. (2008). The feedback between climate and weathering. Mineral Magazine, 72, 317–320. Golubev, S.V., Pokrovsky, O.S., & Schott, J. (2005). Experimental determination of the effect of dissolved CO2 on the dissolution kinetics of Mg and Ca silicates at 25°C. Chemical Geology, 217, 227–238. Gorshkov, A.I., Drits, V.A., Dubinina, G.A., Bogdanova, O.A., & Sivtsov, A.V. (1992). The role of bacterial activity in the formation of hydrothermal Fe–Mn‐formations in the northern part of the Lau Basin (south‐western part of the Pacific Ocean). Izvestiya ‐ Akademiya Nauk, Seriya Geologicheskaya, 9, 84–93. Graham, R.C., Rossi, A.M., & Hubbert, K.R. (2010). Rock to regolith conversion: Producing hospitable substrates for terrestrial ecosystems. GSA Today, 20, 4–9. Grandstaff, D.E. (1977). Some kinetics of bronzite orthopyroxene dissolution. Geochimica et Cosmochimica Acta, 41, 1097–1103. Gyssels, G., & Poesen, J. (2003). The importance of plant root characteristics in controlling concentrated flow erosion rates. Earth Surface Processes and Landforms, 28, 371–384. Hagerberg, D., Thelin, G., & Wallander, H. (2003). The production of ectomycorrhizal mycelium in forests: Relation between forest nutrient status and local mineral sources. Plant and Soil, 252, 279–290. Heimsath, A.M. (2014). Limits of soil production? Science, 343, 617–618.

Heimsath, A.M., Fink, D., Hancock, G.R. (2009). The “humped” soil production function: eroding Arnhem Land, Australia. Earth Surface Processes and Landforms, 34, 1674–1684. Heinonsalo, J., Hurme, K.‐R., & Sen, R. (2004). Recent 14C‐ labelled assimilate allocation to Scots pine seedling root and mycorrhizosphere compartments developed on reconstructed podzol humus, E‐ and B‐ mineral horizons. Plant and Soil, 259, 111–121. Hinsinger, P., Barros, O.N.F., Benedetti, M.F., Noack, Y., & Callot, G. (2001). Plant‐induced weathering of a basaltic rock: Experimental evidence. Geochimica et Cosmochimica Acta, 65, 137–152. Hoffland, E., Giesler, R., Jongmans, T., & Breemen, N.v. (2002). Increasing feldspar tunneling by fungi across a North Sweden podzol chronosequence. Ecosystems, 5, 11–22. Hoffland, E., Giesler, R., Jongmans, A.G., & Breemen, N.v. (2003). Feldspar tunneling by fungi along natural productivity gradients. Ecosystems, 6, 739–746. Jenny, H. (1941). The factors of soil formation. New York: McGrawHill. Johnson‐Maynard, J.L., Graham, R.C., Shouse, P.J., & Quideau, S.A. (2005). Base cation and silicon biogeochemistry under pine and scrub oak monocultures: implications for weathering rates. Geoderma, 126, 353–365. Johnson, M.O., Gloor, M., Kirkby, M.J., & Lloyd, J. (2014). Insights into biogeochemical cycling from a soil evolution model and long‐term chronosequences. Biogeosciences, 11, 6873– 6894. Jongmans, A.G., vanBreemen, N., Lundstrom, U., vanHees, P.A.W., Finlay, R.D., Srinivasan, M., et  al. (1997). Rock‐ eating fungi. Nature, 389, 682–683. Kantola, I.B., Masters, M.D., Beerling, D.J., Long, S.P., & DeLucia, E.H. (2017). Potential of global croplands and bioenergy crops for climate change mitigation through deployment for enhanced weathering. Biology Letters, 13. https:// doi.org/10.1098/rsbl.2016.0714 Kardjilov, M.I., Gíslason, S.R., & Gísladóttir, G. (2006). The effect of gross primary production, net primary production and net ecosystem exchange on the carbon fixation by chemical weathering of basalt in northeastern Iceland. Journal of Geochemical Exploration, 88, 292–295. Kawano, M., Tomita, K. (2001). Microbial biomineralization in weathered volcanic ash deposit and formation of biogenic minerals by experimental incubation. American Mineralogist, 86, 400–410. Ketchie, D.O., & Lopushinsky, W. (1981). Composition of root pressure exudate from conifers. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. Khan, N., Seshadri, B., Bolan, N., Saint, C.P., Kirkham, M.B., Chowdhury, S., et  al. (2016). Root iron plaque on wetland plants as a dynamic pool of nutrients and contaminants. In D.L. Sparks, (Ed.), Advances in agronomy (pp. 1–96). Academic Press. Knauss, K.G., Nguyen, S.N., & Weed, H.C. (1993). Diopside dissolution kinetics as a function of pH, CO2, temperature, and time. Geochimica et Cosmochimica Acta, 57, 285–294.

Plants as Drivers of Rock Weathering  55 Koele, N., Dickie, I.A., Blum, J.D., Gleason, J.D., & de Graaf, L. (2014). Ecological significance of mineral weathering in ectomycorrhizal and arbuscular mycorrhizal ecosystems from a field‐based comparison. Soil Biology and Biochemistry, 69, 63–70. Koele, N., & Hildebrand, E.E. (2008). The ecological significance of the coarse soil fraction for Picea abies (L.) Karst. seedling nutrition. Plant and Soil, 312, 163–174. Koele, N., Hildebrand, E.E., & Schack‐Kirchner, H. (2010). Effects of weathering state of coarse‐soil fragments on tree‐ seedling nutrient uptake. Journal of Plant Nutrition and Soil Science, 173, 245–251. Koele, N., Storch, F., & Hildebrand, E.E. (2011). The coarse‐ soil fraction is the main living space of fungal hyphae in the BhBs horizon of a Podzol. Journal of Plant Nutrition and Soil Science, 174, 750–753. Koele, N., Turpault, M.‐P., Hildebrand, E.E., Uroz, S., & Frey‐ Klett, P. (2009). Interactions between mycorrhizal fungi and mycorrhizosphere bacteria during mineral weathering: Budget analysis and bacterial quantification. Soil Biology and Biochemistry, 41, 1935–1942. Kohler, B., Singer, A., & Stoffers, P. (1994). Biogenic nontronite from marine white smoker chimneys. Clays and Clay Minerals, 42, 689–701. Kraemer, S.M., Crowley, D., & Kretzschmar, R. (2006). Siderophores in plant iron acquisition: Geochemical aspects. Advances in Agronomy, 91, 1–46. Lagache, M. (1965). Contribution a l’etude de l’alteration des feldspaths, dans l’eau, entre 100 et 200 C, sous diverses pressions de CO2, et application a la synthese des mineraux argileux. Bulletin de la Société Française de Minéralogie et de Cristallographie, 88, 223–253. Landeweert, R., Hoffland, E., Finlay, R.D., Kuyper, T.W., & van Breemen, N. (2001a. Linking plants to rocks: ectomycorrhizal fungi mobilize nutrients from minerals. Trends in Ecology and Evolution, 16, 248–254. Landeweert, R., Hoffland, E., Finlay, R.D., Kuyper, T.W., & van Breemen, N. (2001b. Linking plants to rocks: ectomycorrhizal fungi mobilize nutrients from minerals. Trends in Ecology and Evolution, 16, 248–254. Larsen, I.J., Almond, P.C., Eger, A., Stone, J.O., Montgomery, D.R., & Malcolm, B. (2014). Rapid soil production and weathering in the Western Alps, New Zealand. Science, 343(6171), 637–640. Leake, J.R., Donnelly, D.P., Saunders, E.M., Boddy, L., & Read, D.J. (2001). Rates and quantities of carbon flux to ectomycorrhizal mycelium following 14C pulse labeling of Pinus sylvestris seedlings: effects of litter patches and interaction with a wood‐decomposer fungus. Tree Physiology, 21, 71–82. Leake, J., Johnson, D., Donnelly, D., Muckle, G., Boddy, L., & Read, D. (2004). Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Canadian Journal of Botany, 82, 1016–1045. Leake, J.R., & Read, D.J. (2017). Mycorrhizal symbioses and pedogenesis throughout Earth’s history. In N.C. Johnson, C. Gehring, J. Jansa (Eds.), Mycorrhizal mediation of soil (pp. 9–33). Elsevier.

Levan, M.A., & Riha, S.J. (1986). The precipitation of black oxide coatings on flooded conifer roots of low internal porosity. Plant and Soil, 95, 33–42. Li, G., Hartmann, J., Derry, L.A., West, A.J., You, C.‐F., Long, X., et al. (2016). Temperature dependence of basalt weathering. Earth and Planetary Science Letters, 443, 59–69. Li, P., Bohnert, H.J., & Grene, R. (2007). All about FACE— plants in a high‐[CO2] world. Trends in Plant Science, 12. https://doi.org/10.1016/j.tplants.2007.01.003 Liermann, L.J., Kalinowski, B.E., Brantley, S.L., & Ferry, J.G. (2000). Role of bacterial siderophores in dissolution of hornblende. Geochimica et Cosmochimica Acta, 64, 587–602. Liu, Z.‐J., Weller, D.E., Correll, D.L., & Jordan, T.E. (2000). Effects of land cover and geology on stream chemistry in watersheds of Chesapeake Bay. Journal of the American Water Resources Association, 36, 1349–1365. Lucas, Y. (2001). The role of plants in controlling rates and products of weathering: Importance of biological pumping. Annual Review of Earth and Planetary Sciences, 29, 135–163. Lynch, J.M. (1990). The rhizosphere, Chichester, UK: J. Wiley & Sons. Lyons, W.B., & Bird, D.A. (1995). Geochemistry of the Madeira River, Brazil: comparison of seasonal weathering reactions using a mass balance approach. Journal of South American Earth Sciences, 8, 97–101. Markewitz, D., & Richter, D.D. (1998). The Bio in Aluminum and Silicon Geochemistry. Biogeochemistry, 42, 235–252. Marschner, H. (2002). Mineral nutrition of higher plants, 2nd edn. Academic Press. Marschner, H., Romheld, V., Horst, W.J., & Martin, P. (1986). Root‐induced changes in the rhizosphere: Importance for the mineral nutrition of plants. Zeitschrift für Pflanzenernährung und Bodenkunde, 149, 441–456. Matson, P. (1990). Plant–soil interactions in primary succession at Hawaii Volcanoes National Park. Oecologia, 85, 241–246. McCully, M.E., & Boyer, J.S. (1997). The expansion of maize root‐cap mucilage during hydration. 3. Changes in water potential and water content. Physiologia Plantarum, 99, 169–177. McNear Jr., D.H. (2013). The rhizosphere—roots, soil and everything in between. Nature Education Knowledge, 4(3), 1. Meheruna, A., & Akagi, T. (2006). Role of fine roots in the plant‐induced weathering of andesite for several plant species. Geochemical Journal, 40, 57–67. Meunier, J.D., Guntzer, F., Kirman, & S., Keller, C. (2008). Terrestrial plant‐Si and environmental changes. Mineral Magazine, 72, 263–267. Moulton, K.L., West, J., & Berner, R.A. (2000). Solute flux and mineral mass balance approaches to the quantification of plant effects on silicate weathering. American Journal of Science, 300, 539–570. Navarre‐Sitchler, A., & Thyne, G. (2007). Effects of carbon dioxide on mineral weathering rates at earth surface conditions. Chemical Geology, 243, 53–63. Naveed, M., Brown, L.K., Raffan, A.C., George, T.S., Bengough, A.G., Roose, T., et al. (2017). Plant exudates may stabilize or weaken soil depending on species, origin and time. European Journal of Soil Science, 68, 806–816.

56  BIOGEOCHEMICAL CYCLES Neaman, A., Chorover, J., & Brantley, S.L. (2005). Implications of the evolution of organic acid moieties for basalt weathering over geological time. American Journal of Science, 305, 147–185. Neaman, A., Chorover, J., & Brantley, S.L. (2006). Effects of organic ligands on granite dissolution in batch experiments at pH 6. American Journal of Science, 306, 451–473. Nezat, C.A., Blum, J.D., Klaue, A., Johnson, C.E., & Siccama, T.G. (2004). Influence of landscape position and vegetation on long‐term weathering rates at the Hubbard Brook Experimental Forest, New Hampshire, USA. Geochimica et Cosmochimica Acta, 68, 3065–3078. O’Reilly, S.E., Furukawa, Y., & Newell, S. (2006). Dissolution and microbial Fe(III) reduction of nontronite (NAu‐1). Chemical Geology, 235, 1–11. Ochs, M. (1996). Influence of humified and non‐humified natural organic compounds on mineral dissolution. Chemical Geology, 132, 119–124. Ochs, M., Brunner, I., Stumm, W., & Cosovic, B. (1993). Effects of root exudates and humic substances on weathering kinetics. Water, Air, and Soil Pollution, 68, 213–229. Oh, N.‐H., & Raymond, P.A. (2006). Contribution of agricultural liming to riverine bicarbonate export and CO2 sequestration in the Ohio River basin. Global Biogeochemical Cycles, 20, GB3012. Osthols, E., & Malmstrom, M. (1995). Dissolution kinetics of ThO2 in acids and carbonate media. Radiochimica Acta, 68, 113–119. Pagani, M., Caldeira, K., Berner, R., & Beerling, D.J. (2009). The role of terrestrial plants in limiting atmospheric CO2 decline over the past 24 million years. Nature, 460, 85–88. Pawlik, Ł., Phillips, J.D., & Šamonil, P. (2016). Roots, rock, and regolith: Biomechanical and biochemical weathering by trees and its impact on hillslopes—a critical literature review. Earth‐Science Reviews, 159, 142–159. Pawlik, Ł., & Šamonil, P. (2018). Biomechanical and biochemical effects recorded in the tree root zone—soil memory, historical contingency and soil evolution under trees. Plant Soil, 426, 109–134. Perdrial, J.N., Warr, L.N., Perdrial, N., Lett, M.‐C., & Elsass, F. (2009). Interaction between smectite and bacteria: implications for bentonite as backfill material in the nuclear waste disposal. Chemical Geology, 264, 281–294. Phillips, J.D., Turkington, A.V., & Marion, D.A. (2008). Weathering and vegetation effects in early stages of soil formation. Catena, 72, 21–28. Plamboeck, A.H., Dawson, T.E., Egerton‐Warburton, L.M., North, M., Bruns, T.D., & Querejeta, J.I. (2007). Water transfer via ectomycorrhizal fungal hyphae to conifer seedlings. Mycorrhiza, 17, 439–447. Quideau, S.A., Chadwick, O.A., Graham, R.C., & Wood, H.B. (1996). Base cation biogeochemistry and weathering under oak and pine: a controlled long‐term experiment. Biogeochemistry, 35, 377–398 Quirk, J., Andrews, M.Y., Leake, J.R., Banwart, S.A., Beerling, D.J. (2014). Ectomycorrhizal fungi and past high CO2 atmospheres enhance mineral weathering through increased below‐ground carbon‐energy fluxes. Biology Letters, 10. https://doi.org/10.1098/rsbl.2014.0375

Quirk, J., Beerling, D.J., Banwart, S.A., Kakonyi, G., Romero‐ Gonzalez, M.E., & Leake, J.R. (2012). Evolution of trees and mycorrhizal fungi intensifies silicate mineral weathering. Biology Letters, 8, 1006–1011. Quirk, J., Leake, J.R., Banwart, S.A., Taylor, L.L., & Beerling, D.J. (2014). Weathering by tree‐root‐associating fungi diminishes under simulated Cenozoic atmospheric CO2 decline. Biogeosciences, 11, 321–331. Raich, J.W., Russell, A.E., Crews, T.E., Farrington, H., & Vitousek, P.M. (1996). Both nitrogen and phosphorus limit plant production on young Hawaiian lava flows. Biogeochemistry, 32, 1–14. Raymond, P.A. (2017). Temperature versus hydrologic controls of chemical weathering fluxes from United States forests. Chemical Geology, 458, 1–13. Raymond, P.A., & Cole, J.J. (2003). Increase in the export of alkalinity from North America’s largest river. Science, 301, 88–91. Raymond, P.A., & Hamilton, S.K. (2018). Anthropogenic influences on riverine fluxes of dissolved inorganic carbon to the oceans. Limnology and Oceanography Letters, 3(3), 143–155. Raymond, P.A., Oh, N.‐H., Turner, R.E., & Broussard, W. (2008). Anthropogenically enhanced fluxes of water and carbon from the Mississippi River. Nature, 451, 449–452. Reicosky, D.C., Gesch, R.W., Wagner, S.W., Gilbert, R.A., Wente, C.D., & Morris, D.R. (2008). Tillage and wind effects on soil CO2 concentrations in muck soils. Soil Tillage Research, 99, 221–231. Remiszewski, K.A., Bryce, J.G., Fahnestock, M.F., Pettitt, E.A., Blichert‐Toft, J., Vadeboncoeur, M.A., & Bailey, S.W. (2016). Elemental and isotopic perspectives on the impact of arbuscular mycorrhizal and ectomycorrhizal fungi on mineral weathering across imposed geologic gradients. Chemical Geology, 445, 164–171. Riebe, C.S., Kirchner, J.W., & Finkel, R.C. (2004). Erosional and climatic effects on long‐term chemical weathering rates in granitic landscapes spanning diverse climate regimes. Earth and Planetary Science Letters, 224, 547–562. Riotte, J., Ruiz, L., Audry, S., Sekhar, M., Mohan Kumar, M.S., Siva Soumya, B., & Braun, J.J. (2014). Impact of vegetation and decennial rainfall fluctuations on the weathering fluxes exported from a dry tropical forest (Mule Hole). Procedia Earth and Planetary Science, 10, 34–37. Roelandt, C., Goddéris, Y., Bonnet, M.‐P., & Sondag, F. (2010). Coupled modeling of biospheric and chemical weathering processes at the continental scale. Global Biogeochemical Cycles, 24. https://doi.org/10.1029/2008GB003420 Roering, J.J., Marshall, J., Booth, A.M., Mort, M., & Jin, Q. (2010). Evidence for biotic controls on topography and soil production. Earth and Planetary Science Letters, 298, 183–190. Römheld, V. (1991). The role of phytosiderophores in acquisition of iron and other micronutrients in graminaceous species: An ecological approach. Plant and Soil, 130, 127–134. Rosenberg, D.R., & Maurice, P.A. (2003). Siderophore adsorption to and dissolution of kaolinite at pH 3 to 7 and 22°C. Geochimica et Cosmochimica Acta, 67, 223–229. Rosenstock, N.P., Berner, C., Smits, M.M., Krám, P., & Wallander, H. (2016). The role of phosphorus, magnesium and potassium availability in soil fungal exploration of

Plants as Drivers of Rock Weathering  57 ­ ineral nutrient sources in Norway spruce forests. New m Phytologist, 211, 542–553. Rosling, A., Lindahl, B.D., Finlay, R.D. (2004). Carbon allocation to ectomycorrhizal roots and mycelium colonising different mineral substrates. New Phytologist, 162, 795–802. Saccone, L., Gazzè, S.A., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdóttir, K.V., Smits, M.M., & McMaster, T.J. (2012). High resolution characterization of ectomycorrhizal fungal‐mineral interactions in axenic microcosm experiments. Biogeochemistry, 111, 411–425. Samuels, T., Bryce, C., Landenmark, H., Loudon, C., Nicholson, N., Stevens, A., Cockell, C. (2019). Microbial weathering of minerals and rocks in natural environments. In: Dontsova, K., Balogh‐Brunstad, Z., Le Roux, G. (Eds.). Biogeochemical Cycles: Ecological Drivers and Environmental Impact. Wiley. Schmalenberger, A., Duran, A.L., Bray, A.W., Bridge, J., Bonneville, S., Benning, L.G., et al. (2015). Oxalate secretion by ectomycorrhizal Paxillus involutus is mineral‐specific and controls calcium weathering from minerals. Nature Sientific Reports, 5(12187). Schreiner, O., & Reed, H.S. (1909). Studies on the oxidizing power of roots. Botanical Gazette, 47, 355–388. Schroth, A.W., Friedland, A.J., Bostick, B.C. (2007). Macronutrient depletion and redistribution in soils under conifer and northern hardwood forests. Soil Science Society of America Journal, 71, 457–468. Schwartzman, D.W. (2013). The geobiology of weathering: a 13th hypothesis. http://arxiv.org/ftp/arxiv/papers/1509/ 1509.04234.pdf Shi, Z., Balogh‐Brunstad, Z., Grant, M., Harsh, J., Gill, R., Thomashow, L., et al. (2014). Cation uptake and allocation by red pine seedlings under cation‐nutrient stress in a column growth experiment. Plant Soil, 378, 83–98. Smith, S.E., & Read, D. (2008). Mycorrhizal symbiosis, 3rd edn (pp. 145–187). London: Academic Press. Smits, M.M., Bonneville, S., Benning, L.G., Banwart, S.A., & Leake, J.R. (2012). Plant‐driven weathering of apatite – the role of an ectomycorrhizal fungus. Geobiology, 10, 445–456. Smits, M.M., Hoffland, E., Jongmans, A.G., & van Breemen, N. (2005). Contribution of mineral tunneling to total feldspar weathering. Geoderma, 125, 59–69. Smits, M.M., Johansson, L., & Wallander, H. (2014). Soil fungi appear to have a retarding rather than a stimulating role on soil apatite weathering. Plant and Soil, 385, 217–228. Smits, M.M., & Wallander, H. (2017). Role of mycorrhizal symbiosis in mineral weathering and nutrient mining from soil parent material. In N.C. Johnson, C. Gehring, J. Jansa (Eds.), Mycorrhizal mediation of soil (pp. 35–46). Elsevier. Song, Z., Zhao, S., Zhang, Y., Hu, G., Cao, Z., & Wong, M. (2011). Plant impact on CO2 consumption by silicate weathering: The role of bamboo. The Botanical Review, 77, 208–213. Stallard, R.F., & Edmond, J.M. (1987). Geochemistry of the Amazon: 3. Weathering chemistry and limits to dissolved inputs. Journal of Geophysical Research: Oceans, 92, 8293–8302. Steefel, C.I. (2008). Geochemical kinetics and transport. In S.L. Brantley, J.D. Kubicki, A.F. White (Eds.), Kinetics of water– rock interaction (pp. 545–589). New York: Springer Science & Business Media.

Steefel, C.I., DePaolo, D.J., & Lichtner, P.C. (2005). Reactive transport modeling: An essential tool and a new research approach for the Earth sciences. Earth and Planetary Science Letters, 240, 539‐ 558. Stephens, J.C., & Hering, J.G. (2004). Factors affecting the dissolution kinetics of volcanic ash soils: dependencies on pH, CO2, and oxalate. Applied Geochemistry, 19, 1217–1232. Stotzky, G., & Pramer, D. (1972). Activity, ecology, and population dynamics of microorganisms in soil. Critical Reviews in Microbiology, 2, 59–137. Sverdrup, H. (2009). Chemical weathering of soil minerals and the role of biological processes. Fungal Biology Reviews, 23, 94–100. Taylor, L.L., Banwart, S.A., Valdes, P.J., Leake, J.R., Beerling, D.J. (2012). Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global‐scale process‐based approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 565–582. Taylor, L.L., Leake, J.R., Quirk, J., Hardy, K., Banwart, S.A., & Beerling, D.J. (2009). Biological weathering and the long‐ term carbon cycle: integrating mycorrhizal evolution and function into the current paradigm. Geobiology, 7, 171–191. Terhune, C.L., Harden, J.W. (1991). Seasonal variations of carbon dioxide concentrations in stony, coarse‐textured desert soils of Southern Nevada, USA. Soil Science, 151. Thorley, R.M.S., Taylor, L.L., Banwart, S.A., Leake, J.R., & Beerling, D.J. (2015). The role of forest trees and their mycorrhizal fungi in carbonate rock weathering and its significance for global carbon cycling. Plant, Cell, and Environment, 38, 1947–1961. Torres Acosta, V., Schildgen, T.F., Clarke, B.A., Scherler, D., Bookhagen, B., Wittmann, H., von Blanckenburg, F., & Strecker, M.R. (2015). Effect of vegetation cover on millennial‐scale landscape denudation rates in East Africa. Lithosphere, 7, 408–420. Traoré, O., Groleau‐Renaud, V., Plantureux, S., Tubeileh, A., & Boeuf‐Tremblay, V. (2000). Effect of root mucilage and modelled root exudates on soil structure. European Journal of Soil Science, 51, 575–581. Turner, B.F., White, A.F., & Brantley, S.L. (2010). Effects of temperature on silicate weathering: Solute fluxes and chemical weathering in a temperate rain forest watershed, Jamieson Creek, British Columbia. Chemical Geology, 269, 62–78. Turpault, M.‐P., Nys, C., & Calvaruso, C. (2009). Rhizosphere impact on the dissolution of test minerals in a forest ecosystem. Geoderma, 153, 147–154. Uhlig, D., Schuessler, J.A., Bouchez, J., Dixon, J.L., & von Blanckenburg, F. (2017). Quantifying nutrient uptake as driver of rock weathering in forest ecosystems by magnesium stable isotopes. Biogeosciences, 14, 3111–3128. van Breemen, N., Finlay, R., Lundstrom, U., Jongmans, A.G., Giesler, R., & Olsson, M. (2000). Mycorrhizal weathering: A true case of mineral plant nutrition? Biogeochemistry, 49, 53–67. van Breemen, N., Lundström, U.S., & Jongmans, A.G. (2000). Do plants drive podzolization via rock‐eating mycorrhizal fungi? Geoderma, 94, 163–171.

58  BIOGEOCHEMICAL CYCLES van der Heijden, M.G.A., Martin, F.M., Selosse, M.‐A., & Sanders, I.R. (2015). Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytologist, 205, 1406–1423. van Hees, P.A.W., Lundström, U.S., & Mörth, C.M. (2002). Dissolution of microcline and labradorite in a forest O horizon extract: the effect of naturally occurring organic acids Chemical Geology, 189, 199–211. Van Schöll, L., Hoffland, E., & Van Breemen, N. (2006). Organic anion exudation by ectomycorrhizal fungi and Pinus sylvestris in response to nutrient deficiencies. New Phytologist, 170, 153–163. Van Schöll, L., Smits, M.M., & Hoffland, E. (2006). Ectomycorrhizal weathering of the soil minerals muscovite and hornblende. New Phytologist, 171, 805–814. Violante, A., Barberis, E., Pigna, M., & Boero, V. (2003). Factors affecting the formation, nature, and properties of iron precipitation products at the soil–root interface. Journal of Plant Nutrition, 26, 1889–1908. Violante, A., & Glanfreda, L. (2000). Role of biomolecules in the formation and reactivity toward nutrients and organics of variable charge minerals and organomineral complexes in soil environment. In J.‐M. Bollag, G. Stotzky (Eds.), Soil biochemistry (Vol. 10, pp. 207–270). New York: Marcel Dekker. Vitousek, P.M., Chadwick, O.A., Crews, T.E., Fownes, J.H., Hendricks, D.M., & Herbert, D. (1997). Soil and ecosystem development across the Hawaiian Islands. GSA Today, 7, 1–8. Vitousek, P.M., Walker, L.R., Whiteaker, L.D., & Matson, P.A. (1993). Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biogeochemistry, 23, 197–215. Wainwright, J., Parsons, A.J., & Abrahams, A.D. (2000). Plot‐ scale studies of vegetation, overland flow and erosion interactions: case studies from Arizona and New Mexico. Hydrological Processes, 14, 2921–2943. Wallander, H. (2000). Uptake of P from apatite by Pinus sylvestris seedlings colonised by different ectomycorrhizal fungi. Plant and Soil, 218, 249–256. Wallander, H., Arnebrant, K., Östrand, F., & Kårén, O. (1997). Uptake of 15N‐labelled alanine, ammonium and nitrate in Pinus sylvestris L. ectomycorrhiza growing in forest soil treated with nitrogen, sulphur or lime. Plant and Soil, 195, 329–338. Wallander, H., & Hagerberg, D. (2004). Do ectomycorrhizal fungi have a significant role in weathering of minerals in forest soil? Symbiosis, 37, 249–257.

Wallander, H., Nilsson, L.O., Hagerberg, D., & Bååth, E. (2001). Estimation of the biomass and seasonal growth of external mycelium of ectomycorrhizal fungi in the field. New Phytologist, 151, 753–760. Warren, J.M., Brooks, J.R., Meinzer, F.C., & Eberhart, J.L. (2008). Hydraulic redistribution of water from Pinus ponderosa trees to seedlings: evidence for an ectomycorrhizal pathway. New Phytologist, 178, 382–394. Welch, S.A., & Ullman, W.J. (1993). The effect of organic acids on plagioclase dissolution rates and stoichiometry. Geochimica et Cosmochimica Acta, 57, 2725–2736. Welch, S.A., & Ullman, W.J. (1996). Feldspar dissolution in acidic and organic solutions: Compositional and pH dependence of dissolution rate. Geochimica et Cosmochimica Acta, 60, 2939–2948. Welch, S.A., & Ullman, W.J. (1999). The effect of microbial glucose metabolism on bytownite feldspar dissolution rates between 5° and 35°C. Geochimica et Cosmochimica Acta, 63, 3247–3259. Weller, D.E., Jordan, T.E., Correll, D.L., & Liu, Z.‐J. (2003). Effects of land‐use change on nutrient discharges from the Patuxent River Watershed. Estuaries, 26, 244–266. Wendling, L.A., Harsh, J.B., Ward, T.E., Palmer, C.D., Hamilton, M.A., Boyle, J.S., & Flury, M. (2005). Cesium desorption from illite as affected by exudates from rhizosphere bacteria. Environmental Science and Technology, 39, 4505–4512. White, A.F., & Blum, A.E. (1995). Effects of climate on chemical weathering in watersheds. Geochimica et Cosmochimica Acta, 59, 1729–1747. Wieland, E., Wehrli, B., & Stumm, W. (1988). The coordination chemistry of weathering: III. A generalization on the dissolution rates of minerals Geochimica et Cosmochimica Acta, 52, 1969–1981. Winnick, M.J., & Maher, K. (2018). Relationships between CO2, thermodynamic limits on silicate weathering, and the strength of the silicate weathering feedback. Earth and Planetary Science Letters, 485, 111–120. Wogelius, R.A., & Walther, J.V. (1991). Olivine dissolution at 25°C: Effects of pH, CO2, and organic acids. Geochimica et Cosmochimica Acta, 55, 943–954. Zaharescu, D.G., Burghelea, C.I., Dontsova, K., Presler, J.K., Maier, R.M., Huxman, T., et al. (2017). Ecosystem composition controls the fate of rare earth elements during incipient soil genesis. Nature Scientific Reports, 7, 43208. Zeebe, R.E., & Caldeira, K. (2008). Close mass balance of long‐ term carbon fluxes from ice‐core CO2 and ocean chemistry records. Nature Geoscience, 1, 312.

3 Microbial Weathering of Minerals and Rocks in Natural Environments Toby Samuels1,2, Casey Bryce3, Hanna Landenmark1, Claire Marie-Loudon1, Natasha Nicholson1, Adam H. Stevens1, and Charles Cockell1

ABSTRACT Microbes are active agents of environmental change. From the depths of the Earth’s crust to the heights of the upper atmosphere, microorganisms alter the physicochemical conditions surrounding them. Their activity provides important ecosystem services to the wider biosphere, making essential elements such as carbon, sulfur, phosphorus, and iron available for higher organisms. One such environmental interaction is the weathering of minerals and rocks by microbial communities, a key process that underpins soil formation and global biogeochemical cycles. By facilitating mineral dissolution and rock degradation, microbes enhance the release of elements from their geological reservoirs and perform significant elemental transformations. Under what conditions microbes perform these activities and to what extent they impact their surrounding environment, are key topics in geobiology. In this chapter, various aspects of microbial mineral and rock weathering will be explored. Key concepts and terminology will be introduced, followed by an overview of the mechanisms used by microorganisms to perform weathering activity. Sections covering the methodological approaches used by researchers to study microbial rock weathering processes, including detecting the traces or “biosignatures” such microbial activity leaves behind on geological materials, will be provided. The effect of microbial rock weathering on the wider microbial ecosystem, including the endurance and functional capacity of microbial communities, is also explored. Finally, this chapter will discuss the emerging field of microbial biogeomorphology, the study of how biological activity at the micron scale can impact the environment at meter to kilometer scales, contributing to the processes that shape Earth’s landscapes.

3.1. INTRODUCTION

how Beggiatoa alba (a Gammaproteobacteria) oxidized hydrogen sulfide gas to form intracellular granules of sulfur and aqueous sulfuric acid, providing B. alba with energy to sustain its metabolism and growth (Dworkin & Gutnick, 2012). It is for this work, along with his ­distinguished career, that many consider him to be one of the founders of a range of scientific fields, including biogeochemistry and microbial ecology.

Sergei Winogradsky, a Ukrainian scientist working at the University of Strasbourg, is attributed with documenting the first example of chemolithotrophy (chemo— chemical, lith—stone, troph—feed) in 1887. He described 1  UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK 2   Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK 3  Geomicrobiology Group, Centre for Applied Geoscience, University of Tübingen, Tübingen, Germany

“…it suddenly occurred to me that sulfur might be ­oxidized by Beggiatoa to sulfuric acid. I could at once appreciate all the significance and implications of my conjecture… The work was humdrum, it dragged on and on sluggishly, and

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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60  BIOGEOCHEMICAL CYCLES all of a sudden it developed into an interesting result and was finished…Even so, I could not see that my discovery would become an epoch‐making discovery.” Memoirs of Sergei Winogradsky (Zavarzin, 1989).

Today, the scientific community has come a long way in understanding the sulfur cycle and how it forms part of a much larger global network of biogeochemical cycles that supports all life on Earth. Microbes play a key role in the cycling of numerous elements such as carbon, sulfur, phosphorus, and iron, supplying the biosphere with essential nutrients for metabolism (Falkowski et  al., 2008). Microbial mineral and rock weathering is an important component of these cycles (Ehrlich, 1996), liberating these elements from their geological reservoirs so that they can be made available for biological uptake (Uroz et al., 2009). Weathering is the breakdown or dissolution of minerals and rocks at the Earth’s surface, via a range of physical, chemical, and biological processes (Nichols, 2009). It  should be distinguished from erosion, which is the transport of broken material from the source to another location. Microbial weathering contributes to biological weathering (which includes the activity of other organisms, e.g., plants), and includes the growth and activity of all major groups of microorganisms including both prokaryotes (bacteria and archaea) and eukaryotes (microscopic fungi and algal species) (Brantley et  al., 2012; Ehrlich et al., 2015). This chapter starts by providing an overview on the numerous approaches that can be used to investigate microbial rock weathering activity, and gives a perspective on why that activity is a useful, and often necessary, adaptation for survival (section 3.2). Section 3.3 covers the various known mechanisms used by microbes to weather rocks and minerals and section 3.4 reviews of the methodologies that can be used to study these mechanisms and their impacts on geological environments. Microbial communities can be found in almost every environment on Earth, including extreme environments that are uninhabitable to other forms of life (Cockell et al., 2016). The oligotrophic (nutrient deplete) nature of many extreme environments, such as caves and the deep biosphere that have limited influx of photosynthetic carbon, require microbial communities to use rock weathering activity to access nutrients or produce energy (autolithotrophy) that would otherwise be unavailable (Tebo et al., 2015). Rock weathering microbes therefore act as producers within these microbial ecosystems, supporting phylogenetically diverse communities that carry out a range of elemental cycling processes (Fullerton et al., 2017; Kelly et al., 2010). Exploring the various interspecies interactions and connected biogeochemical cycles of such communities is the focus of section 3.5. The products of microbial rock and mineral weathering, along with the extensive elemental transformations that accompany it, leave unique traces in both contemporary geological environments and in the geological record. These traces, or

biosignatures, of microbial rock and mineral weathering activity are the topic of section 3.6. An increasing number of studies in the field of microbial biogeomorphology (section 3.7) are also starting to highlight the role of microbial rock weathering communities in the shaping of a wider range of geological environments, including caves and coastal areas (Coombes et al., 2015; Engel et al., 2004; Phillips, 2016; Viles, 2012). This chapter aims to introduce the reader to the core concepts of microbial weathering of minerals and rocks in natural environments, from the mechanisms that operate at the micron scale to their effects that directly alter and shape the geological landscape of this planet. 3.2. CONCEPTS IN MICROBIAL WEATHERING STUDIES 3.2.1. Approaches to Investigate Microbial Rock Weathering The study of microbial rock weathering can be surmised, at a fundamental level, as a series of interactions between microorganisms and geological materials. Within natural rock weathering systems, a complex web of these interactions occur due to the diversity of microbial species present within a geological environment comprised of numerous mineral and rock types (Uroz et  al., 2015). Many studies choose to investigate microbial rock weathering at this whole‐ environment scale, attempting to determine the numerous effects an in situ microbial community has on a specific rock type (Olsson‐Francis et al., 2016) or even an entire rock profile (J. Li et al., 2014). Such studies provide a comprehensive overview of their chosen environment, identifying the numerous types of interactions that could be contributing to weathering activity. However, they often fail to directly demonstrate the actual activity of individual interactions, rather inferring that activity based upon the presence of individual microbial species and geological components that have been previously demonstrated to interact (J. Li et  al., 2014). As directly demonstrating that a specific microbial activity is occurring in situ can be technically challenging, many studies provide supporting evidence that the microbial species has the potential to perform this activity, through tests such as enzyme assays (Liermann et al., 2015) or identification of the key genes involved (Berlendis et al., 2014). An alternative and commonly used approach to investigate microbial rock weathering is to reduce the complexity of the study system to a single interaction, or a subset of the total interactions believed to be active within that system. Biological complexity can be reduced, for example, by determining the rock weathering activity of a single microbial species (Bryce et al., 2016) or a defined mixture of microbial isolates (Matlakowska et al., 2012); geological complexity can be reduced by examining microbial weathering activity on individual minerals rather than the

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whole rock (Kelly et al., 2016). The greatest level of simplification focuses on the interaction of a single microbial species with a single mineral, in which precise mechanisms behind the weathering activity can be studied in detail. The interaction of Acidithiobacillus ferrooxidans with pyrite, where the bacterium oxidizes the iron within the mineral to obtain energy (chemolithoautotrophy), is an example of such an interaction (Mielke et  al., 2003). Bacterial iron oxidation within pyrite has been studied extensively, both in naturally occurring and anthropogenic environments, and the microbial mechanisms of weathering and the adaptive advantage of the activity are well understood (reviewed in Vera et  al., 2013). An advantage of these “reduced‐complexity” over “whole‐environment” studies is that they are experimentally easier to constrain, allowing individual interactions to be studied to greater depth (Z. Li et al., 2016; Uroz & Frey‐Klett 2011). However, the extent of complexity reduction within a study system limits the application of the findings back to understanding microbial rock weathering in the natural environment. Both laboratory (in vitro) and field studies (in situ) can be used to study microbial rock weathering interactions at varying levels of complexity. Naturally occurring in situ communities within a field site can be used to study community interactions with a mineral or rock (Zhu & Reinfelder, 2012), or a microbial community could be enriched in a growth medium within a laboratory setting (Sonnlietner et al., 2011). Studies investigating the interaction of a single species with a mineral or rock tend to be confined to laboratory work, where the exclusion of other microbial species within the experimental setup can be controlled (Zhao et al., 2013). The use of approaches outlined above has enabled the identification of a large number of microbial rock weathering mechanisms across various microbial species and mineral/ rock types. A review of these mechanisms is provided in section  3.3, but excellent reviews can also be found in Uroz et al. (2009) and Gadd (2010). A few mechanisms have been studied far more extensively than others, such as microbial pyrite oxidation or quartz dissolution, often due to the economic value that can be gained in understanding them (Vera et  al., 2013) or their relative importance in natural environments (Bennett et  al., 2001). However, for many other microbial weathering mechanisms, a lack of extensive research or difficulties in studying the interactions involved means that the factors constraining them are not fully understood, such as for the microbial degradation of sedimentary organic matter (Berlendis et al., 2014). 3.2.2. Microbial Rock Weathering Activity—an Adaptation for Survival? Microbial rock weathering activity is often primarily considered from a geological perspective—the impact of the microbial activity on mineral deposits and rocks.

This perspective is useful when attempting to understand how biology can alter the environment, important for understanding processes such as acid mine drainage (Méndez‐Garcia et  al., 2015) or for how microbial rock weathering activity can be used for human purposes (Watling, 2016). However, weathering activity can also be viewed from the perspective of the microbe—what is the advantage of performing weathering activity for the microbial species involved? For most weathering mechanisms involving redox reactions (mainly with metals), the activity provides metabolic energy to support growth, as with A. ferrooxidans and the oxidation of iron within pyrite (Vera et al., 2013). In other cases, such as the release of chelating agents (e.g., organic acids), weathering activity leaches minerals and rocks releasing nutrients such as phosphates, which subsequently can be used to support growth (Rogers & Bennett, 2004). When considering individual mechanisms within a weathering activity, it is useful to consider whether they are active or passive, i.e., whether they require direct energy expenditure, or whether they are a by‐ product of another energy‐consuming process (Uroz et  al., 2009). This consideration allows greater comprehension of the conditions under which an activity may be carried out and the factors that could constrain it. For example, an active weathering mechanism of an organism is more likely to be a necessary requirement for growth, and therefore the presence of that organism within an environment is likely to be concurrent with evidence for that activity. In contrast, a passive mechanism might only be carried out under specific environmental conditions (Phillips, 2016; Uroz et  al., 2009). Understanding this distinction is necessary to fully comprehend the implication of identifying microbial species within rock weathering environments. Identification of obligate autotrophic iron‐oxidizing bacteria within a rock profile is considered indicative of iron oxidizing (and potentially rock weathering) activity—these species cannot maintain metabolic activity without oxidizing iron (J. Li et al., 2014; Liermann et al., 2015; Yesavage et al., 2012). In contrast, the identification of heterotrophic bacteria within a weathered rock profile is not as informative, as the rock weathering activity of such species is known to be dependent upon nutrient availability and environmental conditions (Akers & Magee, 1985). 3.3. MECHANISMS OF MINERAL AND ROCK WEATHERING There is a diversity of mechanisms used by microbes to weather rocks and minerals, all of which vary in their specificity and their efficacy in different geological substrates. It can be useful to break these mechanisms into

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categories, so that the interactions between them can be better considered (Gadd, 2010; Uroz et  al., 2009). Alteration of the pH surrounding the mineral, at either a microscopic or bulk‐solution scale, is one of the main microbial contributions to mineral and rock weathering (Drever & Stillings, 1997). Chelation, the removal of elements (particularly heavy metals) from within a mineral matrix or a bulk rock, is another important weathering process (Liermann et al., 1999; Włodarczyk et al., 2015). A third category consists of redox reactions involved in weathering that directly oxidize or reduce a specific element within a mineral. Redox weathering generally results in a mineral transformation, with the primary mineral containing the redox element being dissolved and the constituent elements or compounds forming new mineral products (Barker et al., 1998; Gadd, 2010). A  final group of mechanisms covered in this chapter focus on the weathering of geological organic matter, which is abundant in some sedimentary rocks such as shales. A summary of microbial weathering systems can be found in Figure 3.1. 3.3.1. Altering pH—Proton Promoted Dissolution

Fresh zone

Weathered zone

Air/solution

The growth and activity of microorganisms can substantially alter the pH of their surrounding environment. Although both pH increases and decreases can enhance

weathering processes, the production of acidity through the release of both organic (including biopolymers) and inorganic acids is the most significant contribution (Konhauser et al., 2006). Organic acids, sometimes specified to be low molecular weight organic acids (see Thorley et al., 2015), are often produced as a by‐product of cellular metabolism, oxidizing carbon sources to form organic acids such as citrate and oxalate. Other organic acids are actively synthesized, such as amino acids or phenolic acids, but their primary purpose is generally not weathering related. Regardless, the release of these acids into the surrounding environment can make a direct contribution to mineral dissolution and rock weathering. When considering organic acids as agents of proton promoted dissolution, they can act via several mechanisms to weather minerals and rocks. Acid‐derived protons can cleave bonds such as siloxane (Si–O–Si) or aluminosilicate bonds (Al–O–Si) in silicates, weakening the mineral matrix that these bonds support and facilitating dissolution. This process is commonly referred to as acidolysis, and in many weathering environments is believed to be the most effective microbial contribution to weathering processes (Gadd, 2010). Furthermore, these protons can displace metal cations (such as K, Al, and Fe) within aluminosilicate mineral matrices, releasing them into the surrounding solution. These displacing

Elemental/compound leaching

Photoautotrophs

CO2 release/fixation All weathering organisms including chemoheterotrophs

Siderophores

Carbonate ions Increase alkalinity

Organic acids chelate metals Increase acidity, Catabolising enzymes chelate metals

Mineral dissolution

Autolithotrophs

Solubilise elements/compounds

Breakdown of organic compounds

Secondary mineral formation

Biocatalysts Facilitate redox reactions

Solubilisation

Figure 3.1 A summary of important microbial weathering mechanisms and their impact on geological substrates. Different microbial groups are present on the interface between air or water and a substrate, with the grey layer indicating the weathered zone of the substrate. Beneath this is the weathering front, where fresh rock below (black layer) is being actively weathered and releasing weathering products. Labels within the weathered zone are examples of microbial weathering mechanisms, while the white labeling within the fresh zone are the weathering processes that these mechanisms contribute to. Labels in the air/solution zone are weathering products that are released from the rock through the weathered zone into the surrounding environment or absorbed in the case of carbon fixation.

Microbial Weathering of Minerals and Rocks in Natural Environments  63

protons are subsequently oxidized to form hydroxyl ions or water, which diffuse out of the matrix, leaving a gap (Bennett, 1991; Bennett et al., 2001). Microbially produced organic acids are found ubiquitously in soils (typically 1–50 μM), with oxalic acid (COOH)2 being the most abundant (D.L. Jones et al., 2003). Fungi are important producers of oxalate within soil environments (Gadd et al., 2014), with ectomycorrhizal species (fungi in symbiosis with plants at root surfaces) associated with trees being highlighted as important weathering agents in calcareous soils (Thorley et al., 2015). Recent work has shown that the ectomycorrhizal fungus Paxillus involutus has mineral‐specific release rates of oxalate, dependent upon the geological environment (Schmalenberger et al., 2015). Fungal hyphae growing on calcium rich rocks (limestone, gabbro) released more acid (rock surface < pH 5) compared to those grown on rocks with a low calcium content (rock surface > pH 6) such as granite. Furthermore, oxalate and calcium accumulation was greater on gabbro and limestone compared to granite, indicating that the lowered pH caused by oxalate production had resulted in enhanced leaching of calcium. The authors hypothesize that as 95% of phosphorus in the Earth’s crust is associated with calcium, preferential weathering of calcium-bearing rocks enhances the bioavailability of this element. Ectomycorrhiza supply ­ phosphorus to their host plant in return for photosynthesized organic carbon, so this calcium‐specific release of oxalate is likely to be a specialized adaptation of this symbiosis (Schmalenberger et al., 2015). Organic acids form only one part of biologically sourced acidity within a weathering environment. The release of respiratory CO2 and the subsequent formation of carbonic acid, the production of acidic exopolymeric substances (EPS), and the release of inorganic acids from redox reactions (e.g., sulfuric and nitric acids) are all significant drivers of pH alteration. Almost all microbial species produce and excrete EPS onto their substrate and into the environment for a range of purposes including attachment and adherence to physical surfaces, protection from stressors, and facilitation of biofilm formation (Hall‐Stoodley et  al., 2004). Welch et  al. (1999) found that a variety of acidic EPS molecules (including starch, xanthan, pectin, and alginate polymers) all increased weathering of feldspar by a factor of 50–100 compared to abiotic controls. Furthermore, some EPS molecules can bind to silicon and metallic elements leached from minerals and rocks, promoting ligand‐enhanced dissolution (Pokrovsky et al., 2009). The metabolism of heterotrophic organisms degrades organic compounds into carbon dioxide (CO2), which subsequently reacts with water to form carbonic acid (H2CO3, disassociating into HCO3− and H+). The formation of carbonic acid can occur abiotically, or can

be enhanced by carbonic anhydrase, an enzyme released by some weathering organisms (Thorley et  al., 2015). Although carbonic acid has a weathering effect on most mineral and rock types (Montross et al., 2013), it is most effective at promoting carbonate dissolution (Thorley et al., 2015). Xiao et al. (2014) identified that the expression levels of carbonic anhydrase biosynthesis genes in Bacillus mucilaginosus were upregulated when this organism was grown in Ca2+‐depleted conditions when calcite (CaCO3) was added to the medium. Furthermore, the authors cloned the carbonic anhydrase biosynthetic genes into Escherichia coli (a model microorganism), an organism that was not able to weather calcite. They demonstrated that the supernatant (lacking cells) of the genetically modified E. coli culture could enhance calcite dissolution, indicating that carbonic anhydrase release is the responsible mechanism for B. mucilaginosus calcite weathering (Xiao et al., 2014). One of the main sources of redox generated inorganic acid is from microbially mediated pyrite (FeS2) oxidation, which produces sulfuric acid from the oxidation of liberated sulfur (Nordstrom & Southam, 1997). Species such as Acidothiobacillus ferrooxidans can oxidize both the iron and sulfur components that are released from weathered pyrite, forming a range of oxidized sulfur compounds including thiosulfate and sulfuric acid (Schippers et al., 1996). Although pyrite oxidation can occur solely through abiotic reactions, microbial mediation of the oxidation reactions has been shown to accelerate pyrite oxidation rate by 10–20 fold (Boon & Heijnen, 1993). Rocks rich in pyrite therefore can become significant sources of acid rock drainage, which, although often attributed to mining activity (Baker & Banfield, 2003; Johnson & Hallberg, 2005), is also found in completely natural environments (Konhauser et  al., 2011; Kwong et al., 2009). This type of microbial acid production can have profound weathering affects, for example, pyrite‐ derived sulfuric acid reduced bulk rock pH from circumneutral (pH 6–7) to highly acidic (pH ~2.5) in a natural shale weathering profile. The pH gradient across this rock profile strongly correlated with its weathering intensity index (J. Li et al., 2014). Away from the rock/mineral surface, both organic and inorganic acids act to reduce pH, which alters the solubility of ions (e.g. Fe3+) within the surrounding solution. This disrupts ion concentration equilibria at the mineral– solution interface, resulting in enhanced mineral dissolution to restore equilibrium. The pH itself can also influence kinetics of redox reactions, either enhancing or inhibiting the rate of oxidation/reduction dependent upon the redox couple reacting. For example, the rate of abiotic oxidation of ferrous iron, Fe(II), to ferric iron, Fe(III), drops with decreasing pH and is significantly reduced below pH 3–4 (Hedrich et  al., 2011). These various mechanisms of

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action can act in unison to enhance weathering, or can actually oppose each other, depending upon the environmental conditions and local geochemistry (Drever & Stillings, 1997). Photosynthetic microbes can also influence rock weathering processes by raising pH rather than reducing it, which occurs because photosynthetic mechanisms ­produce carbonate anions. For example, the growth of cyanobacterial species Anabaena cylindrica was shown to increase the pH of solution–rock mixtures (basalt and rhyolite) from 6.5 to 8.5, which subsequently enhanced the release rate of Ca, Mg, Si, and K from these rocks (Olsson‐Francis et al., 2012). 3.3.2. Metal Chelation—Ligand Promoted Dissolution Organic acids, in addition to their ability to alter pH, can also act as potent chelators of heavy metals (Bennett et al., 2001). Chelation is the ability of organic acids to bind to a heavy metal cation, immobilizing it and potentially causing it to precipitate as a mineral product. The singular or multiple hydroxyl groups (OH−) that are complexed with protons (H+) can become disassociated in solution, allowing individual metal cations (Me+) to complex with an organic acid hydroxyl group (Konhauser, 2006). The secretion of organic acids (also known as organic ligands in this context) into geological environments by microbes can therefore facilitate the release of heavy metals from rocks and minerals (Ahmed & Holmström, 2014; Bray et al., 2015; Saad et al., 2017). Enhancement of metal leaching occurs when deprotonated organic ligands complex with dissolved metals in solution, effectively reducing the concentration of freely available cations of that metal. This acts to decrease the metal’s saturation state in solution, increasing metal release from the mineral. Organic acids that are bidentate (have two hydroxyl groups, e.g., oxalate) or tridentate (three groups, e.g., citrate) are generally more effective weathering agents than are monodentate (one group, e.g., acetate) (Konhauser, 2006; Welch & Ullman, 1993). 3,4‐Dihydroxybenzoic acid (DHBA) is a commonly found organic acid in mineral weathering environments (Hiebert & Bennett, 1992) and has been used as a microbially produced model ligand for mineral weathering studies (Rogers & Bennett, 2004). Rogers and Bennett (2004) found that DHBA enhanced the dissolution of numerous feldspars, and microbial communities that produced DHBA also enhanced the dissolution of these minerals. Furthermore, when the weathering activity of the microbial community was tested on similar feldspar minerals, those with high concentrations of phosphorus were more strongly weathered. This suggests that their weathering activity is an active process in nutrient acquisition. The organic acids produced from this activity form

complexes with a range of metal cations, and were found to form either soluble or insoluble complexes (Rogers & Bennett, 2004). In both cases, the metals are essentially removed from the solution and therefore enhance mineral dissolution to reestablish concentration equilibria (Drever & Stillings, 1997). The proton‐mediated and ligand‐mediated weathering mechanisms of an organic acid can be active within a weathering system simultaneously, but their relative impact on mineral/rock dissolution may differ (Fomina et  al., 2004, 2005; Gadd et  al., 2014). The ectomycorrhizal (also entomopathogenic) fungal species Beauveria caledonica was investigated for its ability to weather the minerals hopeite (Zn3(PO4)2·4H2O) and pyromorphite (Pb5(PO4)3Cl), and its ability to solubilize other metal phosphates. Initial work demonstrated that B. caledonica mineral weathering was largely constrained by microbial alteration of pH, indicating that acidolysis was the primary weathering mechanism (Fomina et al., 2004). However, when the fungus was stimulated to overexcrete the organic acids oxalate and citrate, the primary mechanism of mineral weathering changed to ligand promoted dissolution (Fomina et al., 2005). This indicates that organic acid concentration within a mineral weathering is one factor in determining the relative contribution of proton‐mediated and ligand‐mediated mineral dissolution (Gadd et al., 2014). A specific group of organic compounds with an incredibly high affinity for Fe3+ are known as siderophores. These molecules are tetradendate or hexadendate compared to mono‐, bi‐, or tridendate organic acids, and have oxygen‐bearing moieties such as hydroxamate or catecholate that directly bind to the complexed metal (Akafia et al., 2014). The number of ligand binding sites and the differing structure of siderophores results in a much greater affinity for metals compared to organic acids; formation constants for metals with siderophores range from 1025–1050 log Kf, considerably higher than for oxalic acid (108 log Kf) (Konhauser, 2006). Siderophores are released by a diverse range of microorganisms to complex Fe along with other heavy metals including Cu, Co, Mn and Ni, in order to increase their solubility and biological availability (Gadd, 2004, 2010). Ferric Fe and other metal oxides have low solubility indices at circumneutral pH, so the use of siderophores to obtain Fe and other biological utilized metals such as Mn and Cu is vital for growth in natural environments (Kalinowski et al., 2006; Liermann et  al., 1999; Matlakowska & Sklodowska, 2009; Włodarczyk et al., 2015). Early work showed that siderophores could be potent weathering agents of soil comprising minerals (Liermann et al., 1999). Liermann et al. (1999) demonstrated that a Streptomyces sp. soil isolate could weather hornblende (Ca2(Mg, Fe, Al)5 (Al, Si)8O22(OH)2). The authors

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hypothesized that the organism’s production of a siderophore molecule, desferrioxamine mesylate (DFAM), was primarily responsible for this weathering activity. When purified DFAM (24 μM) was added to a solution containing hornblende, the resulting mineral dissolution rates were similar to that of hornblende weathered by the Streptomyces sp. isolate (Liermann et  al., 1999). More recent research has characterized the ability of numerous siderophores to enhance the dissolution of a range of minerals including Fe, Co, and Mn oxyhydroxides (Akafia et al., 2014), Fe–Cr oxyhydroxides (Saad et al., 2017), Fe–As minerals (Liu et al., 2015), and biotite (Bray et al., 2015). 3.3.3. Redox Reactions All known biological metabolisms gather energy through redox reactions, the transfer of an electron ­between a redox couple, comprised of an electron donor and an electron receiver. The electron is transferred from a higher energy state in the electron donor to a lower energy state in the electron receiver, and the loss of energy is harnessed to produce adenosine triphosphate (ATP), the universal energy storage molecule of life. Microbial metabolisms make use of a diverse array of redox couples, many of which are used by rock weathering organisms. When one or both of the redox couples are comprised of a geological component (e.g., a mineral), the component is transformed and can be said to be weathered or altered. Pyrite dissolution and the subsequent oxidation of sulfur to sulfuric acid has already been highlighted as an important mechanism of weathering via redox reactions. Although important, Fe oxidation within pyrite weathering is just one from an array of biogeochemical reactions involving a large number of elements across the periodic table. Metallic elements such as Fe, Mn, and Cu, and nonmetals such as C, S, N, and P can all undergo redox cycling within geological environments (Falowski et  al., 2008; Gadd, 2010), with individual microbial species contributing to oxidative or reductive processes. In some cases, individual species can contribute to both oxidative and reductive processes of a single element. Geobacter sp. and other Betaproteobacteria isolated from freshwater sediment were shown to be capable of both iron reduction with acetate as an electron donor, and iron oxidation with nitrate as an electron acceptor (Coby et al., 2011). Manganese redox reactions provide a good case study for biogeochemical cycles within natural geological environments. Numerous informative review articles on Mn oxidation can be found in the literature (Geszvain et al., 2012; Tebo et al., 2004, 2005). Mn can form (II), (III) and (IV) compounds, with (II) being more soluble than (III) and (IV) compounds. The rates of abiotic Mn oxidative

processes are slow compared to those of microbial processes, and as such biogeochemical cycling of Mn dominates Mn redox reactions. Mn oxidation occurs in a phylogenetically diverse microbial group including both bacteria and fungi (Gadd, 2007). For the bacterial members of this group the primary mechanism for oxidative activity is multicopper oxidase‐like enzymes that can be found within the cytoplasm, the cell wall, or in spore coats (Tebo et al., 2004). Mn oxidation, although thermodynamically favorable, has not been directly shown to provide energy through a lithoautotrophic metabolism in any organism; despite this, there are several adaptive purposes that the Mn oxidation pathway may have. The oxidation of Mn could act as a mechanism to store Mn for use as an electron acceptor for Mn reduction, which does drive known metabolisms (Tebo et al., 2005). Furthermore, the scavenging of free radicals by Mn oxides may protect cells from radiation damage (Daly et al., 2004). Mn oxides are known catalysts for the degradation of organic matter (Sundra & Kieber, 1994), so Mn oxidation could also play a role in organic carbon weathering and subsequent nutrient acquisition for Mn‐oxidizing heterotrophs. Strains of Leptothrix discophora, Pseudomonas putida, and Bacillus subtilis are among the best‐characterized bacterial Mn oxidizers, and are found in a range of natural geological settings including carbonate cave walls (Carmichael et al., 2013), desert rock varnish (Kuhlman et al., 2006), and marine sediments (Francis et al., 2002), including ferromanganese nodules within the sediment (Stein et al., 2001). These microbial species, among others within the weathering community, actively weather the rock, which causes reduced Mn(II) minerals such as rhodochrosite (MnCO3) to dissolve, following which the leached Mn(II) becomes oxidized to produce oxide minerals such as birnessite (MnO2) (Ehrlich, 1998; Marshall, 1979; Tebo et al., 2004). 3.3.4. Breakdown of Organic Compounds Although minerals form the bulk composition of most rocks, organic matter (OM) from various sources can be found in all types of rock. The amount of OM varies, from trace amounts in volcanic rocks to minor amounts (~10%) in black shales, to the majority of the rock in the case of coal (Nichols, 2009). Microbes are able to transform or degrade such organic matter, contributing to biological weathering processes. The type of transformation/degradation is dependent on a number of variables including the composition of the OM, environmental conditions, and the types of microbes involved. For example, under anaerobic conditions in the subsurface, rock‐bound OM can be transformed by methanogens to form gaseous methane (Meslé et al., 2013). At the Earth’s

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surface, aerobic conditions enable microbes to obtain energy from oxidative breakdown of OM in rocks, including shales and coal (Berlendis et al., 2014; Petsch et al., 2001; Wengel et al., 2006). The OM formed in sedimentary deposits is primarily in the form of a kerogen, a macromolecular structure of OM above 1 kD in size and largely nonhydrolyzable and insoluble. Due to these properties, kerogen is highly recalcitrant to microbial degradation, but despite this numerous species have been found associated with sedimentary OM and are thought likely to use it as a sole carbon source (Petsch et  al., 2001). Although the mechanisms used by these microbes to degrade kerogen remain largely unknown, several studies suggest that enzymes that degrade other forms of recalcitrant OM (such as polycyclic aromatic hydrocarbons) may be used (Berlendis et al., 2014). 3.4. TECHNIQUES AND METHODOLOGY IN MICROBIAL WEATHERING STUDIES Studying microbial weathering systems requires approaches that cover several fields within traditional disciplines of science, including microbiology and genetics within biology, or geochemistry and geomorphology within geology. Such requirements mean that geomicrobiologists must be truly interdisciplinary scientists. In this section, representative examples of some techniques and methodologies used to investigate microbial weathering systems will be discussed. Individual techniques such as culturing and enrichment techniques within microbiology can fall neatly into one discipline, but some straddle disciplines, such as the use of Raman spectroscopy to examine mineralogy and biomarkers.

An obvious addition to this culturing methodology when studying microbial weathering is to add the geological substrate of interest to the culture medium. This substrate is likely to be the same mineral/rock type that the organisms were isolated from. There are several purposes the addition of mineral/rock can have in the study of microbial weathering processes. Microbial strains that are active weathering agents in the studied environment likely require nutrients they access from that substrate for growth, so addition of the substrate to the medium aids in obtaining a richer diversity of weathering isolates (Hirsch et al., 1995). If the experiment is aimed at characterizing weathering microbial activity, the very traits or phenotypes of interest may only become active when the mineral/rock is present, or the effect of microbial weathering activity on the geological substrate can be observed using geochemical and geological techniques. Depending upon the chosen culturing methodology, cultures of single microbial isolates or enriched microbial communities (a subset of the community that would have been present within the inoculum sample) can be grown. Both types of culture have advantages and disadvantages. For example, use of axenic cultures allows a single strain to be characterized and specific attributes, for example physiological or biochemical capabilities, assigned to it. However, microbes are almost never isolated within an environment but are surrounded by other species that will interact with them and affect their behavior. Therefore, study of axenic cultures can lack environmental relevance. The converse is true for community culturing, where the activity of individual species cannot be discerned, but observing the activity of communities as a whole is a more reliable proxy of microbial processes within natural environments.

3.4.1. Culturing Techniques A direct way of examining rock weathering microbes is to grow them within controlled laboratory conditions. This requires an environmental sample, believed to harbor these microbes, that can be inoculated onto solid or liquid‐based media containing nutrients under specific conditions (temperature, pH, oxygen, etc.), which can in turn support the growth of those organisms. Dependent upon the nutrients and conditions provided, differing types of organism (with differing metabolisms) can be grown. Specific conditions are normally required for specific groups of weathering microbes, so when inoculating an environmental sample onto a specific growth medium, only organisms that can grow under those conditions are enriched. For example, to enrich for aerobic Fe oxidizers the culture medium needs to be partially or full oxygenated, contain a high concentration of ferrous Fe, and be mildly to strongly acidic (pH 3–4 or below) (Emerson & Floyd, 2005).

3.4.2. Rock Weathering Phenotype Tests Chemical assays within growth media can be used to test a strain or community for specific traits or phenotypes, normally via a colorimetric result that is qualitative (binary or semiquantitative) or quantitative. Some examples of qualitative, agar‐based assays can be seen in Figure  3.2. Matlakowska and Sklodowska (2009) used some of these plate assays, among others, to isolate rock‐weathering isolates with multiple weathering phenotypes from weathered shale rock. One such agar‐plate‐based test has been developed for the detection of siderophore production using CAS (chrome azurol S) (Schwyn & Neilands, 1987). When complexed with Fe(III) the dye is blue, however, if siderophores are present in the agar they can sequester the Fe(III) from the dye. The resultant color change of the dye is from blue to an orange/yellow, meaning that zones surrounding microbial colonies containing secreted siderophores can be identified (see Figure 3.2).

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Figure 3.2  Agar‐plate‐based phenotypic assays for rock weathering capabilities. In these images, a rock sample suspension has been plated onto each agar type to obtain isolates that tested positive for each phenotype. Top left: an acidic medium with a high concentration of Fe(II) sulfate; Fe oxidizing isolates produce Fe hydroxides/oxides within the colony, giving them a distinctive rust coloring and morphology. [Johnson (1995)]. Top right: a medium containing Mn(II) sulfate and Leucoberberlin blue (LBB) dye; LBB changes color from light blue to dark blue in the presence of Mn oxides, so active Mn‐oxidizing isolates can be identified by dark blue colonies (red arrow) compared to non‐oxidizing colonies (black arrow). Bottom left: skimmed milk agar for testing organic acid and/ or protease secretion; the release of organic acids and/or proteases destabilize the milk‐derived colloids that make the agar cloudy, producing clear halos (red arrow) around the colonies (black arrow). [Frazier & Rupp (1928) and Grube et al. (2009).] Bottom right: chrome Azurol S (CAS) blue agar for testing siderophore production; CAS dye is blue when ligated to Fe(III), but if a siderophore molecule competitively displaces the Fe the dye turns orange/yellow. Orange/yellow halos (red arrow) around colonies indicate that siderophores are being secreted into the agar (Schwyn & Neilands, 1987). The black arrow points to an area of blue agar on the plate that has no iron sequestration from CAS. Rock powder has been added to all of these agar types to stimulate growth of rock weathering organisms. [Schwyn & Neilands (1987), Cockell et al. (2013) and Hirsch et al. (1995).]

The CAS siderophore assay is well known to be toxic to fungi due to the addition of the hexadecyltrimethylammonium bromide surfactant within the agar, so Andrews et al. (2016) developed a CAS‐based alternative medium with reduced fungal toxicity. They used an alternative, less toxic surfactant molecule (N‐dodecyl‐N,N‐ dimethyl‐3‐ammonio‐1‐propanesulfonate, or DDAPS) and also prepared two‐layered agar plates, the bottom layer containing the CAS medium (including DDAPS) and the top layer comprising a basic nutrient agar. The

CAS from the bottom layer diffused into the top layer allowing produced siderophores to be detected. As DDAPS still has some toxicity effects on fungi, the separation of the fungal growth on the top later from the DDAPS in the bottom layer significantly reduced fungal growth inhibition. Andrews et al. (2016) used this modified medium to identify 12 siderophore‐producing fungal genera including Cladosporium and Pyrenochaeta. Quantitative tests are easier to carry out in liquid‐based assays, where the extent of the color change can be

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determined using absorbance measurements of optical density. Variants of the CAS siderophore assay have also been developed for liquid cultures (Yoon et al., 2010). Another example includes the ferrozine assay, which can be used to determine the concentrations of both Fe(III) and Fe(II) ions within a solution, allowing the redox activity of Fe oxidizers or Fe reducers to be monitored (Mejia et al., 2016). 3.4.3. Chemical Analyses of Microbial Rock Weathering Products An alternative approach to test‐based assays is the use of spectroscopic techniques in natural or artificial geological weathering systems. Within the laboratory, microbial communities/isolates grown in a culture medium containing a geological substrate can be examined using spectroscopic techniques that can detect biological and/ or geological molecules within the culture medium or on the substrate itself. Such molecules could include active weathering agents such as siderophores, or products of weathering processes such as biogenic minerals. Weathering of minerals/rocks results in elements and compounds leaching into the surrounding solution. In a rock weathering study where the experimental setup includes both sterile control and biological test samples, leaching can be monitored using techniques such as inductively coupled plasma mass spectrometry (ICP‐MS) or atomic emission spectrometry (ICP‐AES—also called optical emission spectroscopy or OES) that measure elemental concentrations. Olsson‐Francis et  al. (2012) used ICP‐AES to measure elemental concentration in media containing either basalt or rhyolite and inoculated with differing species of cyanobacteria isolated from natural rock weathering environments or taken from strain collections, at various time points over a 45‐day period. By taking measurements at multiple time points they calculated linear release rate (a standard unit in rock leaching) for each element under each culture condition, allowing them to make direct comparisons between rock types and cyanobacterial species. The result of their study was that Anabaena cylindrica was the most potent rock weathering organism tested, and that for A. cylindrica, basalt was more susceptible to biological weathering than rhyolite (Olsson‐Francis et al., 2012). Numerous forms of spectroscopy that can be used to determine the mineralogy of a solid sample including X‐ray diffraction (XRD), near edge X‐ray absorption structure (XANES), Fourier transform infrared (FTIR) spectroscopy or Raman spectroscopy (Cockell et  al., 2011; Joeckel et al., 2005). Each type of spectroscopy has its own advantages and disadvantages, such as characterizing some groups of minerals better than others, and therefore normally multiple forms of spectroscopy are used in conjunction. Joeckel et al. (2005) used both XRD

and FTIR to characterize the minerals within sulfate mineral crusts on two shale rock outcrops in Nebraska, USA. They also used denaturing gradient gel electrophoresis (DGGE) to identify members of the microbial community within the environment. Their analysis showed that the crusts were comprised from a range of relatively soluble sulfate minerals such as alunogen (Al2(SO4)3.17H2O) and copiapite (Fe2+Fe43+(SO4)6(OH)2.20H2O) but lacked relatively insoluble, but often common, minerals associated with pyrite weathering such as jarosite (KFe3(SO4)2(OH)6). These minerals formed as a result of rapid pyrite weathering at low pH and reduced rainfall compared to other pyrite weathering sites. Organism identification revealed the presence of the well‐known pyrite oxidizing organism Acidiothiobacillis ferrooxidans, the activity of which they attribute to the rapid pyrite oxidation. Combined mineralogical and microbiological techniques enabled the authors to gain a good understanding of the geochemical and biogeochemical processes that lead to the morphology of that environment (Joeckel et al., 2005). The interaction of microbes with their surrounding geological environment, including the weathering of minerals and geological components, involves the uptake and metabolism of elements from that environment (Dumont & Murrell, 2005). Isotopic variation of elements often occurs within rocks (e.g., Fe54, Fe56, Fe57, and Fe58) and biological processing of these elements can result in isotopic fractionation. For example, the siderophore desferrioxamine B (DFAM) has been shown to preferentially release isotopically lighter Fe from shale rock taken from the Susquehanna Shale Hills Critical Zone Observatory (SSHO) field site (Liermann et al., 2011). Further in situ analysis of Fe and Mo isotope profiles at SSHO revealed that shale‐derived soil was isotopically light for iron compared to the bedrock, indicating that ligand‐promoted dissolution of shale could be an important process in soil formation (Yesavage et al., 2012). Carbon isotope analysis can be used to determine if biological molecules have been derived from geological carbon, such as sedimentary organic matter (Petsch et al., 2001, 2003, 2005). Carbon has three naturally occurring isotopes (12C, 13C, and 14C) that are found in differing ratios within carbonaceous matter (12C 98.9%, 13C 1.1% and 14C trace). Biological activity generally decreases δ13C (the ratio of 13C to 12C within carbonaceous matter), whereas δ14C (ratio of 14C to 12C) is generally unaltered by biological uptake (Mahmoudi et  al., 2017). Analysis of δ13C and δ14C of biomass and sedimentary organic matter (SOM) can be used to determine if microbes can directly uptake and incorporate SOM into biological molecules (Petsch et al., 2001, 2003, 2005). In Petsch et al. (2005), carbon isotope analysis of fatty acid methyl esters (FAME), membrane components of microbial cells, was used to determine that the heterotrophic microbes Acinetobacter,

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Comamonas, Clostridium, Dechloromonas, and Pseudomonas were capable of assimilating SOM from weathered shale. 3.4.4. Imaging Methods to Visualize Weathering Features The effect of microbial weathering and the resulting dissolution can be seen directly though imaging of the geological surface. Both scanning and transmission electron microscopy (SEM and TEM) can be used for this purpose, along with other microscopic imaging techniques to identify microtopographical features associated with biological activity. For example, Puente et al., (2004) incubated a putative rock‐weathering organism isolated from a cactus rhizosphere, Bacillus chitinolyticus, in a solution containing pulverized particles of volcanic rock for 28 days. Images were taken before and after incubation, and image analysis software was used to obtain a particle size distribution. Microbial incubation decreased the frequency of larger particles (7–46 μm) and increased the frequency of smaller particles (0.1–7 μm). Atomic absorption spectroscopy (AAS), a technique that can be used to determine the chemical composition of a solid once it has undergone a liquid digestion step, was used to determine the chemical content of the powder before and after weathering. The results of the AAS analysis indicated that biological incubation decreased the content of numerous elements and compounds (prominently phosphorus pentoxide and metal ions; Puente et al., 2004). In another study, SEM and epifluorescence microscopy were used to image the surface of shale rock weathered in liquid culture containing several rock weathering isolates (Matlakowska et  al., 2012). Cells were found to preferentially colonize organic‐rich laminae, and biologically formed pits in the rock surface were identified. The weathered rock surface was also found to be “fluffier,” with a larger surface area than the sterile control treatment. The study also used energy dispersive spectroscopy (EDS), a technique that can be used in conjunction with SEM to provide chemical analysis of a spot point on a surface, to analyze their weathered rock surfaces. Elemental abundance data obtained from EDS can be used to calculate mineral phases that potentially could be present within a sample and in this study it was used to identify a range of secondary mineral deposits on biologically weathered rock (Matlakowska et  al., 2012). Confocal laser scanning microscopy (CLSM) can also be used to build three‐dimensional images of the rock weathering microbial communities attached to, or growing through, the rock/mineral substrates (Z. Li et al., 2016). 3.4.5. Sequencing and ‘Omics Technologies in Microbial Rock Weathering Studies Microbial culturing techniques, although effective in  identifying weathering organisms through direct

observation of activity, only capture around 1% of the microbial diversity within a natural environment (Hugenholtz et  al., 1998). Therefore, when characterizing microbial communities, particularly samples taken directly from the study site, molecular analysis can be a powerful tool to capture a broader picture of the organisms present. Particular regions of DNA within genomes can be used for phylogenetic identification, such as 16S rDNA in bacteria or ITS regions in eukaroytes. The most common way of analyzing these sequences is through DNA sequencing, carried out using a range of technologies including Illumina and Sanger sequencing. Sanger sequencing was the first sequencing technology developed, but it still commonly used to sequence single PCR amplicons (such as 16S rDNA) prepared from microbial isolates (Summers et al., 2016). Illumina sequencing and other next generation technologies (NGT) such as 454 sequencing, can be used with greater versatility to study microbial communities. The NGT can be used to sequence 16S rDNA amplicons from a whole microbial community (Archer et  al., 2017), rather than singular isolates, or can be used to sequence whole genomes (Wang et al., 2016) or metagenomes (multiple genomes within one sample, e.g., a microbial community; Lepleux et  al., 2012). Nonsequencing approaches can also be used to analyze extracted DNA from environmental samples or cultures. These include denaturing gradient gel electrophoresis (DGGE), which is primarily used for 16S rDNA phylogenetics (Joeckel et al., 2005), or microarray analysis for both phylogenetic (Kelly et  al., 2010) and transcriptome analysis (Olsson‐Francis et al., 2010). In addition to genomics, other “omics” technologies are being used to study the genes expressed (transcriptomics), proteins produced (proteomics), and metabolites secreted (metabolomics) by microbes in weathering environments (Bryce et  al., 2016; Gutarowska et  al., 2015; Wang et  al., 2015). Transcriptomic analysis, the sequencing of gene mRNA transcripts within a culture, of Aspergillus niger was used to investigate differences in gene expression in a mineral weathering environment. In the absence of soluble K and in the presence of the mineral potassium feldspar, genes involved in carbohydrate metabolism, protein synthesis, and K uptake were significantly upregulated. The authors argue that these changes in gene expression promote the release of organic acids and acidic EPS, which act to enhance mineral weathering (Wang et al., 2015). Bryce et al. (2016) used proteomics, the identification of intra‐ and extracellular proteins, to study the starvation stress response of Cupriavidus metallidurans, a known weathering microorganism (Byloos et  al., 2017). The addition of basalt to minimal media limited in iron and magnesium enhanced the growth of C. metallidurans, demonstrating that the rock could supply essential

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nutrients. However, proteomic analysis also revealed that cells had to respond to stressors introduced by the addition of basalt to the medium, including phosphate limitation. Ca released by the basalt complexed with phosphate, forming insoluble calcium phosphate minerals that reduced soluble phosphate availability. Production of proteins involved in phosphate uptake was shown to be significantly upregulated (Bryce et al., 2016). A combination of high‐throughput sequencing and metabolomic analysis was used to assess the biodeterioration of materials from a historic building (Gutarowska et  al., 2015). The study identified that diverse algal, fungal, and bacterial communities inhabited brick materials, with Proteobacteria and Actinobacteria dominating the bacterial community. Metabolites linked to metabolic pathways involved in the synthesis of organic acids and the degradation of complex organic compounds (e.g. benzoate degradation), were more abundant in samples incubated to promote weathering (higher humidity and temperature; Gutarowska et  al., 2015). The studies described above (Bryce et  al., 2016; Gutarowska et  al., 2015; Wang et  al., 2015) demonstrate how modern “omics” technologies can be used to elucidate previously unseen responses of weathering organisms to their environment. Furthermore, by directly analyzing the gene expression and metabolic activity of weathering organisms, the exact role of previously identified weathering mechanisms can be determined more accurately. 3.5. MICROBIAL ECOLOGY OF WEATHERING ENVIRONMENTS Weathered rock environments, like most environments on Earth, are generally inhabited by diverse microbial communities of bacteria, archaea, and eukaryotes (Archer et al., 2017; Kelly et al., 2014; Uroz et al., 2015). Study of these weathered rock communities has shown that numerous microbial metabolisms such as autolithotrophy, chemotrophy, or autophototrophy, often coexist within the same habitat (Cockell et  al., 2011; J. Li et  al., 2014; Marnocha & Dixon, 2014). In many cases organisms with these differing metabolisms are not isolated, instead, the products of one metabolism (e.g., nitrate from ammonium oxidizers) can support one another (e.g., nitrate reduction). Such interconnected biogeochemical cycles frequently arise within boundary environments, where a geochemical gradient (e.g., [O2]) spans across a physical environment. For example, an oxygen gradient can occur where either atmosphere or oxygen‐rich waters meet sediment, with decreasing oxygen concentration with increasing sediment depth. Montross et al. (2013) investigated the rock‐weathering capability of microbial communities inhabiting sediment (relatively anoxic) and meltwater (relatively oxic) from

subglacial environments. Their study proposed that acetate and reduced Fe, produced from microbial fermentation and Fe reduction respectively with anoxic zones, could become transported to oxic zones and support aerobic metabolisms (e.g., organic respiration and Fe oxidation). Microbial respiration producing CO2, which forms carbonic acid, was demonstrated to be the most potent weathering process of carbonate and silicate minerals within the subglacial sediment (Montross et al., 2013). This study highlights how the activity of some microbial groups (fermenters and Fe reducers) can indirectly support microbial rock weathering by supplying carbon or energy sources for active rock weathering organisms (aerobic heterotrophs). The abundance of weathering organisms within weathered rock environments, and the relative proportion of this group to the rest of the microbial community (including non‐weathering organisms), largely remains unknown. Huang et al. (2014) determined the percentage of the culturable microbial community within soil profiles that were capable of mineral weathering. The overall culturable microbial abundance within the soil profiles was found to be 1.77 × 104 to 1.86 × 107 cells g−1. From this cultured community of isolates (individual organisms isolated on agar plates), 648 out of the 1100 (58.9%) identified isolates were capable of mineral weathering, as tested by the isolates ability to weather biotite (Huang et  al., 2014). This study demonstrates that mineral and rock weathering bacteria represent a significant subset of the soil microbiological community, but they also share their habitat with those that do not share their abilities. It might be expected that the proportion of rock weathering species within a geological habitat would be greater, but it would be unsurprising to find nonweathering microbes even here, supported by the metabolites of the rock weathering organisms (Cockell et al., 2011; Kelly et al., 2014). The combination of culture‐independent (e.g., next generation sequencing) and culture dependent (e.g., enrichment cultures) can provide an effective way to study microbial communities within weathered rock, and to determine which metabolisms and weathering processes could be active within these environments. Olsson‐Francis et  al. (2016) analyzed the microbial community of soil in contact with granitic bedrock using high‐throughput sequencing to produce a 16S rDNA library, and isolation of culturable organisms on agar plates containing crushed granite. Isolates were tested for their ability to produce siderophores and to promote the dissolution of granite in batch culture. The 16S rDNA library revealed a community dominated by Proteo­ bacteria (beta and gamma) and Bacilli, and the eight isolates cultured on agar plates were all identified to be from one of these three phyla. All eight isolates were able to

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grow on agar plates where crushed granite was the only source of bio‐essential elements, but only six were capable of significantly enhancing granite dissolution. These results demonstrate that a significant proportion of the microbial community are active rock weathering organisms. However, they also show that a subset of the community utilizes the nutrients released by microbial rock weathering, without contributing to weathering processes (Olsson‐Francis et al., 2016). The solubilization of phosphorus by rock weathering microbes (particularly fungi) is a common ecosystem service that benefits both microbial and plant communities. Phosphorus is a growth‐limiting nutrient within many environments, so phosphate‐solubilizing microbes such as Burkholderia and Sphingomonas can help support more diverse and abundant microbial communities within weathering environments (Uroz et al., 2009). An important microbial–plant interaction is based on the rock weathering capabilities of mycorrhizal fungi, which obtain phosphorus through rock weathering and supply this to their host plant, among other nutrients and services, in exchange for photosynthesized organic ­ carbon (Gadd, 2007). The geological supply of nutrients and elements can have a significant influence on microbial community structure. Experimental, culture‐independent approaches have been used to study the effect of mineralogy on the abundance, diversity, and functional capabilities of subsurface sulfur‐oxidizing (Jones & Bennett, 2014) and soil (Kelly et al., 2016) microbial communities. For example, Jones and Bennett (2014) found that neutrophilic, sulfur‐ oxidizing microbes such as the Epsilonproteobacteria Thiothrix spp. and Sulfurovum spp. preferentially colonized calcite and limestone surfaces. The pH buffering capacity of these geological substrates was suggested to be beneficial to neutrophilic organisms that were actively producing sulfuric acid as a by‐product of sulfur oxidation. Furthermore, acid‐driven weathering of limestone was suggested to provide growth‐limiting nutrients such as phosphates (Jones & Bennett, 2014). As previously mentioned in this section, oxidized and reduced forms of numerous elements including N, S, and Fe can be used for reduction and oxidation‐based metabolisms respectively. The result of this is that the product of one metabolism can be the source of a reactant for another metabolism (e.g., Fe oxidation produces ferric Fe oxides which can be subsequently reduced). Gault et al. (2011) studied the Fe biogeochemistry of an oxic–anoxic boundary zone within a freshwater seep, where anoxic subsurface waters reach an oxygen gradient within sediment that the seep rises through. Microaerophilic Fe‐ oxidizing species including Leptothrix ochracea and Gallionella ferruginea can be identified within the more oxygenated regions of sediment, associated with the Fe

oxide/hydroxide minerals ferrihydrite, lepidocrocite, and goethite. Anoxic microenvironments adjacent to these Fe oxide/hydroxide deposits were found to be suitable habitats for Fe‐reducing bacteria, such as Rhodoferax ferrireducens and Geothrix fermentans. These Fe‐reducing bacteria will reduce and solubilize the Fe, making it available for reoxidation when entering the microaerophilic zone of the sediment. This study demonstrates how biogeochemical cycling can support numerous metabo­ lisms within microbial communities (Gault et al., 2011). A range of anaerobic lithotrophic metabolisms can simultaneously transform two inorganic electron carriers, such as the combination of Fe oxidation with nitrate reduction (Hedrich et  al., 2011). Fe‐oxidizing, nitrate‐ reducing bacteria, isolated from ditch sediment and brackish lagoon water (Straub et al., 1996) were found to oxidize structural Fe(II) within biotite under laboratory conditions (Shelobolina et al., 2012). In the natural environment, such weathering processes would be controlled by conditions affecting the rate of this metabolism, such as the availability of nitrate and surrounding oxygen levels. Oscillations in environmental conditions such as oxygen level, nitrate concentration, and organic compound availability were found to affect the relative rates of Fe redox cycling between Fe oxidizers (coupled with nitrate reduction) and Fe reducers (coupled with organics oxidation) in an experimental system where Fe hydroxide minerals lepidocrocite and ferrihydrite were being transformed (Mejia et al., 2016). These results show how a microbial weathering community, including Fe(II) oxidizing and nitrate reducers, can interact with other members of a microbial ecosystem such as nitrogen cycling bacteria. 3.6. BIOSIGNATURES OF MICROBIAL MINERAL AND ROCK WEATHERING Within laboratory conditions, experimental design can include abiotic controls so that biological and geochemical weathering processes and their effect on the mineral/ rock can be determined. However, geomicrobiologists working with samples that have been extracted from the environment do not have the luxury of abiotically treated samples to compare them to. This is true of both geological samples that have undergone recent weathering processes, and weathered rock that has been preserved through geological time. As such, biological markers are required to confidently associate a microbial weathering process with a weathered geological substrate. Ideally, abiotic geochemical processes should not be able to replicate such a marker. In reality, many signatures are not uniquely biological in their formation (i.e., they can also be formed by abiotic processes), meaning numerous biosignatures must be combined to confidently associate microbial activity with weathering signatures (Gorbushina, 2007).

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Biosignatures are normally specific to the microbial process and the mineral/rock involved, although some biosignatures can be associated with multiple mineral or rock types. Two examples of weathering biosignatures can be found in Figure 3.3. Morphological biosignatures provide some of the most compelling evidence of microbial activity within a weathering zone. Pitting and etching with mineral surfaces is a commonly provided example of morphological alteration from biological activity in minerals such as quartz, feldspar minerals, and pyrite (Bennett et al., 2001; Buss et al., 2007; Mielke et al., 2003). These features are normally associated with attached microbes either within or to the side of the pit/etch and often with weathering mineral product precipitated on the outer microbial surface (Bennett et al., 2001). The size of pits and etch marks varies significantly between microbes, minerals, and studies, but in combination with sterile controls lacking these alterations, they provide strong evidence of microbially facilitated mineral dissolution. SEM imaging of pits, combined with mineralogical analysis and identification of microfossils, was used to identify microbial Fe oxidation and weathering of pyrite in ~3.4

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Ga rocks, one of the oldest recorded instances of microbial weathering activity (Wacey et al., 2011). Boring through mineral/rock surfaces is another ­morphological alteration mediated by microbes such as cyanobacteria, chlorophyta (algae), and fungi in minerals such as carbonates and volcanic glasses (Cockell & Herrera, 2008; McLoughlin et  al., 2010). The exact adaptive advantages of specific cases of boring have not yet been realized, but several proposals such as nutrient acquisition, protection from radiation or predation, and escape from entrapment during mineralization have all been proposed. Care should be taken when analyzing unknown morphological features before associating them with biological activity, as some features that previously have been believed to be purely biological in origin have been shown to form under abiotic conditions. For example, ferric Fe produced from either biotic or abiotic ferrous Fe oxidation can react with pyrite to produce cell‐shaped pits. These pits look as if they have formed from a direct microbially induced surface reaction, but can be formed in the complete absence of cells (Edwards et al., 2001).

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Figure 3.3  Biosignatures of microbial weathering processes. Left: pyrite that has been incubated within a live (top) or sterilized (bottom) Fe oxidizing enrichment culture. Microbial Fe oxidation of pyritic surfaces produces characteristic rectangular pitting (approximately 0.5 × 2–3 μm) of the mineral surface (top) that is not found under abiotic weathering conditions (bottom) (unpublished). Right: cyanobacteria that reside within a mineral interface (unaltered volcanic glass above, altered glass rich in Fe hydroxides below), with microbial growth producing boreholes through the glass (filaments extending upwards). [Cockell & Herrera (2008). Reproduced with permission of Elsevier.]

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Biogenic minerals, those that are formed through biological processes, can be biosignatures of microbial processes that use leached elements or compounds from a weathered surface. For example, the rate of abiotic Mn oxidation is comparatively very slow to microbially mediated Mn oxidation, so natural deposits of Mn oxide minerals are generally assumed to be at least partially biologically formed (Tebo et al., 2005). Boston et al. (2001) reviewed microbial biosignatures within cave environments, in which they highlight numerous biosignatures including Mn oxide “snow.” This snow, which visibly falls from the roof of the cave, is comprised of corrosion residue formed from bedrock weathering where microbial processes contribute both to rock weathering and to Fe and Mn hydr(oxide) mineral formation (Northup et  al., 2000). Biogenic mineral formation can also occur directly onto biological material such as cellular surfaces or EPS, which act as nucleation sites for mineral growth. Numerous mineral types have been found to form on fungal hyphae, such as magnesium oxalate dihydrate on Penicillium simplicissimum and calcite on Serpula himantioides (Gadd, 2007). There are problems with the use of biogenic minerals as signatures of microbial weathering processes. First, it is often not possible (without additional information) to determine if the microbes involved in biogenic mineral formation were also directly involved in the primary mineral weathering. Second, like other biosignatures, minerals that can be formed by biological processes also can be formed by abiotic processes. As such, other biosignatures (such as the direct association with biomatter) must be used in conjunction with putative biogenic minerals to be confident of their microbial origin (Gorbushina et al., 2002). 3.7. SHAPING LANDSCAPES—MICROBIAL BIOGEOMORPHOLOGY Previous sections have focused on microbial weathering of minerals and rocks from a molecular to microscopic level, the scale at which individual microbes interact with their surrounding environment. This section covers the emerging field of microbial biogeomorphology (Viles, 2012), the study of how microbial activity alters the shape and structure of geological outcrops, with a specific focus on the contribution of microbial weathering processes. The field is itself a subfield of biogeomorphology, which primarily investigates how plants and animals (such as boring invertebrates) alter geomorphology. In relation to biogeomorphology, microbial weathering processes can be categorized by their contribution to weathering processes relative to abiotic mechanisms (Viles, 2012). Microbially induced weathering is where microbial biomass or products (such as organic acids or exopolymeric substances) directly contribute to a weathering process and significantly enhance the rate of that process

from baseline abiotic mechanisms. Microbially influenced weathering does not directly contribute to the weathering process, but the organism’s presence within the environment facilitates the abiotic process to occur and/or enhances the rate of that process (Viles, 2012). Another distinction should be made here between those mechanisms that result in mineral or rock weathering and those that contribute to erosion. Many of the weathering processes mentioned in this chapter contribute to erosion to some extent, but some purely bio‐erosive processes also exist, such as the splitting of mineral planes by the mechanical force of fungal hyphal growth (Hutchens, 2009). Such bio‐erosive processes physically break minerals and rock into smaller pieces but do not alter their geochemical composition, but also generally increase the available surface area exposed to weathering processes. The majority of existing studies within microbial biogeomorphology have largely focused around microbial biomineralization. For example, microbial presence and activity have been implicated in the formation of cave speleothems (Barton & Northup, 2007) and freshwater tufa (Viles, 2012; Coombes, 2016). However, microbial rock weathering processes can also make significant contributions to biogeomorphology (Coombes et  al., 2015; Phillips, 2016; Viles, 2012). The formation of sulfur caves provides a good example, where microbial sulfuric acid production is the primary agent driving cave formation (Boston et al., 2006; Engel et al., 2004). Hydrogen sulfide gas emitted from springs becomes increasingly oxidized (elemental sulfur, sulfates) as it enters cave environments. With increased availability of sulfates, sulfate‐reducing microbes such as Thiobacillus sp. act to form sulfuric acids as a by‐product of their metabolism. Whole cave systems are formed due to these processes, showing that microbial weathering processes, specifically inorganic acid production enhancing rock degradation, can play a significant role in karst biogeomorphology (Boston et al., 2006; Engel et al., 2004; Phillips, 2016). Coastal geomorphology is another area that has undergone increasing focus for studies investigating the role of microbial rock weathering in biogeomorphology (Coombes, 2014; Phillips, 2016; Viles, 2012). Microbial acid production is known to contribute to the formation of boreholes in intertidal carbonate rocks (Naylor et al., 2012), and alters the mechanical properties of the rock surface, potentially enhancing other rock degradation processes (Coombes, 2014). Rock analysis from a limestone coastal platform revealed that cyanobacterially produced endolithic boreholes increased porosity and reduced strength at the rock surface. This author hypothesized that this facilitates the subsequent activity of bioerosive animals such as grazing molluscs to erode the surface (Schneider & Le Campion‐Alusmard, 1999; Viles, 2012).

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Within shale formations, microbial pyrite oxidation and subsequent sulfur oxidation have been shown to play significant role in large‐scale alterations of bedrock stability (Anderson, 2008; Hawkins, 2014; Hoover & Lehmann, 2009). Upon pyrite dissolution by bacteria such as Acidithiobacillus ferrooxidans, sulfur becomes oxidized to sulfuric acid (H2SO4). This acid can then react with other shale minerals such as calcite and illite, forming weathered mineral products such as gypsum and jarosite respectively. These minerals have a greater volume than the unweathered minerals (103% and 115% volume increase respectively), resulting in overall rock expansion. This expansion can be substantial, increasing rock volume by almost 40% (Hoover & Lehmann, 2009). In the Chattanooga Shale Formation within Estill County, Kentucky, pyritic shale expansion had devastating effects on local infrastructure. Numerous roads and several buildings had to undergo significant repairs due to resultant damages (Anderson, 2008). The microbial contribution to the formation of saprolite (weathered rock) on a landscape scale from a quartz diorite bedrock has also been investigated in the Luquillo Mountains, Puerto Rico (Buss et  al., 2005). Buss et  al. (2005) identified that the abundance of aerobic Fe oxidizing microbes increased with depth into the partially weathered zone, and correlated with HCl extractable Fe(II). They further demonstrated that the autotrophic Fe oxidizing community was the primary source of organic substrates for heterotrophic microbes. The combined activity of these microbial communities resulted in saprolite depletion of both Fe(II) and total organic carbon due to Fe oxidation and CO2 release respectively. Buss et al. (2005) hypothesize, and support with numerical geochemical models, that microbial activity likely maintains a gradient of Fe(II) and organic carbon release from the bedrock, driving saprolite formation. This example demonstrates how microbial weathering activity can play a role in altering lithology on a landscape scale. Microbial biogeomorphology provides a new perspective with which to consider microbial rock weathering. It provides the intellectual tools required for geomicrobiologists and biogeochemists to contextualize discoveries at the micrometer scale to kilometer and potentially planetary scales. Although the field is still in its infancy (Phillips, 2016; Viles, 2012), increasing development of sophisticated techniques in microbiology and geomorphology will increasingly enable more ambitious and revealing studies (Viles, 2016). 3.8. CONCLUSIONS AND FUTURE DIRECTIONS Microbial mineral and rock weathering is a significant driver of environmental change, from microscopic transformations at mineral surfaces to landscape‐scale alterations of rocky outcrops. Using a diverse array of mechanisms, microbes act to enhance mineral dissolution

and rock degradation, while also facilitating the formation of secondary mineral structures. Weathering microbial communities are an integral component of the biosphere, driving and interacting with many of Earth’s biogeochemical cycles. These communities often act as ecological producers within ecosystems, supporting the growth of other microorganisms and higher organisms such as plants. Advancements in geomicrobiology allow scientists to investigate and understand these processes, both in the laboratory and in the field. This includes using biosignatures in natural geological formations to recognize microbial weathering activity and understand its contribution to biogeomorphological processes. As such, the study of microbial mineral and rock weathering has relevance to our understanding of Earth’s life system as a whole. Although current research into microbial rock weathering is broad and varied, cutting across numerous scientific disciplines, the authors would like to focus on a few areas of particular interest for future development. Advances in the use of technologies such as a proteomics and metabolomics are enabling an unprecedented understanding of how microbial communities respond to, and interact with, their geological environment (Bryce et  al., 2018; Gutarowska et  al., 2015; Stirflinger et  al., 2018; Włodarczyk et al., 2018). The expanded use of these technologies, and their integration with existing approaches in geomicrobiology is to be encouraged. Through the use of these techniques, the authors believe the relationship of rock weathering with nutrient uptake can be better elucidated. Understanding this relationship is key to identifying active and passive microbial rock weathering activity, enabling more accurate predictions of rock weathering potential within microbial communities to be made. Within microbial biogeomorphology, progress has been made in identifying mechanisms that have the potential to contribute to geomorphological alteration, and in identifying environments that appear to have been shaped by microbial activity. However, within the existing literature there is a lack of experimentally derived evidence to directly link mechanisms with geomorphology through cause and effect. Such work will be technically challenging, and the authors would like to highlight the potential for collaboration between geomicrobiologists/biogeochemists with biogeomorphologists to find approaches to address this problem. By combining knowledge, expertise, and facilities these fields can better tackle important societal challenges, such as increasing our understanding of the processes that drive coastal erosion. ACKNOWLEDGMENTS The first author received funding from Israeli Chemicals Limited (ICL), the Biotechnology and Biological Sciences Research Council (BBSRC), and the Natural Environment Resource Council (NERC) during

Microbial Weathering of Minerals and Rocks in Natural Environments  75

the preparation of this work. The authors would also like to thank editors Katerina Dontsova and Zsuzsanna Balogh‐Brunstad for their invitation to write this chapter, and to the anonymous reviewers for their helpful feedback in its preparation. REFERENCES Ahmed, E., & Holmström, S.J. (2014). Siderophores in environmental research: roles and applications. Microbial Biotechnology, 7(3), 196–208. Akafia, M.M., Harrington, J.M., Bargar, J.R., & Duckworth, O.W. (2014). Metal oxyhydroxide dissolution as promoted by structurally diverse siderophores and oxalate. Geochimica et Cosmochimica Acta, 141, 258–269. Akers, H.A., & Magee, K.P. (1985). The siderophore mediated release of iron and magnesium from Mt St. Helens’ ash and silicate rock standards. Cellular and Molecular Life Sciences, 41(4), 522–523. Anderson, W.H. (2008). Foundation problems and pyrite oxidation in the Chattanooga Shale, Estill County, Kentucky. Kentucky Geological Survey, University of Kentucky. Andrews, M.Y., Santelli, C.M., & Duckworth, O.W. (2016). Layer plate CAS assay for the quantitation of siderophore production and determination of exudation patterns for fungi. Journal of microbiological methods, 121, 41–43. Archer, S.D., de los Ríos, A., Lee, K.C., Niederberger, T.S., Cary, S.C., Coyne, K.J., et  al. (2017) Endolithic microbial diversity in sandstone and granite from the McMurdo Dry Valleys, Antarctica. Polar Biology, 40(5), 997–1006. Baker, B.J., & Banfield, J.F. (2003). Microbial communities in acid mine drainage. FEMS Microbiology Ecology, 44(2), 139–152. Barker, W. W., Welch, S. A., Chu, S., & Banfield, J. F. (1998). Experimental observations of the effects of bacteria on ­aluminosilicate weathering. American Mineralogist, 83(11), 1551–1563. Barton, H.A., & Northup, D.E. (2007). Geomicrobiology in cave environments: past, current and future perspectives. Journal of Cave and Karst Studies, 69(1), 163–178. Bennett, P.C. (1991). Quartz dissolution in organic‐rich aqueous systems. Geochimica et Cosmochimica Acta, 55(7), 1781–1797. Bennett, P.C., Rogers, J.R., Choi, W.J., & Hiebert, F.K. (2001). Silicates, silicate weathering, and microbial ecology. Geomicrobiology Journal, 18(1), 3–19. Berlendis, S., Beyssac, O., Derenne, S., Benzerara, K., Anquetil, C., Guillaumet, M., Esteve, I. & Capelle, B. (2014). Comparative mineralogy, organic geochemistry and microbial diversity of the Autun black shale and Graissessac coal (France). International Journal of Coal Geology, 132, 147–157. Boon, M., & Heijnen, J.J. (1993). Mechanisms and rate limiting steps in bioleaching of sphalerite, chalcopyrite and pyrite with Thiobacillus ferrooxidans. International Biohydrometallurgy Symposium at Jackson Hole, Wyoming, USA, 22–25 August. Boston, P.J., Hose, L.D., Northup, D.E., & Spilde, M.N. (2006). The microbial communities of sulfur caves: a newly

appreciated geologically driven system on Earth and potential model for Mars. Geological Society of America Special Papers, 404, 331–344. Boston, P.J., Spilde, M.N., Northup, D.E., Melim, L.A., Soroka, D.S., Kleina, L.G., et  al. (2001). Cave biosignature suites: microbes, minerals, and Mars. Astrobiology, 1(1), 25–55. Brantley, S.L., Lebedeva, M., & Hausrath, E.M. (2012). A geobiological view of weathering and erosion. In A.H. Knoll, D.E. Canfield, K.O. Konhauser (Eds), Fundamentals of Geobiology (pp. 205–227). Chichester, UK: Wiley‐Blackwell. Bray, A.W., Oelkers, E.H., Bonneville, S., Wolff‐Boenisch, D., Potts, N.J., Fones, G., & Benning, L.G. (2015). The effect of pH, grain size, and organic ligands on biotite weathering rates. Geochimica et Cosmochimica Acta, 164, 127–145. Bryce, C., Franz‐Wachtel, M., Nalpas, N., Miot, J., Benzerara, K., Byrne, J.M., et al. (2018). Proteome response of a metabolically flexible anoxygenic phototroph to Fe (II) oxidation. Applied and Environmental Microbiology, 01166. Bryce, C.C., Le Bihan, T., Martin, S.F., Harrison, J.P., Bush, T., Spears, B., et al. (2016). Rock geochemistry induces stress and starvation responses in the bacterial proteome. Environmental Microbiology, 18(4), 1110–1121. Buss, H.L., Bruns, M.A., Schultz, M.J., Moore, J., Mathur, C.F., & Brantley, S.L. (2005). The coupling of biological iron cycling and mineral weathering during saprolite formation, Luquillo Mountains, Puerto Rico. Geobiology, 3(4), 247–260. Buss, H.L., Lüttge, A., & Brantley, S.L. (2007). Etch pit formation on iron silicate surfaces during siderophore‐promoted dissolution. Chemical Geology, 240(3), 326–342. Byloos, B., Coninx, I., Van Hoey, O., Cockell, C., Nicholson, N., Ilyin, V., et  al. (2017). The impact of space flight on survival and interaction of Cupriavidus metallidurans CH34 with basalt, a volcanic moon analog rock. Frontiers in Microbiology, 8. doi: 10.3389/fmicb.2017.00671 Carmichael, M.J., Carmichael, S.K., Santelli, C.M., Strom, A., & Bräuer, S.L. (2013). Mn (II)‐oxidizing bacteria are abundant and environmentally relevant members of ferromanganese deposits in caves of the upper Tennessee River Basin. Geomicrobiology Journal, 30(9), 779–800. Coby, A.J., Picardal, F., Shelobolina, E., Xu, H., & Roden, E.E. (2011). Repeated anaerobic microbial redox cycling of iron. Applied and Environmental Microbiology, 77(17), 6036–6042. Cockell, C.S., Bush, T., Bryce, C., Direito, S., Fox‐Powell, M., Harrison, J.P., et  al. (2016). Habitability: a review. Astrobiology, 16(1), 89–117. Cockell, C.S., Kelly, L.C., & Marteinsson, V. (2013). Actinobacteria—an ancient phylum active in volcanic rock weathering. Geomicrobiology Journal, 30(8), 706–720. Cockell, C.S., & Herrera, A. (2008). Why are some microorganisms boring? Trends in Microbiology, 16(3), 101–106. Cockell, C.S., Pybus, D., Olsson‐Francis, K., Kelly, L., Petley, D., Rosser, N., Howard, K., & Mosselmans, F. (2011). Molecular characterization and geological microenvironment of a microbial community inhabiting weathered receding shale cliffs. Microbial Ecology, 61(1) 166–181. Coombes, M. A. (2014). The rock coast of the British Isles: weathering and biogenic processes. Geological Society, London, Memoirs, 40(1), 57–76.

76  BIOGEOCHEMICAL CYCLES Coombes, M.A. (2016). Biogeomorphology: diverse, integrative and useful. Earth Surface Processes and Landforms, 41(15), 2296–2300. Coombes, M. A., La Marca, E. C., Naylor, L. A., Piccini, L., De Waele, J., & Sauro, F. (2015). The influence of light attenuation on the biogeomorphology of a marine karst cave: a case study of Puerto Princesa Underground River, Palawan, the Philippines. Geomorphology, 229, 125–133. Daly, M.J., Gaidamakova, E.K., Matrosova, V.Y., Vasilenko, A., Zhai, M., Venkateswaran, A., et al. (2004). Accumulation of Mn (II) in Deinococcus radiodurans facilitates gamma‐ radiation resistance. Science, 306(5698), 1025–1028. Drever, J.I., & Stillings, L.L. (1997). The role of organic acids in mineral weathering. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 120(1–3), 167–181. Dumont, M.G., & Murrell, J.C. (2005). Innovation: Stable isotope probing—linking microbial identity to function. Nature Reviews: Microbiology, 3(6), 499. Dworkin, M., & Gutnick, D. (2012). Sergei Winogradsky: a founder of modern microbiology and the first microbial ecologist. FEMS Microbiology Reviews, 36(2), 364–379. Edwards, K.J., Hu, B., Hamers, R.J., & Banfield, J.F. (2001). A new look at microbial leaching patterns on sulfide minerals. FEMS Microbiology Ecology, 34(3), 197–206. Ehrlich, H.L. (1996) How microbes influence mineral growth and dissolution. Chemical Geology, 132 (1–4), 5–9. Ehrlich, H.L. (1998). Geomicrobiology: its significance for geology. Earth‐Science Reviews, 45(1), 45–60. Ehrlich, H.L., Newman, D.K., & Kappler, A. (Eds.). (2015). Ehrlich’s Geomicrobiology. CRC Press. Emerson, D., & Floyd, M.M. (2005). Enrichment and isolation of iron‐oxidizing bacteria at neutral pH. Methods in Enzymology, 397, 112–123. Engel, A.S., Stern, L.A., & Bennett, P.C. (2004). Microbial contributions to cave formation: new insights into sulfuric acid speleogenesis. Geology, 32(5), 369–372. Falkowski, P.G., Fenchel, T., & Delong, E.F. (2008). The microbial engines that drive Earth’s biogeochemical cycles. Science, 320(5879), 1034–1039. Fomina, M., Alexander, I.J., Hillier, S., & Gadd, G.M. (2004). Zinc phosphate and pyromorphite solubilization by soil plant‐ symbiotic fungi. Geomicrobiology Journal, 21(5), 351–366. Fomina, M., Hillier, S., Charnock, J. M., Melville, K., Alexander, I. J., & Gadd, G. M. (2005). Role of oxalic acid overexcretion in transformations of toxic metal minerals by Beauveria caledonica. Applied and Environmental Microbiology, 71(1), 371–381. Francis, C.A., Casciotti, K.L., & Tebo, B.M. (2002). Localization of Mn (II)‐oxidizing activity and the putative multicopper oxidase, MnxG, to the exosporium of the marine Bacillus sp. strain SG‐1. Archives of Microbiology, 178(6), 450–456. Frazier, W.C., & Rupp, P. (1928). Studies on the proteolytic bacteria of milk I. A medium for the direct isolation of caseolytic milk bacteria. Journal of Bacteriology, 16(1), 57. Fullerton, H., Hager, K.W., McAllister, S.M., & Moyer, C.L. (2017). Hidden diversity revealed by genome‐resolved metagenomics of iron‐oxidizing microbial mats from Lō’ihi Seamount, Hawai’i. The ISME Journal, 11(8), 1900.

Gadd, G.M. (2004). Microbial influence on metal mobility and application for bioremediation. Geoderma, 122(2), 109–119. Gadd, G.M. (2007). Geomycology: biogeochemical transformations of rocks, minerals, metals and radionuclides by fungi, bioweathering and bioremediation. Mycological Research, 111(1), 3–49. Gadd, G.M. (2010). Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology, 156(3), 609–643. Gadd, G.M., Bahri‐Esfahani, J., Li, Q., Rhee, Y.J., Wei, Z., Fomina, M., & Liang, X. (2014). Oxalate production by fungi: significance in geomycology, biodeterioration and bioremediation. Fungal Biology Reviews, 28(2), 36–55. Gault, A.G., Ibrahim, A., Langley, S., Renaud, R., Takahashi, Y., Boothman, C., et al. (2011). Microbial and geochemical features suggest iron redox cycling within bacteriogenic iron oxide‐rich sediments. Chemical Geology, 281(1), 41–51. Geszvain, K., Butterfield, C., Davis, R. E., Madison, A. S., Lee, S. W., Parker, D. L., et al. (2012). The molecular biogeochemistry of manganese (II) oxidation. Biochemical Society Transactions, 40(6), 1244–1248. doi: 10.1042/BST20120229 Gorbushina, A.A. (2007). Life on the rocks. Environmental microbiology, 9(7), 1613–1631. Gorbushina, A.A., Krumbein, W.E., & Volkmann, M. (2002). Rock surfaces as life indicators: new ways to demonstrate life and traces of former life. Astrobiology, 2(2), 203–213. Grube, M., Cardinale, M., de Castro Jr, J.V., Müller, H., & Berg, G. (2009). Species‐specific structural and functional diversity of bacterial communities in lichen symbioses. The ISME Journal, 3(9), 1105. Gutarowska, B., Celikkol‐Aydin, S., Bonifay, V., Otlewska, A., Aydin, E., Oldham, A.L., et  al. (2015). Metabolomic and high‐throughput sequencing analysis—modern approach for the assessment of biodeterioration of materials from historic buildings. Frontiers in Microbiology, 6. doi.org/10.3389/ fmicb.2015.00979 Hall‐Stoodley, L., Costerton, J.W., & Stoodley, P. (2004). Bacterial biofilms: from the natural environment to infectious diseases. Nature Reviews: Microbiology, 2(2), 95. Hawkins, A.B. (2014). Implications of pyrite oxidation for engineering works (pp. 1–98). Springer International Publishing. Hedrich, S., Schlömann, M., & Johnson, D.B. (2011). The iron‐ oxidizing proteobacteria. Microbiology, 157(6), 1551–1564.l Hiebert, F.K., & Bennett, P.C. (1992). Microbial control of silicate weathering in organic‐rich ground water. Science, 258(5080), 278. Hirsch, P., Eckhardt, F.E.W., & Palmer, R.J. (1995). Methods for the study of rock‐inhabiting microorganisms—a mini review. Journal of Microbiological Methods, 23(2), 143–167. Hoover, S.E., & Lehmann, D. (2009). The expansive effects of concentrated pyritic zones within the Devonian Marcellus Shale Formation of North America. Quarterly Journal of Engineering Geology and Hydrogeology, 42(2), 157–164. Huang, J., Sheng, X.F., Xi, J., He, L.Y., Huang, Z., Wang, Q., & Zhang, Z.D. (2014). Depth‐related changes in community structure of culturable mineral weathering bacteria and in weathering patterns caused by them along two contrasting soil profiles. Applied and Environmental Microbiology, 80(1), 29–42.

Microbial Weathering of Minerals and Rocks in Natural Environments  77 Hugenholtz, P., Goebel, B.M., & Pace, N.R. (1998). Impact of culture‐independent studies on the emerging phylogenetic view of bacterial diversity. Journal of Bacteriology, 180(18), 4765–4774. Hutchens, E. (2009). Microbial selectivity on mineral surfaces: possible implications for weathering processes. Fungal Biology Reviews, 23(4), 115–121. Joeckel, R.M., Clement, B.A., & Bates, L.V. (2005). Sulfate‐ mineral crusts from pyrite weathering and acid rock drainage in the Dakota Formation and Graneros Shale, Jefferson County, Nebraska. Chemical Geology, 215(1), 433–452. Johnson, D.B. (1995). Selective solid media for isolating and enumerating acidophilic bacteria. Journal of microbiological methods, 23(2), 205–218. Johnson, D.B., & Hallberg, K.B. (2005). Acid mine drainage remediation options: a review. Science of the Total Environment, 338(1), 3–14. Jones, A.A., & Bennett, P.C. (2014). Mineral microniches control the diversity of subsurface microbial populations. Geomicrobiology Journal, 31(3), 246–261. Jones, D.L., Dennis, P.G., Owen, A.G., & Van Hees, P.A.W. (2003). Organic acid behavior in soils—misconceptions and knowledge gaps. Plant and Soil, 248(1–2), 31–41. Kalinowski, B.E., Johnsson, A., Arlinger, J., Pedersen, K., Ödegaard‐Jensen, A., & Edberg, F. (2006). Microbial mobilization of uranium from shale mine waste. Geomicrobiology Journal, 23(3–4), 157–164. Kelly, L.C., Cockell, C.S., Piceno, Y.M., Andersen, G.L., Thorsteinsson, T., & Marteinsson, V. (2010). Bacterial diversity of weathered terrestrial Icelandic volcanic glasses. Microbial Ecology, 60(4), 740–752 Kelly, L.C., Cockell, C.S., Thorsteinsson, T., Marteinsson, V., & Stevenson, J. (2014). Pioneer microbial communities of the Fimmvörðuháls lava flow, Eyjafjallajökull, Iceland. Microbial Ecology, 68(3), 504–518. Kelly, L.C., Colin, Y., Turpault, M.P., & Uroz, S. (2016). Mineral type and solution chemistry affect the structure and composition of actively growing bacterial communities as revealed by bromodeoxyuridine immunocapture and 16S rRNA pyrosequencing. Microbial Ecology, 72(2), 428–442. Konhauser, K.O. (2006). Introduction to Geomicrobiology. John Wiley & Sons. Konhauser, K.O., Lalonde, S.V., Planavsky, N.J., Pecoits, E., Lyons, T.W., Mojzsis, S.J., et  al. (2011). Aerobic bacterial pyrite oxidation and acid rock drainage during the Great Oxidation Event. Nature, 478(7369), 369. Krumbein, W.E., & Altmann, H.J. (1973). A new method for the detection and enumeration of manganese oxidizing and reducing microorganisms. Helgoländer Wissenschaftliche Meeresuntersuchungen, 25(2), 347. Kuhlman, K.R., Fusco, W.G., La Duc, M.T., Allenbach, L.B., Ball, C.L., Kuhlman, G.M., & Strap, J.L. (2006). Diversity of microorganisms within rock varnish in the Whipple Mountains, California. Applied and Environmental Microbiology, 72(2), 1708–1715. Kwong, Y.J., Whitley, G., & Roach, P. (2009). Natural acid rock drainage associated with black shale in the Yukon Territory, Canada. Applied Geochemistry, 24(2), 221–231.

Lepleux, C., Turpault, M.P., Oger, P., Frey‐Klett, P., & Uroz, S. (2012). Correlation of the abundance of betaproteobacteria on mineral surfaces with mineral weathering in forest soils. Applied and Environmental Microbiology, 78(19), 7114–7119. Li, J., Sun, W., Wang, S., Sun, Z., Lin, S., & Peng, X. (2014). Bacteria diversity, distribution and insight into their role in S and Fe biogeochemical cycling during black shale weathering. Environmental Microbiology, 16(11), 3533–3547. Li, Z., Liu, L., Chen, J., & Teng, H.H. (2016). Cellular dissolution at hypha‐ and spore–mineral interfaces revealing unrecognized mechanisms and scales of fungal weathering. Geology, 44(4), 319–322. Liermann, L.J., Albert, I., Buss, H.L., Minyard, M., & Brantley, S.L. (2015). Relating microbial community structure and geochemistry in deep regolith developed on volcaniclastic rock in the Luquillo Mountains, Puerto Rico. Geomicrobiology Journal, 32(6), 494–510. Liermann, L.J., Kalinowski, B.E., Brantley, S.L., & Ferry, J.G. (1999). Role of bacterial siderophores in dissolution of hornblende. Geochimica et Cosmochimica Acta, 64(4), 587–602. Liermann, L.J., Mathur, R., Wasylenki, L.E., Nuester, J., Anbar, A.D., & Brantley, S.L. (2011). Extent and isotopic composition of Fe and Mo release from two Pennsylvania shales in the presence of organic ligands and bacteria. Chemical Geology, 281(3–4), 167–180. Liu, X., Yang, G.M., Guan, D.X., Ghosh, P., & Ma, L.Q. (2015). Catecholate‐siderophore produced by As‐resistant bacterium effectively dissolved FeAsO4 and promoted Pteris vittata growth. Environmental Pollution, 206, 376–381. Mahmoudi, N., Beaupré, S. R., Steen, A. D., & Pearson, A. (2017). Sequential bioavailability of sedimentary organic matter to heterotrophic bacteria. Environmental Microbiology, 19(7), 2629–2644. Marnocha, C.L., & Dixon, J.C. (2014). Endolithic bacterial communities in rock coatings from Kärkevagge, Swedish Lapland. FEMS Microbiology Ecology, 90(2), 533–542. Marshall, K. C. (1979). Biogeochemistry of manganese minerals. Studies in Environmental Science, 3, 253–292. Matlakowska, R., & Sklodowska, A. (2009). The culturable bacteria isolated from organic‐rich black shale potentially useful in biometallurgical procedures. Journal of Applied Microbiology, 107(3), 858–866. Matlakowska, R., Skłodowska, A., & Nejbert, K. (2012). Bioweathering of Kupferschiefer black shale (Fore‐Sudetic Monocline, SW Poland) by indigenous bacteria: implication for dissolution and precipitation of minerals in deep underground mine. FEMS Microbiology Ecology, 81(1), 99–110. McLoughlin, N., Staudigel, H., Furnes, H., Eickmann, B., & Ivarsson, M. (2010). Mechanisms of microtunneling in rock substrates: distinguishing endolithic biosignatures from abiotic microtunnels. Geobiology, 8(4), 245–255. Mejia, J., Roden, E.E., & Ginder‐Vogel, M. (2016). Influence of oxygen and nitrate on Fe (hydr) oxide mineral transformation and soil microbial communities during redox cycling. Environmental Science and Technology, 50(7), 3580–3588. Méndez‐García, C., Peláez, A.I., Mesa, V., Sánchez, J., Golyshina, O.V., & Ferrer, M. (2015). Microbial diversity

78  BIOGEOCHEMICAL CYCLES and metabolic networks in acid mine drainage habitats. Frontiers in Microbiology, 6, 475. Meslé, M., Dromart, G., & Oger, P. (2013). Microbial methanogenesis in subsurface oil and coal. Research in Microbiology, 164(9), 959–972. Mielke, R.E., Pace, D.L., Porter, T., & Southam, G. (2003). A  critical stage in the formation of acid mine drainage: Colonization of pyrite by Acidithiobacillus ferrooxidans under pH‐neutral conditions. Geobiology, 1(1), 81–90. Montross, S.N., Skidmore, M., Tranter, M., Kivimäki, A.L., & Parkes, R.J. (2013). A microbial driver of chemical weathering in glaciated systems. Geology, 41(2), 215–218. Naylor, L.A., Coombes, M.A., & Viles, H.A. (2012). Reconceptualising the role of organisms in the erosion of rock coasts: a new model. Geomorphology, 157, 17–30. Nichols, G. (2009). Sedimentology and Stratigraphy, 2nd edn. Chichester, UK: John Wiley & Sons. Nordstrom, D.K., & Southam, G. (1997). Geomicrobiology of sulfide mineral oxidation. Reviews in Mineralogy, 35, 361–390. Northup, D.E., Dahm, C.N., Melim, L.A., Spilde, M.N., Crossey, L.J., Lavoie, K.H., et al. (2000). Evidence for geomicrobiological interactions in Guadalupe caves. Journal of Cave and Karst Studies, 62(2), 80–90. Olsson‐Francis, K., Pearson, V.K., Schofield, P.F., Oliver, A., & Summers, S. (2016). A study of the microbial community at the interface between granite bedrock and soil using a culture‐ independent and culture‐dependent approach. Advances in Microbiology, 6(3), 233–245. Olsson‐Francis, K., Simpson, A.E., Wolff‐Boenisch, D., & Cockell, C.S. (2012). The effect of rock composition on ­cyanobacterial weathering of crystalline basalt and rhyolite. Geobiology, 10(5), 434–444. Olsson‐Francis, K., Van Houdt, R., Mergeay, M., Leys, N., & Cockell, C.S. (2010). Microarray analysis of a microbe–mineral interaction. Geobiology, 8(5), 446–456. Petsch, S.T., Edwards, K.J., & Eglinton, T.I. (2003). Abundance, distribution and δ13C analysis of microbial phospholipid‐ derived fatty acids in a black shale weathering profile. Organic Geochemistry, 34(6), 731–743. Petsch, S.T., Edwards, K.J., & Eglinton, T.I. (2005). Microbial transformations of organic matter in black shales and implications for global biogeochemical cycles. Palaeogeography, Palaeoclimatology, Palaeoecology, 219(1), 157–170. Petsch, S.T., Eglinton, T.I., & Edwards, K.J. (2001). 14C‐dead living biomass: evidence for microbial assimilation of ancient organic carbon during shale weathering. Science, 292(5519), 1127–1131. Phillips, J.D. (2016). Biogeomorphology and contingent ecosystem engineering in karst landscapes. Progress in Physical Geography, 40(4), 503–526. Pokrovsky, O. S., Shirokova, L. S., Bénézeth, P., Schott, J., & Golubev, S. V. (2009). Effect of organic ligands and heterotrophic bacteria on wollastonite dissolution kinetics. American Journal of Science, 309(8), 731–772. Puente, M.E., Bashan, Y., Li, C.Y., & Lebsky, V.K. (2004). Microbial populations and activities in the rhizoplane of rock‐weathering desert plants. I. Root colonization and weathering of igneous rocks. Plant Biology, 6(05), 629–642.

Rogers, J.R., & Bennett, P.C. (2004). Mineral stimulation of subsurface microorganisms: release of limiting nutrients from silicates. Chemical Geology, 203(1), 91–108. Saad, E.M., Sun, J., Chen, S., Borkiewicz, O.J., Zhu, M., Duckworth, O.W., & Tang, Y. (2017). Siderophore and organic acid promoted dissolution and transformation of Cr (III)–Fe (III)–(oxy) hydroxides. Environmental Science and Technology, 51(6), 3223–3232. Schippers, A., Jozsa, P., & Sand, W. (1996). Sulfur chemistry in bacterial leaching of pyrite. Applied and Environmental Microbiology, 62(9), 3424–3431. Schmalenberger, A., Duran, A.L., Bray, A.W., Bridge, J., Bonneville, S., Benning, L.G., et al. (2015). Oxalate secretion by ectomycorrhizal Paxillus involutus is mineral‐specific and controls calcium weathering from minerals. Nature: Scientific Reports, 5, 12187. Schneider, J., & Le Campion‐Alsumard, T. (1999). Construction and destruction of carbonates by marine and freshwater cyanobacteria. European Journal of Phycology, 34(4), 417–426. Schwyn, B., & Neilands, J.B. (1987). Universal chemical assay for the detection and determination of siderophores. Analytical biochemistry, 160(1), 47–56. Shelobolina, E., Xu, H., Konishi, H., Kukkadapu, R., Wu, T., Blöthe, M., & Roden, E. (2012). Microbial lithotrophic oxidation of structural Fe (II) in biotite. Applied and Environmental Microbiology, 78(16), 5746–5752. Sonnleitner, R., Redl, B., Pipal, A., & Schinner, F. (2011). Chemolithotrophic metal mobilization from dolomite. Geomicrobiology Journal, 28(8), 651–659. Stein, L.Y., La Duc, M.T., Grundl, T.J., & Nealson, K.H. (2001). Bacterial and archaeal populations associated with freshwater ferromanganous micronodules and sediments. Environmental Microbiology, 3(1), 10–18. Sterflinger, K., Little, B., Pinar, G., Pinzari, F., de los Rios, A., & Gu, J.D. (2018). Future directions and challenges in biodeterioration research on historic materials and cultural properties. International Biodeterioration and Biodegradation, 129, 10–12. Straub, K.L., Benz, M., Schink, B., & Widdel, F. (1996). Anaerobic, nitrate‐dependent microbial oxidation of ferrous iron. Applied and Environmental Microbiology, 62(4), 1458–1460. Summers, S., Thomson, B.C., Whiteley, A.S., & Cockell, C.S. (2016). Mesophilic mineral‐weathering bacteria inhabit the critical‐zone of a perennially cold basaltic environment. Geomicrobiology Journal, 33(1), 52–62. Sunda, W.G., & Kieber, D.J. (1994). Oxidation of humic substances by manganese oxides yields low‐molecular‐weight organic substrates. Nature, 367(6458), 62. Tebo, B.M., Bargar, J.R., Clement, B.G., Dick, G.J., Murray, K.J., Parker, D., Verity, R. & Webb, S.M. (2004). Biogenic manganese oxides: properties and mechanisms of formation. Annual Review in Earth and Planetary Sciences, 32, 287–328. Tebo, B.M., Davis, R.E., Anitori, R.P., Connell, L.B., Schiffman, P., & Staudigel, H. (2015). Microbial communities in dark oligotrophic volcanic ice cave ecosystems of Mt. Erebus, Antarctica. Frontiers in Microbiology, 6, 179. Tebo, B.M., Johnson, H.A., McCarthy, J.K., & Templeton, A.S. (2005). Geomicrobiology of manganese (II) oxidation. Trends in Microbiology, 13(9), 421428.

Microbial Weathering of Minerals and Rocks in Natural Environments  79 Thorley, R., Taylor, L.L., Banwart, S.A., Leake, J.R., & Beerling, D.J. (2015). The role of forest trees and their mycorrhizal fungi in carbonate rock weathering and its significance for global carbon cycling. Plant, Cell and Environment, 38(9), 1947–1961. Uroz, S., Calvaruso, C., Turpault, M.P., & Frey‐Klett, P. (2009). Mineral weathering by bacteria: ecology, actors and mechanisms. Trends in Microbiology, 17(8), 378–387. Uroz, S., & Frey‐Klett, P. (2011). Linking diversity to function: highlight on the mineral weathering bacteria. Open Life Sciences, 6(5), 817–820. Uroz, S., Kelly, L.C., Turpault, M.P., Lepleux, C., & Frey‐Klett, P. (2015). The mineralosphere concept: mineralogical control of the distribution and function of mineral‐associated bacterial communities. Trends in Microbiology, 23(12), 751–762. Vera, M., Schippers, A., & Sand, W. (2013). Progress in bioleaching: fundamentals and mechanisms of bacterial metal sulfide oxidation—part A. Applied Microbiology and Biotechnology, 97(17), 7529–7541. Viles, H.A. (2012). Microbial geomorphology: a neglected link between life and landscape. Geomorphology, 157, 6–16. Viles, H. (2016). Technology and geomorphology: Are improvements in data collection techniques transforming geomorphic science? Geomorphology, 270, 121–133. Wacey, D., Saunders, M., Brasier, M.D., & Kilburn, M.R. (2011). Earliest microbially mediated pyrite oxidation in ~3.4 billion‐year‐old sediments. Earth and Planetary Science Letters, 301(1), 393–402. Wang, Y., Chen, W., He, L., Wang, Q., & Sheng, X.F. (2016). Draft genome sequence of Ensifer adhaerens M78, a mineral‐ weathering bacterium isolated from soil. Genome Announcements, 4(5), e00969–16. Wang, W., Lian, B., & Pan, L. (2015). An RNA‐sequencing study of the genes and metabolic pathways involved in Aspergillus niger weathering of potassium feldspar. Geomicrobiology Journal, 32(8), 689–700. Watling, H. (2016). Microbiological advances in biohydrometallurgy. Minerals, 6(2), 49. Welch, S.A., Barker, W.W., & Banfield, J.F. (1999). Microbial extracellular polysaccharides and plagioclase dissolution. Geochimica et Cosmochimica Acta, 63(9), 1405–1419. Welch, S.A., & Ullman, W.J. (1993). The effect of organic acids on plagioclase dissolution rates and stoichiometry. Geochimica et Cosmochimica Acta, 57(12), 2725–2736.

Wengel, M., Kothe, E., Schmidt, C. M., Heide, K., & Gleixner, G. (2006). Degradation of organic matter from black shales and charcoal by the wood‐rotting fungus Schizophyllum commune and release of DOC and heavy metals in the aqueous phase. Science of the Total Environment, 367(1), 383–393. Włodarczyk, A., Stasiuk, R., Skłodowska, A., & Matlakowska, R. (2015). Extracellular compounds produced by bacterial consortium promoting elements mobilization from polymetallic Kupferschiefer black shale (Fore‐Sudetic Monocline, Poland). Chemosphere, 122, 273–279. Włodarczyk, A., Lirski, M., Fogtman, A., Koblowska, M., Bidziński, G., & Matlakowska, R. (2018). The oxidative metabolism of fossil hydrocarbons and sulfide minerals by the lithobiontic microbial community inhabiting deep subterrestrial Kupferschiefer black shale. Frontiers in Microbiology, 9, doi.org/10.3389/fmicb.2018.00972. Xiao, L., Hao, J., Wang, W., Lian, B., Shang, G., Yang, Y., &  Wang, S. (2014). The up‐regulation of carbonic anhydrase genes of Bacillus mucilaginosus under soluble Ca2+ deficiency and the heterologously expressed enzyme promotes calcite dissolution. Geomicrobiology Journal, 31(7), 632–641. Yesavage, T., Fantle, M. S., Vervoort, J., Mathur, R., Jin, L., Liermann, L. J., & Brantley, S. L. (2012). Fe cycling in the Shale Hills Critical Zone Observatory, Pennsylvania: an analysis of biogeochemical weathering and Fe isotope fractionation. Geochimica et Cosmochimica Acta, 99, 18–38. Yoon, S., Kraemer, S.M., DiSpirito, A.A., & Semrau, J.D. (2010). An assay for screening microbial cultures for chalkophore production. Environmental Microbiology Reports, 2(2), 295–303. Zavarzin, G.A. (1989). Sergei N. Winogradsky and the discovery of chemosynthesis. In G.H. Schlegel, B. Bowien (Eds.), Autotrophic bacteria (pp. 17–32). Madison, WI: Science Tech Publishers. Zhao, F., Qiu, G., Huang, Z., He, L., & Sheng, X. (2013). Characterization of Rhizobium sp. Q32 isolated from weathered rocks and its role in silicate mineral weathering. Geomicrobiology Journal, 30(7), 616–622. Zhu, W., & Reinfelder, J.R. (2012). The microbial community of a black shale pyrite biofilm and its implications for pyrite weathering. Geomicrobiology Journal, 29(2), 186–193.

4 Micro‐ and Nanoscale Techniques to Explore Bacteria and Fungi Interactions with Silicate Minerals Zsuzsanna Balogh‐Brunstad1, Kyle Smart1, Alice Dohnalkova2, Loredana Saccone3, and Mark M. Smits4 ABSTRACT Biogeochemical interfaces such as bacteria–mineral, fungi–mineral, and water–mineral are hot spots for important processes and reactions in all environments. Study of these interfaces is useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro‐ and nanoscale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state‐of‐the‐art analytical techniques allow for studying solid, liquid, and gas phases and their relations with biological components and often permit simultaneous study of chemical and physical properties in a site‐specific manner. Improved techniques not only increase spatial and temporal resolution with site specificity but also decrease sample preparation, artifact generation, and sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe–mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offers some solutions. This review chapter focuses on a subset of the available and most promising micro‐ and nanoscale techniques: electron, helium ion, and atomic force microscopy, and X‐ray‐based spectroscopy with traditional and synchrotron‐ based applications.

4.1. INTRODUCTION

formation since Jenny (1941, 1980) defined biota as one of the soil forming factors. In addition, it is recognized that understanding the processes at biogeochemical interfaces such as, plant–soil (rhizosphere), bacteria–mineral, fungi– mineral, and water–mineral provides information about sequestration and stabilization of carbon, transport, toxicity, speciation and bioavailability of nutrients, metals, and contaminants in the environment, which are essential to sustainable land management and remediation efforts (Sparks, 2005; Gadd, 2013). Terrestrial ecosystems, agriculture, and forestry are dependent on the transformation of rocks to soils (weathering), where silicate minerals are the primary source of inorganic nutrients (Smits et al., 2009). Over the past two decades, numerous studies were ­dedicated to identifying, describing, and quantifying the effects of bacteria and fungi on soil processes and primary silicate mineral weathering (e.g., Balogh‐Brunstad, Keller,

Microbe–mineral interactions greatly influence processes in the soil environment, which include sorption, desorption, oxidation‐reduction, dissolution, and precipitation (Sparks, 2013). These processes vary on spatial and temporal scales, forming a highly heterogeneous media that defines a particular soil (Smits et al., 2009). It is generally accepted that biology plays a pivotal role in soil   Department of Geology and Environmental Sciences, Hartwick College, Oneonta, New York, USA 2   Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA 3   Department of Architecture and Civil Engineering, University of Bath, Bath, UK 4   Applied Biology, HAS University of Applied Sciences, Hertogenbosch, the Netherlands 1

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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Dickinson, et  al., 2008; Balogh‐Brunstad, Keller, Gill, et al. 2008; Banfield et al., 1999; Bonneville et al., 2009, 2011; Burford et al., 2003; Calvaruso et al., 2009; Gazzè et  al., 2012; Koele et  al., 2009, 2014; Landeweert et  al., 2001; Leake et al., 2008; Li et al., 2016; Pinzari et al., 2016; Saccone et al., 2012; Turpault et al., 2009; van Hees et al., 2006). These works demonstrated that although most of the chemical reactions at biogeochemical interfaces can be described by inorganic reactions, there is growing evidence that plants and their associated microbes, directly and indirectly, control the availability of acids, carbon dioxide, and oxygen (and other gases), in addition to organic ligands, enzymes, and extracellular polymeric substances (EPS) that are largely influencing the processes at these interfaces (Balogh‐Brunstad et  al., 2017; Bonneville et  al., 2016; Bray et  al., 2015; Dohnalkova et al., 2017; Gadd, 2010, 2013; Hoffland et al., 2004; Quirk et  al., 2014; Saccone et  al., 2012; Schmalenberger et  al., 2015; Smits & Wallander, 2016; Uroz et al., 2009). Interdisciplinary research on microbial interactions with mineral surfaces is mainly done in laboratory settings (for a recent review see Smits & Wallander, 2016). Although at this scale the interactions can be studied in much detail and under highly controlled conditions, extrapolation to field‐ and global‐scale processes remains difficult. In addition, a number of studies were carried out in mesocosms (Balogh‐ Brunstad, Keller, Gill, et al., 2008; Balogh‐Brunstad et al., 2017; Calvaruso et  al., 2006; Dohnalkova et  al., 2017; Koele et al., 2009; Shi et al., 2014; Wallander & Wickman, 1999) or in field settings using in‐growth mesh bags (Gobran et al., 2005; Koele et al., 2014; Rosenstock et al., 2016; Wallander & Hagerberg, 2004). The more realistic setups are challenging because studying microbe–mineral processes on the scale of the interaction (micro‐ to nanometer scale) poses a difficulty. New developments in microscopic imaging techniques open new opportunities to study microbe–mineral interactions under (near) field conditions. Controlled microcosm experiments have demonstrated the power of the advancement of these techniques. Etch‐pit and channel formation by bacteria and fungi were documented on various silicate mineral surfaces such as feldspars and micas (Balogh‐ Brunstad, Keller, Dickinson, et al., 2008; Balogh‐Brunstad, Keller, Gill, et  al., 2008; Barker et  al., 1998; Bonneville et  al., 2009, 2011; Gazzè et  al., 2012; Miot et  al., 2014; Saccone et al., 2012; Ward et al., 2013). Bonneville et al. (2009, 2011) confirmed mechanical forcing and chemical depletion by single fungal hyphae over a short time period (weeks to months) and determined that iron oxidation in the lattice of biotite is the initiating factor in the biomechanical weathering through fungal interactions (Bonneville et al., 2016). The main goals of these studies were to understand the processes at the microbe–mineral interface (including both bacteria and fungi), describe the reactions, determine how these processes affect water

chemistry and ecosystem nutrient status, and quantify the contribution of bacterial and fungal weathering to large‐ scale (watershed/ecosystem and global) weathering. To accomplish these goals, an integrated approach is needed that ideally connects e­ cosystem‐scale rates with microbial‐ scale processes. How much these microbial actions contribute to ecosystem and global weathering rates is under debate (Smits & Wallander, 2016). Questions still remain regarding the best methods of studying heterogeneous biochemical interfaces. Methods should gain meaningful information on the solid, liquid, and gas phases present at the interface simultaneously. In addition, there are several challenges that need to be overcome in order to quantify these processes to provide information for soil formation, biogeochemical cycles of elements, and climate models. The greatest difficulties are dealing with the heterogeneity, scaling from experiments to real ecosystems, setting up realistic laboratory conditions, field testing the laboratory results, quantifying various components of the system, such as direct versus indirect effects, and bacteria, fungi, plant, and abiotic/inorganic contributions. A multitude of techniques has been tested to visualize the morphology of microbe–mineral attachment, the topography of the minerals and the interface, and to detect and describe chemical and physical changes and composition at the microbe–mineral interface. These techniques include scanning electron microscopy (SEM; Balogh‐Brunstad, Keller, Dickinson, et  al., 2008; Balogh‐Brunstad, Keller, Gill, et al., 2008; Balogh‐Brunstad et al., 2017; Dohnalkova et al., 2011; Glowa et al., 2003; Karcz et al., 2012; Minyard et al., 2011; Pinzari et al., 2016; Turpault et al., 2009), transmission electron microscopy (TEM; Benzerara et al., 2005; Bonneville et  al., 2009, 2011; Dohnalkova et  al., 2011; Knowles et al., 2012; Minyard et al., 2011; Ward et al., 2013), atomic force microscopy (AFM; Balogh‐Brunstad, Keller, Dickinson, et al., 2008; Balogh‐Brunstad et al., 2017; Gazzè et al., 2012, 2013; Grantham & Dove, 1996; Li et al., 2016; Lower et al., 2001; Maurice et al., 2001; McMaster, 2012; Saccone et  al., 2012), helium ion microscopy (HeIM; Dohnalkova et  al., 2017; Joens et  al., 2013; Notte et  al., 2007), vertical scanning interferometry (VSI; Arvidson et al., 2004; Quirk et al., 2012), and complementary spectroscopic techniques, such as X‐ray absorption spectroscopy (XAS; Ginder‐Vogel & Sparks, 2010; Herndon et al., 2014; Miot et  al., 2014; Sparks, 2013; Templeton & Knowles, 2009), secondary ion mass spectroscopy (SIMS; McLoughlin et  al., 2011; Szczepanowska & Goreva, 2014), X‐ray diffraction (XRD; Adamo & Violante, 2000; Arocena & Velde, 2009; Calvaruso et al., 2009; Dohnalkova et al., 2017; Minyard et al., 2011; Price & Velbel, 2014; Singh et al., 2010; Welch & Banfield, 2002; Wongfun et al., 2014), X‐ray fluorescence (XRF; Castillo‐Michel et al., 2017; Hunter et al., 1997; Kemner et  al., 2004, 2005; Majumdar et  al., 2012; Sutton et  al., 2002), Fourier‐transform infrared (FTIR; Hind et al., 2001), Raman (Johnston et al., 2002), nuclear

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  83 (a)

(b)

(c)

Figure 4.1  Examples of scanning electron micrographs (SEM) are shown. (a) Fungal hyphae (*) attached to a hornblende grain and some desiccated biolayer is also observed (#); it was imaged in low voltage mode in high vacuum without conductive coating using an XL30 field emission gun (FEG) SEM. (b) A biotite surface is analyzed from a field sample using environmental SEM mode of an FEI Quanta 3D FEG SEM, fungal hyphae (dark lines) looks embedded in a layer of weathered coating on the mineral surface. (c) Biofilm layer with embedded bacteria (*) on a biotite surface was imaged using low‐temperature (cryo) SEM mode of an FEI Quanta 3D FEG SEM. Scale bars: (a) 2 μm, (b) 20 μm, (c) 4 μm. [Images by Balogh‐Brunstad, unpublished.]

magnetic resonance (NMR; Bleam, 1991), and Mössbauer (McCammon, 2004; Rancourt, 1998) spectroscopies. In this chapter we summarize a subset of these techniques focusing on the applicable and most promising electron, helium ion, and atomic force microscopy techniques and X‐ray‐based spectroscopy with traditional and synchrotron‐based applications. 4.2. ELECTRON MICROSCOPY 4.2.1. Conventional Scanning Electron Microscopy Scanning electron microscopy (SEM) is one of the high‐ resolution surface analytical techniques, which uses a focused electron beam to scan the material surface and produce an image. It is primarily a technique that documents topographic information of bulk surfaces and spatial relationships within the samples (Goldstein et al., 2012). There are various modes of operation. The conventional operation is at ambient temperatures and in high‐vacuum mode, using either a secondary electron or a backscattered electron detector to produce an image of the surface (Goldstein et al., 2012). Studying bacteria– mineral and fungi–mineral interfaces is difficult in this mode, because it requires an involved specimen preparation for biological samples, and a conductive coating to prevent charge buildup on the surfaces of the nonconductive samples, which results in frequent artifact formation, spatial collapse or “deflation” of biological material, and beam damage (Bozzola, 2014). Thus, image interpretation of microbe–mineral interactions should be treated carefully, as the biological material must be desiccated, and is most likely reduced in size, important details may have been washed away during the sample processing, and contracted due to the fixation process, and

the mineral surfaces can also be altered. Nevertheless, the traditional high‐vacuum SEM has been successfully used to ­document the attachment of bacteria and fungi to mineral and rock surfaces, and their spatial relationships on the substrate (Balogh‐Brunstad, Keller, Dickinson, et  al., 2008; Balogh‐Brunstad, Keller, Gill, et  al., 2008; Glowa et  al., 2003; Turpault et  al., 2009). In addition, with the emergence of SEMs that are constructed to work effectively in the low‐voltage regions (mostly with field emission gun electron sources), surface details of nonconductive materials can be achieved at high resolution without the need of conductive layer coatings (Figure 4.1a; Goldstein et al., 2012; Balogh‐Brunstad et al., 2017). These advances in conventional SEM technology reduced cost and preparation time, which increased the accessibility and affordability of SEM to study microbe–mineral interactions. 4.2.2. Environmental Scanning Electron Microscopy A more novel mode of operation that can be used for studying the microbe–mineral interface with preservation of the sample is the environmental scanning electron microscopy (ESEM). This mode allows for imaging samples in their hydrated state without coating with any conductive materials (Egerton, 2016; Kaminskyj & Dahms, 2008). The ESEM uses a series of pressure‐limiting apertures to separate and seal the specimen chamber, which is maintained at higher pressures with gaseous water vapor, from the rest of the electron‐source chambers in a low‐ pressure and high‐vacuum environment (Goldstein et al., 2012). It uses a gaseous secondary electron detector (GSED) that produces positive ions to limit charging of nonconductive samples (Egerton, 2016; Goldstein et al., 2012). While ESEM is useful for hydrated materials with

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controlled relative humidity and pressure 107 times in the specimen chamber than the electron‐source chamber (Egerton, 2016), samples sensitive to dehydration still collapse or deform. Specimen charging remains a problem  for nonconductive materials, which can result in low image resolution and contrast without providing sufficient morphological details of the specimen (Egerton, 2016; Karcz et al., 2012). The great advantage of ESEM is that the samples can be imaged without any preparation, and experiments can be conducted in situ. For example, the ESEM was successfully applied to study properties of weathering products (clays) as water pressure and temperature were manipulated during the experiment (Lin & Cerato, 2014). The ESEM has also been essential in documenting microbial presence on mineral surfaces without altering the sample, thus the samples could be subjected to further analytical work (e.g., Minyard et  al., 2011). Karcz et  al. (2012) demonstrated that sensitive biofilms and EPS layers stayed the most intact using ESEM among other SEM modes, but point out limited contrast and resolution of this technique. However, ESEM scanning causes charge buildup on the surfaces of nonconductive materials, which can be related to composition or structural differences, and it can be utilized in charge contrast imaging (CCI; Watt et al., 2000). This technique has been used to study geological materials for some time (Watt et  al., 2000) and Clode (2006) developed a method to use this technique for biological materials; however, a limitation remains as the specimen surface has to be flat for CCI. Recently, an ESEM with CCI documented dark halos around fungal hyphae on phlogopite surfaces, which was interpreted as fungal alteration of the mineral structure and secretion of organic material from the fungi (Pinzari et al., 2016). ESEM remains to be limited to bulk surface imaging of uncoated materials (e.g., Figure 4.1b). 4.2.3. Low Temperature Scanning Electron Microscopy Low temperature scanning electron microscopy (cryo‐ SEM) offers great promise for studying microbe–mineral interfaces. It has been successfully used to image biological materials in a frozen hydrated state (Dohnalkova et al., 2011; Hess, 2007). Typically, the samples are frozen by quench cooling as they are plunged into a liquid nitrogen slush (–210°C), which results in ice‐ crystal‐free specimens (Goldstein et al., 2012). The most modern field emission cryo‐SEMs are equipped with a cold stage holder and a cryopreparation chamber, where the extra amorphous ice on the specimen surface is sublimated, then the specimen usually is coated with a few nanometers of platinum before it is transferred to the cooled stage of the SEM (Dohnalkova et  al., 2011). Usually, imaging takes place between –180 and –60°C,

but above –135°C water recrystallization damages biological tissue (Kaminskyj & Dahms, 2008), and sensitive materials such as biofilms start to collapse around –90°C (Dohnalkova et al., 2011). Cautious interpretation of the surface morphology is recommended, as certain artifacts are commonly associated with incomplete sublimation of surface moisture (Figure 4.1c) and water recrystallization artifacts such as redistribution and formation of debris on surfaces (Kaminskyj & Dahms, 2008; Karcz et  al., 2012). An additional difficulty arises when internal structures of the microbe–mineral interface are investigated due to limitations of sample preparation. Biological materials typically can be cut using a cryo‐ ultramicrotome if they are prepared outside the cryo‐ SEM (Miot et al., 2014). Materials can be fractured within the cryo sample preparation chamber, but the fracture plane is random and density differences between biological and mineral material can influence the success of the fracture production (Miot et al., 2014). Hammer et al. (2014) visualized the internal structure of biochar and demonstrated exudate deposition in the pores of biochar using cryo‐SEM. Also, volcanic glass was successfully fractured in a cryo‐SEM preparation chamber to study contact between biofilm and the altered glass and/or clay minerals (Cuadros et  al., 2012, 2013). When Balogh‐Brunstad (unpublished) investigated fungi and biotite interactions, the cryofracturing within the preparation chamber did not provide meaningful results because it was difficult to fracture the biotite across its cleavage planes. 4.2.4. Dual‐beam SEM and Focused Ion Beam Sample preparation has been the limiting step in analysis of the microbe–mineral interface as the ultramicrotome, typically used to slice biological materials (ambient and low temperature (cryo) versions), was not capable of slicing brittle and hard materials such as minerals, and did not provide high enough resolution of the small particles embedded in biofilms or encapsulated by the microbes (Miot et  al., 2014). With advances in focused ion beam (FIB) technology (Benzerara et al., 2005; Obst et al., 2005) and the development of the most advanced dual‐beam SEM/FIB instruments, these limitations disappeared. Typically, each system has a gallium ion FIB column and a field emission electron source SEM column, which are aligned at the coincident point where the two beams intersect. Any added detectors are also aligned to the coincident point, which speeds up the process of analyzing the samples (Young & Moore, 2005). The dual‐ beam SEM/FIB systems improve sample manipulations, allow localization of areas of interest, and in combination with other techniques and analytical capabilities, the interactions between microbes and minerals can be investigated in the third dimension with ease at the micro‐ and

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nanoscales (Dohnalkova et al., 2011; Miot et al., 2014). This also provides a very site‐specific investigation of the sample (desirable outcome), contrary to other fracturing methods. Care has to be taken to reduce beam damage because the gallium ion milling is operated at high energy, which causes alterations of the surface up to 20–30 nm in depth (Young & Moore, 2005). To reduce charging and avoid surface alteration by the ion beam, a protective layer of 50–100 nm of gold or platinum is deposited, usually using a sputter coater, which is followed by about a 1 μm deposition of a carbon–platinum alloy on the top of the area of interest using the ion beam before initiation of milling (Young & Moore, 2005). One of the analytical uses of the dual‐beam SEM/ FIB is the “slice and view” which allows for recording of images of successively milled slices (Young & Moore, 2005), and then the stack of SEM micrographs of these consecutive cross‐sections are aligned and the image is  processed to reconstruct the sample in three ­dimensions. This technique can be based on X‐rays (X‐ray tomography) and provide a three‐dimensional high‐­resolution information of the internal structure of any solid material, including the microbe–mineral interface (Yao et  al., 2013). The most advanced dual‐ beam SEM/FIB systems are also equipped with a cryostage and preparation chambers, thus the samples can be analyzed in the frozen stage, preserving the liquids at the interface, which is the most challenging component of the microbe–mineral interface to analyze (Wierzbicki et al., 2013). Another great advantage of the dual‐beam SEM/FIB system is that it revolutionized sample preparation to investigate the microbe–mineral (or biofilm–mineral) interface by keeping the interface intact while providing a thin lamella for transmission electron microscopy (TEM) investigation (Benzerara et  al., 2005; Obst et  al., 2005). This is called the “lift‐out” specimen preparation technique, which was originally developed for the microelectronics industry and it was applied to biomaterials later on (Giannuzzi & Stevie, 2005). Benzerara et  al. (2005) successfully applied this technique to study microbe–mineral interfaces for the first time. It has become more accessible to researchers, but it is still training‐intensive and a very expensive tool. Details of the “lift‐out” processes are found elsewhere (Bonneville et  al., 2009; Dohnalkova et  al., 2011; Giannuzzi & Stevie, 2005), briefly, after the area of interest is selected a protective strip of carbon‐platinum is deposited, then the milling begins on both sides of the strip using gallium ion (30 kV, 1–10 nA beam current), once the trench is big enough, the holder is rotated, a micromanipulator needle is welded to the top of the lamella using tungsten or carbon, and a “U” cut is made to release the lamella. The needle is moved to lift out the lamella, and the sample is

transferred to the TEM grid positioned within the SEM chamber. Once the correct position is achieved, the lamella is welded to the TEM grid and the needle is released. Once the lamella is in place on the TEM grid, it is thinned to 80–100 nm in thickness using the low current gallium ion beam, and the sample is ready for TEM and spectroscopy analysis. There are multiple positions on the TEM grid, thus more than one cross‐section can be prepared, and transferred to the TEM, again cutting down on preparation time. This process preserves the microbe– mineral interface and allows further investigation with TEM and coupled spectroscopic methods. It can also be completed in a cryostate, preserving the frozen liquid portion of the interface (Miot et  al., 2014; Wierzbicki et al., 2013). 4.2.5. Transmission Electron Microscopy Transmission electron microscopy (TEM) is a high‐resolution technique that uses a focused electron beam to study thin sections (< 100 nm) of materials in a vacuum (Egerton, 2016). The condenser lenses focus the electrons into a uniform beam that traverses the sample, then the transmitted beam is focused by the objective lens to form an image. The diffracted electrons can also be captured by focusing the intermediate lens into the back focal plane of the objective lens and generating a diffraction pattern of the imaged area. An image and a diffraction pattern can be recorded for the same area consecutively, which gives an advantage to TEM (Egerton, 2016). Traditionally, it is one of the main tools for microstructural characterization of materials (Fultz & Howe, 2013), and investigation of structures of cells usually combined with staining and labeling to emphasize the regions of interest (Egerton, 2016). With the development of field emission electron guns, the advancement of the optical components and sample preparation techniques (as described in section 4.2.4), and the improvement of various operation modes, TEM has become one of the most powerful tools for studying the microbe–mineral interface (Miot et al., 2014). The analytical functionalities of the TEM were improved by adding optical components and various detectors to the electron gun and advancing computational technology (Egerton, 2016). In addition, the resolution of the TEM was further increased by the development of high‐resolution (HR) TEM (Fultz & Howe, 2013; Welch & Banfield, 2002). Recently developed atomic scale scanning‐transmission electron microscopy (STEM) can provide subatomic‐scale spatial information from a larger area of the specimen than the fixed‐beam mode, using high‐angle annular dark‐field detectors (HAADF; Egerton, 2016). Spatial resolution of structural and chemical compositional changes are one of the main interests in studying

86  BIOGEOCHEMICAL CYCLES (a)

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Figure 4.2 A series of images illustrating site‐specific chemical and physical properties of a FIB lamella of EPS covered biotite collected by transmission electron microscopy (TEM). (a) A TEM image taken using a Tecnai T‐12 (FEI, Hillsboro, OR) at 120 kV, scale bar 50 nm; (b) aberration corrected atomic scale structure of the same biotite imaged with FEI Titan 80–300 STEM using a HAADF detector at 300 kV, scale bar 1 nm; (c) crystal structure of the biotite using SAED; (d) chemical composition of the biotite collected by EDS. [Data provided by Dohnalkova, unpublished.]

the microbe–mineral interface, which can be achieved by adding energy dispersive X‐ray spectroscopy (EDS; described in section 4.5.1), electron energy‐loss spectroscopy (EELS), and selected area electron diffraction (SAED) to TEM, STEM, or HRTEM. Both structural and chemical composition information can be collected on the area of interest (site specific) and provide high spatial resolution to describe the changes at the microbe– mineral interface. The EELS is best for light elements (low atomic number), which have sharp and well‐defined excitation edges (Egerton, 2011). Some portion of the primary electrons undergoes inelastic scattering and loses energy, and the amount of energy lost (small differences in kinetic energy) can be measured with a scintillator (Egerton, 2016). An EELS spectrum contains a zero‐loss peak, representing the sum of unscattered and elastically scattered electrons, and several element‐specific ionization edge peaks (Egerton, 2016). Interpreting the collected data provides information about atomic composition, chemical bonding, oxidation state, surface properties, and element‐specific pair‐distance distribution functions (Egerton, 2011). A SAED analysis can be completed for the same area of the sample. The SAED is a crystallographic experimental technique that adds

structural information to the chemical data, identifies mineral alterations or transformations, and determines formation of new mineral phases in a site‐specific manner (Minyard et al., 2011; Ward et al., 2013). The most powerful aspect of combining SEM/FIB sample preparation with (S)TEM‐EDS/EELS/SAED is the site‐specific co‐localization of chemical and physical information about both the biological and geological material at the interface (Figure 4.2). Analyzing the liquid component of the microbe–mineral interface is still challenging, but the utilization of the cryo‐SEM/FIB and cryo‐ TEM systems allows investigation of both the solid and liquid components at high spatial resolution (Dohnalkova et al., 2011; Miot et al., 2014). Additional breakthroughs in the understanding of microbe–mineral interactions are expected from correlative microscopy combined with complementary spectroscopy and other methods (see review by Miot et al., 2014). The drawbacks of the various TEM systems are that they require intensive training and practice to master the sample preparation and operation of the instruments, and they are very expensive. Availability and affordability are improving through national user laboratories or facilities and research institutions, but these are still limited resources for the research community.

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4.3. HELIUM ION MICROSCOPY Helium ion microscopy (HeIM) is the newest scanning microscopy technique; it became commercially available in 2006 (Economou et  al., 2012). The application and the operating principle of HeIM are similar to SEM, but here a high brightness focused helium ion beam scans across the material surface and provides a very high‐resolution image (0.35 nm; Notte et al., 2007). The helium ion beam is high energy (30 keV) and penetrates deeply into the sample before diverging, so the secondary electrons are detected from a narrow (about beam width) region near the surface providing a sharp image with a long depth of field (micrometers), about five times that of a SEM under the same conditions (Notte et  al., 2007). Getting true surface details from biological materials has been challenging because they consist of low atomic number materials, which cause a decrease in resolution, increase in charge buildup on the nonconductive surface, and increase in noise of the image when using electron microscopes. HeIM overcomes these difficulties as the forward scattered ions penetrate deep into the sample and energy is not deposited on the surface of the sample. As a result, the charge buildup is reduced and the sample damage is minimized (Joens et al., 2013). HeIM can generate high contrast without the need for conductive metal coatings and is able to provide very high‐resolution details about microbe–mineral, soil organic matter (SOM)–mineral, and SOM–microbial community interfaces (Figure 4.3; Dohnalkova et al., 2017; Joens et  al., 2013). The high resolution, the outstanding depth of field, the minimal sample preparation, and the reduced charge on uncoated organic material makes HeIM especially suitable to study the spatial organization of

(a)

(b)

organo–mineral associations, SOM and mineral interactions (Dohnalkova et al., 2017), and opens great potential to investigate bacteria–mineral and fungi–mineral interactions. These qualities make HeIM superior to SEM in imaging microtopography (see Joens et  al. (2013) for a detailed comparison of the two techniques). Analyzing the liquid component of the microbe–mineral interface is still  challenging, and as of now, HeIM is only capable of  analyzing dry materials. However, a potential future combination of cryo‐sample preparation and cryo‐ operating mode with HeIM could open up further possibilities for interrogation of the heterogeneous interface. There is a major disadvantage to HeIM; while it is superior with imaging it cannot provide chemical and structural information because there are no X‐rays produced. 4.4. ATOMIC FORCE MICROSCOPY Atomic force microscopy (AFM), a member of the scanning probe microscopy (SPM) family of techniques, is a surface‐sensitive technique that provides nanometer resolution of surface topography using a sharp tip attached at the end of a flexible cantilever (Binnig et al., 1986). In addition to high‐resolution imaging, this technique is advantageous for studying the surfaces of microbe–mineral interaction, because it requires relatively simple sample preparation and no coating (the sample does not need to be conductive). Most importantly, the sample can be analyzed under ambient conditions, either air or fluid (Ma et al., 2005). The tip is lowered to the surface repeatedly as it scans the sample, and the varying topographical features cause the deflection of the cantilever and tip. The amount of deflection, or force applied

(c)

Figure 4.3 Helium ion micrographs (HeIM) of biological material attached to biotite surfaces from a column experiment. (a) Fungal hypha and associated bacteria; (b) individual bacteria cells attached to a thin biolayer coating of the mineral; (c) very detailed surface features of bacteria cells embedded in extracellular polymeric substances (EPS) matrix. Scale bars: (a) 1 μm, (b) 500 nm, (c) 200 nm. [Images by Balogh‐Brunstad, unpublished.]

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to the sample, causes the displacement of the laser beam from the cantilever to the photodiode. By using the spring constant of the cantilever, deflection is converted to force, and a force versus time readout provides topographic information (Hassenkam et al., 2009). The main modes of operation are contact (nonvibrating) and tapping (vibrating) mode. In contact mode, the tip remains in continuous contact with the surface when scanning over the sample surface. This mode is best for smooth and hard surfaces, but it is not suitable for delicate biological samples because the tip can damage soft samples (Ubbink & Schar‐Zammaretti, 2005). In tapping mode, the cantilever oscillates vertically, near resonance, and this greatly reduces or eliminates any shear forces that could damage either the sample or tip while scanning. Tapping mode is therefore useful for very soft biological materials and highly irregular samples that could damage the tip if dragged across the surface (Johnson et al., 2017). Choosing the right cantilever stiffness and the correct geometry of the tip plays an important role in effective imaging of a surface with the AFM. Commercially available probes are made from Si/SiO2 or Si3N4 (Ubbink & Schar‐Zammaretti, 2005), with tips pyramidal or conical‐ shaped, and with a spring constant ranging from 0.006 N m−1 to 200 N m−1 (Last et  al., 2010). Contact mode imaging (in air and fluid) and tapping mode in fluid on soft biological materials, normally require cantilevers with low spring constants < 0.2 N m−1, whereas a stiffer cantilever (~45 N m−1) is needed for tapping mode in air to reduce noise (Last et al., 2010). A more advanced mode of AFM widely used for characterizing biological samples is force spectroscopy (or force mapping), where the tip‐sample force is measured, providing information about mechanical properties, such as elasticity, adhesion, and variation in composition, of the sample surface, in addition to the topographic information (Cappella & Dietler, 1999; Weisenhorn, 1989). A force curve shows the force felt by the tip when approaching towards, and retracting from, a specific point on the surface, and the information extracted from force curves can be used to build property maps and correlated with the topographical map (Figure 4.4; Hassenkam et al., 2009). Force curves have been used to measure properties such as adhesion forces (Bowen et al., 1999; Diao et al., 2014; Saccone et al., 2012), long‐range interactions (Gillies et al., 2005; Hartley et al., 1999), surface compressibility and elasticity (Hassenkam et al., 2009), and biomolecular bond strengths (Allen et al., 1997; Best et al., 2003). The surface properties can be further investigated with tips that are “functionalized,” which means that various organic or inorganic molecules (chemical probes), or even living bacteria cells (biological probes) are attached to either a tip or to a tipless cantilever (Diao et  al., 2014; Huang et al., 2015; Kendall & Hochella, 2003).

Applications of AFM have been increasing over the past three decades since its development in the early to mid‐1980s (Binnig et  al., 1986). High resolution and surface sensitivity allowed applications to investigate ­ mineral structures, dissolution, and recrystallization of carbonates (Stipp et al., 1994; Teng et al., 2000). Using a fluid cell, mineral dissolution can be initiated by variable fluid properties such as varying pH, ionic strength, organic and inorganic composition, and concentration of  the solutions (Johnson et  al., 2017). Many mineral dissolution experiments have been conducted using ­ “biomolecules” such as oxalate and other organic acids (Haward et al., 2011), and siderophores (Buss et al., 2007; Kendall & Hochella, 2003). It also has been extensively used to visualize and study biological cells and biofilm properties for medical applications, microbiology, molecular biology, soil and environmental sciences (Huang et al., 2015; Kaminskyj & Dahms, 2008; Ma et al., 2005). The AFM has mostly been used to investigate bacteria– mineral interactions during weathering (Grantham & Dove, 1996; Lower et al., 2001; Maurice et al., 2001) and bioleaching in mining applications (Diao et  al., 2014). Force spectroscopy was used in experiments of bacterial adhesion to mineral surfaces under various environmental conditions in order to maximize the effectiveness of bacteria in bioleaching processes (Diao et  al., 2014) and to understand the role of bacteria in contamination transport and soil aggregate stability (Huang et al., 2015). Only a few studies applied AFM and force spectroscopy to fungi–mineral interactions. Fungal hyphae attachment, exudate and biolayer formation, and fungal hyphae sized etching patterns on mica and other mineral surfaces were documented (Balogh‐Brunstad, Keller, Dickinson, et  al., 2008; Balogh‐Brunstad, et  al., 2017; Gazzè et al., 2012, 2013; McMaster, 2012; Saccone et al., 2012). Examples of visualization of fungal attachment to biotite surface are illustrated in Figure  4.5. Force spectroscopy measurements of adhesion forces revealed that fungal hypha’s hydrophobicity and hydrophilicity changes along the length; with tips being most hydrophilic (Saccone et  al., 2012). In addition, the bacteria‐free microcosm experiment also found clear evidence that the hyphae attached to mineral surfaces produce EPS which covers the mineral surface at 10–35 nm in thickness (Saccone et  al., 2012) and forms thicker distinct halos around the fungal hyphae (Gazzè et al., 2013). Li et al. (2016) used AFM in combination with other techniques to quantify polished lizardite (magnesium silicate mineral) dissolution in laboratory fungi growth experiments. With technology advancement, AFM became combined with infrared absorption (IR) techniques, and this novel AFM‐IR approach enabled the research team to record absorption spectra with about a 10 nm spatial resolution to positively identify ancient organic matter trapped in

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0.0

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Figure 4.4  Atomic force microscopy (AFM) height image (a) shows a fungal hypha growing on a silicate substrate and (b) is a close up of the hyphal tip. (c, d) Examples of the associated adhesion maps extracted from the force curve data of the respective height image, indicating higher adhesion (hydrophilic properties) at the hyphal tip. [Images by Saccone, unpublished.]

garnets of a 3.7 Ga metasedimentary rock (Hassenkam et  al., 2017). This has implications for potential use to characterize the exudates at the microbe–mineral interface with a very high spatial resolution. AFM is a great complementary technique to electron microscopy, because it provides measurable three‐dimensional topography at a very high resolution, with some AFMs capable of providing atomic‐scale information (Johnson et  al., 2017). In addition, force spectroscopy allows measurements of the mechanical properties of an

unaltered specimen surface, a measurement that is not available in electron microscopy (Kaminskyj & Dahms, 2008). However, AFM needs to be combined with a range of other techniques to provide information about chemical composition and structure (Oberle‐Kilic et al., 2013). Another limitation is that the investigated area has to be fairly flat and horizontal (< 10°) because at the high spatial resolution of 10 nm (Hassenkam et al., 2017) the field of view is restricted to less than 100 × 100 μm area (Smits et al., 2009).

90  BIOGEOCHEMICAL CYCLES (a)

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Figure 4.5  AFM topographic images of a freshly cleaved biotite surface, where (a) several crystallographic steps are seen, and (b) the same area of the biotite was imaged after colonization by fungal hyphae. (c) The crossing point of two hyphae imaged at higher resolution and (d) a further close up of the crossing point. The scale bar on each image represents the z‐scale of each. [Images by Saccone, unpublished.]

4.5. X‐RAY‐BASED SPECTROSCOPY ANALYSIS 4.5.1. Energy Dispersive X‐ray Spectroscopy Energy dispersive X‐ray spectroscopy (EDS) is one of the most common additions to SEM and TEM. It provides information on the elemental composition of the imaged materials (Goldstein et  al., 2012). As the electron beam is applied to the sample, it excites an electron in an inner shell of an atom and ejects it from the shell. As it leaves, an electron from an outer, higher‐energy shell fills the electron vacancy, and the difference in energies between the two shells can be released as X‐ rays. The X‐rays emitted from a sample are measured using an energy‐dispersive detector. The energy

difference between electron shells is a specific or characteristic value for each element, thus it can be used to identify the elemental composition of the sample (Goldstein et al., 2012). Quantification is possible if the samples are analyzed together with standards of either pure elements, or materials of interest. In any case, the concentration ratio and peak intensity ratio of standards and elements in samples are compared, and the composition is expressed as weight percent of the elements. There are several corrections needed to be included in the calculations if quantitative values are sought, such as absorption and working distance (Z) corrections, which can make the data processing difficult. Details about these calculations are found in Goldstein et al. (2012).

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Recent advances allow EDS mapping, which provides a spatial distribution of elements in the sample and relative abundance of elements over large areas of the sample (Figure  4.6; e.g. McLoughlin et  al., 2011; Pedersen et  al., 2015). One of the advantages of this spectroscopy is that it is fast and inexpensive. On the other hand, it is not surface sensitive, due to the large sampling volume. In addition, the resolution is only about 0.1 wt%, which allows quantification of only major element composition of the sample (Smits et al., 2009). When EDS is coupled with TEM, information is gained from a smaller sample volume as the electron beam travels through a thin specimen (about 100 nm) of  a few square‐nanometers area, and thus is able to provide a higher spatial resolution of the chemical composition of the specimen than when it is used with SEM (Ward et al., 2013).

4.5.2. X‐ray Diffraction X‐ray diffraction (XRD) has been a fundamental tool for crystallography and mineralogy for over a century (Flemming, 2007). Traditional XRD uses monochromatic X‐rays generated when a high voltage is applied to an anode, generally copper. The sample is scanned through a large range of incident angles to produce an X‐ray diffractogram (Singh et al., 2010). XRD has been used to identify minerals based on the crystal structure and long‐range ordering information that is unique to each mineral, so it is limited to crystalline materials (Moore & Reynolds, 1989). The collected diffractograms are compared to mineral databases and identification can be made. While heterogeneous samples such as soils and clays are commonly analyzed with XRD, it requires a complex sample preparation, which uses various identifying characteristics of

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Figure 4.6  (a) An example of a silicate surface covered with bacteria embedded in an extracellular polymeric substances (EPS) matrix, scale bar is 2 μm. The subsequent panels show elemental maps of five elements collected with an energy dispersive X‐ray spectroscopy (EDS) detector of an FEI Quanta 3D FEG SEM, the identity of the mapped element is shown in the lower right‐hand corner of each map. The sample was uncoated and the carbon content of the biological material (b) correlates well with the visual location of the bacteria (a). The other selected elements are more dispersed in the sample (c–f). [Data collected by Balogh‐Brunstad, unpublished.]

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the clays (Arocena et al., 1999). Improvements in the computing power allow separation of overlapping peaks on the X‐ray diffractograms, which also help with heterogeneous samples (Lanson, 1997). However, in studying the microbe–mineral interface, traditional XRD has several limitations. One is that it is a bulk technique. Sample preparation involves producing uniformly sized, randomly oriented fine (ideally 1–2 μm) powder mounts, as a result, the interface is destroyed and no spatial information can be gained. Second, it often requires a relatively large sample volume, and it is a time‐consuming technique (Singh et al., 2010). However, it has been widely used to identify biominerals and weathering products (e.g., Adamo & Violante, 2000; Arocena & Velde, 2009; Calvaruso et al., 2009; Minyard et al., 2011; Price & Velbel, 2014; Welch & Banfield, 2002; Wongfun et al., 2014) because it is available in most geosciences laboratories and it has been the most effective method of identifying mineral transformation through weathering (Flemming, 2007; Moore & Reynolds, 1989). Quantification of the mineral phases in soils and heterogeneous media is also possible through using internal standards, and peak intensities with the application of the Rietveld method (e.g. Hillier, 2000; Prandel et al., 2014; Singh et al., 2010). Development of the microbeam‐XRD and the use of synchrotron radiation significantly improved the spatial resolution of the analysis and sped up the process. A traditional microbeam‐XRD is capable of analyzing samples in forms of: (a) fiber mount (small particles or aggregates); (b) flat samples (x–y mapping of petrographic thin sections), and (c) capillary tubes (anaerobic samples) at a resolution of 10–50 μm (Flemming, 2007; Sutton & Newville, 2014). Sample preparation is reduced to grinding and homogenizing of the sample, or in the case of thin sections, no extra sample preparation is needed (Flemming, 2007; Singh et al., 2010). Microbeam‐ XRD requires only a small amount of sample. Coupling (microbeam‐) XRD with a synchrotron radiation source further improves the resolution to a submicrometer level, and the identification and quantification of trace phases become possible, which is not achievable using conventional X‐ray sources (Singh et  al., 2010). The high‐brilliance synchrotron X‐rays also allow for time‐resolved measurements in situ with minimal sample preparation, which could be applicable to the analysis of the microbe– mineral interface (Lombi & Susini, 2009). In addition, microbeam‐XRD and synchrotron‐radiation‐based XRD reduced the acquisition times to a couple of minutes, thus a high number of samples can be analyzed in a relatively short time (Flemming, 2007; Sutton & Newville, 2014). The increased computing power allows for a lot faster qualitative and quantitative assessment of the results with separating overlapping peaks and fast searches of the mineral databases (Arocena et  al., 2012; Lanson,

1997; Mouas‐Bourbia et al., 2015). Most silicate dissolution is an incongruent process whereby primary minerals weather to secondary (clay) minerals. During these processes, amorphous materials, which cannot be identified with conventional XRD, can also form. However, amorphous materials, thin films, and hydrated samples can be analyzed using the high flux of synchrotron X‐ray sources and specialized instrumental configuration (Singh et al., 2010). Dohnalkova et al. (2017) were able to determine the weight fraction of amorphous materials in a rhizospheric growth medium using a corundum internal standard, peak broadening and intensities in the TOPAS v4.2 program, which can help with interpreting the interactions between microbes and minerals. 4.5.3. Micro‐X‐ray Fluorescence Spectroscopy X‐ray fluorescence spectroscopy (XRF) is a bulk chemical technique and has been used for determining the composition of geologic and synthetic materials for major and some trace elements as oxides (Majumdar et al., 2012; Sutton et al., 2002). Solid, powder, and liquid samples can be measured by XRF, but only in a bulk form, which does not provide the needed information about microbe and mineral interactions (Buhrke et al., 1998). A potential of applying XRF techniques to microbe–mineral interfaces arose with the development of μ‐XRF coupled with high‐ brilliance synchrotron‐sourced small intense X‐ray beams (Lombi & Susini, 2009; Majumdar et al., 2012; Petibois, 2010). Synchrotron μ‐XRF is capable of in situ mapping at nanometer to submicrometer scale with a high sensitivity of multiple elements, negligible sample damage, less elaborate sample preparation, and at ambient or low (cryo) temperatures, which allows for a time and energy efficient analysis (Castillo‐Michel et al., 2017; De Samber et al., 2008; Lombi & Susini, 2009; Majumdar et al., 2012). These advantages make the technique desirable for wider application to investigate diverse matrices of environmental samples such as the microbe–mineral interface. Micro‐XRF has been increasingly applied to study the elemental distribution and localization in soils and sediments (Croudace & Rothwell, 2015; Singer et  al., 2009; Voegelin et al., 2007), atmospheric samples (Cozzi et al., 2012; MacLean et  al., 2011), and biological materials (Castillo‐Michel et al., 2017; Hunter et al., 1997; Kemner et al., 2004, 2005), including medical studies (e.g., Hummer & Rompel, 2013; Vergucht et  al., 2015). The greatest advantages compared to other techniques are that it allows analysis at a high resolution in situ, maintaining the samples’ natural integrity, and, at the same time, it can be combined with other complementary techniques such as μ‐XAS, μ‐XRD, and μ‐FTIR to gain a full understanding of the microenvironment (Kemner et al., 2005; Majumdar et al., 2012; Singer et al., 2009). There are also

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  93

limitations that can compromise resolution as the X‐rays have a high penetrating power, the depth average signal depends on the sample thickness, and the angle of the sample and beam interaction, which can increase the analytical error in highly heterogeneous samples (Castillo‐ Michel et  al., 2017; Lombi & Susini, 2009; Majumdar et  al., 2012). When studying bacteria and fungi interactions with minerals, cell damage cannot be completely ruled out because of the high brilliance of the incident beams (Templeton & Knowles, 2009). Also, the beams may cause alteration of redox‐sensitive elements (Majumdar et al., 2012). It has been applied to the study of biological matrices, but studying microorganisms and their interactions with physical matrices has been limited because the resolution for spectral analysis at the nanometer scale is still challenging (Majumdar et  al., 2012). However, based on the successful applications of μ‐XRF to study soils, sediments, and biota, it is an emerging technology with high potential to probe redox transformations, mineral dissolution, biomineralization processes, and distribution of elements in microorganism, and at the microbe–mineral interface, especially with the continuous development of detector and focusing technologies (Castillo‐Michel et al., 2017; Majumdar et al., 2012). 4.5.4. X‐ray Absorption Spectroscopy Hayes et al. (1987) published the first study using XAS to observe the sorption of selenite and selenate at the goethite and water interface, which initiated and fueled hundreds of studies using bulk XAS for determining sorption properties of metals and oxyanions on hydroxides, oxides, phyllosilicates, humic substances, etc. (e.g., Borda & Sparks, 2007; Brown & Sturchio, 2002). The XAS spectrum is divided into two sections: X‐ray absorption near‐edge spectroscopy (XANES) and the extended X‐ray absorption fine‐structure spectroscopy (EXAFS). The XANES region (at lower energy) is more sensitive to bound electron levels, so it can determine the valence‐state of the metals, and the EXAFS region is more sensitive to the nature of neighboring atoms such as coordination numbers, identity, and distances (Sparks, 2013). It is important to keep in mind that XAS is based on the synchrotron’s capacity to provide a controlled energy‐tunable source of X‐rays, which allows scanning of the specimen with a range of X‐ray energies to obtain the desired information about the element of interest (Lombi & Susini, 2009; Sparks, 2013). XAS can be used for any material (physical and biological matter, and liquids) and it probes an area of several square millimeters and provides information on the local chemical environment of a surface at a nanometer scale resolution (Ginder‐Vogel & Sparks, 2010; Sparks, 2013). It can provide structural information, coordination numbers of

elements, bond distance, orbital symmetries, oxidation state, and presence of multinuclear complexes and precipitate phases (Lombi & Susini, 2009; Sparks, 2013). The main advantages of using XAS are elemental specificity, sensitivity to local chemical and structural state of an element, and the ability to analyze in situ (Ginder‐ Vogel & Sparks, 2010; Senkovska & Bon, 2016; Sparks, 2013). The limitations of XAS include technical difficulties in analyzing alkaline earth cations and their complexes, as well as limitations in determining the type of surface complexes with NO3−, Cl−, and ClO4− on mineral surfaces (Sparks, 2013). Bulk XAS probes a relatively large area (several square millimeters), so the information gained shows an average of the local chemical environment, which can be resolved by using micro‐XAS and micro‐XRF with smaller spot sizes and higher resolution (Sparks, 2013). XAS has played crucial roles in determining mobility and environmental toxicity of many transition metals (Fomina et  al., 2007; Herndon et  al., 2014; Violante et al., 2010), identifying and quantifying iron (hydr)oxide mineralogy (Hansel et  al., 2003, 2005; Singh et  al., 2010), and studying biomineralization processes at the microbe–mineral interface (Miot, Benzerara, Morin, et  al., 2009; Miot, Benzerara, Obst, et  al., 2009). With the advancement of the “tunability and focusability” of high‐brilliance hard X‐rays, and the combination of various synchrotron‐based X‐ray spectroscopy techniques, for example micro‐XRD or micro‐ XRF with micro‐XAS, further improvement can be expected for studying the highly heterogeneous microbe– mineral interfaces (Castillo‐Michel et  al., 2017; Miot et al., 2014; Sutton & Newville, 2014). 4.6. SUMMARY AND FUTURE PERSPECTIVES Bacteria and fungi play significant roles in biogeochemical cycling of elements, transformation of minerals and rocks to soils through biochemical and biomechanical weathering, biomineralization, and bioremediation. Over the past three decades, with the advancement of electron and atomic force microscopy, and X‐ray‐based methodologies, and the development of the helium ion microscope, new discoveries about reactions and interactions at the heterogeneous interface of bacteria– mineral and fungi– mineral were made possible. The increase in accessibility and use of cryo techniques allow the investigation of the liquid phase between the cells and the minerals. With the advancement of X‐ray optics, detectors, computing powers, and technical configurations, the spatial and temporal resolution of analysis has improved, which allows investigation of highly heterogeneous material, and also permits in situ experiments at the micro‐ and nanoscales on minutes to seconds timescales, often with living cells. While electron microscopy is the primary

94  BIOGEOCHEMICAL CYCLES Table 4.1  A summary of the main applications and limitations of each technique discussed and their spatial resolution Spatial resolution (detection limits)

Technique

Applications/advantages

Conventional scanning electron microscopy (SEM)

Three‐dimensional spatial imaging of bulk, dry, solid, conductive materials, high vacuum

10–15 nm

Low‐voltage (LV) SEM

Three‐dimensional spatial imaging of bulk, dry, solid, any materials, high vacuum

10–15 nm

Environmental (E) SEM

Three‐dimensional spatial imaging of specimen in hydrated state (include bio‐geo interface), experiments in situ, minimal sample preparation Three‐dimensional spatial imaging of specimen in frozen hydrated state (include bio‐geo interface), freeze fracturing Site‐specific cross‐sectional analysis of specimen; three‐dimensional image building with tomography; TEM sample preparation Two‐dimensional spatial imaging

0.5–1 μm

Two‐dimensional spatial imaging with ultra‐high resolution Crystal structure and long‐range ordering

Atomic scale

Low temperature (cryo) SEM Dual‐beam SEM‐FIB (focused ion beam) Conventional transmission electron microscopy (TEM) High‐resolution (HR) TEM Selected area electron diffraction (SAED) Electron energy‐loss spectroscopy (EELS) Helium ion microscopy (HeIM) Atomic force microscopy (AFM) Force spectroscopy (FS) AFM Energy dispersive X‐ray spectroscopy (EDS) X‐ray diffraction (XRD) Micro‐XRD Micro‐X‐ray fluorescence (XRF) X‐ray absorption spectroscopy (XAS)

10–15 nm

5–10 nm

0.1–0.2 nm

Challenges and limitations Sophisticated specimen preparation, artifacts, beam damage, need of conductive coatings Deflation of biological cells, surface charging, beam damage Surface charge buildup, lower resolution, and contrast Sublimation and recrystallization artifacts, redistribution and formation of debris Ion beam damage, training intensive Requires very thin samples, preparation, and training intensive

< 0.5 μm

Elemental compositional measurements, best for light elements Three‐dimensional spatial imaging of dry uncoated materials, five times the depth of field of an SEM Three‐dimensional surface microtopography determination Physical property determination through interfacial force measurements; chemical probing Chemical analysis of surfaces and thin‐ sections, elemental maps, coupled with SEM and TEM Crystallographic and mineralogical analysis of crystalline materials

1 nm (0.0001%)

High‐resolution analysis of crystalline and amorphous materials, early detection of weathering transformation Coupled with other techniques on synchrotron beam lines, can provide spatially resolved chemical analysis, mapping in situ Determine the valence‐state of the metals, and coordination numbers, identity, and distances between atoms, any materials (physical, biological, and liquids)

10–50 μm Submicrometer with synchrotron Nanometer to submicrometer

0.25–0.35 nm < 10 nm < 10 nm Depends on applications < 5 um (0.1%) > 0.5 mm

mm (bulk XAS) 5–30 nm (0.001%) with synchrotron

No chemical and structural analysis is possible; only dry samples Requires fairly flat and horizontal (< 10°); restricted field of view, and sample size Quantification requires complicated corrections, large sampling volume (SEM) Complex sample preparation for clays, no spatial information, relatively large sample volume Data analysis requires high computing power Sample thickness and angle influences results, beam damage, alteration of redox‐ sensitive material Hard to analyze alkaline earth cations, their complexes, and some of the anion complexes

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  95

choice for the analysis of microbe–mineral interactions, the use of AFM also increased, because it provides complementary information about true three‐dimensional topography and mechanical surface properties. The superior high resolution of HeIM also opened up new avenues to investigate the microtopography of biological materials on mineral surfaces. The current state‐of‐the‐art (Table  4.1) allows studying the solid, liquid, and gas phases, and the biological interactions with these phases simultaneously at the most advanced third generation synchrotron facilities (e.g., μ‐XRF coupled with μ‐XRD and μ‐XAS). Even at traditional (nonsynchrotron) research facilities, the new equipment allows studying simultaneous chemical and physical characteristics of the microbe–mineral interface at high resolution and in a very site‐specific manner (e.g., cryo‐HRTEM with EDS, EELS, and SAED; AFM force spectroscopy with functionalized tips, or combined with IR spectroscopy). These advances also cut down the sample preparation, artifact generation, sample size requirements, and the analytical time tremendously, and open up exciting new possibilities to further investigate the interactions between bacteria, fungi, and minerals. However, there are still limitations relating to each of, or combinations of, the techniques listed in Table 4.1. In addition, the greatest limitations are accessibility to beam time at the synchrotron facilities, instrument time at the user laboratories, and the usage of various instrument‐ specific data collection software that are incompatible with the regular user software packages. As our understanding and knowledge about the bacteria– mineral and fungi–mineral interfaces advances, it is crucial that the analytical techniques also advance to overcome the current limitations. Further improvements in resolution and detection limits can tackle the micro‐ and nanoscale heterogeneity of the microbe–mineral interface. While more realistic experimental designs of in situ experiments, where simultaneous detection of various physical and chemical parameters, coupled with modeling approaches, could improve the implications of nanoscale mechanisms to large spatial scales (e.g., pedon to watershed to global) and temporal scales. These advances would allow for practical translation of the knowledge to sustainable agriculture, forestry, remediation, and detoxification of the environment. In addition, standardization of collection and analytical software would streamline the data transfer, processing, and interpretation, which could allow finding solutions for a new generation of complex environmental, agricultural, and industrial challenges. ACKNOWLEDGMENTS The People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007‐2013/ under REA grant agreement no.

235879 funded the SEM and EDS work by Balogh‐ Brunstad, which was completed at the Core Facility for Integrated Microscopy with the help of Klaus Qvortrup at University of Copenhagen. The HeIM work was funded by NSF/EAR 09‐52052 grant to Balogh‐ Brunstad, and it was completed at the Environmental Molecular Science Laboratory, at Pacific Northwest National Laboratory, Richland, WA with the help of Bruce Arey, and two Hartwick College undergraduate students: Kyle Greenberg and Sheila Niedziela. The AFM work by Saccone was funded by and completed at the Department of Physics at the University of Bristol, and at the Department of Physics at University of Sheffield. The TEM work by Dohnalkova was also completed at the Environmental Molecular Science Laboratory, at Pacific Northwest National Laboratory, Richland, WA. Authors thank the two reviewers and Dr. Katerina Dontsova for their helpful comments to improve this manuscript, and Dr. David Griffing for his help with English editing. REFERENCES Adamo, P., & Violante, P. (2000). Weathering of rocks and neogenesis of minerals associated with lichen activity. ­ Applied  Clay Science, 16(5), 229–256. doi: 10.1016/ S0169‐1317(99)00056‐3 Allen, S., Chen, X., Davies, J., Davies, M.C., Dawkes, A.C., Edwards, J C., et al. (1997). Detection of antigen–antibody binding events with the atomic force microscope. Biochemistry, 36(24), 7457–7463. doi: 10.1021/bi962531z Arocena, J.M., Glowa, K.R., Massicotte, H.B., & Lavkulich, L. (1999). Chemical and mineral composition of ectomycorrhizosphere soils of subalpine fir (Abies lasiocarpa (Hook.) Nutt.) in the Ae horizon of a luvisol. Canadian Journal of Soil Science, 79(1), 25–35. doi: 10.4141/S98‐037 Arocena, J. M., & Velde, B. (2009). Transformation of chlorites by primary biological agents—a synthesis of X‐ray diffraction studies. Geomicrobiology Journal, 26(6), 382–388. doi: 10.1080/01490450902929316 Arocena, J.M., Velde, B., & Robertson, S.J. (2012). Weathering of biotite in the presence of arbuscular mycorrhizae in selected agricultural crops. Applied Clay Science, 64, 12–17. doi: 10.1016/j.clay.2011.06.013 Arvidson, R.S., Beig, M.S., & Luttge, A. (2004). Single‐crystal plagioclase feldspar dissolution rates measured by vertical scanning interferometry. American Mineralogist, 89(1), 51– 56. doi: 10.2138/am‐2004‐0107 Balogh‐Brunstad, Z., Keller, C.K., Dickinson, J.T., Stevens, F., Li, C.Y., & Bormann, B.T. (2008). Biotite weathering and nutrient uptake by ectomycorrhizal fungus, Suillus tomentosus, in liquid‐culture experiments. Geochimica et Cosmochimica Acta, 72(11), 2601–2618. doi: 10.1016/j.gca.2008.04.003 Balogh‐Brunstad, Z., Keller, C.K., Gill, R.A., Bormann, B.T., & Li, C.Y. (2008). The effect of bacteria and fungi on chemical weathering and chemical denudation fluxes in pine growth

96  BIOGEOCHEMICAL CYCLES experiments. Biogeochemistry, 88(2), 153–167. doi: 10.1007/ s10533‐008‐9202‐y Balogh‐Brunstad, Z., Keller, C.K., Shi, Z., Wallander, H., & Stipp, S.L. (2017). Ectomycorrhizal fungi and mineral interactions in the rhizosphere of Scots and red pine seedlings. Soils, 1(1), 5. doi: 10.3390/soils1010005 Banfield, J.F., Barker, W.W., Welch, S.A., & Taunton, A. (1999). Biological impact on mineral dissolution: application of the lichen model to understanding mineral weathering in the rhizosphere. Proceedings of the National Academy of Sciences, 96(7), 3404–3411. doi: 10.1073/pnas.96.7.3404 Barker, W.W., Welch, S.A., Chu, S., & Banfield, J.F. (1998). Experimental observations of the effects of bacteria on aluminosilicate weathering. American Mineralogist, 83(11), 1551–1563. doi: 10.2138/am‐1998‐1116 Benzerara, K., Yoon, T.H., Menguy, N., Tyliszczak, T., & Brown, G.E. (2005). Nanoscale environments associated with  bioweathering of a Mg–Fe‐pyroxene. Proceedings of the  National Academy of Sciences of the United States of America, 102(4), 979–982. doi: 10.1073/pnas.0409029102 Best, R.B., Brockwell, D.J., Toca‐Herrera, J.L., Blake, A.W., Smith, D.A., Radford, S.E., & Clarke, J. (2003). Force mode atomic force microscopy as a tool for protein folding studies. Analytica Chimica Acta, 479(1), 87–105. doi: 10.1016/ S0003‐2670(02)01572‐6 Binnig, G., Quate, C.F., & Gerber, C. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930. Bleam, W. F. (1991). Soil science applications of nuclear magnetic resonance spectroscopy. Advances in Agronomy, 46, 91–155. doi: 10.1016/S0065‐2113(08)60579‐9 Bonneville, S., Bray, A.W., & Benning, L.G. (2016). Structural Fe (II) oxidation in biotite by an ectomycorrhizal fungi drives mechanical forcing. Environmental Science and Technology, 50(11), 5589–5596. doi: 10.1021/acs.est.5b06178 Bonneville, S., Morgan, D.J., Schmalenberger, A., Bray, A., Brown, A., Banwart, S.A., & Benning, L.G. (2011). Tree‐ mycorrhiza symbiosis accelerate mineral weathering: evidences from nanometer‐scale elemental fluxes at the hypha–mineral interface. Geochimica et Cosmochimica Acta, 75(22), 6988–7005. doi: 10.1016/j.gca.2011.08.041 Bonneville, S., Smits, M.M., Brown, A., Harrington, J., Leake, J.R., Brydson, R., & Benning, L.G. (2009). Plant‐driven fungal weathering: Early stages of mineral alteration at the nanometer scale. Geology, 37(7), 615–618. doi: 10.1130/ G25699A.1 Borda, M.J., & Sparks, D.L. (2007). Kinetics and mechanisms of sorption‐desorption in soils: a multiscale assessment. In A. Violante, P.M. Huang, G.M. Gadd (Eds.), Biophysico‐ chemical processes of heavy metals and metalloids in soils environments (pp. 97–124). Chichester UK: J.Wiley & Sons. Bowen, W.R., Hilal, N., Lovitt, R.W., & Wright, C.J. (1999). An atomic force microscopy study of the adhesion of a silica sphere to a silica surface—effects of surface cleaning. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 157(1), 117–125. doi: 10.1016/S0927‐7757(99)00045‐X Bozzola, J.J. (2014). Conventional specimen preparation techniques for scanning electron microscopy of biological specimens. In J. Kuo, (Ed.), Electron microscopy: methods and protocols. Methods in Molecular Biology, 1117, 133–150. doi 10.1007/978‐1‐62703‐776‐1_7

Bray, A.W., Oelkers, E.H., Bonneville, S., Wolff‐Boenisch, D., Potts, N.J., Fones, G., & Benning, L.G. (2015). The effect of pH, grain size, and organic ligands on biotite weathering rates. Geochimica et Cosmochimica Acta, 164, 127–145. doi: 10.1016/j.gca.2015.04.048 Brown, G.E., & Sturchio, N.C. (2002). An overview of synchrotron radiation applications to low‐temperature geochemistry and environmental science. Reviews in Mineralogy and Geochemistry, 49(1), 1–115. doi: 10.2138/gsrmg.49.1.1 Buhrke, V.E., Jenkins, R., Smith, D.K., & Kingsley, D. (1998). Practical guide for the preparation of specimens for x‐ray fluorescence and x‐ray diffraction analysis. Wiley‐VCH. Burford, E.P., Fomina, M., & Gadd, G.M. (2003). Fungal involvement in bioweathering and biotransformation of rocks and minerals. Mineralogical Magazine, 67(6), 1127– 1155. doi: 10.1180/0026461036760154 Buss, H.L., Lüttge, A., & Brantley, S.L. (2007). Etch pit formation on iron silicate surfaces during siderophore‐promoted dissolution. Chemical Geology, 240(3), 326–342. doi: 10.1016/j.chemgeo.2007.03.003 Calvaruso, C., Mareschal, L., Turpault, M.P., & Leclerc, E. (2009). Rapid clay weathering in the rhizosphere of Norway spruce and oak in an acid forest ecosystem. Soil Science Society of America Journal, 73(1), 331–338. doi: 10.2136/ sssaj2007.0400 Calvaruso, C., Turpault, M.P., & Frey‐Klett, P. (2006). Root‐ associated bacteria contribute to mineral weathering and to mineral nutrition in trees: a budgeting analysis. Applied and Environmental Microbiology, 72(2), 1258–1266. doi: 10.1128/ AEM.72.2.1258‐1266.2006 Cappella, B.,. & Dietler, G. (1999) Force–distance curves by atomic force microscopy. Surface Science Reports, 34(1–3), 1–104. doi: 10.1016/S0167‐5729(99)00003‐5 Castillo‐Michel, H. A., Larue, C., del Real, A. E. P., Cotte, M., & Sarret, G. (2017). Practical review on the use of synchrotron based micro‐and nano‐X‐ray fluorescence mapping and X‐ray absorption spectroscopy to investigate the interactions between plants and engineered nanomaterials. Plant Physiology and Biochemistry, 110, 13–32. doi: 10.1016/j. plaphy.2016.07.018 Clode, P. L. (2006). Charge contrast imaging of biomaterials in a variable pressure scanning electron microscope. Journal of Structural Biology, 155(3), 505–511. doi: 10.1016/j. jsb.2006.04.004 Cozzi, F., Gržinić, G., Cozzutto, S., Barbieri, P., Bovenzi, M., & Adami, G. (2012). Dimensional characterization of selected elements in airborne PM10 samples using μ‐SRXRF. X‐Ray Spectrometry, 41(1), 34–41. doi: 10.1002/xrs.1377 Croudace, I.W., & Rothwell, R.G. (Eds.). (2015). Micro‐XRF studies of sediment cores: Applications of a non‐destructive tool for the environmental sciences (Vol. 17). Springer‐Verlag. Cuadros, J., Afsin, B., Jadubansa, P., Ardakani, M., Ascaso, C., & Wierzchos, J. (2013). Microbial and inorganic control on the composition of clay from volcanic glass alteration experiments. American Mineralogist, 98(2–3), 319–334. doi: 10.2138/am.2013.4272 Cuadros, J., Afsin, B., Michalski, J.R., & Ardakani, M. (2012). Fast, microscale‐controlled weathering of rhyolitic obsidian to quartz and alunite. Earth and Planetary Science Letters, 353, 156–162. doi: 10.1016/j.epsl.2012.08.009

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  97 De Samber, B., Silversmit, G., Evens, R., De Schamphelaere, K., Janssen, C., Masschaele, B., et al. (2008). Three‐dimensional elemental imaging by means of synchrotron radiation micro‐XRF: developments and applications in environmental chemistry. Analytical and Bioanalytical Chemistry, 390(1), 267–271. doi: 10.1007/s00216‐007‐1694‐0 Diao, M., Taran, E., Mahler, S., Nguyen, T.A., & Nguyen, A.V. (2014). Quantifying adhesion of acidophilic bioleaching bacteria to silica and pyrite by atomic force microscopy with a bacterial probe. Colloids and Surfaces B: Biointerfaces, 115, 229–236. Dohnalkova, A.C., Marshall, M.J., Arey, B.W., Williams, K.H., Buck, E.C., & Fredrickson, J.K. (2011). Imaging hydrated microbial extracellular polymers: comparative analysis by electron microscopy. Applied and Environmental Microbiology, 77(4), 1254–1262. doi: 10.1128/AEM.02001‐10 Dohnalkova, A.C., Tfaily, M.M., Smith, A.P., Chu, R.K., Crump, A.R., Brislawn, C.J., et al. (2017). Molecular and microscopic insights into the formation of soil organic matter in a red pine rhizosphere. Soils, 1(1), 4. doi: 10.3390/soils1010004 Economou, N.P., Notte, J.A., & Thompson, W.B. (2012). The history and development of the helium ion microscope. Scanning, 34(2), 83–89. doi: 10.1002/sca.20239 Egerton, R.F. (2011). Electron energy‐loss spectroscopy in the electron microscope, 3rd edn. Springer Science & Business Media. Egerton, R. F. (2016). Physical principles of electron microscopy: An introduction to TEM, SEM, and AEM, 2nd edn. Springer‐Verlag. Flemming, R. L. (2007). Micro X‐ray diffraction (μXRD): a versatile technique for characterization of Earth and planetary materials. Canadian Journal of Earth Sciences, 44(9), 1333–1346. doi: 10.1139/e07‐020 Fomina, M., Charnock, J., Bowen, A.D., & Gadd, G.M. (2007). X‐ray absorption spectroscopy (XAS) of toxic metal mineral transformations by fungi. Environmental Microbiology, 9(2), 308–321. doi: 10.1111/j.1462‐2920.2006.01139.x Fultz, B., & Howe, J.M. (2013). Transmission electron microscopy and diffractometry of materials, 4th edn. Springer Science & Business Media. Gadd, G.M. (2010). Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology, 156(3), 609–643. doi: 10.1099/mic.0.037143‐0 Gadd, G.M. (2013). Microbial roles in mineral transformations and metal cycling in the Earth’s critical zone. In J. Xu, D. L. Sparks (Eds.), Molecular environmental soil science (pp. 115–165). Dordrecht, Netherlands: Springer. doi: 10.1007/978‐94‐007‐4177‐5_6 Gazzè, S.A., Saccone, L., Ragnarsdottir, K.V., Smits, M.M., Duran, A.L., Leake, J.R., Banwart, S.A. & McMaster, T.J. (2012). Nanoscale channels on ectomycorrhizal‐colonized chlorite: Evidence for plant‐driven fungal dissolution. Journal of Geophysical Research: Biogeosciences, 117(G3). doi: 10.1029/2012JG002016 Gazzè, S.A., Saccone, L., Smits, M.M., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdottir, K.V., & McMaster, T.J. (2013). Nanoscale observations of extracellular polymeric substances deposition on phyllosilicates by an ectomycorrhizal fungus. Geomicrobiology Journal, 30(8), 721–730. doi: 10.1080/01490451.2013.766285

Giannuzzi, L.A., & Stevie, F.A. (2005) Introduction to focused ion beams. Boston, MA: Springer. Gillies, G., Kappl, M., & Butt, H.J. (2005). Surface and capillary forces encountered by zinc sulfide microspheres in aqueous electrolyte. Langmuir, 21(13), 5882–5886. doi: 10.1021/la050226l Ginder‐Vogel, M., & Sparks, D.L. (2010). The impacts of X‐ray absorption spectroscopy on understanding soil processes and reaction mechanisms. Developments in soil science, 34, 1–26. doi: 10.1016/S0166‐2481(10)34001‐3 Glowa, K.R., Arocena, J.M., & Massicotte, H.B. (2003). Extraction of potassium and/or magnesium from selected soil minerals by Piloderma. Geomicrobiology Journal, 20(2), 99–111. doi: 10.1080/01490450303881 Gobran, G.R., Turpault, M.P. & Courchesne, F. (2005). Contribution of rhizospheric processes to mineral weathering in forest soils. In P.M. Huang, G.R. Gobran (Eds.), Biogeochemistry of trace elements in the rhizosphere (pp. 3– 28). Amsterdam: Elsevier B.V. Goldstein, J., Newbury, D.E., Echlin, P., Joy, D.C., Romig Jr, A.D., Lyman, C.E., Fiori, C., & Lifshin, E. (2012). Scanning electron microscopy and X‐ray microanalysis: a text for biologists, materials scientists, and geologists. Springer Science & Business Media. Grantham, M.C., & Dove, P.M. (1996). Investigation of bacterial–mineral interactions using Fluid Tapping Mode™ atomic force microscopy. Geochimica et Cosmochimica Acta, 60(13), 2473–2480. doi: 10.1016/0016‐7037(96)00155‐X Hammer, E.C., Balogh‐Brunstad, Z., Jakobsen, I., Olsson, P. A., Stipp, S.L., & Rillig, M.C. (2014). A mycorrhizal fungus grows on biochar and captures phosphorus from its surfaces. Soil Biology and Biochemistry, 77, 252–260. doi: 10.1016/j. soilbio.2014.06.012 Hansel, C. M., Benner, S. G., & Fendorf, S. (2005). Competing Fe (II)‐induced mineralization pathways of ferrihydrite. Environmental Science and Technology, 39(18), 7147–7153. doi: 10.1021/es050666z Hansel, C.M., Benner, S. G., Neiss, J., Dohnalkova, A., Kukkadapu, R.K., & Fendorf, S. (2003). Secondary mineralization pathways induced by dissimilatory iron reduction of ferrihydrite under advective flow. Geochimica et Cosmochimica Acta, 67(16), 2977–2992. doi: 10.1016/S0016‐7037(03)00276‐X Hartley, P.G., Grieser, F., Mulvaney, P., & Stevens, G.W. (1999). Surface forces and deformation at the oil−water interface probed using AEM force measurement. Langmuir, 15(21), 7282–7289. doi: 10.1021/la9817563 Hassenkam, T., Skovbjerg, L. L., & Stipp, S. L. S. (2009). Probing the intrinsically oil‐wet surfaces of pores in North Sea chalk at subpore resolution. Proceedings of the National Academy of Sciences, 106(15), 6071–6076. doi: 10.1073/ pnas.0901051106 Hassenkam, T., Andersson, M.P., Dalby, K.N., Mackenzie, D.M.A., & Rosing, M.T. (2017). Elements of Eoarchean life trapped in mineral inclusions. Nature, 548(7665), 78–81. doi: 10.1038/nature23261 Haward, S.J., Smits, M.M., Ragnarsdóttir, K.V., Leake, J.R., Banwart, S.A., & McMaster, T.J. (2011). In situ atomic force microscopy measurements of biotite basal plane reactivity in the presence of oxalic acid. Geochimica et Cosmochimica Acta, 75(22), 6870–6881. doi: /10.1016/j.gca.2011.09.010

98  BIOGEOCHEMICAL CYCLES Hayes, K.F., Roe, A.L., Brown Jr, G.E., Hodgson, K.O., Leckie, J.O., & Parks, G.A. (1987). In situ X‐ray absorption study of surface complexes: selenium oxyanions on alpha‐FeOOH. Science, 238(4828), 783–787. Herndon, E.M., Martínez, C.E., & Brantley, S.L. (2014). Spectroscopic (XANES/XRF) characterization of contaminant manganese cycling in a temperate watershed. Biogeochemistry, 121(3), 505–517. doi:10.1007/s10533‐ 014‐0018‐7 Hess, M.W. (2007). Cryopreparation methodology for plant cell biology. Methods in Cell Biology, 79, 57–100. doi: 10.1016/ S0091‐679X(06)79003‐3 Hillier, S. (2000). Accurate quantitative analysis of clay and other minerals in sandstones by XRD: comparison of a Rietveld and a reference intensity ratio (RIR) method and the importance of sample preparation. Clay Minerals, 35(1), 291–302. doi: 10.1180/000985500546666 Hind, A.R., Bhargava, S.K., & McKinnon, A. (2001). At the solid/liquid interface: FTIR/ATR—the tool of choice. Advances in Colloid and Interface Science, 93(1), 91–114. doi: 10.1016/S0001‐8686(00)00079‐8 Hoffland, E., Kuyper, T.W., Wallander, H., Plassard, C., Gorbushina, A.A., Haselwandter, K., et al. (2004). The role of fungi in weathering. Frontiers in Ecology and the Environment, 2(5), 258–264. doi: 10.1890/1540‐9295(2004)002[0258:TROFI W]2.0.CO;2 Huang, Q., Wu, H., Cai, P., Fein, J. B., & Chen, W. (2015). Atomic force microscopy measurements of bacterial adhesion and biofilm formation onto clay‐sized particles. Nature: Scientific Reports, 5. doi: 10.1038/srep16857 Hummer, A.A., & Rompel, A. (2013). The use of X‐ray absorption and synchrotron based micro‐X‐ray fluorescence spectroscopy to investigate anti‐cancer metal compounds in vivo and in vitro. Metallomics, 5(6), 597–614. doi: 10.1039/ C3MT20261E Hunter, D.B., Bertsch, P.M., Kemner, K.M., & Clark, S.B. (1997). Distribution and chemical speciation of metals and metalloids in biota collected from contaminated environments by spatially resolved XRF, XANES, and EXAFS. Le Journal de Physique IV, 7(C2), C2–767. doi: 10.1051/ jp4:1997232 Jenny, H. (1941) Factors of soil formation; a system of quantitative pedology. New York: McGraw‐Hill. Jenny, H. (1980) The soil resource, origin and behaviour, New York: Springer‐Verlag. Joens, M. S., Huynh, C., Kasuboski, J. M., Ferranti, D., Sigal, Y. J., Zeitvogel, F., … & Stern, L. A. (2013). Helium ion microscopy (HIM) for the imaging of biological samples at sub‐nanometer resolution. Nature: Scientific Reports, 3, 3514. doi:10.1038/srep03514 Johnson, D., Oatley‐Radcliffe, D.L., & Hilal, N. (2017). Atomic force microscopy (AFM). In N. Hilal, A.F. Ismail, T. Matsuura, & D. Oatley‐Radcliffe (Eds.), Membrane characterization (pp. 115–144). Amsterdam: Elsevier. doi:10.1016/ B978‐0‐444‐63776‐5.00007‐3 Johnston, C.T., Wang, S.L., Bish, D.L., Dera, P., Agnew, S.F., & Kenney, J.W. (2002). Novel pressure‐induced phase transformations in hydrous layered materials. Geophysical Research Letters, 29(16). doi: 10.1029/2002GL015402

Kaminskyj, S.G., & Dahms, T.E. (2008). High spatial resolution  surface imaging and analysis of fungal cells using SEM  and AFM. Micron, 39(4), 349–361. doi: 10.1016/j. micron.2007.10.023 Karcz, J., Bernas, T., Nowak, A., Talik, E., & Woznica, A. (2012). Application of lyophilization to prepare the nitrifying bacterial biofilm for imaging with scanning electron microscopy. Scanning, 34(1), 26–36. doi: 10.1002/sca.20275 Kemner, K.M., Kelly, S.D., Lai, B., Maser, J., O’loughlin, E.J., Sholto‐Douglas, D., et al. (2004). Elemental and redox analysis of single bacterial cells by X‐ray microbeam analysis. Science, 306(5696), 686–687. doi: 10.1126/science.1103524 Kemner, K.M., O’Loughlin, E.J., Kelly, S.D., & Boyanov, M.I. (2005). Synchrotron X‐ray investigations of mineral– microbe–metal interactions. Elements, 1(4), 217–221. doi: 10.2113/gselements.1.4.217 Kendall, T.A., & Hochella, M.F. (2003). Measurement and interpretation of molecular‐level forces of interaction between the siderophore azotobactin and mineral surfaces. Geochimica et Cosmochimica Acta, 67(19), 3537–3546. doi: 10.1016/S0016‐7037(03)00166‐2 Knowles, E., Wirth, R., & Templeton, A. (2012). A comparative analysis of potential biosignatures in basalt glass by FIB‐ TEM. Chemical Geology, 330, 165–175. doi: 10.1016/j. chemgeo.2012.08.028 Koele, N., Turpault, M.P., Hildebrand, E.E., Uroz, S., & Frey‐ Klett, P. (2009). Interactions between mycorrhizal fungi and mycorrhizosphere bacteria during mineral weathering: budget analysis and bacterial quantification. Soil Biology and  Biochemistry, 41(9), 1935–1942. doi: 10.1016/j.soilbio. 2009.06.017 Koele, N., Dickie, I.A., Blum, J.D., Gleason, J.D., & de Graaf, L. (2014). Ecological significance of mineral weathering in ectomycorrhizal and arbuscular mycorrhizal ecosystems from a field‐based comparison. Soil Biology and Biochemistry, 69, 63–70. doi: 10.1016/j.soilbio.2013.10.041 Landeweert, R., Hoffland, E., Finlay, R.D., Kuyper, T.W. and van Breemen, N. (2001). Linking plants to rocks: Ectomycorrhizal fungi mobilize nutrients from minerals. Trends in Ecology and Evolution, 16, 248–254. doi: 10.1016/ S0169‐5347(01)02122‐X Lanson, B. (1997). Decomposition of experimental X‐ray diffraction patterns (profile fitting): a convenient way to study clay minerals. Clays and Clay Minerals, 45(2), 132–146. Last, J.A., Russell, P., Nealey, P.F., & Murphy, C.J. (2010). The applications of atomic force microscopy to vision science. Investigative Ophthalmology and Visual Science, 51(12), 6083–6094. doi: 10.1167/iovs.10‐5470 Leake, J.R., Duran, A.L., Hardy, K.E., Johnson, I., Beerling, D.J., Banwart, S.A., & Smits, M.M. (2008). Biological weathering in soil: the role of symbiotic root‐associated fungi biosensing minerals and directing photosynthate‐energy into grain‐scale mineral weathering. Mineralogical Magazine, 72(1), 85–89. doi: 10.1180/minmag.2008.072.1.85 Li, Z., Liu, L., Chen, J., & Teng, H.H. (2016). Cellular dissolution at hypha‐ and spore–mineral interfaces revealing ­unrecognized mechanisms and scales of fungal weathering. Geology, 44(4), 319–322. doi: 10.1130/G37561.1

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  99 Lin, B., & Cerato, A.B. (2014). Applications of SEM and ESEM in microstructural investigation of shale‐weathered expansive soils along swelling–shrinkage cycles. Engineering Geology, 177, 66–74. doi: 10.1016/j.enggeo.2014.05.006 Lombi, E., & Susini, J. (2009). Synchrotron‐based techniques for plant and soil science: opportunities, challenges and future perspectives. Plant and Soil, 320(1–2), 1–35. doi: 10.1007/s11104‐008‐9876‐x Lower, S.K., Tadanier, C.J., & Hochella Jr, M.F. (2001). Dynamics of the mineral? Microbe interface: Use of biological force microscopy in biogeochemistry and geomicrobiology. Geomicrobiology Journal, 18(1), 63–76. doi: 10.1080/01490450151079798 Ma, H., Snook, L.A., Kaminskyj, S.J.G., & Dahms, T.E.S. (2005). Surface ultrastructure and elasticity in growing tips and mature regions of Aspergillus hyphae describe wall maturation. Microbiology, 151, 3679–3688. doi: 10.1099/ mic.0.28328‐0 MacLean, L.C., Beauchemin, S., & Rasmussen, P.E. (2011). Lead speciation in house dust from Canadian urban homes using EXAFS, micro‐XRF, and micro‐XRD. Environmental Science and Technology, 45(13), 5491–5497. doi: 10.1021/ es2001503 Majumdar, S., Peralta‐Videa, J.R., Castillo‐Michel, H., Hong, J., Rico, C.M., & Gardea‐Torresdey, J.L. (2012). Applications of synchrotron μ‐XRF to study the distribution of biologically important elements in different environmental matrices: a review. Analytica Chimica Acta, 755, 1–16. doi: 10.1016/j. aca.2012.09.050 Maurice, P.A., Vierkorn, M.A., Hersman, L.E., Fulghum, J.E., & Ferryman, A. (2001). Enhancement of kaolinite dissolution by an aerobic Pseudomonas mendocina bacterium. Geomicrobiology Journal, 18(1), 21–35. doi: 10.1080/01490450151079752 McCammon, C.A. (2004). Mössbauer spectroscopy: applications. Spectroscopic Methods in Mineralogy, 6, 369–398. McLoughlin, N., Wacey, D., Kruber, C., Kilburn, M.R., Thorseth, I.H., & Pedersen, R.B. (2011). A combined TEM and NanoSIMS study of endolithic microfossils in altered seafloor basalt. Chemical Geology, 289(1), 154–162. doi: 10.1016/j.copbio.2012.05.006 McMaster, T.J. (2012). Atomic Force Microscopy of the fungi– mineral interface: applications in mineral dissolution, weathering and biogeochemistry. Current Opinion in Biotechnology, 23(4), 562–569. doi: 10.1016/j.copbio.2012.05.006 Minyard, M.L., Bruns, M.A., Martínez, C.E., Liermann, L.J., Buss, H.L., & Brantley, S.L. (2011). Halloysite nanotubes and bacteria at the saprolite–bedrock interface, Rio Icacos watershed, Puerto Rico. Soil Science Society of America Journal, 75(2), 348–356. doi: 10.2136/sssaj2010.0126nps Miot, J., Benzerara, K., & Kappler, A. (2014). Investigating microbe–mineral interactions: Recent advances in X‐ray and electron microscopy and redox‐sensitive methods. Annual Review of Earth and Planetary Sciences, 42, 271–289. doi: 10.1146/annurev‐earth‐050212‐124110 Miot, J., Benzerara, K., Morin, G., Kappler, A., Bernard, S., Obst, M., et  al. (2009). Iron biomineralization by anaerobic  neutrophilic iron‐oxidizing bacteria. Geochimica et  Cosmochimica Acta, 73(3), 696–711. doi: 10.1016/j. gca.2008.10.033

Miot, J., Benzerara, K., Obst, M., Kappler, A., Hegler, F., Schädler, S., et  al. (2009). Extracellular iron biomineralization by photoautotrophic iron‐oxidizing bacteria. Applied and Environmental Microbiology, 75(17), 5586–5591. doi: 10.1128/AEM.00490‐09 Moore, D.M., & Reynolds, R.C. (1989). X‐ray diffraction and the identification and analysis of clay minerals (Vol. 378, p. 155). Oxford: Oxford University Press. Mouas‐Bourbia, S., Barre, P., Kaci, M.B.N., Mouffok, M., Rebbouh, M., Kessouri, L., et al. (2015). Influence of Olea europea L. and Ficus carrica L. fine root activity on the K biodisponibility and clay mineralogy of the rhizosphere. Eurasian Journal of Soil Science, 4(4), 220. Notte, J., Ward, B., Economou, N., Hill, R., Percival, R., Farkas, L., & McVey, S. (2007). An introduction to the helium ion microscope. In AIP Conference Proceedings (Vol. 931, No. 1, pp. 489–496). College Park, MD: American Institute of Physics. Oberle‐Kilic, J., Dighton, J., & Arbuckle‐Keil, G., 2013. Atomic force microscopy and micro‐ATR‐FT‐IR imaging reveals fungal enzyme activity at the hyphal scale of resolution. Mycology, 4, 44–53. doi: 10.1080/21501203.2012.759631 Obst, M., Gasser, P., Mavrocordatos, D., & Dittrich, M. (2005). TEM‐specimen preparation of cell/mineral interfaces by focused ion beam milling. American Mineralogist, 90(8–9), 1270–1277. doi: 10.2138/am.2005.1743 Pedersen, L.E.R., McLoughlin, N., Vullum, P.E., & Thorseth, I.H. (2015). Abiotic and candidate biotic micro‐alteration textures in subseafloor basaltic glass: a high‐resolution in‐ situ textural and geochemical investigation. Chemical Geology, 410, 124–137. doi: 10.1016/j.chemgeo.2015.06.005 Petibois, C. (2010). Imaging methods for elemental, chemical, molecular, and morphological analyses of single cells. Analytical and Bioanalytical Chemistry, 397(6), 2051–2065. doi: 10.1007/s00216‐010‐3618‐7 Pinzari, F., Cuadros, J., Napoli, R., Canfora, L., & Bardají, D.B. (2016). Routes of phlogopite weathering by three fungal strains. Fungal Biology, 120(12), 1582–1599. doi: 10.1016/j. funbio.2016.08.007 Prandel, L.V., Saab, S.C., Brinatti, A.M., Giarola, N.F.B., Leite, W.C., & Cassaro, F.A.M. (2014). Mineralogical analysis of clays in hardsetting soil horizons, by X‐ray fluorescence and X‐ray diffraction using Rietveld method. Radiation Physics and Chemistry, 95, 65–68. doi: 10.1016/j.radphyschem.2012.12.017 Price, J.R., & Velbel, M.A. (2014). Rates of biotite weathering, and clay mineral transformation and neoformation, determined from watershed geochemical mass‐balance methods for the Coweeta Hydrologic Laboratory, Southern Blue Ridge Mountains, North Carolina, USA. Aquatic Geochemistry, 20(2–3), 203–224. doi: 10.1007/s10498‐013‐9190‐y Quirk, J., Andrews, M.Y., Leake, J.R., Banwart, S.A., & Beerling, D.J. (2014). Ectomycorrhizal fungi and past high CO2 atmospheres enhance mineral weathering through increased below‐ground carbon‐energy fluxes. Biology Letters, 10(7), 20140375. doi: 10.1098/rsbl.2014.0375 Quirk, J., Beerling, D.J., Banwart, S.A., Kakonyi, G., Romero‐ Gonzalez, M.E., & Leake, J.R. (2012). Evolution of trees and mycorrhizal fungi intensifies silicate mineral weathering. Biology Letters, 8(6), 1006–1011. doi: 10.1098/rsbl.2012.0503

100  BIOGEOCHEMICAL CYCLES Rancourt, D.G. (1998). Mössbauer spectroscopy in clay ­science. Hyperfine interactions, 117(1), 3–38. doi: 10.1023/A: 1012651628508 Rosenstock, N.P., Berner, C., Smits, M.M., Krám, P., & Wallander, H. (2016). The role of phosphorus, magnesium and potassium availability in soil fungal exploration of mineral nutrient sources in Norway spruce forests. New Phytologist, 211(2), 542–553. doi: 10.1111/nph.13928 Saccone, L., Gazzè, S.A., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdóttir, K.V., Smits, M.M. & McMaster, T.J. (2012). High resolution characterization of ectomycorrhizal fungal–mineral interactions in axenic microcosm experiments. Biogeochemistry, 111(1–3), 411–425. doi: 10.1007/ s10533‐011‐9667‐y Schmalenberger, A., Duran, A.L., Bray, A.W., Bridge, J., Bonneville, S., Benning, L. G., et al. (2015). Oxalate secretion by ectomycorrhizal Paxillus involutus is mineral‐specific and controls calcium weathering from minerals. Nature: Scientific Reports, 5. doi: 10.1038/srep12187 Senkovska, I., & Bon, V. (2016). In situ X‐ray diffraction and XAS Methods. In: S. Kaskel (Ed.), The chemistry of metal? Organic frameworks: Synthesis, characterization, and applications (pp. 691–727). Holboken, NJ: John Wiley & Sons, Inc. doi: 10.1002/9783527693078.ch23 Shi, Z., Balogh‐Brunstad, Z., Grant, M., Harsh, J., Gill, R., Thomashow, L., et al. (2014). Cation uptake and allocation by red pine seedlings under cation‐nutrient stress in a column growth experiment. Plant and Soil, 378(1–2), 83–98. doi: 10.1007/s11104‐013‐2016‐2 Singer, D.M., Zachara, J.M., & Brown Jr, G.E. (2009). Uranium speciation as a function of depth in contaminated Hanford sediments—a micro‐XRF, micro‐XRD, and micro‐and bulk‐ XAFS study. Environmental Science and Technology, 43(3), 630–636. doi: 10.1021/es8021045 Singh, B., Gräfe, M., Kaur, N., & Liese, A. (2010). Applications of synchrotron‐based X‐ray diffraction and X‐ray absorption spectroscopy to the understanding of poorly crystalline and metal‐substituted iron oxides. Developments in Soil Science, 34, 199–254. doi: 10.1016/S0166‐2481(10)34008‐6 Smits, M.M., Herrmann, A.M., Duane, M., Duckworth, O.W., Bonneville, S., Benning, L.G. & Lundström, U. (2009). The fungal–mineral interface: Challenges and considerations of micro‐analytical developments. Fungal Biology Reviews, 23, 122–131. doi: 10.1016/j.fbr.2009.11.001 Smits, M.M., & Wallander, H. (2016). Role of mycorrhizal symbiosis in mineral weathering and nutrient mining from soil parent material. In N. Johnson, C. Gehring, J. Jansa (Eds.), Mycorrhizal mediation of soil: Fertility, structure, and carbon storage, (pp. 35–46). Amsterdam: Elsevier. doi: 10.1016/ B978‐0‐12‐804312‐7.00003‐6 Sparks, D.L. (2005). Toxic metals in the environment: the role  of surfaces. Elements, 1(4), 193–197. doi: 10.2113/ gselements.1.4.193 Sparks, D.L. (2013). Advances in the use of synchrotron radiation to elucidate environmental interfacial reaction processes and mechanisms in the earth’s critical zone. In J. Xu, D.L. Sparks (Eds.), Molecular environmental soil science (pp. 93–114). Dordrecht, Netherlands: Springer. doi 10.1007/978‐94‐007‐ 4177‐5 5.

Stipp, S.L.S., Eggleston, C.M., & Nielsen, B.S. (1994). Calcite surface structure observed at microtopographic and molecular scales with atomic force microscopy (AFM). Geochimica et Cosmochimica Acta, 58(14), 3023–3033. doi: 10.1016/ 0016‐7037(94)90176‐7 Sutton, S.R., Bertsch, P.M., Newville, M., Rivers, M., Lanzirotti, A., & Eng, P. (2002) Microfluorescence and microtomography analyzes of heterogeneous earth and environmental materials. In P. Fenter, M. Rivers, N. Sturchio, S. Sutton (Eds.), Reviews in mineralogy and geochemistry: Applications of synchrotron radiation in low‐temperature and environmental science (Vol. 49, pp. 429–483). Washington, DC: Mineralogical Society of America. doi: 10.2138/rmg.2002.49. Sutton, S.R., & Newville, M. (2014). Synchrotron X‐ray spectroscopic analysis. In H.D. Holland, K.K. Turekian (Eds.), Treatise on geochemistry, 2nd edn (Vol. 15, pp. 213–230). Amsterdam: Elsevier. doi: 10.1016/B978‐0‐08‐095975‐7.01415‐7 Szczepanowska, H. M., & Goreva, Y. (2014). SEM and ToF‐ SIMS ion imaging applied to characterization of fungal biodeterioration of paper in the context of cultural heritage collections. Microscopy and Microanalysis, 20(S3), 2036– 2037. doi: 10.1017/S143192761401191X Templeton, A., & Knowles, E. (2009). Microbial transformations of minerals and metals: recent advances in geomicrobiology derived from synchrotron‐based X‐ray spectroscopy and X‐ray microscopy. Annual Review of Earth and Planetary Sciences, 37, 367–391. doi: 10.1146/annurev.earth.36.031207.124346 Teng, H.H., Dove, P.M., & De Yoreo, J.J. (2000). Kinetics of calcite growth: surface processes and relationships to macroscopic rate laws. Geochimica et Cosmochimica Acta, 64(13), 2255–2266. doi: 10.1016/S0016‐7037(00)00341‐0 Turpault, M.‐P., Nys, C., & Calvaruso, C. (2009). Rhizosphere impact on the dissolution of test minerals in a forest ecosystem. Geoderma, 153, 147–154. doi: 10.1016/j.geoderma.2009.07.023 Ubbink, J., & Shar‐Zammaretti, P. (2005). Probing bacterial interactions: integrated approaches combining atomic force microscopy, electron microscopy and biophysical techniques. Micron, 36, 293–320. Uroz, S., Calvaruso, C., Turpault, M. P., & Frey‐Klett, P. (2009). Mineral weathering by bacteria: ecology, actors and mechanisms. Trends in microbiology, 17(8), 378–387. doi: 10.1016/j. tim.2009.05.004 van Hees, P.A., Rosling, A., Lundström, U.S., & Finlay, R.D. (2006). The biogeochemical impact of ectomycorrhizal conifers on major soil elements (Al, Fe, K and Si). Geoderma, 136(1), 364–377. doi: 10.1016/j.geoderma.2006.04.001 Vergucht, E., De Samber, B., Izmer, A., Vekemans, B., Appel, K., Tolmachev, S., Vincze, L. & Vanhaecke, F. (2015). Study of the distribution of actinides in human tissues using synchrotron radiation micro X‐ray fluorescence spectrometry. Analytical and Bioanalytical Chemistry, 407(6), 1559–1566. doi: 10.1007/s00216‐014‐8421‐4 Violante, A., Cozzolino, V., Perelomov, L., Caporale, A. G., & Pigna, M. (2010). Mobility and bioavailability of heavy metals and metalloids in soil environments. Journal of Soil Science and Plant Nutrition, 10(3), 268–292. doi: 10.4067/ S0718‐95162010000100005 Voegelin, A., Weber, F.A., & Kretzschmar, R. (2007). Distribution and speciation of arsenic around roots in a

MICRO‐ AND NANOSCALE TECHNIQUES TO EXPLORE BACTERIA AND FUNGI INTERACTIONS  101 contaminated riparian floodplain soil: Micro‐XRF element mapping and EXAFS spectroscopy. Geochimica et Cosmochimica Acta, 71(23), 5804–5820. doi: 10.1016/j. gca.2007.05.030 Wallander, H., & Hagerberg, D. (2004). Do ectomycorrhizal fungi have a significant role in weathering of minerals in forest soil? Symbiosis, 37(1–3), 249–257. Wallander, H. & Wickman, T. (1999). Biotite and microcline as potassium sources in ectomycorrhizal and non‐mycorrhizal Pinus sylvestris seedlings. Mycorrhiza, 9(1), 25–32. doi: 10.1007/s005720050259 Ward, M.B., Kapitulčinová, D., Brown, A.P., Heard, P.J., Cherns, D., Cockell, C.S., et al. (2013). Investigating the role of microbes in mineral weathering: Nanometre‐scale characterisation of the cell–mineral interface using FIB and TEM. Micron, 47, 10–17. doi: 10.1016/j.micron.2012.12.006 Watt, G.R., Griffin, B.J., & Kinny, P.D. (2000). Charge contrast imaging of geological materials in the environmental scanning electron microscope. American Mineralogist, 85(11–12), 1784–1794. doi: 10.2138/am‐2000‐11‐1221 Welch, S.A., & Banfield, J.F. (2002). Modification of olivine surface morphology and reactivity by microbial activity

­ uring chemical weathering. Geochimica et Cosmochimica d Acta, 66(2), 213–221. doi: 10.1016/S0016‐7037(01)00771‐2 Weisenhorn, A.L, Hansma, P.K. Albrecht, T.R., & Quate, C.F. (1989) Forces in atomic force microscopy in air and water. Applied Physics Letters, 54(26), 2651–53. Wierzbicki, R., Købler, C., Jensen, M.R., Łopacińska, J., Schmidt, M.S., Skolimowski, M., et al. (2013). Mapping the complex morphology of cell interactions with nanowire substrates using FIB‐SEM. PLoS One, 8(1), e53307. doi: 10.1371/ journal.pone.0053307 Wongfun, N., Plötze, M., Furrer, G., & Brandl, H. (2014). Weathering of granite from the Damma glacier area: the contribution of cyanogenic bacteria. Geomicrobiology Journal, 31(2), 93–100. doi: 10.1080/01490451.2013.802396 Yao, M., Lian, B., Teng, H.H., Tian, Y., & Yang, X. (2013). Serpentine dissolution in the presence of bacteria Bacillus mucilaginosus. Geomicrobiology Journal, 30(1), 72–80. doi: 10.1080/01490451.2011.653087 Young, R.J., & Moore, M.V. (2005) Dual‐beam (FIB‐SEM) systems. In L.A. Giannuzzi, F.A. Stevie (Eds.), Introduction to focused ion beams (pp. 247–268). Boston, MA: Springer. doi: 10.1007/0‐387‐23313‐X_12

5 Modeling Microbial Dynamics and Heterotrophic Soil Respiration: Effect of Climate Change Elsa Abs1,2 and Régis Ferrière1,2,3 ABSTRACT Microbial respiration is the largest flux of carbon (C) out of the soil. The responses of microbes to climate change will determine the amplitude of the feedbacks between the carbon cycle and climate. Carbon models linking soil organic carbon turnover and microbial ecophysiology can predict better transient and long‐term responses of soil C stocks and respiration to climate change. Yet microbial models are not used in Earth system models. We propose here a roadmap for how to build simple mechanistic microbial carbon models sensitive to climate conditions and provide a toolbox of functions and parameters used in current models. We show that they can predict general empirical patterns of responses to warming and give better predictions of global C stocks. Because there is still a gap to fill in C stock global distributions, we discuss directions to complexify microscale microbial models and the challenges to tackle when scaling them up from the micro‐ to the macroscale. We aim for this overview to clarify the properties and utilities of microbial models, and to expose the opportunities for future work to better assess the uncertainty of future carbon cycle projections.

5.1. INTRODUCTION One major source of uncertainty in global climate predictions is the extent to which global warming will increase atmospheric CO2 concentrations through enhanced microbial decomposition of soil organic carbon. Through microbial respiration, the decomposition of soil organic matter releases ten times more CO2 to the atmosphere than human‐caused emissions (Schlesinger, 1997). Furthermore, soils store ~2300 Pg of C, nearly four times the amount of C in plant biomass (Jobbágy & Jackson, 2000). Therefore, even small changes in soil C turnover could have large ­consequences for atmospheric CO2 concentrations and the  Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA 2  Institute of Biology of Ecole Normale Superieure (IBENS), CNRS, INSERM, PSL University, Paris, France 3   International Center for Interdisciplinary and Global Environmental Studies (iGLOBES), CNRS, ENS, University of Arizona, Tucson, Arizona, USA 1

stability of the global climate system. Yet most current global models do not represent direct microbial control over decomposition. Instead, all of the coupled climate models reviewed in the Fifth Assessment Report of the  last Intergovernmental Panel on Climate Change (IPCC) (Pachauri et  al., 2014) assume that decomposition is a first‐order decay process, proportional to the size of the soil carbon pool. There is therefore a critical need for models that mechanistically link decomposition  to the size and activity of microbial communities, and integration of these mechanistic models in global projection models of the Earth system (Todd‐Brown et al., 2012). This chapter gives a brief introduction to mathematical models of soil microbial dynamics and how these models can be used to predict soil respiration, especially in the context of climate change. We recognize the importance of abiotic controls on soil carbon accessibility (through soil physical structure, mineral–organic associations, soil moisture—see sections 5.4 and 5.5; Bradford et al., 2016; Dungait et  al., 2012; J.P. Schimel & Schaeffer, 2012;

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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104  BIOGEOCHEMICAL CYCLES

Van Veen & Kuikman, 1990), however, our presentation focuses on the microbial and enzymatic processes driving litter decomposition (Fontaine & Barot, 2005; Moorhead & Sinsabaugh, 2006; J.P. Schimel & Weintraub, 2003). In section  5.2, we present the construction of fine‐scale models of soil microbial decomposition that capture fundamental processes without excessive mathematical complexity. In section 5.3, we show how the effect of climate change can be incorporated in these models, and address the issue of parameterization. In section 5.4, we review some key insights that have been gained from these models regarding the effect of climate change on the soil C cycle. In section  5.5, we highlight some major challenges faced by current and future research. Finally, section 5.6 provides a summary of the chapter’s key points.

microbial enzyme production. To more accurately describe the kinetics of catalyzed reactions the concentration of the catalyst must be part of the rate equation (Roberts, 1977). This can be undertaken using the Michaelis–Menten relation:



Early models of soil organic matter (SOM) decomposition, which are still widely used in Earth system models, assume first‐order decomposition kinetics and take the form:



KC (5.1)

where C is the size of a soil carbon pool and K is a first‐ order rate constant, which may differ among SOM pools depending on their “quality.” Such first‐order kinetics models assume that SOM decomposition is controlled by microbial activity, but that the rate of decomposition is independent of microbial biomass (Bradford & Fierer, 2012; J. P. Schimel, 2001; Wieder et al., 2015). In reality, as SOM breakdown is catalyzed by extracellular enzymes that are produced by microorganisms, its rate should therefore vary with the abundance of active microbes and (a)

(b)

SOC input

r ta lity

M

M

CO2

C

or

DOC input

Decomp.

Decomp. C

(c)

SOC input

Mo

KZ C (5.2) Km C

where K is the fundamental kinetic constant as defined by the quality of the substrate, Z the concentration of enzymes, and Km is the half‐saturation constant of the enzymatic reaction. If we assume that Z is constant, as commonly done, then Z can be combined with K into a vmax term, the maximum reaction rate. Under some conditions (e.g., low C), this relationship can be effectively simplified to a pseudo‐first‐order equation, but even in that case, the enzyme concentration Z remains part of the rate (D. Schimel, 2001). While the rate at which accessible SOM is processed is strongly controlled by the quality of the material (the fundamental K value), how much carbon is available locally to the microorganisms is controlled by the activity of extracellular (exo) enzymes. To model this, we need to introduce the pool of small dissolved organic carbon molecules (DOC, with concentration denoted by D) as an additional compartment besides soil organic carbon (SOC, concentration C), exoenzymes (concentration Z) and soil microbes (biomass M) (all variables being measured in C mass per soil volume unit). The DOC molecules can be taken up and used by microbes to provide for their needs (in order of priority): exoenzyme synthesis; cellular maintenance (respired as CO2); biomass production. The production of exoenzymes and cell biomass both require energy. Some fraction of the available C pool is assimilated and respired to provide that energy. The processes shown in Figure  5.1b can be translated into

5.2. FROM FIRST‐ORDER KINETICS TO FOUR‐ POOL CDMZ SOIL MICROBIAL MODELS

dC dt

dC dt

SOC input

ta lity

Decomp.

Uptake D

M

Enz. decay Z

z.

En

M

pro

d.

CO2

C

or

DOC input

ta lity

Uptake D

Enz. So rp decay . Z

M Q

.

rod

.p nz

E

Figure 5.1  Structure of microbial decomposition models compared in this review. (a) CM model: two‐pool microbial model with SOC (C) and microbial biomass (M) pools. (b) CDMZ model. This four‐pool microbial model includes enzymatic (Z) decomposition of SOC and subsequent assimilation (uptake) of DOC (D). (c) CDMZ model with soil C stabilization. This five‐pool microbial model includes sorption of DOC (D) onto mineral surfaces to form mineral‐associated DOC (Q) that is protected from enzymatic attack. [Adapted from Georgiou et al., 2017.]

CO2

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  105

time‐continuous differential equations that govern the change rate of C, D, M, and Z. The resulting set of equations varies depending on specific assumptions on the process rates. Here we use a slightly simpler model of microbe resource allocation to present the different versions that J.P. Schimel and Weintraub (2003) derived for their model. Our simplification amounts to assuming that at low resource availability, rather than dying, microbes can reduce their maintenance cost by tuning down their metabolism (He et  al., 2015; Salazar et  al., 2018; G. Wang et al., 2013, 2015). In their simplest model, J.P. Schimel and Weintraub (2003) assume that the SOM pool is large and relatively unchanging on the microbial timescale. As a consequence, the SOC pool is treated as constant, hence dC/dt = 0. Microbes uptake DOC, and uptake is assumed to be fast, so that the total uptake rate, U, is assumed to be constantly equal to the total production rate of DOC from SOC by decomposition. By treating the decomposition kinetics as first‐order with respect to both substrate (C) and exoenzymes (Z), this can be written as KDZC. Thus, U = KDZC at any time, and dD/dt = 0. Using our notation for resource allocation parameters, J.P. Schimel and Weintraub’s (2003) simplest model has only two state variables (M and Z) and reads



dM dt dZ dt

1

M

K D Z dM M (5.3a)

 (5.3b) Z KD Z dZ Z

where K D is the “decomposition constant” for a particular SOM pool, multiplied by the constant SOC concentration (C). Microbial death is modeled as due primarily to external factors (predation, infection, and accidental abiotic causes) and is assumed to occur at a constant rate, dM. Exoenzymes decay at a constant rate, dZ. This simplest model predicts either unbounded population growth, or extinction. To obtain a stable system, J.P. Schimel and Weintraub (2003) consider the possibility of competition among enzymes for binding substrates. This leads to model decomposition kinetics with a “reverse Michaeli–Menten” model, with total decomposition rate equal to (KDCZ)/(Kes + Z), where Kes is the half saturation constant for enzymes on substrate. Keeping the assumption that C is approximately constant, this also writes as (K DZ)/ (Kes + Z). The “reverse” Michaelis–Menten model assumes that there is functionally a saturating level of enzymes on the substrate, rather than a saturating level of substrates on the enzyme; see Vetter et al. (1998) for empirical support. The corresponding model is:

dM dt



1

dZ dt

M

Z

K D Z K esD Z

K D Z K es Z

dMM (5.4a)

d Z Z (5.4b)

With such nonlinear kinetics, the system is stabilized and converges to a stable equilibrium (Moorhead & Weintraub, 2018; Sihi et al., 2016). The fully dynamical version of the Schimel–Weintraub model relaxes the assumptions of constant SOC and DOC pools. Time variation of C, D, M, and Z obey equations (5.5a–d), hereafter dubbed the “CDMZ model”:



dC dt dD dt

dM dt dZ dt

I

D vmax C Z D Km C

1 p dMM eCC (5.5a)

D vmax C Z pdM d Z Z D Km C vUmax D (5.5b) M eD D KU D m

1

M

Z

vUmax D M dMM (5.5c) KU D m

vUmax D M d Z Z (5.5d) KU D m

In equation  (5.5a), decomposition follows from Michaelis–Menten kinetics of Z binding substrate C; there is a constant input, I, of soil organic (nondecomposed) carbon from aboveground litter, an input from microbial necromass (fraction 1 − p of necromass produced per unit time, at constant rate dM), and a loss due to leaching at constant rate eC. Note that in those early models, C gathers litter and SOC because they do not make explicit the mechanisms of soil carbon stabilization that characterize SOC. As we present models of increasing complexity, paralleling their history, SOC initially refers to any nondecomposed organic carbon (includes both litter and microbial residues), and we introduce more specific pools as we build on the models’ complexity. In equation (5.5b), D is produced by decomposition and the recycling of microbial necromass (fraction p of necromass produced per unit time) and inactive enzymes; D is consumed by microbial uptake, and lost by leaching at constant rate eD. In equation (5.5c), growth of microbial biomass M is driven by the rate of DOC uptake (a Monod function of D) times the fraction of uptaken DOC turned into biomass, (1 − φ) γM, minus microbial death at rate dM. In equation (5.5d), enzyme variation is driven by the rate of DOC uptake times the fraction allocated to

106  BIOGEOCHEMICAL CYCLES

enzyme production, φ, and production efficiency, γZ, minus enzyme decay at constant rate, dZ. Depending on parameter values (see Box 5.2 later), the CDMZ model (equation 5.5) possesses either one globally stable equilibrium or three equilibria, one of which is always unstable. There are thresholds φmin and φmax such that the globally stable equilibrium exists for φ < φmin or φ > φmax and is given by M = 0, Z = 0, C = I/eC, D = 0. Thus, at this equilibrium, the microbial population is extinct and no decomposition occurs. For φmin < φ < φmax, the microbial population can either go extinct (then the system stabilizes at the same equilibrium as before) or persists at or around a nontrivial equilibrium, which can be solved for analytically. Note that φmin and φmax depend on all microbial and model parameters. Variants of the basic CDMZ model (equation 5.5) have been introduced (Table 5.1; Allison et al., 2010; Tang & Riley, 2015; G. Wang et al., 2013; Y. Wang et al., 2014; Zhang et al., 2014). All these models greatly simplify the spatial structure of soil and treat soil carbon and microbial pools as spatially and chemically homogeneous. In reality, soil microbes and substrates interact at microscales, and at any given time, state variables may vary greatly among microsites. Moreover, heterogeneity exists in the physiological and biochemical traits present in the system, which results in a distri­ bution of parameters controlling microbial growth, extracellular enzyme production, and enzyme affinity. Trait‐based computational models have been developed to account for such spatial and functional heterogeneity. Trait‐based models trade off mathematical ­formalism for biological complexity. By explicitly representing diversity, trait‐based models can simulate ecosystem processes based on spatial and functional trait distributions in a community. Allison (2012) constructed a trait‐based model that links microbial community composition with physiological and enzymatic traits to predict litter decomposition rates. The model, dubbed DEMENT, is spatially explicit and integrates processes from micrometer to millimeter scales (Box 5.1, Figure 5.2). Microbial cells interact on a square grid. The grid is analogous to the surface of a decomposing leaf, and multiple microbial cells may occupy the same grid box. The microbial community is made up of multiple strains. Each strain is characterized by phenotypic traits including: the enzymes that the strain produces, the rates at which the strain produces them, the strain’s microbial growth efficiency (MGE). There is a given list of enzymes and substrates, and each enzyme is characterized by its substrates’ binding affinities and kinetics parameters. The trait‐based model captures the interaction between the diversity of enzymes and substrates, and how this interaction feeds back on the community of microbial strains and shapes their diversity and abundances. For  example, in response to an input of litter this

“chemo‐ecological” feedback determines the time trajectory of each  substrate, when different strains peak in density, what densities they actually reach, and how these properties depend on the strains’ trait values (Figure 5.2b). The system state variables (e.g., litter, strain abundances) can then be aggregated at the scale of the whole grid to characterize the dynamics of total microbial biomass and organic matter at that scale. Parameterizations of the CDMZ models (Box 5.2) have been based on observational measurements and incubation experiments (e.g. German et al., 2012; Sulman et  al., 2014), involving in some cases inverse modeling and parameter optimization (German et al., 2012). Trait‐ based models can also tap into ‘omics’ data to assess the diversity of microbial and enzyme communities, estimate relative abundances, and measure activity and specificity (Fierer et al., 2014; Trivedi et al., 2013). 5.3. INCORPORATING CLIMATE PARAMETERS Three climate‐change drivers that may alter soil biogeochemistry and change future soil respiration and C stocks are: changing temperature, altered precipitation regime, and elevated CO2 (Wieder et  al., 2015). Mathematical models that include explicit microbial traits and parameters provide the opportunity to mechanistically represent the effect of such abiotic factors on decomposition. Then the response of decomposition and soil respiration to climate change becomes an emerging property, integrated across the individual physiological responses of microorganisms and upscaled by individual interactions through population and community levels. As a consequence, the response of microbial biomass and respiration to climate change may be decoupled (Todd‐Brown et  al., 2012). Numerous laboratory studies support the assumption that microbial respiration increases exponentially with temperature (Davidson et al., 2006; Lloyd & Taylor, 1994). But even though biomass‐specific respiration tends to increase with temperature, community‐level respiration is ultimately mediated by the emerging response (increase or decrease) of microbial biomass (Allison et al., 2010). 5.3.1. Temperature In the models reviewed in section 5.2, the vmax parameter of Michaelis–Menten kinetics represents the proportionality constant between enzyme concentration and process rate (hydrolysis, uptake, and metabolism). This general parameter has a well‐established dependence on temperature as defined by the Arrhenius equation, which has an exponential form:

vmax

v0 e

Ea R T 273

(5.6)

Table 5.1  Structural and operational characteristics of six recent microbial models. Model features are presented with “yes/no” and details Model reference

Model C pools and fluxes

German et al. (2012)

No CM model. C inflow: constant litter input, recycling of necromass; C outflow: decomposition (Michaelis); M inflow: uptake of decomposition products; M outflow: respiration, constant death rate No CMP model. C inflow: constant litter input; C outflow: microbial decomposition (Michaelis) and constant stabilization rate (into P); M inflow: uptake of decomposition products, M outflow: respiration, constant death rate; P inflow: microbial residues and direct stabilization of C from litter input; P outflow: microbial decomposition CMP model. Same as Wieder Two functional types for each C et al. (2013) pool; all affect Michaelis– Menten (reverse in Wieder et al., 2018) kinetic parameters; C functional types also affect fraction of C directly stabilised and microbial CUE; M functional types also affect constant death rate and partitioning of necromass recycled Three functional types for C and CMP model. C inflow: constant P: differ in litter inputs, litter input and turnover of P; C decomposition and protection outflow: decomposition (reverse rates; Affect microbial uptake Michaelis with M: C ratio) and rate protection; M inflow: uptake of decomposition products; M outflow: respiration, constant death rate; P inflow: protection of C; P outflow: constant turnover rate

Wieder et al. (2013)

Wieder et al. (2014, 2015b) MIMICS

Sulman et al. (2014) CORPSE

Functional diversity

Soil environmental dependency

SOC protection (yes/no, Vertical degree, details) resolution

Temperature; Arrhenius No function for Michaelis–Menten kinetic parameters; linear function for CUE Temperature; Arrhenius Low: P is accessible to microbial function for decomposition and Michaelis–Menten not slower than C. kinetic parameters; linear function for CUE

Temperature; Arrhenius Intermediate: P is accessible to function for microbial Michaelis–Menten decomposition, but kinetic parameters. slower than C

Horizontal resolution

No

Five locations: differ in decomposition kinetics parameters and initial temperature Global: differ in litter Two layers: inputs, initial 0–30 cm and temperature, 30–100 cm; Michaelis–Menten differ in litter kinetic parameters inputs (65% in top layer)

No

Temperature; Arrhenius Full: P is inaccessible to No function for reverse microbial Michaelis–Menten decomposition. Its vmax parameter. formation is not microbe‐mediated, Moisture; Non‐linear but instead is a function for the functional type‐ decomposition rate dependent abiotic process

Global: same as Wieder et al. (2013).

Ecosystem: differ in temperature, moisture, litter inputs, root exudate inputs, and protection rates. Global: model integrated into a global land model with dynamical vegetation growth and soil physical and hydrological processes (Continued)

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Table 5.1 (Continued) Model reference

Model C pools and fluxes

Abramoff et al. (2018) MILLENIAL

Two types of P: differ in CDMP model. C inflow: litter formation and breakdown input, breakdown of P; C processes outflow: decomposition (double Michaelis) and protection. D inflow: litter input, decomposition of C; D outflow: microbial uptake, leaching, protection. M inflow: uptake of D; M outflow: respiration, constant death rate. P inflow: protection; P outflow: breakdown

Allison et al. (2010)

No CDMZ. C inflow: constant litter input, recycling of necromass; C outflow: decomposition (Michaelis); D inflow: litter input, recycling of necromass and enzymes, decomposition; D outflow: microbial uptake; Z inflow: microbial production; Z outflow: constant turnover; M inflow: uptake of decomposition products; M outflow: respiration, constant death rate No CDMZ. Same as Allison et al. (2010), except microbial biomass is divided into structural biomass and reserve, and CUE is derived from the model using the dynamic energy budget theory

Tang & Riley (2015) SUMMS

0004534000.INDD 108

Functional diversity

Soil environmental dependency

SOC protection (yes/no, Vertical degree, details) resolution

No: 1 m soil Multiple. Two types of Temperature; profile P: mineral‐associated arctangent function. organic carbon Moisture; reverse (MAOC), and exponential function. aggregate carbon All processes, except (AC). litter input, are MAOC inflow: temperature and adsorption of D moisture dependent. following a non‐linear pH; reverse saturating function of exponential function D and microbial for adsorption necromass, binding affinity breakdown of AC; (stronger adsorption MAOC outflow: at lower pH). aggregation of MAOC Texture (% clay); into AC. AC inflow: exponential function aggregation of MAOC for the maximum and C; AC outflow: sorption capacity breakdown (stronger adsorption at high % clay) Temperature; Arrhenius No No function for Michaelis–Menten vmax parameter; linear function for Michaelis–Menten half‐saturation constant KM; linear function for CUE

Temperature; Arrhenius Implicit; mineral association with D functions in and Z is included competition for implicitly as a binding (adsorption), reaction at decomposition, equilibrium with the uptake, and enzyme other reactions of turnover binding for uptake and decomposition

No

Horizontal resolution No

No

No

2/7/2020 8:38:35 AM

Functional diversity

Soil environmental dependency

SOC protection (yes/no, Vertical degree, details) resolution

Model reference

Model C pools and fluxes

G. Wang et al. (2013) MEND

No Temperature; Arrhenius Multiple. Two types of Two types of Z for the CDMZP. Same as Allison et al. P: mineral‐associated function for decomposition of C and P. Two (2010) for CDMZ. P inflow: organic carbon Michaelis–Menten types of P that differ in mineral‐association, adsorption; (MAOC) that can be kinetic parameters, formation and breakdown P outflow: decomposition, decomposed by adsorption and processes desorption specific enzymes, and desorption; linear adsorbed D (AD) that function for CUE is inaccessible to decomposition. MAOC inflow: mineral‐ association of C; MAOC outflow: decomposition; AD inflow: adsorption of D; AD outflow: desorption

Horizontal resolution No

Note: Adapted from German et al. (2012), Wieder et al. (2013), and Wieder et al. (2014, 2015b). C denotes accessible complex C compounds (litter or soil C); D, accessible C available for microbial uptake; M, microbial C biomass; Z, extracellular enzymatic C; P, protected soil C, which includes aggregate C and mineral‐associated D or C, and may or may not be accessible to microbial decomposition. SOC protection qualifies as “low” when P is accessible to microbial decomposition at a rate that is not much lower than C, as “intermediate” when P is accessible to microbial decomposition but at a significantly lower rate than C, as “full” when P is inaccessible to microbes, and as “multiple” when there are at least one accessible and one inaccessible P pools. We define a process as “constant” when it occurs at a constant rate.

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110  BIOGEOCHEMICAL CYCLES

Box 5.1  Microbial and decomposition processes in trait‐based models Here we summarize some important details of Allison’s (2012) trait‐based model DEMENT. ••Microbial cells interact on a two‐dimensional square grid. Boundary conditions are periodic, i.e., the grid edges contact each other, forming a torus. The grid is initialized with a homogeneous distribution of each substrate and monomer. Each grid box is colonized at random by each taxon with a 1% probability, meaning that each strain is expected to occupy ~100 grid boxes on the 100 × 100 grid. Each strain in an occupied grid box is initialized with biomass C concentration 1 mg cm−3, such that the average biomass density across the grid is ~1 mg cm−3 with 100 different strains. ••Each polymeric substrate can be degraded into monomers by multiple (randomly chosen) enzymes, and each enzyme can degrade at least one substrate. Substrate degradation obeys Michaelis–Menten regulation. ••Microbial uptake rates are also modeled as Michaelis–Menten functions of monomer concentrations and microbial strain biomass. Each strain is characterized by its transporters; each transporter binds to at least one monomer, and each monomer corresponds to at least one transporter. Uptake is measured first per transporter and then scaled up to strain biomass. When the monomer pool size is limiting, the available monomer is partitioned among strains in proportion to their calculated uptake rates. ••Microbial growth is represented implicitly as the difference between uptake and loss processes. Microbial growth is modeled within each grid box, where the microbes present have the first opportunity to take up DOC (monomers). Microbes respire a fraction of C upon uptake, representing the energy cost of monomer metabolism, and additional respiration occurs as a consequence of processing a fraction of biomass and uptaken monomers to produce enzymes (the use of a biomass fraction represents constitutive enzyme production). There is also a daily maintenance cost for each uptake transporter, expressed as a fraction of microbial biomass C. ••C, N, P stoichiometry is explicit. There are thresholds on microbial biomass C, N, P such that excess elements are mineralized into CO2 or inorganic nutrients. Stoichiometry is assumed constant across all enzymes. ••The state variables of each grid box are updated on a daily timescale. Typical runs are 500 time steps,

i.e., the simulations describe the system dynamics over 500 days. ••Grid boxes are spatially connected by (a) diffusion of excess monomers after uptake by microbes within each grid box. Diffusion is instantaneous and excess monomers from any grid box are uniformly distributed across the entire grid; (b) biomass dispersal, which occurs when local (i.e., within a grid box) biomass of a given strain reaches some threshold; then that biomass splits and one half disperses to a grid box in any direction up to some maximum dispersal distance. Dispersal is independent of whether or not the destination grid box is occupied. ••The distribution of feasible traits is constrained by physiological tradeoffs. Three tradeoffs are considered: (a) between enzyme substrate generalism and maximum reaction rate (i.e., more generalist enzymes have lower); (b) between and binding affinity (resulting in a positive correlation between and); (c) between microbial growth and enzyme production (the strain position along this tradeoff is measured by φ in our formalism, cf. Figure 5.1b). In Allison (2012)’s model, microbial stoichiometry is flexible and enzymes are randomly attributed to microbial taxa and substrates, and therefore distinct traits among microbial taxa (enzyme types and quantities produced, and C, N, P stoichiometry) are an emerging property of the model. Kaiser et al. (2014) specifically address the question of the link between C and N availability community dynamics and decay rates, and they build on Allison’s (2012) model by adding the following features. ••They initialize their model with preselected microbial functional groups with distinct life‐history traits: maximal cell size, maximum turnover rate, cell chemical composition, and C/N ratio, and enzyme production rate and types. ••Secondary substrates are divided into C‐rich (C/N ratio of 150) and N‐rich (C/N ratio of 5) microbial products. ••Diffusion is not instantaneous but is instead modeled using Brownian motion. ••A fraction of enzyme production is constitutive but a fraction of it is resource dependent, such as growth, after maintenance and constitutive production are completed. ••Nutrient excess released from cells is mineralized for both C and N.

(b)

Enzyme cost

Trait assignment

Vmax

Mass remaining (%)

100

Km

Resource gain

Trait distributions and tradeoffs

Initialize

0.15

Litter Microbial byproducts

80

Microbial density (mg cm–3)

(a)

60 40 20 0 0

100

200

300

400

0.10

0.05

0.00

500

0

Time (days)

Dead microbe Inactive enzyme Cellulose Hemicellulose Starch Chitin Lignin Protein 1 Protein 2 Protein 3 Phospholipid Nucleic acid

Taxon sorting and community interactions

Substrate C (mg cm–3)

120 100 80 60 40 20 Predicted composition and function

0 0

100

200

300

Time (days)

200

300

400

500

Days

400

500

–0.5 Maximum density [log10 (mg cm–3)]

140

100

–1.0

–1.5

–2.0 0

5

10

15

Number of enzymes

Figure 5.2  (a) Structure of Allison’s (2012) trait‐based model DEMENT and (b) examples of outputs. Multiple strains with different traits are sampled in a set of feasible trait values constrained by trade‐offs. Initialization: strain‐specific microbial biomass is randomly distributed across the microsites of a spatial grid. Dynamics: the state of all microsites is updated as within‐site and between‐sites processes are iterated. [Allison (2012). Reproduced with permission of John Wiley & Sons.]

20

112  BIOGEOCHEMICAL CYCLES

Box 5.2  Model parameterization Using CDMZ models to extrapolate short‐term empirical results and assess their significance for long‐term ecosystem states requires model parameterization with realistic values. Parameter values are obtained from the empirical literature, derived from experiments specifically designed to measure them (e.g., German et al., 2012), or constrained to promote model stability and reasonable quantitative outputs. Allison et  al. (2010), Allison (2012), German et  al. (2012), and Kaiser et al. (2014, 2015) provide useful lists of values and ranges to parameterize CDMZ models and their trait‐based extensions. ••Target outputs. The target C is of the order of 100 mg cm−3, biomass M is about 2% of C, Z is about 1% of M and D is limiting, hence close to 0.60% of the microbial biomass is estimated to be in the first 5 cm, 70% in the first 10 cm and 80% in the first 20 cm below surface (Fierer et  al., 2009), therefore the vertical resolution of microbial processes is not as relevant as for other processes of soil formation. ••Carbon inputs. Using global predictions of the fraction of NPP that goes into litter from the CMIP5 averaged over 2005–2020, litter inputs range between 0 (e.g., in high latitudes) and ~1700 gC cm−2 year−1 (e.g., in tropical forests). Most mechanistic microbial models do not include coarse wooden debris in litter inputs and litter inputs are therefore usually below 100 gC cm−2 year−1 (Allison, 2010; German et  al., 2012; G. Wang & Post, 2012; G. Wang et al., 2013). ••Microbial parameters. The microbial death rate is  typically fixed between 0.05 and 0.0002 h−1 corresponding to an expected lifespan of between 1 and 200 days (Allison et  al., 2010; German et  al., 2010; Sulman et  al., 2014; Wieder et  al., 2013). Dead biomass is recycled in equal proportion between SOC and DOC. The uptake kinetics are poorly known and are therefore assumed to follow enzymatic properties. Assuming that DOC substrate is

where v0 is a pre‐exponential coefficient, Ea is the activation energy for the reaction, and R is the ideal gas constant. The activation energy represents the temperature sensitivity and biochemical resistance of the substrate to catalysis. The temperature response of half‐saturation constants Km is uncertain. There is some evidence from animal physiology literature that enzyme Km values tend to increase with temperature, thereby reducing affinity for

close to 0 and does not saturate the uptake rate, KmU is fixed to 0.3 mg cm−3. The temperature sensitivity function for KmU is assumed to be linear positive and the proportional factor is fixed to 0.01 mg cm−3 °C−1. U The 20°C value  and Arrhenius relation for vmax are D the same as for vmax (Allison et al., 2010). In models integrating rules of stoichiometry with nitrogen and phosphorous, predicted stoichiometry is compared with the empirical ratios of 8.6 for C/N and 7 for N/P (Cleveland & Liptzin, 2007). ••MGE temperature dependence. Empirical studies in soils suggest that MGE declines by at least 0.009 °C−1 (Steinweg et  al., 2008). Allison et  al. (2010) assumed MGE = 0.63 – 0.016 T for temperature T between 0 and 25°C and they also tested decreasing its sensitivity to half. ••Enzyme production. In Allison et  al.’s (2010) CDMZ model, the microbial rate of enzyme production is fixed at 0.012% of microbial biomass per day. In models where enzyme production is a fraction of resources taken, this fraction ranges between 0.0005 and 0.2 (Burns et al., 2013; Kaiser et al., 2014, 2015; Schimel & Weintraub, 2003). Models integrating N and P assume no cost in these nutrients for enzymes. ••Enzyme parameters. The deactivation rate is typically fixed around 0.02 day−1, corresponding to an expected lifespan of 50 days (Allison et  al., 2010; Allison, 2012; Kaiser et al., 2014, 2015; Wang et al., 2013). Assuming that the SOC substrate does not saturate enzyme reactions, KmD is fixed to 600 mg cm−3. The temperature sensitivity function for KmD is assumed to be linear positive and the proportional factor is fixed to 5 mg cm−3 °C−1. The pre‐exponential coefficient in D the Arrhenius relation for vmax was constrained by equilibrium stability at 20°C. Activation energies for soil hydrolytic enzymes vary from 13 to 94 kJ mol−1 with most values in the range of 20–50 kJ mol−1. [Adapted from McClaugherty & Linkins (1990) and Trasar‐Cepeda et al. (2007).]

substrate and slowing catalysis (Hochochka & Somero, 2002; Somero, 1978, 2004). Although recent work showed a declining response of Km of multiple enzymes, probably due the production of isoenzymes (Sihi et al., 2019), Km has most commonly been hypothesized to increase with temperature in soil (Davidson et  al., 2006; Davidson & Janssens, 2006). Todd‐Brown et al. (2012) suggested representing Km for extracellular enzymes as a linear function of temperature, whereas German et al. (2012) opted for

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  113

an exponential function, leading to an Arrhenius‐like model of Km sensitivity to temperature. Because intracellular enzymatic catalysis is limited by the availability of DOC whereas availability of SOC limits extracellular catalysis, models of temperature dependence need to use parameters that are specific to extracellular enzymatic catalysis versus microbial uptake and metabolism (Todd‐Brown et  al. 2012). Consistently with model choices made by Allison et  al. (2010) and German et  al. (2012), this leads to the following set of equations that can be incorporated in the CDMZ model (equation 5.5):



D vmax



K mD

v0D e

E vD R T 273

(5.7a)

EKD R T 273 D K0 e

vUmax



KU m

vU0 e

R T 273

(5.7c)

U EK R T 273 U K0 e



M

M,ref

m T Tref (5.8)

with Tref = 20°C. To represent a positive effect of temperature on microbial mortality, Hagerty et al. (2014) used the Arrhenius model:



dM

dM0 e

EdM R T 273

(5.9)

5.3.2. Soil Moisture and Precipitation Regime (5.7b)

E Uv



sensitivity of MGE to temperature. To represent a negative effect of temperature on MGE, Allison et al. (2010), German et al. (2012), and G. Wang et al. (2013) used a linear relationship (see also Todd‐Brown et al., 2012; Li et al., 2014):

(5.7d)

where T is temperature in Celsius, R is the ideal gas constant, and the E parameters denote the corresponding activation energies. By increasing kinetic energy, warming accelerates enzyme‐catalyzed reactions and stimulates C consumption by soil microbes. Thus, the response of microbial respiration to warming is determined by the abundance (microbial biomass M in the models introduced in the previous section) and function of the microbial community. For a given mass‐specific uptake rate, warming may affect M through two demographic mechanisms: ••decreasing MGE, as a consequence of increasing the energy cost of maintaining existing biomass (Sinsabaugh et al., 2013); ••increasing the microbial death rate (Hagerty et  al., 2014; Joergensen et al., 1990). The effect of temperature on maintenance energy cost, resulting in MGE decreasing with warming, has been observed in pure culture experiments (Crowther & Bradford, 2013; Manzoni et  al., 2012). However, responses of MGE to warming are generally equivocal, possibly due to methodological reasons, or to actual processes related to substrate type (Frey et al., 2013), or to physiological acclimation (Allison, 2014), or to the magnitude of warming (Sihi, Davidson, et  al., 2018). Hagerty et al. (2014) reported data from short‐term laboratory soil incubation showing a significant increase in turnover rate of microbial biomass with warming, but no

With climate change, frequency and intensity of precipitation will become increasingly variable (Pachauri et al., 2014). Variation in soil moisture can have strong transient effects on soil respiration that have long been observed in laboratory as well as field experiments. The well‐known “Birch effect” (Birch, 1958) refers to the dramatic increase in soil respiration caused by pulsed wetting after drought periods. To include the effect of variation in soil moisture due to variable precipitation in CDMZ models, Zhang et al. (2014) introduced controls of enzyme activity and DOC uptake by water saturation (θ/θs in Figure 5.3), and distinct pools of wet versus dry enzymes and DOC (Figure 5.3). These pools reflect the heterogeneous soil structure, in particular pore‐size distribution and wet and dry zones within soil pores. Microbes can only access DOC in the wet pores. SOC decomposition occurs in the dry pores at a reduced efficiency due to enzyme immobilization; enzymes in the dry pores are also expected to have a lower deactivation rate due to protection from decay (Alster et al., 2013). It remains unclear whether soil moisture constrains microbial activity primarily through direct (via desiccation stress) or indirect (via its impact on diffusion of substrates or enzymes) mechanisms. Zhang et al. (2014) used linear functions to relate enzyme and uptake rates to soil moisture. However, at very low and high moisture levels, the relationship is likely nonlinear (Davidson et al., 2012; Lawrence et al., 2009; Moyano et al., 2013). For example, at high water content, O2 becomes a limiting factor, whereas at low water content, diffusion is constrained by thin and discontinuous water films (Abramoff et  al., 2017; Davidson et al., 2012; Sihi, Davidson, et al., 2018). On the other hand, Homyak et al. (2018) argue that dry periods increase C substrate availability through abiotic processes.

114  BIOGEOCHEMICAL CYCLES

Death

Wet ENZ

Catalysis θ/θs

Vmax

Wet DOC SOC

Vmax

Km

Uptake θ/θs

Km

Respiration MIC

1.0–CUE

CO2

Dry DOC Catalysis 1 – θ/θs

Dry ENZ

Enzyme decay

Enzyme production

Figure 5.3  CDMZ model extended to account for variation in soil moisture. SOC decomposition and microbial uptake rates are controlled by water saturation, θ/θs, where θ is volumetric water content and θs is porosity (dependent on soil texture). The model splits DOC and enzyme pools into two, respectively, one for the wet zone and the other for the dry zone of soil pore space. Microbial uptake of DOC occurs only in the wet zone, and the uptake is linearly related to θ/θs. The enzyme catalytic rate is proportional to θ/θs in the wet zone, and to 1 – θ/θs in the dry zone. [Zhang et al. (2014). Reproduced with permission of John Wiley & Sons.]

5.3.3. Elevated CO2 Many field studies have found that elevated atmospheric CO2 (eCO2) leads to higher carbon assimilation by plants leading to higher carbon storage in soils, through higher root production, higher litter production and enhanced root exudation (Liu et  al., 2005; Norby et  al., 2005; Phillips et  al., 2011; Pregitzer et  al., 2008), and even more so in surface soils (Hicks Pries et al., 2018). Individual microbes have shown no response to eCO2 in laboratory experiments (Carney et al., 2007; Norby et al., 2001), in which case soils should accumulate carbon at eCO2. However, enhanced litter and root exudate inputs can increase soil respiration and SOC decay rates (called “priming effect”) and induce soil C losses (Kuzyakov, 2010; Pendall et al., 2014), through enhanced production of enzymes decomposing recalcitrant substrates (Phillips et al., 2011), or changes in the microbial community composition (Blagodatskaya et al., 2010; Carney et al., 2007; Cheng et al., 2012). Blagodatskaya et al. (2010) found a linear relationship between microbial growth rate and atmospheric CO2 concentration. This relationship has not been included in soil microbial models, but priming effect has been modeled with higher litter inputs (Drake et al., 2013; Sulman et al., 2014). In Sulman et al. (2014)’s model, three classes of soil C compounds are included (simple, chemically resistant plant‐derived, chemically resistant microbe‐derived), characterized by different maximum decomposition and microbial uptake rates, and each existing in both unprotected and protected forms (Figure  5.4). Only unprotected carbon is accessible to microbes. The model shed light on the role played by priming effects in the response of C stocks to warming, which was done by comparing a

rhizosphere model with increasing root exudation (calibrated with data from eCO2 field experiments), and a bulk soil model without root exudates. At the regional scale, the model predicted either net loss of soil C or sequestration, depending on litter quality (determined by the plant community) and soil texture. At the global scale, the model predicted a loss of soil C. First‐order models would fail to account for priming effects (Zaehle et  al., 2014). This is because without microbial‐driven decomposition, soil C increases linearly with inputs (Li et al., 2014). Recent nonmicrobial models have tried phenomenologically to capture priming effects by representing multiple SOC pools for which decay rates vary with litter inputs, using observed responses of plant growth and microbial respiration for parameterization (van Groenigen et al., 2015; Z. Luo et al., 2017). However, priming effects might not persist over time in certain ecosystems (Drake et  al., 2018), raising the need for more long‐term experiments to better represent priming effects over time across biomes. If enhanced root exudation in eCO2 leads to stimulated root and microbial respiration, eCO2 may also result in enhanced weathering because higher soil CO2 concentration from respiration supplies more protons for dissolution reactions (Goddéris et  al., 2006). Root exudates also promote weathering because they contain organic acids (e.g., oxalic acid) and organic phenolic compounds (Keiluweit et al., 2015; McGill, 1996; Natali et  al., 2009) that disrupt mineral–organic associations. The breakdown of mineral‐associated organic compounds releases previously inaccessible organic C and inorganic nutrients such as phosphorus (Amundson, 2003) that could further stimulate microbial activity in  nutrient‐limiting conditions. Although some studies

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  115

Leaf litter

Leaf litter

s

ate

d xu te

Root litter

o Ro

Unprotected C

Root litter Protected C

Unprotected C

Protected C

Simple C

Simple C

Chem. resistant C

Chem. resistant C

Dead microbe C

Dead microbe C

Dead microbe C

Dead microbe C

ial turn

Rhizosphere

Decomposition Live microbe C

Microbial

Live microbe C

Microb

Decomposition

turnover

Simple C Chem. resistant C

over

Simple C Chem. resistant C

Bulk soil

Figure 5.4  Structure of Sulman et al.’s (2014) model. Soil carbon is divided into three chemical classes, which can be protected or unprotected. Decomposition is mediated by microbial biomass, which takes up a portion of decomposed carbon and loses carbon to CO2 and the dead microbial C pool over time. Soil is separated into the rhizosphere, which receives root exudate inputs, and bulk soil, which does not. [Sulman et al. (2014). Reproduced with permission of Springer Nature.]

provide strong indication that eCO2 will promote weathering (Karberg et  al., 2005), specific experiments are needed to predict the magnitude of the feedbacks between atmospheric CO2, microbial activity and weathering in different ecosystems over time (Brantley et al., 2011). 5.4. WHAT DO WE LEARN FROM CDMZ MODELS? In this section we focus on what models of soil microbial dynamics tell us about the response of soil respiration to climate warming. Field experiments have documented an initial increase in CO2 efflux from soils, followed by a decline in CO2 loss, down to control levels within a few years (see, e.g., Y. Luo et al., 2001; Melillo et  al., 2002; Oechel et  al., 2000). Recent long‐term warming experiments show that the response might be more complex over time (Melillo et al., 2017). Can models help us explain empirical patterns and their variation? How can models help us explain the Birch effect, i.e., the sudden pulse of soil respiration after precipitation pulses? And moving up to larger scales (Huang et al., 2018; Sihi, Davidson, et  al., 2018; K. Wang et  al., 2017), how can mechanistic models of microbial dynamics and soil respiration be used to improve coupled projections of the C cycle and Earth climate?

5.4.1. Explaining Empirical Patterns of Soil Respiration Responses to Climate Change Short‐term laboratory and field experiments have shown consistently that soil respiration increases exponentially with temperature. On longer timescales, soil respiration tends to decline, but it has been very difficult to tease apart possible explanatory mechanisms, such as a decreasing SOC stock, a decreasing microbial biomass, or a reduced production of enzyme that may be caused by individual‐level physiological acclimation, or population‐ level genetic adaptation, or community‐level ecological shift (Karhu et al., 2014). Allison et  al. (2010) sought to explain the nonlinear, hump‐shaped pattern of soil CO2 loss with warming by using the basic CDMZ model with constant or temperature‐dependent MGE. Their goal was to evaluate the model’s ability to reproduce the transitory increase in soil respiration as well as generate plausible changes in C, M, and Z. They focused on ecosystems for which no dramatic changes in SOC pools had been reported (Schuur et al., 2009) while microbial biomass declined with warming (Allison & Treseder, 2008; Bradford et al., 2008; Rinnan et  al., 2007). This pattern may not be general (Sulman et al. (2018)’s synthesis of field manipulation experiments reveals a wide diversity of responses), however Allison

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et al.’s (2010) analysis is a first attempt to understand the mechanistic link between heterotrophic respiration and soil C loss in response to climate warming. Specifically, Allison et  al. (2010) simulated the consequences over time, with a 30‐year horizon, of 5°C warming. Warming was abrupt (instantaneous) and represented a perturbation of the model’s parameters (previously set at 20°C). Transient dynamics over 30 years were simulated by monitoring the state variables initialized at their equilibrium value at 20°C. Their baseline model assumes that temperature affects the microbial uptake rate and the exoenzyme catalysis rate. They compare model simulations run (Figure 5.5) under the following scenarios: ••MGE may be constant, decrease with warming, or “acclimate,” i.e., show reduced sensitivity to temperature (Figure 5.5a). ••The temperature sensitivity of enzymes itself may be constant, or “acclimate,” i.e., decrease with warming through a 50% reduction in maximal activity (vDmax) and 50% increase in the half‐saturation constant (KmD) (Figure 5.5b). The conclusion from these simulations is that, without enzyme “acclimation” (Figure  5.5a), only the temperature‐dependent MGE scenario (dotted line in Figure 5.5a) can produce the target pattern of transient increase in the soil CO2 loss rate, little change in the SOC pool, and reduced microbial biomass. Assuming that enzyme activity can acclimate (Figure  5.5b) the best match of the empirical target is achieved by the “acclimating” MGE scenario. What happens if we use simpler models in which the enzyme pool is not explicitly represented (“CDM models”) to predict the effect of warming on soil CO2 loss, SOC stock, and microbial biomass? In these models (such as the “conventional model” sensu Allison et al., 2010), microbial processes, including degradative enzyme production and activity, are not explicitly coupled to soil C turnover, so temperature‐driven changes in microbial biomass and enzymes cannot interact with the response of decomposition to warming. Allison et  al. (2010) report simulations of a CDM model for constant, temperature‐dependent or “acclimating” MGE. The CDM model can predict the transient rise in respiration along with the decline in microbial biomass, but in all cases the CDM model predicts strong SOC stock decline, which, according to Allison et al. (2010) is at odds with empirical observations. Li et al. (2014) reported a model comparison encompassing Allison et al.’s (2010) “conventional” (CDM) and CDMZ models and G. Wang et al.’s (2013) CDMZ model with multiple SOC and enzyme pools (and also German et  al.’s (2012) simplified “CM model” (Figure  5.1a), which does not explicitely represent the DOC and enzyme pools). Like Allison et  al. (2010), they assume initial

equilibrium at 20°C and instantaneous +5°C warming, and they monitor the subsequent system dynamics under different scenarios of MGE temperature dependence (no dependence, constant decline, reduced decline). Whereas the main effect of warming in the “conventional” microbial model is to reduce the equilibrium SOC pool, the direction of SOC change in the CDMZ models depends on the balance between increases in Km parameters and declines in MGE with warming, both of which tend to increase SOC; and increases in vmax coefficients, which tend to reduce SOC (Li et al., 2014). The Li et al. (2014) model comparison also shows that in all CDMZ models and scenarios, there is a critical temperature that minimizes the equilibrium SOC stock. This critical temperature, Tcrit, determines whether warming causes a gain or loss of soil C in a given ecosystem. Cooler ecosystems with mean temperature below Tcrit are expected to lose soil C in response to warming, whereas warmer ecosystems with mean temperature above Tcrit are predicted to store more C in response to warming. The critical temperature depends on the temperature‐dependence scenario and model complexity. The temperature dependence of the microbial death rate was ignored in the models that Li et al. (2014) compared. In a laboratory study, Hagerty et  al. (2014) documented the case of a forest soil in which MGE is temperature‐ independent while microbial turnover (death rate) accelerates with warming. By using Allison et al.’s (2010) CDMZ model with either constant or temperature‐dependent MGE and microbial death rate, Hagerty et al. (2014) evaluated the long‐term consequences of increasing microbial mortality with warming. Focusing on equilibrium SOC sampled at 3°C and incubated in temperatures between 5° and 20°C, they found that temperature‐dependent mortality results in significant decrease in microbial biomass (equilibrium M) and increase SOC (equilibrium C), these effects being similar in direction but much larger than in the temperature‐dependent MGE/temperature‐ independent mortality scenario. G. Wang et  al. (2013) built on Allison et  al.’s (2010) CDMZ model by considering two SOC pools (particulate organic carbon, POC, and mineral‐associated organic carbon, MOC) and associated exoenzymes. It is the first CDMZ model to account for the soil C stabilization mechanisms of DOC adsorption and SOC association with minerals. In this model, only adsorbed DOC is protected, i.e., inaccessible to microbes; MOC can still be decomposed but at a slower rate than POC. Their model can reproduce the response of total SOC to warming predicted by Allison et al.’s (2010) CDMZ model. As climate change affects water availability and therefore accessibility of microbes to SOC and mineral–organic matter interactions, G. Wang et al.’s (2013) model is well suited to study the effect of multivariate climate change.

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  117 (a)

(b) Enzyme acclimation

Base model Control Warm + const CUE Warm + vary CUE Warm + acclim CUE

Control Warm + const CUE Warm + vary CUE Warm + acclim CUE 1.6 CO2 efflux (μg g–1 h–1)

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Figure 5.5 Response of microbial dynamics and soil respiration to 5°C warming predicted by Allison et  al.’s (2010) CDMZ model. (a) Enzyme sensitivity to temperature is fixed. (b) Enzyme parameters vmaxD and KmD are adjusted to compensate their intrinsic response to warming. Scenario “vary CUE” assumes a constant temperature‐dependence of carbon use efficiency (sensu Allison et al., 2010). Scenario “acclim CUE” assumes that CUE responded to warming with reduced temperature sensitivity. [Allison et al. (2010). Reproduced with permission of Springer Nature.]

Tang and Riley (2015) introduced another CDMZ model accounting for mineral–organic interactions. The model does not account explicitly for adsorbed or mineral‐ associated carbon pools, instead mineral associations with

enzymes and DOC are represented as competing reactions at equilibrium, in which enzymes bind with SOC and microbes bind with DOC. This mechanism alters the fraction of accessible SOC for enzymes and DOC for

118  BIOGEOCHEMICAL CYCLES

microbes. Additionally, Tang and Riley’s (2015) model represents daily and seasonal change of temperature. This makes their model well designed to investigate the consequences of dynamical climate variation across multiple timescales. Zhang et  al. (2014) used the extended CDMZ model (Figure  5.3) to investigate the causes of Birch pulses of soil respiration in response to episodic rainfall pulses. Their approach goes beyond the kind of model‐based extrapolation of empirical data that studies such as Allison et  al. (2010) and Hagerty et  al. (2014) implemented. They evaluated alternate hypotheses by constructing CDMZ models corresponding to their different hypotheses and fitting these models to a set of field measurements of soil respiration from a semiarid savannah ecosystem driven by episodic rainfall pulses (Figure 5.6). They used some known parameter values (see previous section) and performed Bayesian parameter estimation of MGE, enzyme production rate, microbial death rate, and enzyme deactivation rate. They evaluated and compared the models using three assessment criteria considering both goodness of fit and model complexity. The best model turns out to be the one depicted in Figure 5.3; this model accounts for the moisture‐dependence of enzyme activity and microbial uptake rates, and for the processes

of accumulation and storage of DOC in the dry soil pores during dry periods (which is temporarily inaccessible to microbes), along with the facilitation of SOC decomposition during dry periods by enzymes localized in dry soil pores (Homyak et  al., 2018). Soil microbial models are also effective in reproducing drought events in a primarily temperature‐limited system like that in temperate and boreal forests (Sihi, Inglett, et  al., 2018). These results emphasize the need to better understand and quantify the mechanisms of DOC accumulation in dry soil pores. 5.4.2. Projecting Global Soil C Stocks in a Changing Climate Mechanistic models are useful to investigate the significance of short‐term experimental results to long‐term ecosystem states, and to evaluate alternative hypotheses for explaining empirical data. They also exhibit two general properties that are relevant for scaling up microbial processes to the global Earth system. ••First, the C stock response to warming depends on the initial temperature. Soil carbon losses are expected in cold biomes, such as Arctic tundra; minimal carbon losses or even carbon gains are predicted in warm regions such as the tropics. 320

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Figure 5.6 Data assimilation and model selection for the effect of rainfall pulses on soil respiration. (Top) Observed half‐hourly volumetric soil moisture (in m3 m‐3) and temperature (in K) at 10 cm. (Middle and bottom) CO2 efflux measured half hourly at the soil surface (in μmol m‐2 s‐1) compared with (middle) the basic CDMZ model of Figure 5.1b, and (bottom) the six‐pool CDMZ model shown in Figure 5.3. The shaded area (in red) represents the 95% credible interval, while the green line is for the best realization. [Zhang et al. (2014). Reproduced with permission of John Wiley & Sons.]

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••Second, the C stock at steady state appears to be decoupled from inputs. In Allison et al.’s (2010) CDMZ model, equilibrium C depends on the ratio of SOC to DOC inputs, not the total amount (Li et al., 2014). This result is consistent with experiments that demonstrate that increased plant inputs and SOC responses are not linearly related (Lajtha et al., 2014, 2015). The actual integration of microbial models of soil respiration into broad‐scale land models remains a challenge. Different model structures and parameterization lead to different patterns of soil C response to warming. Continuous change in climate over time may prevent soils from reaching equilibrium and require considering transient dynamics (which are prone to develop strong oscillations, e.g., Li et  al., 2014; Y. Wang et  al., 2014). Properly describing even the short‐term response of soil C dynamics to warming may raise the need to account for mechanisms such as physicochemical changes, priming, and the interaction of C and N cycles (e.g., Fontaine et al., 2003). Microbial model complexity will need to be optimized before integration into larger scale models. In the first attempt to represent soil microbial dynamics in Earth system models (ESMs), Wieder and contributors built a temperature‐dependent CM model into the Community Land Model (CLM) and thus produced a “CLM microbial model” (Wieder et  al., 2013). This model represents aboveground and belowground processes. Two belowground layers are included, surface (0–30 cm) and subsurface (30–100 cm). Microbial biomass is the source of enzymatic activity, but degradative enzymes are not modeled explicitly (no Z pool); rather, microbes directly catalyze the mineralization of litter and SOC according to Michaelis–Menten kinetics, hence the model CM structure is applied to all three layers (one aboveground and two belowground horizons). The parameterization of the CLM microbial model uses German et  al.’s (2012) data (cf. Box 5.2). Temperature affects microbial uptake parameters and MGE. To validate the model, global simulations were run using globally gridded data: observed (rather than simulated) litter inputs and mean annual soil temperature. Model outputs were compared with observations from the Harmonized World Soils Database. The CLM model explains 50% of the spatial variation in observed soil C stock, in contrast to 28–30% for the best traditional, nonmicrobial models. They concluded that models with explicit microbes should show greater agreement with actual measurements of soil C than models without them. To simulate soil C responses to global warming, control simulations were run using observationally derived litter inputs distributed throughout the year and mean monthly soil temperature data from 1985 to 2005 from a single community ESM (CESM) ensemble member from archived Coupled Model Intercomparison Project Phase 5

(CMIP5) experiments. Projections of the CLM microbial model were run from 2006 to 2100 by using CESM projected soil temperature (obtained from ensemble member one of CESM simulations for the Representative Concentration Pathway 8.5 (RCP 8.5)) corresponding to a 4.8°C increase in mean global temperature by 2100. The salient result is a massive soil C loss (~300 Pg C by year 2100) in the case of temperature‐independent MGE (Figure  5.7a). In a temperature‐dependent MGE scenario, this effect can be completely offset (Figure 5.7a). These results emphasized that microbial processes and their temperature dependence should be critical to model global soil C responses to warming on decadal to century‐ long timescales and to properly evaluate the uncertainties of model projections. In a more recent study comparing a first‐order model (CASA‐CNP) and two microbial models (MIMICS and CORPSE), Wieder et al. (2018) pointed to limitations of ranking models based on their capacity to simulate the spatial distribution of soil carbon stocks, especially considering that different models are not calibrated similarly. Rather, they highlighted and embraced the diversity of projections generated by alternate models, and focused on explaining the mechanistic causes of such differences. Considering transient dynamics, the three models were forced with litterfall inputs calculated by running the CASA‐CNP vegetation model, and used historical data of temperature and moisture from 1901 to 2010 (instead of projected data). According to these data, global mean annual soil temperature increased by 1.1°C and mean annual soil moisture by 0.5%, relative to the initial conditions. Again, the three models showed dramatically different patterns of soil carbon gains and losses despite identical litter inputs and climate forcing, with a net accumulation of soil carbon in CASA‐CNP (+18.1 Pg C) and MIMICS (+24.1 Pg C), and a net loss in CORPSE (−21.7 Pg C) over the same period (Figure 5.7b). Ultimately, the development, validation, and verification of models crucially depends on our ability to constrain them with empirical data, but we face a lack of relevant global data sets. Table 5.2 provides a short list of global data sets that can be used to initialize global projections that incorporate CDMZ models. As the analysis of Wieder et al. (2013) demonstrates, explicitly incorporating microbial dynamics into ESMs will increase the uncertainty of the projections. This is not a negative result: comparing multiple structurally different models to better assess the uncertainties of coupled carbon–climate projections is highly desirable (Bradford et al., 2016; Wieder et  al., 2015, 2018). In addition, moving beyond first‐order models is required for a mechanistic and quantitative understanding of soil carbon–climate feedbacks in the context of multivariate climate change (Abramoff et al., 2018; Monroe et al., 2018).

120  BIOGEOCHEMICAL CYCLES (b) 100

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Figure 5.7  Predictions of cumulative change of soil carbon stocks by structurally different models. (a) Future predictions in response to a 4.8°C warming scenario over 2006–2100. Thick lines (black and blue): conventional CLM models. Green lines: CLM microbial model, with temperature‐dependent MGE (dotted line) or t­ emperature‐ independent MGE (plain line). (b) Historical predictions in response to a 1.1°C warming over 1910–2010. Cyan line: first‐order CASA‐CNP. Purple line: MIMICS. Brown line: CORPSE. See text for details. [(a) Wieder et  al. (2013). Reproduced with permission of Springer Nature. (b) Wieder et al. (2018). Reproduced with permission of John Wiley & Sons.] Table 5.2  Large‐scale data sets available to parameterize and initialize global projections of CDMZ models Soil stocks and properties Microbial biomass Soil respiration

Number of pools

Key variables

Scale and resolution

Reference

Harmonized World Soil Database ISRIC‐WISE and SoilsGrid1km Soil microbial abundance Biome extrapolated M, N, and P Soil respiration database (SRDB)

Soil C, physical properties, top soil and subsoil (0‐1m) Soil C, physical properties at six depths M, soil C, physical properties

Global, 30 arc sec (0.008333°) resolution Global, 1 km resolution

FAO & ISRIC (2012) Hengl et al. (2014)

Global, 0.5° resolution

Microbial and soil C, N, and P to 1m depth Soil respiration, T, Q10, biome

Global, 3422 data points for 14 biomes > 800 studies and > 3300 records

Serna‐Chavez et al. (2013) Xu et al. (2013) Bond‐Lamberty & Thomson (2010)

Note: Adapted from table 3 in Wieder et al. (2015).

5.5. CHALLENGES AND PERSPECTIVES The development of microbial models of soil respiration and the application of these models to improve our projections of climate and ecosystem change face multiple challenges, all revolving around the general issue of scaling in ecology (Levin, 1992). Mechanistic models of the CDMZ family capture processes at a spatial scale that is intermediate between the microscopic (micro) scale of cellular and physicochemical processes at which SOM decomposition and stabilization occur (10−6 to 10−3 m) and the mesoscale at which these processes are commonly

measured (10−2 to 1 m). Projections of ecosystem rates of soil respiration involve macroscales that are relevant to global climate projections (103 to 1014 m) (Hinckley et al., 2014). There are thus two major scaling issues when considering the derivation and application of CDMZ models (Bradford & Fierer, 2012; Burd et  al., 2016; Davidson et al., 2014; Hararuk et al., 2015; Ise & Moorcroft, 2006; Sulman et al., 2014, Wieder et al., 2013, 2015). First, we need CDMZ models that are consistent with microscale processes and can be integrated with mesoscale empirical data (Kaiser et  al., 2014, 2015). Second, we need to ­integrate CDMZ models in ESMs and validate their

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  121

CDMZ models typically assume a 10−2 m spatial scale and iterate over hourly or daily time steps. The underlying processes, in contrast, involve individual cells with characteristic length of order 1 μm and interaction range of order 10 μm (Raynaud & Nunan, 2014). Microbial accessibility to substrates on this microscale is key to the timescale of microbial influence on SOM decomposition (J. P. Schimel & Schaeffer, 2012). The C that is associated with mineral fractions and that is not available for decomposition may be irrelevant for short‐term soil respiration dynamics, whereas on a longer timescale, the dynamics of mineral adsorption/desorption will change substrate accessibility and impact microbial activity and respiration. In addition, the physical diffusion of labile compounds (enzymes, DOC) couples the microscale dynamics of their production with accessibility on longer spatial and temporal scales. Ultimately, to describe measurable soil C dynamics on the mesoscale, scaling up from micro‐ to mesoscales requires us to (a) integrate microbial functional and enzyme chemical diversity across microsite variations in

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Figure 5.8 Spatio‐temporal dynamics of microbial decomposition emerging from microscale processes. Each square shows a 100 × 100 grid of microsites, each 10 mm of side length, corresponding to a 1 × 1 mm area of leaf litter. (Top row) Spatial distribution of individual microbes of three functional groups (blue, plant degrader; green, microbial‐necromass degrader; red, opportunists). Bottom row, spatial distribution of litter material (SOC). Quantity ranges from low (blue) to high (red); sites empty of microbes appear in black. Panels on the right show the aggregated sizes of the respective pools. [Kaiser et al. (2014). Licensed under CC BY SA 3.0.]

Microbial biomass (mg C g–1 litter C)

5.5.1. Consistency of CDMZ Models with Microscale Decomposition Processes

substrate availability and environmental conditions (temperature, water content, CO2, pH, etc.) while (b) accounting for spatial environmental correlations induced by physical diffusion among microsites. Computational trait‐based modeling as done by Allison (2012) and Kaiser et  al. (2014, 2015) (see section  5.2 and Box 5.1) represents seminal steps in this direction, where numerical simulations of the microscale processes are used to derive the dynamics of aggregate variables. This is illustrated by the run shown in Figure 5.8, where considerable heterogeneity in the spatial distribution of microbes (Figure 5.8, top) and SOC (Figure 5.8, bottom) collapses into aggregated variables that vary smoothly over time (Figure 5.8, right‐hand panels). Considering the aggregated dynamics also informs us on the characteristic timescale (1 year) of decomposition at that scale. The critical question that such work raises is how should microscale parameters of C–D–M–Z activity and interaction be rescaled for a mesoscale CDMZ model to fit the aggregated variables dynamics precisely? Trait‐based models as constructed and analyzed by Allison (2012) and Kaiser et al. (2014, 2015) are fundamentally designed to investigate the consequences of diverse functional types in the soil microbial community (as illustrated in Figures 5.2 and 5.8). Because exoenzyme production is key to the decomposition function of soil microbes, it is essential to understand how variation in exoenzyme production across strains influences decomposition and soil respiration. In particular, “cheater” strains that invest little resource or none in exoenzyme production may still reap off the benefits of decomposition, i.e., access to DOC, brought about by “cooperative” strains that pay the cost of producing exoenzymes.

% of initial litter C

projections at macroscales (Todd‐Brown et  al., 2012; Wieder et al., 2013, 2015). What is at stake here ultimately is a better understanding of how soil microbial processes and dynamics influence the global, long‐term (decadal to centennial) dynamics of Earth coupled carbon–climate, and how global effects feed back and shape soil microbial communities and function across temporal and spatial scales.

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Allison (2005) studied the ecological stability of such a “social” system of cooperation and cheating; Kaiser et al. (2015) explored the consequences of cooperator–cheater social dynamics for decomposition of a given amount of  organic matter (Figure  5.8). It turns out that the equilibrium ratio between enzyme producers and cheaters downregulates to a minimum the total amount of enzymes produced per total microbial biomass, thus  slowing down decomposition and causing an accumulation of soil carbon (and nitrogen). To explore consequences on longer time and spatial scales, one challenge now is to consider a fully evolutionary version of this model, which will account for genetic mutation generating variation in enzyme production, and natural selection arising from spatial and temporal variation in environmental conditions. Mathematical and modeling techniques exist to achieve this goal (e.g., Champagnat et al., 2006). There are other microscale processes that are potentially critical for soil respiration dynamics at the mesoscale, which need to be incorporated in CDMZ‐type models. One such process involves how soil moisture affects the supply of substrates at the microscopic scale, for which we need a mechanistic representation. This could be achieved by explicitly modeling water transport of the substrates at the scale of soil pores. Also, a spatial representation of soil heterogeneous structure could account for the distribution of available substrates in the pore network. This is important because substrate concentrations vary enormously in the pore network and microbes may not be collocated with the available C (Baldock & Skjemstad, 2000; Van Veen & Kuikman, 1990). Accounting for interactions among the carbon, nitrogen, and phosphorus cycles is another challenge. Community‐level competition for C and N sources is likely to be important for determining the overall response of soil respiration to warming (Conant et al., 2011). For example, if the temperature sensitivity of key N‐cycle processes is greater than some C‐cycle processes, then it is possible that N availability limits microbial activity. This could influence plastic responses or evolutionary adaptations in microbial allocation to N versus C acquisition. Trait‐based models are ideally suited to address the microbial dynamics of C, N, and P simultaneously (Allison, 2012). On the mathematical side, we need to build further on models such as J.P. Schimel & Weintraub’s (2003) to represent N availability, C/N soil and microbial stoichiometry, and the coupling of C cycling to N. These models will require additional parameterization of the enzymatic processes that convert organic N to other forms (Abramoff et  al., 2017; Allison, 2012; Averill & Waring, 2018; Kaiser et al., 2014, 2015; Moorhead et al., 2012; J.P. Schimel & Bennett, 2004).

5.5.2. Incorporating Soil C Stabilization in  CDMZ Models Soil‐carbon–climate feedbacks involve timescales (year to century) over which mechanisms of soil C stabilization may not be ignored (Abramoff et al., 2017). Using CDMZ models to improve long‐term and large‐scale climate forecasting requires incorporation of such mechanisms. Soil C is stabilized by interacting with minerals, through sorption (physical or chemical binding of organic matter with mineral), occlusion (blocking of organic matter within a mineral frame), or aggregation (association of mix of minerals and organic compounds including pores and live microbes) (Keil & Mayer, 2014). Microaggregates break down over time due to mechanical stresses or the gradual degradation of binding agents, and carbon in chemically protected organo‐mineral complexes is slowly released through desorption (Sollins et al., 1996). As presented earlier, there have been few attempts to incorporate organic–mineral interactions in CDMZ models. G. Wang et al.’s (2013) model includes the slower decomposable mineral‐associated SOC and the nondecomposable adsorbed DOC. Adsorption and desorption are first‐order temperature‐dependent functions. In Tang and Riley (2015), mineral‐associated C pools are not explicit, however, SOC “compete” with mineral surface to bind enzymes, while microbes compete with mineral surface to bind DOC. Recent microbial models intended for use in global numerical models usually simplify the decomposition process (CM instead of CDMZ models, i.e., no explicit enzymes or DOC), but they do account for processes of C stabilization (Abramoff et  al., 2018; Sulman et  al., 2014; Wieder et al., 2014). What makes “stabilized SOC” conceptually different from “recalcitrant SOC” with slower decomposition kinetics, is that it is mainly formed at the end of the microbial decomposition chain and is little or not accessible to microbial decomposition. In Sulman et  al. (2014)’s model (cf. section  5.3), there are three chemically distinct classes of SOC (simple, chemically resistant, and dead microbes), each of them divided into an accessible form and a fully inaccessible form. Carbon moves from accessible to inaccessible forms at a class‐specific protection rate that also depends on soil texture (clay content); inaccessible carbon moves back to the accessible form after 45 years. In Wieder et al. (2014)’s model, formation of inaccessible SOC is, like decomposition, a microbial process. Most aboveground to belowground carbon inputs start as accessible SOC, then are converted into microbial biomass (through decomposition, uptake, and growth captured with one reaction). Microbial residues then form the bulk of stabilized SOC (not fully inaccessible to decomposition). Only a small fraction of carbon inputs bypasses accessible SOC and

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microbial biomass and is transformed directly into stabilized SOC. Abramoff et  al. (2018) synthesized both Sulman and Wieder’s models by including two pools of stabilized SOC: one fully inaccessible pool formed by mineral sorption of microbial products (“mineral‐associated organic C”), and one little‐accessible pool formed by aggregation of accessible SOC and microbial residues (“aggregate C”). They compared their predictions with the nonmicrobial Century model, which has long been a standard for large‐scale simulations of soil C stocks (Bonan et  al., 2013; Parton et  al., 1987, 1995; Paustian et  al., 1992). The microbial model exhibits distinct nonlinear responses due to the choice of functions affecting SOC in the pool; importantly, the two models diverge regarding the direction of SOC change (sink vs. source) in the case of multiple varying environmental factors. 5.5.3. Integrating CDMZ Models in Global Carbon–Climate Projections To scale up the ecosystem effects of microscale microbial dynamics to continental and global scales, the general principle is to use statistical distributions of parameters, such as vmax, Km, and the death rates of microbes and decay rates of exoenzymes and assemble them across ecosystems and soil types. The spatial resolution of ESMs then dictates the spatial scale at which projections must be run (German et al., 2012; Sinsabaugh et  al., 2008). In addition to climate drivers, vegetation and soil physico‐chemical variables are well characterized at that scale; for example, soil texture and pH data have been shown to be effective at scaling microbial respiration rates across landscapes (Pansu et al., 2010). Seminal attempts of global scaling such as Wieder et al. (2013) pave the way forward. Further developments could draw from progress in global modeling of vegetation dynamics: ••Williams et al. (1997) developed a protocol for scaling up models of gross primary productivity. A fine‐scale mechanistic model is used to predict productivity across a wide range of conditions, and these predictions are then aggregated across time and space. A broad‐scale model with simplified equations is then developed to replicate the aggregated output from the fine‐scale model. Projections from the broad‐scale model of gross primary productivity across disparate ecosystems using broad environmental drivers, such as daily irradiance and leaf area index, can be successfully validated. ••The “Ecosystem Demography” (Moorcroft et  al., 2001) and “Perfect‐Plasticity Approximation” (Strigul et  al., 2008) models apply individual‐based forest gap dynamics to derive properties of size and age‐classes at larger ecosystem scale, which can be related to forest structure, productivity, and C storage (Fischer et al., 2015).

••Likewise, scaling terrestrial photosynthesis from leaf to Earth scale is now achieved by scaling leaf traits within plant canopies and across plant functional types while still using microscale enzyme kinetics to model aboveground C balance and project its response to climate change (Bonan et al., 2011, 2012, 2014). Similar developments are needed for modeling soil microbial dynamics across scales (see Bond‐Lamberty et al. (2016) for a discussion of how modeling approaches to decomposition functional types differ from, and complement models of, plant functional types). A critical issue when moving up from meso‐ to global scales is to account for the multiple exogenous and endogenous processes that generate variation at spatial and temporal scales above that of mechanistic CDMZ models, including: ••The quantity and quality of organic matter inputs, which are determined primarily by vegetation type and productivity at scales larger than the mesoscale of decomposition. Temperature as well as its indirect effects via soil moisture will alter plant production, partitioning of that carbon to roots and leaves and to litter, and litter quality (Conant et al., 2011), ••Large‐scale and long‐term ecological feedbacks of small‐scale, short‐term physiological, ecological, and evolutionary processes. Increased C inputs, as might occur with CO2 or nutrient fertilization, may cause little change to soil C stock, yet microbial respiration will return more CO2 to the atmosphere. This could be a significant influence of global warming, which would then feed back to the microscale dynamics of respiration. ••Environmental correlates of global warming, such as extreme climatic events (droughts and floods) and changes in their distribution, frequency, and intensity, are likely to generate wide variation at regional scale in soil properties and microbial responses. Physiological consequences may include microbial dormancy, while episodes of severe ecological filtering on community composition and strong natural selection on genetic variation of microbial traits can be expected. Validating soil microbial models of decomposition at the global scale is the ultimate challenge. Global datasets on soil carbon stocks and fluxes offer promising opportunities to validate the emerging ESM microbial models. Many of the land submodels from current Global Circulation Models (GCMs) are tested at continental scales, and a similar approach could be applied to microbial ESM projections, possibly using CO2 flux data from networks such as the North American Carbon Program and CarboEurope (Schwalm et al., 2010; Suzuki & Ichii, 2010). As we tackle challenges in scaling, parameterization, and validation, a new generation of microbially based decomposition models will eventually improve predictions of carbon–climate feedbacks in the Earth system and help quantify projection uncertainty.

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5.6. SUMMARY

ACKNOWLEDGMENTS

Here we provide a brief recap of the chapter key points. ••As the flaws of first‐order decay kinetics still used in most coupled carbon–climate models were recognized, a new generation of SOC decomposition models have been developed to link SOC turnover to microbial ecophysiology and degradative enzyme kinetics. The basic processes represented are: litter input, microbial enzyme production, enzymatic SOC degradation, microbial DOC uptake and assimilation, microbial death, and enzyme deactivation. The models take the form of systems of nonlinear ordinary differential equations with at least four state variables corresponding to the main four pools represented at a mesoscale of space and time: C (measuring SOC), D (measuring DOC), M (microbial biomass), Z (enzyme concentration). Trait‐based models are computational in essence and allow representation of a diversity of microbial functional types, that high‐throughput sequencing and “omics” data can help monitor in real systems. ••To investigate how soil microbes and exoenzymes mediate the effects of environmental (e.g., climate) change on soil respiration and C stock, these models implement the dual Arrhenius and Michaelis–Menten kinetics. The Arrhenius function represents dependence of microbial and enzyme activity on temperature and substrate quality (through activation energy); the Michaelis–Menten function represents the limitation of substrate availability and chemical affinity of substrates on temperature sensitivity. ••Accounting for climate factors other than temperature (moisture, CO2 concentration) allows the “apparent” versus “intrinsic” temperature sensitivity of SOC decomposition and soil heterotrophic respiration to be distinguished. The effect of variation in soil moisture due to variable precipitation can be addressed by including controls of enzyme activity and DOC uptake by water saturation, and possibly distinguishing pools of wet versus dry enzymes and DOC. ••Temperature‐dependent CDMZ models of decomposition and soil respiration are used to explain observational and experimental responses of soil respiration to warming. They can explain a pattern of increasing respiration, decreasing microbial biomass, and relatively stable SOC. Temperature‐dependent CDMZ can be integrated in ESMs. Processes that need to be integrated in large‐scale models include microbial evolutionary adaptation and eco‐evolutionary feedbacks between soil C and climate. ••Microbial respiration is the largest flux of C out of the soil. Inclusion of explicit microbial processes of decomposition in ESMs will likely increase the intermodel range of soil C projections in model intercomparison projects, which may in fact provide a more accurate assessment of uncertainty in future carbon cycle projections.

We thank the Editors for their invitation to write this chapter, and Rachel Gallery, Pierre‐Henri Gouyon, Hélène Leman, Nicolas Loeuille, Scott Saleska, and Will Wieder for discussion. E.A. received support for the graduate program Frontières du Vivant (University Paris 7), Chaire Modélisation Mathématique de la Biodiversité, PSL University, Laboratory of Excellence (LabEx) MemoLife (PIA‐10‐LABX‐54). This work was also supported by CNRS Pépinière de site PSL “Eco‐Evo‐Devo”) and the Partner University Fund for ENS‐University of Arizona cooperation. REFERENCES Abramoff, R.Z., Davidson, E.A., & Finzi, A.C. (2017). A parsimonious modular approach to building a mechanistic belowground carbon and nitrogen model. Journal of Geophysical Research: Biogeosciences, 122(9), 2418–2434. Abramoff, R., Xu, X., Hartman, M., O’Brien, S., Feng, W., Davidson, E., et al. (2018). The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry, 137(1), 51–71. Allison, S.D. (2005). Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments: Constraints on enzymatic decomposition. Ecology Letters, 8(6), 626–635. Allison, S.D. (2012). A trait‐based approach for modelling microbial litter decomposition. Ecology Letters, 15(9), 1058–1070. Allison, S.D. (2014). Modeling adaptation of carbon use efficiency in microbial communities. Frontiers in Microbiology, 5, 571. Allison, S.D., & Treseder, K.K. (2008). Warming and drying suppress microbial activity and carbon cycling in boreal forest soils. Global Change Biology, 14(12), 2898–2909. Allison, S.D., Wallenstein, M.D., & Bradford, M.A. (2010). Soil‐carbon response to warming dependent on microbial physiology. Nature: Geoscience, 3, 336. Alster, C.J., German, D.P., Lu, Y., & Allison, S.D. (2013). Microbial enzymatic responses to drought and to nitrogen addition in a southern California grassland. Soil Biology and Biochemistry, 64, 68–79. Amundson, R. (2003). Soil formation. Treatise on Geochemistry, 5, 605. Averill, C., & Waring, B. (2018). Nitrogen limitation of decomposition and decay: How can it occur? Global Change Biology, 24(4), 1417–1427. Baldock, J.A., & Skjemstad, J.O. (2000). Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Organic Geochemistry, 31(7), 697–710. Birch, H.F. (1958). The effect of soil drying on humus decomposition and nitrogen availability. Plant and Soil, 10(1), 9–31. Blagodatskaya, E., Blagodatsky, S., Dorodnikov, M., & Kuzyakov, Y. (2010). Elevated atmospheric CO2 increases microbial growth rates in soil: results of three CO2 enrichment experiments. Global Change Biology, 16(2), 836–848.

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  125 Bonan, G.B., Hartman, M.D., Parton, W.J., & Wieder, W.R. (2013). Evaluating litter decomposition in earth system models with long‐term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology. https://doi.org/10.1111/gcb.12031 Bonan, G.B., Lawrence, P.J., Oleson, K.W., Levis, S., Jung, M., Reichstein, M., et al. (2011). Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. Journal of Geophysical Research, 116(G2), GB1008. Bonan, G.B., Oleson, K.W., Fisher, R.A., Lasslop, G., & Reichstein, M. (2012). Reconciling leaf physiological traits and canopy flux data: Use of the TRY and FLUXNET databases in the Community Land Model version 4. Journal of Geophysical Research: Biogeosciences, 117(G2). https:// onlinelibrary.wiley.com/doi/abs/10.1029/2011JG001913 Bonan, G.B., Williams, M., Fisher, R.A., & Oleson, K.W. (2014). Modeling stomatal conductance in the earth system: linking leaf water‐use efficiency and water transport along the soil–plant–atmosphere continuum. Geoscientific Model Development, 7(5), 2193–2222. Bond‐Lamberty, B., Epron, D., Harden, J., Harmon, M. E., Hoffman, F., Kumar, J., et al. (2016). Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps. Ecosphere, 7(6), e01380. Bond‐Lamberty, B., & Thomson, A. (2010). A global database of soil respiration data. Biogeosciences , 7(6), 1915–1926. Bradford, M.A., Davies, C.A., Frey, S.D., Maddox, T.R., Melillo, J.M., Mohan, J.E., et al. (2008). Thermal adaptation of soil microbial respiration to elevated temperature. Ecology Letters, 11(12), 1316–1327. Bradford, M.A., & Fierer, N. (2012). The biogeography of microbial communities and ecosystem processes: implications for soil and ecosystem models. In D.H. Wall, K. Ritz, J. Six, D.R. Strong, W.H. van der Putten (eds), Soil Ecology and Ecosystem Services (Vol. 18, pp. 189–200). Oxford University Press. Bradford, M.A., Wieder, W.R., Bonan, G.B., Fierer, N., Raymond, P.A., & Crowther, T.W. (2016). Managing uncertainty in soil carbon feedbacks to climate change. Nature: Climate Change, 6, 751. Brantley, S.L., Megonigal, J.P., Scatena, F.N., Balogh‐Brunstad, Z., Barnes, R.T., Bruns, M.A., et al. (2011). Twelve testable hypotheses on the geobiology of weathering. Geobiology, 9(2), 140–165. Burd, A.B., Frey, S., Cabre, A., Ito, T., Levine, N.M., Lønborg, C., et al. (2016). Terrestrial and marine perspectives on modeling organic matter degradation pathways. Global Change Biology, 22(1), 121–136. Burns, R.G., DeForest, J.L., Marxsen, J., Sinsabaugh, R.L., Stromberger, M.E., Wallenstein, M.D., et  al. (2013). Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biology and Biochemistry, 58, 216–234. Carney, K.M., Hungate, B.A., Drake, B.G., & Megonigal, J.P. (2007). Altered soil microbial community at elevated CO2 leads to loss of soil carbon. Proceedings of the National Academy of Sciences of the United States of America, 104(12), 4990–4995.

Champagnat, N., Ferrière, R., & Méléard, S. (2006). Unifying evolutionary dynamics: from individual stochastic processes to macroscopic models. Theoretical Population Biology, 69(3), 297–321. Cheng, L., Booker, F.L., Tu, C., Burkey, K.O., Zhou, L., Shew, H.D., et  al. (2012). Arbuscular mycorrhizal fungi increase organic carbon decomposition under elevated CO2. Science, 337(6098), 1084–1087. Cleveland, C.C., & Liptzin, D. (2007). C:N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry, 85(3), 235–252. Conant, R.T., Ryan, M.G., Ågren, G.I., Birge, H.E., Davidson, E.A., Eliasson, P.E., et  al. (2011). Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Global Change Biology, 17(11), 3392–3404. Crowther, T.W., & Bradford, M.A. (2013). Thermal acclimation in widespread heterotrophic soil microbes. Ecology Letters, 16(4), 469–477. Davidson, E.A., & Janssens, I.A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440(7081), 165–173. Davidson, E.A., Janssens, I.A., & Luo, Y. (2006). On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biology, 12(2), 154–164. Davidson, E.A., Samanta, S., Caramori, S.S., & Savage, K. (2012). The Dual Arrhenius and Michaelis‐–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Global Change Biology, 18(1), 371–384. Davidson, E.A., Savage, K.E., & Finzi, A.C. (2014). A big‐ microsite framework for soil carbon modeling. Global Change Biology, 20(12), 3610–3620. Drake, J.E., Darby, B.A., Giasson, M.‐A., Kramer, M.A., Phillips, R.P., & Finzi, A.C. (2013). Stoichiometry constrains microbial response to root exudation—insights from a model and a field experiment in a temperate forest. Biogeosciences, 10(2), 821–838. Drake, J E., Macdonald, C.A., Tjoelker, M.G., Reich, P.B., Singh, B.K., Anderson, I.C., & Ellsworth, D.S. (2018). Three years of soil respiration in a mature eucalypt woodland exposed to atmospheric CO2 enrichment. Biogeochemistry, 139(1), 85–101. Dungait, J.A.J., Hopkins, D.W., Gregory, A.S., & Whitmore, A.P. (2012). Soil organic matter turnover is governed by accessibility not recalcitrance. Global Change Biology, 18(6), 1781–1796. FAO, & ISRIC (2012). JRC: Harmonized World Soil Database (version 1.2). Rome: Food and Agriculture Organization; Laxenburg, Austria: International Soil Reference and Information Centre. Fierer, N., Barberán, A., & Laughlin, D.C. (2014). Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Frontiers in Microbiology, 5, 614. Fierer, N., Strickland, M.S., Liptzin, D., Bradford, M.A., & Cleveland, C.C. (2009). Global patterns in belowground communities. Ecology Letters, 12(11), 1238–1249. Fischer, R., Ensslin, A., Rutten, G., Fischer, M., Schellenberger Costa, D., Kleyer, M., et al. (2015). Simulating carbon stocks

126  BIOGEOCHEMICAL CYCLES and fluxes of an African tropical montane forest with an individual‐based forest model. PloS One, 10(4), e0123300. Fontaine, S., & Barot, S. (2005). Size and functional diversity of microbe populations control plant persistence and long‐term soil carbon accumulation: Modelling long‐term SOM dynamics. Ecology Letters, 8(10), 1075–1087. Fontaine, S., Mariotti, A., & Abbadie, L. (2003). The priming effect of organic matter: a question of microbial competition? Soil Biology and Biochemistry, 35, (6), 837–843. Frey, S.D., Lee, J., Melillo, J.M., & Six, J. (2013). The temperature response of soil microbial efficiency and its feedback to climate. Nature: Climate Change, 3, 395. Georgiou, K., Abramoff, R.Z., Harte, J., Riley, W.J., & Torn, M.S. (2017). Microbial community‐level regulation explains soil carbon responses to long‐term litter manipulations. Nature Communications, 8(1), 1223. German, D.P., Marcelo, K.R.B., Stone, M.M., & Allison, S.D. (2012). The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: a cross‐latitudinal study. Global Change Biology, 18(4), 1468–1479. Goddéris, Y., François, L. M., Probst, A., Schott, J., Moncoulon, D., Labat, D., & Viville, D. (2006). Modelling weathering processes at the catchment scale: The WITCH numerical model. Geochimica et Cosmochimica Acta, 70(5), 1128–1147. Hagerty, S. B., van Groenigen, K. J., Allison, S. D., Hungate, B. A., Schwartz, E., Koch, G. W., et  al. (2014). Accelerated microbial turnover but constant growth efficiency with warming in soil. Nature: Climate Change, 4, 903. Hararuk, O., Smith, M.J., & Luo, Y. (2015). Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change. Global Change Biology, 21(6), 2439–2453. Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., et al. (2014). SoilGrids1km— global soil information based on automated mapping. PloS One, 9(8), e105992. He, Y., Yang, J., Zhuang, Q., Harden, J. W., McGuire, A.D., Liu, Y., et al. (2015). Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests. Journal of Geophysical Research: Biogeosciences, 120(12), 2596–2611. Hicks Pries, C.E., Sulman, B.N., West, C., O’Neill, C., Poppleton, E., Porras, R.C., et al. (2018). Root litter decomposition slows with soil depth. Soil Biology and Biochemistry, 125, 103–114. Hinckley, E.‐L.S., Wieder, W., Fierer, N., & Paul, E. (2014). Digging into the world beneath our feet: Bridging across scales in the age of global change. Eos, Transactions American Geophysical Union, 95(11), 96–97. Hochochka, P.W., & Somero, G.N. (2002). Biochemical adaptation. Oxford University Press, Oxford. Homyak, P.M., Blankinship, J.C., Slessarev, E.W., Schaeffer, S.M., Manzoni, S., & Schimel, J.P. (2018). Effects of altered dry season length and plant inputs on soluble soil carbon. Ecology, 99(10), 2348–2362. Huang, Y., Guenet, B., Ciais, P., Janssens, I. A., Soong, J. L., Wang, Y., et al. (2018). ORCHIMIC (v1.0), a microbe‐mediated model for soil organic matter decomposition. Geoscientific Model Development, 11(6), 2111–2138.

Ise, T., & Moorcroft, P.R. (2006). The global‐scale temperature and moisture dependencies of soil organic carbon decomposition: an analysis using a mechanistic decomposition model. Biogeochemistry, 80(3), 217–231. Jobbágy, E.G., & Jackson, R.B. (2000). The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecological Applications: A Publication of the Ecological Society of America, 10(2), 423–436. Joergensen, R.G., Brookes, P.C., & Jenkinson, D.S. (1990). Survival of the soil microbial biomass at elevated temperatures. Soil Biology and Biochemistry, 22(8), 1129–1136. Kaiser, C., Franklin, O., Dieckmann, U., & Richter, A. (2014). Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecology Letters, 17(6), 680–690. Kaiser, C., Franklin, O., Richter, A., & Dieckmann, U. (2015). Social dynamics within decomposer communities lead to nitrogen retention and organic matter build‐up in soils. Nature: Communications, 6, 8960. Karberg, N.J., Pregitzer, K.S., King, J.S., Friend, A.L., & Wood, J.R. (2005). Soil carbon dioxide partial pressure and dissolved inorganic carbonate chemistry under elevated carbon dioxide and ozone. Oecologia, 142(2), 296–306. Karhu, K., Auffret, M.D., Dungait, J.A.J., Hopkins, D.W., Prosser, J.I., Singh, B.K., et al. (2014). Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature, 513(7516), 81–84. Keiluweit, M., Bougoure, J.J., Nico, P.S., Pett‐Ridge, J., Weber, P.K., & Kleber, M. (2015). Mineral protection of soil carbon counteracted by root exudates. Nature Climate Change, 5, 588. Kuzyakov, Y. (2010). Priming effects: Interactions between living and dead organic matter. Soil Biology and Biochemistry, 42(9), 1363–1371. Lajtha, K., Townsend, K.L., Kramer, M.G., Swanston, C., Bowden, R.D., & Nadelhoffer, K. (2014). Changes to particulate versus mineral‐associated soil carbon after 50 years of litter manipulation in forest and prairie experimental ecosystems. Biogeochemistry, 119(1), 341–360. Lajtha, K., Peterson, F., Nadelhoffer, K., & Bowden, R.D. (2015). Twenty years of litter and root manipulations: insights into multi‐decadal som dynamics. Soil Science Society of America Journal. Soil Science Society of America. https:// dspace.allegheny.edu/handle/10456/37712 Lawrence, C.R., Neff, J.C., & Schimel, J.P. (2009). Does adding microbial mechanisms of decomposition improve soil organic matter models? A comparison of four models using data from a pulsed rewetting experiment. Soil Biology and Biochemistry, 41(9), 1923–1934. Levin, S.A. (1992). The problem of pattern and scale in ecology: The Robert H. MacArthur Award Lecture. Ecology, 73(6), 1943–1967. Li, J., Wang, G., Allison, S.D., Mayes, M.A., & Luo, Y. (2014). Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial‐ecosystem models of varying complexity. Biogeochemistry, 119(1), 67–84. Liu, L., King, J.S., & Giardina, C.P. (2005). Effects of elevated concentrations of atmospheric CO2 and tropospheric O3 on leaf litter production and chemistry in trembling aspen and  paper birch communities. Tree Physiology, 25(12), 1511–1522.

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  127 Lloyd, J., & Taylor, J.A. (1994). On the temperature dependence of soil respiration. Functional Ecology, 8(3), 315–323. Luo, Y., Wan, S., Hui, D., & Wallace, L.L. (2001). Acclimatization of soil respiration to warming in a tall grass prairie. Nature, 413(6856), 622–625. Luo, Z., Baldock, J., & Wang, E. (2017). Modelling the dynamic physical protection of soil organic carbon: Insights into carbon predictions and explanation of the priming effect. Global Change Biology, 23(12), 5273–5283. Manzoni, S., Taylor, P., Richter, A., Porporato, A., & Ågren, G. I. (2012). Environmental and stoichiometric controls on microbial carbon‐use efficiency in soils. The New Phytologist, 196(1), 79–91. McClaugherty, C.A., & Linkins, A.E. (1990). Temperature responses of enzymes in two forest soils. Soil Biology and Biochemistry, 22(1), 29–33. McGill, W.B. (1996). Soil sustainability: Microorganisms, and electrons. In Solo/Suelo’96 XIII Congresso Latino Americano de Ciéncia do Solo (pp. 4–8). Melillo, J.M., Frey, S.D., DeAngelis, K.M., Werner, W.J., Bernard, M.J., Bowles, F.P., et al. (2017). Long‐term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358(6359), 101–105. Melillo, J.M., Steudler, P.A., Aber, J.D., Newkirk, K., Lux, H., Bowles, F.P., et  al. (2002). Soil warming and carbon‐cycle feedbacks to the climate system. Science, 298(5601), 2173–2176. Monroe, J.G., Markman, D.W., Beck, W.S., Felton, A.J., Vahsen, M.L., & Pressler, Y. (2018). Ecoevolutionary dynamics of carbon cycling in the Anthropocene. Trends in Ecology and Evolution, 33(3), 213–225. Moorcroft, P.R., Hurtt, G.C., & Pacala, S.W. (2001). A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecological Monographs, 71(4), 557–586. Moorhead, D.L., Lashermes, G., & Sinsabaugh, R.L. (2012). A theoretical model of C‐ and N‐acquiring exoenzyme activities, which balances microbial demands during decomposition. Soil Biology and Biochemistry, 53, 133–141. Moorhead, D.L., & Sinsabaugh, R.L. (2006). A theoretical model of litter decay and microbial interaction. Ecological Monographs, 76(2), 151–174. Moorhead, D.L., & Weintraub, M.N. (2018). The evolution and application of the reverse Michaelis‐Menten equation. Soil Biology and Biochemistry, 125, 261–262. Moyano, F.E., Manzoni, S., & Chenu, C. (2013). Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models. Soil Biology and Biochemistry, 59, 72–85. Natali, S.M., Sañudo‐Wilhelmy, S.A., & Lerdau, M.T. (2009). Plant and soil mediation of elevated CO2 impacts on trace metals. Ecosystems , 12(5), 715–727. Norby, R.J., Todd, D. E., Fults, J., & Johnson, D.W. (2001). Allometric determination of tree growth in a CO2‐enriched sweetgum stand. The New Phytologist, 150(2), 477–487. Norby, R.J., Delucia, E.H., Gielen, B., Calfapietra, C., Giardina, C.P., King, J.S., et al. (2005). Forest response to elevated CO2 is conserved across a broad range of productivity. Proceedings of the National Academy of Sciences of the United States of America, 102(50), 18052–18056.

Oechel, W.C., Vourlitis, G.L., Hastings, S.J., Zulueta, R.C., Hinzman, L., & Kane, D. (2000). Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature, 406(6799), 978–981. Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., et al. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (R.K. Pachauri, L. Meyer (Eds.), p. 151). Geneva, Switzerland: IPCC. Pansu, M., Sarmiento, L., Rujano, M.A., Ablan, M., Acevedo, D., & Bottner, P. (2010). Modeling organic transformations by microorganisms of soils in six contrasting ecosystems: Validation of the MOMOS model. Global Biogeochemical Cycles, 24(1). https://onlinelibrary.wiley. com/doi/abs/10.1029/2009GB003527 Parton, W.J., Schimel, D.S., Cole, C.V., & Ojima, D.S. (1987). Analysis of factors controlling soil organic matter levels in Great Plains Grasslands 1. Soil Science Society of America Journal. Soil Science Society of America, 51(5), 1173–1179. Parton, W.J., Scurlock, J.M.O., Ojima, D.S., Schimel, D.S., Hall, D.O., & SCOPEGRAM Group Members (1995). Impact of climate change on grassland production and soil carbon worldwide. Global Change Biology, 1(1), 13–22. Paustian, K., Parton, W.J., & Persson, J. (1992). Modeling soil organic matter in organic‐amended and nitrogen‐fertilized long‐term plots. Soil Science Society of America Journal. Soil Science Society of America, 56, 476–488. Pendall, E., Bridgham, S., Hanson, P.J., Hungate, B., Kicklighter, D.W., Johnson, D.W., et  al. (2004). Below‐ground process responses to elevated CO2 and temperature: a discussion of observations, measurement methods, and models. The New Phytologist, 162(2), 311–322. Phillips, R.P., Finzi, A.C., & Bernhardt, E.S. (2011). Enhanced root exudation induces microbial feedbacks to N cycling in a pine forest under long‐term CO2 fumigation. Ecology Letters, 14(2), 187–194. Pregitzer, K.S., Burton, A.J., King, J.S., & Zak, D.R. (2008). Soil respiration, root biomass, and root turnover following long‐term exposure of northern forests to elevated atmospheric CO2 and tropospheric O3. The New Phytologist, 180(1), 153–161. Raynaud, X., & Nunan, N. (2014). Spatial ecology of bacteria at the microscale in soil. PloS One, 9(1), e87217. Rinnan, R., Michelsen, A., Bååth, E., & Jonasson, S. (2007). Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem. Global Change Biology, 13(1), 28–39. Roberts, D.V. (1977). Enzyme kinetics. http://www.sidalc.net/ cgi‐bin/wxis.exe/?IsisScript=UACHBC.xis&method=post& formato=2&cantidad=1&expresion=mfn=054326 Salazar, A., Sulman, B.N., & Dukes, J.S. (2018). Microbial dormancy promotes microbial biomass and respiration across pulses of drying‐wetting stress. Soil Biology and Biochemistry, 116, 237–244. Schimel, D. (2001). Preface. In E.‐D. Schulze, M. Heimann, S. Harrison, E. Holland, J. Lloyd, I. C. Prentice, D. Schimel (Eds.), Global Biogeochemical Cycles in the Climate System (pp. xvii–xxi). San Diego: Academic Press.

128  BIOGEOCHEMICAL CYCLES Schimel, J. P. (2001). 1.13—Biogeochemical models: Implicit versus explicit microbiology. In E.‐D. Schulze, M. Heimann, S. Harrison, E. Holland, J. Lloyd, I. C. Prentice, D. Schimel (Eds.), Global Biogeochemical Cycles in the Climate System (pp. 177–183). San Diego: Academic Press. Schimel, J.P., & Bennett, J. (2004). Nitrogen mineralization: challenges of a changing paradigm. Ecology, 85(3), 591–602. Schimel, J.P., & Schaeffer, S.M. (2012). Microbial control over carbon cycling in soil. Frontiers in Microbiology, 3, 348. Schimel, J.P., & Weintraub, M.N. (2003). The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biology and Biochemistry, 35(4), 549–563. Schlesinger, W.H. (1997). Biogeochemistry: An analysis of global change. San Diego, CA: Academic Press. Schuur, E.A.G., Vogel, J.G., Crummer, K.G., Lee, H., Sickman, J.O., & Osterkamp, T.E. (2009). The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature, 459(7246), 556–559. Schwalm, C.R., Williams, C.A., Schaefer, K., Anderson, R., Arain, M.A., Baker, I., et al. (2010). A model–data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research: Biogeosciences, 115(G3). h t t p s : / / a g u p u b s . o n l i n e l i b r a r y. w i l e y. c o m / d o i / abs/10.1029/2009JG001229 Serna‐Chavez, H.M., Fierer, N., & Van Bodegom, P.M. (2013). Global drivers and patterns of microbial abundance in soil. Global Ecology and Biogeography: A Journal of Macroecology, 22(10), 1162–1172. Sihi, D., Davidson, E.A., Chen, M., Savage, K.E., Richardson, A.D., Keenan, T.F., & Hollinger, D.Y. (2018). Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA. Agricultural and Forest Meteorology, 252, 155–166. Sihi, D., Gerber, S., Inglett, P.W., & Inglett, K.S. (2016). Comparing models of microbial–substrate interactions and their response to warming. Biogeosciences, 13(6), 1733–1752. Sihi, D., Inglett, P.W., Gerber, S., & Inglett, K.S. (2018). Rate of warming affects temperature sensitivity of anaerobic peat decomposition and greenhouse gas production. Global Change Biology, 24(1), e259–e274. Sihi, D., Inglett, P.W., & Inglett, K.S. (2019). Warming rate drives microbial nutrient demand and enzyme expression during peat decomposition. Geoderma, 336, 12–21. Sinsabaugh, R.L., Lauber, C.L., Weintraub, M.N., Ahmed, B., Allison, S.D., Crenshaw, C., et  al. (2008). Stoichiometry of soil enzyme activity at global scale. Ecology Letters, 11(11), 1252–1264. Sinsabaugh, R.L., Manzoni, S., Moorhead, D.L., & Richter, A. (2013). Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecology Letters, 16(7), 930–939. Sollins, P., Homann, P., & Caldwell, B.A. (1996). Stabilization and destabilization of soil organic matter: mechanisms and controls. Geoderma, 74(1), 65–105.

Somero, G.N. (1978). Temperature adaptation of enzymes: Biological optimization through structure‐function compromises. Annual Review of Ecology and Systematics, 9(1), 1–29. Somero, G.N. (2004). Adaptation of enzymes to temperature: searching for basic “strategies.” Comparative Biochemistry and Physiology. Part B, Biochemistry and Molecular Biology, 139(3), 321–333. Steinweg, J.M., Plante, A.F., Conant, R.T., Paul, E.A., & Tanaka, D.L. (2008). Patterns of substrate utilization during long‐term incubations at different temperatures. Soil Biology and Biochemistry, 40(11), 2722–2728. Strigul, N., Pristinski, D., Purves, D., Dushoff, J., & Pacala, S. (2008). Scaling from trees to forests: tractable macroscopic equations for forest dynamics. Ecological Monographs, 78(4), 523–545. Sulman, B.N., Moore, J.A.M., Abramoff, R., Averill, C., Kivlin, S., Georgiou, K., et al. (2018). Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry, 141(2), 109–123. Sulman, B.N., Phillips, R.P., Oishi, A.C., Shevliakova, E., & Pacala, S.W. (2014). Microbe‐driven turnover offsets mineral‐ mediated storage of soil carbon under elevated CO2. Nature: Climate Change, 4, 1099. Suzuki, T., & Ichii, K. (2010). Evaluation of a terrestrial carbon cycle submodel in an Earth system model using networks of eddy covariance observations. Tellus. Series B, Chemical and Physical Meteorology, 62(5), 729–742. Tang, J., & Riley, W.J. (2015). Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nature: Climate Change, 5, 56. Todd‐Brown, K E.O., Hopkins, F.M., Kivlin, S.N., Talbot, J.M., & Allison, S.D. (2012). A framework for representing microbial decomposition in coupled climate models. Biogeochemistry, 109(1), 19–33. Trasar‐Cepeda, C., Gil‐Sotres, F., & Leirós, M.C. (2007). Thermodynamic parameters of enzymes in grassland soils from Galicia, NW Spain. Soil Biology and Biochemistry, 39(1), 311–319. Trivedi, P., Anderson, I.C., & Singh, B.K. (2013). Microbial modulators of soil carbon storage: integrating genomic and  metabolic knowledge for global prediction. Trends in Microbiology, 21(12), 641–651. van Groenigen, K.J., Xia, J., Osenberg, C.W., Luo, Y., & Hungate, B.A. (2015). Application of a two‐pool model to soil carbon dynamics under elevated CO2. Global Change Biology, 21(12), 4293–4297. Van Veen, J.A., & Kuikman, P.J. (1990). Soil structural aspects of decomposition of organic matter by micro‐organisms. Biogeochemistry, 11(3), 213–233. Vetter, Y.A., Deming, J.W., Jumars, P.A., & Krieger‐Brockett, B.B. (1998). A predictive model of bacterial foraging by means of freely released extracellular enzymes. Microbial Ecology, 36(1), 75–92. Wang, G., Jagadamma, S., Mayes, M.A., Schadt, C.W., Steinweg, J.M., Gu, L., & Post, W.M. (2015). Microbial dormancy improves development and experimental validation of ecosystem model. The ISME Journal, 9(1), 226–237.

MODELING MICROBIAL DYNAMICS AND HETEROTROPHIC SOIL RESPIRATION  129 Wang, G., & Post, W.M. (2012). A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling. FEMS Microbiology Ecology, 81(3), 610–617. Wang, G., Post, W.M., & Mayes, M.A. (2013). Development of microbial‐enzyme‐mediated decomposition model parameters through steady‐state and dynamic analyses. Ecological Applications: A Publication of the Ecological Society of America, 23(1), 255–272. Wang, K., Peng, C., Zhu, Q., Zhou, X., Wang, M., Zhang, K., & Wang, G. (2017). Modeling global soil carbon and soil microbial carbon by integrating microbial processes into the ecosystem process model TRIPLEX‐GHG: Global soil carbon and microbial carbon. Journal of Advances in Modeling Earth Systems, 9(6), 2368–2384. Wang, Y., Chen, B., Wieder, W.R., Leite, M.C., Medlyn, B.E., Rasmussen, M., et  al. (2014). Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition. Biogeosciences , 11(7), 1817–1831. Wieder, W.R., Allison, S.D., Davidson, E.A., Georgiou, K., Hararuk, O., He, Y., et al. (2015). Explicitly representing soil microbial processes in Earth system models: Soil microbes in earth system models. Global Biogeochemical Cycles, 29(10), 1782–1800. Wieder, W.R., Bonan, G.B., & Allison, S.D. (2013). Global soil carbon projections are improved by modelling microbial processes. Nature Climate Change, 3, 909.

Wieder, W.R., Grandy, A.S., Kallenbach, C.M., & Bonan, G.B. (2014). Integrating microbial physiology and physio‐ chemical principles in soils with the MIcrobial‐MIneral Carbon Stabilization (MIMICS) model. Biogeosciences, 11(14), 3899–3917. Wieder, W.R., Hartman, M.D., Sulman, B.N., Wang, Y.‐P., Koven, C.D., & Bonan, G.B. (2018). Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models. Global Change Biology, 24(4), 1563–1579. Williams, M., Rastetter, E.B., Fernandes, D.N., Goulden, M.L., Shaver, G.R., & Johnson, L.C. (1997). Predicting gross primary productivity in terrestrial ecosystems. Ecological Applications: A Publication of the Ecological Society of America, 7(3), 882–894. Xu, X., Thornton, P.E., & Post, W.M. (2013). A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Global Ecology and Biogeography: A Journal of Macroecology, 22(6), 737–749. Zaehle, S., Medlyn, B.E., & De Kauwe, M.G. (2014). Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate free‐air CO2 enrichment studies. New Phytologist. 202(3), 803–822. Zhang, X., Niu, G.‐Y., Elshall, A.S., Ye, M., Barron‐Gafford, G.A., & Pavao‐Zuckerman, M. (2014). Assessing five evolving microbial enzyme models against field measurements from a semiarid savannah—what are the mechanisms of soil respiration pulses? Geophysical Research Letters, 41(18), 6428–6434.

Part II Elemental Cycles

6 Critical Zone Biogeochemistry: Linking Structure and Function Bryan Moravec and Jon Chorover ABSTRACT Recent studies of the critical zone (CZ)—which extends from impermeable bedrock upward through fractured rock, saprolite, soil, and aboveground biomass—have begun to reveal how this zone functions as a single system, responsive on an event basis, but also evolving its structure over geologic timescales. In order to understand the interrelationships between biogeochemical dynamics and CZ structure, CZ observatories across the globe have been established that utilize a common set of measurements (i.e., measurements in situ in the x, y, z, and t domains) to enable the testing of relations among processes that are postulated to be tightly coupled in CZ function. In this context, CZ biogeochemistry is probed across a range of space and timescales (pore to watershed, hydrologic events to millennia) to better understand linkages between hydrology, biological activity, and geochemical reactions that can be incorporated into predictive models. In this chapter, we provide an overview of biogeochemistry through the lens of CZ science, and present our understanding of how tightly linked biogeochemical processes both drive and derive from landscape and ecosystem structure and evolution.

6.1. INTRODUCTION: WHAT IS CRITICAL ZONE BIOGEOCHEMISTRY? The critical zone (CZ) is the porous skin of the Earth’s land surface extending from the top of the vegetation canopy to the lower limits of freely circulating groundwater (NRC, 2001). This region of the Earth’s surface encompasses tightly linked abiotic and biotic processes that have co‐evolved to form landscapes over pedogenic and geologic timescales (Chorover et al., 2011; Rasmussen, Brantley, de Richter, et al., 2011a; Wagener et al., 2010). A strong research focus in recent years has emerged from the perspective that the CZ behaves as a single open system wherein interactions among biota, minerals, and fluids create a “reactor” at the Earth’s land surface that gives rise to soils, thereby supporting the nutritional needs of ecosystems, and stabilizing carbon (C) in the subsurface for millennia. While the dynamics of CZ materials can be

Department of Environmental Science, University of Arizona, Tucson, Arizona, USA

observed on short timescales (e.g., typical of hydrologic events) the long‐term evolution of CZ structure can be best inferred from such process dynamics when they are combined with space‐for‐time substitutions, such as those associated with climosequences or chronosquences (Chadwick & Chorover, 2001; Vitousek, 2004). In a general sense, CZ evolution can be conceived as the biological colonization of a rock template under climatic forcing and its progressive transformation by meteoric inputs modulated by a successional series of functional ecosystems. However, CZ evolution occurs on a timescale that is longer than the succession of vegetation series (Anderson et al., 2012), and the progressive changes that occur to regolith structure may support multiple cycles of ecosystem secondary successions, punctuated by disturbances such as wildfire or hurricanes (Grant & Dietrich, 2017; O’Geen et  al., 2018). Key controls over CZ structure evolution include radiant inputs, hydrologic partitioning and fluid fluxes, biogeochemical weathering reactions, and associated soil production and erosion (Heimsath et al., 1997; Rasmussen, Troch, Chorover, et al., 2011b). CZ structure itself is multifaceted and can be represented in terms of

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

133

134  BIOGEOCHEMICAL CYCLES Water, gas, and solute fluxes

Biogeochemical weathering reactions

Distribution and connectivity of the microbiome

Evolution

Dynamics

Aggregation and porosity distribution

Structure

Soil production and erosion

Lithology, fracture density

Ecosystem or Critical Zone Services Carbon stabilization Water purification and storage Climate control Food and fiber production

Figure 6.1 Critical zone science aims to link dynamics (measured in real time) with CZ structure, and thereby develop conceptual and quantitative models of CZ evolution. Doing so enables an improved societal understanding of the role the CZ plays in provisioning ecosystem services (Field et al., 2015). As a result, CZ science links processes that occur on time scales ranging from short‐term hydrologic events to long‐term landscape evolution time scales.

the distribution of biota, porosity, mineral composition, geochemistry, etc. (Figure 6.1). Biogeochemical processes occurring in the CZ are highly variable vertically (e.g., extending from organotrophic in the near surface to oligotrophic conditions at depth) and laterally (e.g., with landscape position exerting strong control on water and C throughflux), as well as with time (across hydrologic events and seasons up to geologic timescales) (Pelletier et  al., 2018; Riebe et  al., 2017). The long‐term development of the CZ occurs as a result of a complex set of feedbacks where energy and matter inputs are distributed and dissipated through biological activity, weathering, and vertical and lateral flow dynamics (Brantley et  al., 2011; Chorover et  al., 2007). Understanding that these feedbacks are coupled processes is critical, not only to understanding CZ evolution and dynamics, but also to developing better predictive models of how this life‐support system responds to changing climate and land use. Essential features of CZ biogeochemistry are the coupling of biological and abiotic processes and reactions (Amundson et  al., 2007; Finzi et  al., 2011). Biological processes include small‐ and large‐scale dynamics, for example, tree effects on CZ architecture development and plumbing (Johnson & Lehmann, 2006), floral/faunal effects on soil and nutrient (re)distribution (Gabet et al., 2003), and microbial‐induced weathering dynamics (Gadd, 2013). Abiotic controls include regional climatic forcings (Pelletier & Rasmussen, 2009a), hydrologic partitioning and water storage (Brooks et  al., 2015; Lohse

et al., 2009; Rempe & Dietrich, 2018), mineral weathering rates (McIntosh et al., 2017; Zapata‐Rios et al., 2015), and microclimatic effects (Anderson et al., 2014). Landscape evolution (from the pedon‐ to watershed‐scales) results from a system of interdependent processes that have co‐ evolved over time (Figure 6.2; Troch et al., 2015). The goal of linking CZ structure and dynamics (Figure  6.1) motivates an observatory approach, where real‐time data (collected on diurnal, event, and seasonal timescales) of CZ response can be overlain on a progressively developed understanding of CZ architecture (Brantley et al., 2017). Hence, a foundational aspect of CZ observatory (CZO) science has been the use of in situ sensor/sampler networks to collect parallel (x, y, z, t) data series in a sufficiently dense (spatiotemporal) format to enable the testing of relations among processes that are postulated to be tightly coupled in CZ function (e.g., Olshansky et al., 2018), while also conducting geophysical and drilling surveys to observe CZ structure (e.g., Flinchum, Holbrook, Rempe, et al., 2018; Holbrook et al., 2014). Such sensor/sampler networks commonly include eddy covariance towers, meteorological stations, precipitation and throughfall collectors, soil moisture, temperature and matric potential sensors, piezometers, gas (CO2 and O2) sensors, vadose zone pore‐water samplers, groundwater monitoring wells, and surface water flumes or weirs. Such a sensor/sampler network enables an improved closure of mass and energy balances, enabling the assessment of element cycles at the watershed scale (Perdrial et  al., 2018). Toward the goal of resolving coupled processes in

Critical Zone Biogeochemistry: Linking Structure and Function  135 Climate • Seasonality • Drought • Extreme events • Snowmelt

Soil water/shallow groundwater • CO2 and nutrient flux • Latent heat flux

Precipitation Sha

llow

grou nd

wate r

• Clay formation and porosity development

• Antecedent geology and lithologic transformations • Armoring and infilling of fractures

ET

• Biologically mediated weathering reactions • Solute release and transport

Potential feedbacks

• Aggregation and porosity distribution • Altered climate/ precipitation dynamics • Distribution and connectivity of the microbiome

Deep groundwater

Deep groundwater • Fracture dominated • Water/rock interactions • Long residence times • Susceptibility to contamination • Contribution to baseflow • Solute transport

Figure 6.2  Conceptual model linking climate, biogeochemical processes, and groundwater flow to understand critical zone (CZ) and watershed evolution. Shallow biogeochemical processes are driven by biological activity, fluid and gas flux, latent heat flux, weathering, clay formation, and solute release and transport. Fractures provide an important storage for water and solutes in the deep CZ and may serve as an important buffer to climate variability. Subsurface architecture, antecedent geology and weathering, fracture filling, and porosity distribution determine flow dynamics and is important to groundwater resource availability and water quality.

the Earth’s living skin, the vertical and lateral distribution of instrumentation in a watershed setting is emblematic of the CZO network approach (Banwart et al., 2011; Brantley et al., 2017). Furthermore, making such “big data” publicly available enhances the capacity of the surface Earth science community to probe how biogeochemical processes, such as mineral weathering, C stabilization, and redox reactions are affected by dynamic changes in hydrology, geomorphology, and biology (Bui, 2016). By funding a worldwide network of CZOs, science foundations across the globe have laid the groundwork for understanding linkages between CZ function, structure, and evolution across a much wider lithologic and climatic parameter space than is possible within any single CZO (White et al., 2015). Over the past decade, observatories with comparable monitoring instrumentation have been set up across the globe to provide common measurement data sets, with the collective goal of developing improved predictive models for landscape evolution, biogeochemical processes, hydrologic dynamics, ecosystem services, and soil development across

a variety of ecological and climatic regimes (Banwart et al., 2012). While most of the published studies from CZOs have focused on data deriving from individual observatories, the availability of similarly formatted data from multiple observatories is expected to lead, in future years, to enhanced insights deriving from intersite comparisons. Such comparisons should help to resolve the impacts of climate and land‐use change on CZ structure and function over a wide range of parameter space. 6.2. BIOLOGICAL AND GEOCHEMICAL PROCESS COUPLING ACROSS THE CRITICAL ZONE 6.2.1. Water, Carbon, and Lithogenic Element Cycles in the Critical Zone Geochemical weathering in Earth’s CZ is inseparable from the resident biological activity. For example, the long‐term global C cycle is represented by the drawdown of atmospheric CO2 in silicate weathering being balanced

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by CO2 emissions from volcanic activity. In contrast, the rates and impacts of silicate weathering reactions in the CZ are a direct function of subsurface CO2 partial pressures, which are, in turn, driven by the rates and locations of autotrophic and heterotrophic respiration. The role of plants and microbes is further underscored by the fact that they generate metal complexing ligands, including, e.g., siderophores and low molecular mass organic acids, that promote the rate of mineral dissolution, and alter the speciation of pore waters to affect the tendency for secondary minerals to precipitate (Berner, 1992; Drever, 1994; Moulton et al., 2000). Consequently, while mineral weathering via carbonic acid neutralization is, at its basic level, considered the primary “abiotic” weathering reaction in the CZ, it too is greatly affected by the activities of biota. Plants and their fungal symbionts provide an additional role in weathering in the CZ by physically opening the regolith while mining for nutrients and seeking out water resources. It has been well established that root propagation in regolith provides physical disturbance at both micro‐ and macroscales (Hasenmueller et al., 2017; Pawlik et  al., 2016; Wilkinson et  al., 2009) as well as inducing preferential flowpaths to develop as part of double funneling of precipitation by trees (Johnson & Lehmann, 2006). Fungal symbionts have also been shown to increase fracturing in regolith, to the extent that fungal hyphae are able to exploit microfractures otherwise inaccessible to plant roots alone (Balogh‐Brunstad et  al., 2008). For example, Hasenmueller et al. (2017) found that fine tree roots exploit relatively large aperture (> 50 μm) fractures down to at least 180 cm depth in a nonwater‐ limited shale soil system. It was also found that the fracture fill material was similar to that found in the upper soil horizons, suggesting pedogenesis in situ rather than translocation of fine materials from the surface to fractures (Hasenmueller et al., 2017). Biotic weathering reactions in the CZ are complex, diverse, and dependent on a set of multifaceted and interdependent factors, such as proximity to the vegetated surface, climate, temperature, water availability, soil ­ aggregation and C availability, nutrient availability, microbial distribution, etc. Contributions of biotic agents to weathering reactions include the production of extracellular enzymes and complexing ligands, such as siderophores (Uroz et  al., 2009), pH modification via CO2 respiration, organic acid production (Drever, 1994), and mineral mining by roots and fungal hyphae (Balogh‐ Brunstad et  al., 2008). Bio‐ligand promoted dissolution reactions are fundamental to nutrient cycling in forest ecosystems (Holmstrom et al., 2004; Reichard et al., 2007), and bio‐enhanced weathering may reflect microbial diversity and distribution within the CZ (Balland et al., 2010; Uroz et al., 2009, 2011). Microbial colonization of mineral surfaces has been shown to result from a biological

requirement for particular cations, resulting in colonization on select minerals (e.g., biotite, pyrite, Fe oxides, and other ferromagnesian minerals), accelerating preferential weathering reactions of these colonized mineral surfaces (Hutchens, 2009; Saccone et al., 2012). Physical evidence of microbially mediated weathering has been shown by biogenic etch‐pit formation on mineral surfaces, illustrating the importance of microbial community to biogeochemical cycles and weathering (Ahmed & Holmström, 2015; Balogh‐Brunstad et  al., 2017; Buss et  al., 2007; Hutchens, 2009; Uroz et al., 2011, 2015). Biomass tends to decrease with depth in the CZ, with highest biomass concentrations at or proximal to the sunlit surface. Here, in the region directly impacted by photosynthesis, photoautotrophs generate significant inputs of fixed C that, upon introduction to the subsurface, serves as a reducing agent for the gaseous (O2), dissolved (NO3−, SO42−), and solid phase (Mn(IV), Fe(III)) oxidants that exist there. Heterotrophic microorganisms colonizing CZ interfaces, whether they be roots, air–water boundaries, soil particles, or fracture surfaces, capture the free energy associated with oxidation of reduced C, and in doing so, promote mineral transformations either actively (e.g., by dissimilatory Fe(III) mineral dissolution) or passively (e.g., via production of respiratory carbonic acid). Photoautotrophic and associated heterotrophic microbial consortia produce a wide array of specific extracellular metabolites that are variously utilized as primary substrates by different members of the microbial community (Baran et al., 2015). Hence, as the exometabolite composition changes with depth in the CZ, so (one might expect) does the microbial diversity and function. Eilers et al. (2012) examined how bacterial and archaeal community structure changed with depth in soil profiles of a forested, granitic montane watershed. They found that both organic C and microbial biomass decreased exponentially with depth, and that microbial diversity also decreased, but much less steeply. Importantly, they reported that there was as much variation with depth in the top meter of the soil zone as there was across surface soils from a wide range of biomes, indicating the strong influence of depth as an environmental gradient structuring soil communities (Eilers et  al., 2012). In a companion study, the team found that this large depth‐ dependent change in microbial community structure was correlated with variation in the molecular structure of dissolved organic matter (DOM). Whereas in the near surface, DOM exhibited a strong plant‐derived signature and low C oxidation state, with increasing depth DOM took on an increasingly microbial‐derived signature, and exhibited a higher degree of oxidation, with a correlation between organic matter chemistry and microbial community composition and diversity (Gabor et al., 2014).

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In contrast to the wealth of studies focused on surface soil microbiology, much less is known about the abundance, composition, and diversity of microorganisms occurring in the deeper CZ subsurface, e.g., within the water‐wetted fractures of partially weathered bedrock (Akob & Kuesel, 2011). However, at such depth the influence of photosynthate as a C and electron source diminishes, and primary producer composition shifts to one more characteristic of the deep biosphere. In that environment, microbes gain energy and electrons from oxidation of reduced metals (e.g., Fe(II) and Mn(II)) in primary minerals, and C is sourced from dissolved or gas phase CO2 (Figure  6.3). These chemolithoautotrophs drive weathering reactions as well, but in this case, through the active oxidative transformation of minerals, in addition to constituents such as nitrogen, sulfur, and hydrogen. This chemolithoautotrophic assimilation of C

is found in close association with opportunistic heterotrophs who utilize the chemoautotrophically fixed C for heterotrophic fermentation and anaerobic respiration (Dutta et al., 2018; Itavaara et al., 2016). While inorganic and organic C molecules are key reactants in mineral transformation reactions, they are also incorporated into solid phase products, both as inorganic carbonates (e.g., calcite, magnesium calcite), and as organo‐mineral complexes. Hence, the evolution of CZ structure often results in the accumulation of C in the weathering zone (relative to its concentration in unweathered silicate rock). Indeed, this stabilization of C against release to the atmosphere is a crucial “Critical Zone Service” to society that is variously distributed across the globe (Figure 6.1). For example, the C pool stored in soil carbonate is comparable in size to that stored in soil organic C (SOC), but the relative importance of each in

CO2

CO2

Photosynthesislinked cycle Litter inputs Storage

Surface processes Subsurface processes Root exudates

O2

CO2

Oxic

Electron donor (e.g., Fe(II))

CO2

Organic carbon

Aerobic heterotrophic prokaryotes & fungi

Anaerobic heterotrophic prokaryotes & fungi

Oxidized product (e.g., Fe(III)) CO2

H2O

O2 Oxic

O2

Chemolithoautotrophic prokaryotes

Nonphototrophic CO2 fixation

Organic matter decomposition

Anoxic CO2

Electron acceptors (e.g., NO3, Fe(III), SO42-)

Reduced products (e.g., N2, Fe(II), H2S)

Figure 6.3  The critical zone biological C cycle. The C pathways are illustrated by arrows: solid arrows show how C enters the CZ and dashed arrows show how C exits the CZ. Pathway intensity is shown by the arrow thickness, and green arrows indicate contribution of processes to surface habitats and red arrows indicate contributions of subsurface habitats. [Akob & Kuesel (2011). Licensed under CC BY SA 3.0.]

Anoxic

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any particular location is highly dependent on climate. In arid and semi‐arid regions, where flushing of nonhydrolyzing cations, such as Ca2+ is limited, sequestration of C in carbonate minerals is the principal form of soil C (Zamanian et  al., 2016). Indeed, whereas organic C is dominant in more humid climates, inorganic forms are most prevalent in more arid systems such that on a global scale, inorganic C as soil carbonate (940 Pg C) is of a magnitude similar to that of SOC (1530 Pg C) (Monger et al., 2015). In addition to their impacts on the global C cycle, the precipitation of pedogenic carbonates in soil profiles has a strong impact on subsequent hydrologic response (Hirmas et al., 2010). Similarly, organo‐mineral associations strongly influence the stabilization of soil organic C, as well as soil structure evolution, the latter through their impact on porosity and conductivity of the soil fabric. Proposals have been put forth to view SOC stocks along a continuum of bioavailability rather than as they have been viewed historically, as discrete pools of labile and recalcitrant C based on alkali solubility measured in the laboratory (Lehmann & Kleber, 2015; Schmidt et al., 2011). This new SOC paradigm integrates recent work on soil aggregate formation, the spatial micro‐ architecture of soils, SOC–mineral‐surface interaction, microbial community composition, distribution, and activity, the role of pyrogenic C, SOC molecular structure, and physical access to SOC by soil biota (Czarnes et al., 2000; Gupta & Germida, 2015; Kleber et al., 2015; Simpson et  al., 2007; Sutton & Sposito, 2005; Verchot et  al., 2011). The emerging understanding of SOC dynamics, key to soil biological function, derives from the observation that the stabilized organic matter in soil retains, to a large extent, its parent biomolecular structure, and so it is inherently susceptible to oxidative degradation. However, it resists such microbial degradation over relatively long timescales as a result of its occlusion within aggregates or its complexation with metals or surfaces (Schmidt et  al., 2011). Organic matter represents a continuum of C degradation from fragmented litter to biomolecules to supramolecular structures containing biomolecular fragments held together by H‐ bonds, hydrophobic interactions, and ion bridging interactions, to low molar mass organic molecules. These molecular entities enter into organo‐mineral interactions and aggregate formation, and as a result of their proximity to microbial populations, exhibit varying resistance to biodegradation. This conceptual framework is replacing the previously universal viewpoint that soil organic matter is composed of inherently recalcitrant humic substances whose resistance to degradation was considered largely a result of their postulated nonbiomolecular structure, and therefore independent of soil association (Lehmann & Kleber, 2015).

Whereas much of the organic C introduced to soil is plant‐derived, recent studies have reported that mineral‐ stabilized forms of SOC that exhibit long turnover times are the organic products of plant decomposition by ­heterotrophic microbes, i.e., microbial exometabolites and necromass (Kallenbach et  al., 2015). Hence, cycling of plant‐derived photosynthate through microbial biomass produces necromass that is subject to subsequent adsorption to, or coprecipitation with, high surface area products of geochemical weathering. It is these organo‐mineral products, comprising microbial biomolecules and secondary minerals (such as colloid sized phyllosilicate clays and oxides, whose composition depends on weathering environment) that result in the long‐term stability of organic matter in soils (Rasmussen et al., 2018). 6.2.2. Geomorphology–Hydrology–Ecology– Geochemistry Geomorphic features of the CZ impart important controls on key functions including water, C, and nutrient distribution and flux, soil development, distribution of micro‐ and macroflora, etc. Landscape position, aspect, and lithology are key drivers for spatial variability in CZ processes, soil depth, and slope steepness and wetness (Pelletier et al., 2018). For instance, Pelletier et al. (2018) examined latitude, aridity indices, mean‐annual precipitation, and slope gradients to develop a conceptual model of soil production rates, water storage potential, vegetation cover, and erosion on equator and pole facing slopes. They found that at high latitudes and high elevations, vegetation growth on pole facing slopes is limited due to reduced solar insolation, which in turn reduces soil production rates, water storage potential, and increases erosional efficiency (Pelletier et al., 2018). However, at lower latitudes and elevations, which are generally water limited, the tendency is reversed, where pole facing slopes have higher soil production rates and water storage potential as well as more vegetation cover (Pelletier et al., 2018). In this case, the observed asymmetry in slope morphology is a result of aspect and position, which is a key feedback to CZ evolution. Water and gases connect the various portions of the porous CZ in both the vertical and lateral domains (Giardino & Houser, 2015). Water availability and flow are key drivers for CZ development and function both at short‐ and long‐time scales. Short‐term hydrologic processes may consist of regular threshold events whereby biogeochemical processes may “lie in wait” for a water pulse to bring the CZ to life (e.g. annual snowmelt) (McIntosh et al., 2017; Olshansky et al., 2018). Shifts in climate (e.g. regional drying or wetting) may significantly alter plant and microbial populations and disrupt long‐ term CZ steady state, leading to future re‐equilibration

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and altered function (Andersen et al., 2013; Drigo et al., 2010; Reynolds et al., 1999). Despite the recognized biological role in CZ weathering processes, comparisons of mineral transformation rates measured in natural settings versus laboratory weathering experiments have shown that they are orders of magnitude lower in natural systems than in the laboratory (Maher et al., 2009). This difference has been attributed to slow moving pore waters in the field that more closely approach chemical equilibrium than far‐from‐equilibrium flow‐through weathering experiments conducted in the lab, as well as the effects of secondary mineral precipitates that coat the surfaces of primary minerals that would otherwise be subjected fluid flow and mineral dissolution (Maher et al., 2009). This implies that CZ structure plays a significant role in regulating weathering dynamics across both spatial and temporal dimensions. The rate of transport of O2 rich fluids from the surface is also crucial to drive redox weathering reactions (e.g. Fe(II) oxidation to Fe(III)) that may limit downward propagation of initial porosity and, hence, the weathering front. For example, oxidation of Fe‐silicate minerals (e.g., biotite), has been suggested to be the incipient weathering reaction in otherwise pristine rock, causing expansion cracking and opening surfaces to further weathering (Buss et  al., 2008). However, O2 is also consumed by resident aerobic heterotrophic microbes, thereby limiting O2 concentrations in the subsurface (Akob & Kuesel, 2011). Stinchcomb et  al. (2018) examined weathering‐induced fracturing (WIF), which develops secondary porosity at depth, and found that WIF is governed by a combination of CO2 and O2 consumption. As O2 is consumed by microbial populations and abiotic redox reactions (e.g., oxidation of Fe(II) to Fe(III)) under saturated conditions, a negative feedback may result, inhibiting WIF and overall deep weathering potential (Stinchcomb et al., 2018). Finally, disturbances, such as fire, significantly alter the landscape, initiating a systems‐wide recalibration of CZ processes towards reestablishing steady‐state conditions. For example, short‐term effects of disturbance can alter surface hydrophobicity of the soil, resulting in increased run‐off and soil erosion (Ice et al., 2004) and such erosive events may serve as a primary burial and preservation mechanism for soil C (Berhe et al., 2007), especially pyrogenic C (Abney & Berhe, 2018). 6.2.3. Bottom‐up and Top‐down Controls on Critical Zone Evolution Models of CZ evolution have included both bottom‐up controls (i.e., pore development and the weatherability of underlying lithology, upward migration of rock, subsequent weathering, and erosion), and top‐down ­controls (i.e., energy and mass flux from the surface and

subsequent dissipation via weathering processes) to explain CZ evolution over geologic timescales (Brantley & Lebedeva, 2011; Rasmussen, Troch, Chorover, et  al., 2011b; Rempe & Dietrich, 2014). Bottom‐up controls on CZ evolution are based on lithologic controls on weathering front propagation. Rempe and Dietrich (2014) proposed a model to predict the vertical extent of the CZ underlying soil‐mantled hillslopes that was based on the development of sufficient bedrock porosity and an associated lateral head gradient to drive drainage waters through weathering bedrock toward a channel, thereby affecting propagation of the weathering front. Deep weathering profiles in granites (as opposed to shallower weathering profiles in basalt) were determined to be controlled by incipient weathering of biotite, expansion of the altered biotite minerals, and subsequent microfracturing, leading to pore formation and permeability to weathering fluids (Buss et al., 2008). In addition, the interface where bedrock becomes regolith in deep, ridge‐top weathering fronts observed at CZOs in Pennsylvania (Shale Hills CZO) and Puerto Rico (Luquillo CZO) were hypothesized to be a result of a balance between weathering and erosion (e.g., chemical and physical denudation) (Brantley et  al., 2011). Buss et al. (2017) examined weathering rates as a function of lithologic differences (granite and volcaniclastics) in two analogous catchments at the Luquillo CZO in Puerto Rico. They found that weathering reactions differed between the catchments based on parent material composition (chlorite and illite weathering in the volcaniclastics, and biotite weathering in the granite), resulting in different long‐term weathering rates in the two watersheds (Buss et al., 2017). As a result, bottom‐up controls on CZ evolution is an outcome of vertical and lateral fluid migration, weathering at the fluid–rock interface, lithology, elemental mobilization, mass loss, and porosity and permeability development (Brantley & White, 2009). A model of top‐down controls on CZ evolution is represented in the effective energy and mass transfer (EEMT) model developed by Rasmussen, Troch, Chorover, et al. (2011b). This model predicts soil depth and weathering‐ front propagation as a function of climatic forcing and its biological modulation by surface‐colonizing vegetation (i.e., precipitation, evapotranspiration, net primary productivity). The EEMT provided a first approximation for soil production rates (Pelletier & Rasmussen, 2009b) based on open‐system thermodynamics and established soil‐forming factors (Jenny, 1941; Rasmussen & Tabor, 2007). The covariation of climate (decreasing temperature and increasing precipitation) with elevation in the Catalina Mountains CZO in the SW United States enabled Pelletier et al. (2013) to test the effects of climate on key parameters of landscape evolution. Both numerical landscape evolution modeling (using EEMT as

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an input parameter) and CZO data sets showed an increase in biomass, soil thickness, relief, and a decrease in valley density with increasing elevation, highlighting the strong influence of climatic variation within the CZO on regolith and landscape formation (Pelletier et  al., 2013). Zapata‐Rios et al. (2015) utilized EEMT to understand the interrelationships between climate, landscape, hydrology, and geochemistry in CZ function and weathering in the Jemez River Basin CZO in New Mexico. They found that EEMT effectively predicted hydrologic response based on landscape position (i.e., north versus south facing slopes), which corresponded to differences in water transit time and chemical weathering dynamics at the watershed scale (Zapata‐Rios et al., 2015). 6.3. RESOLVING CRITICAL ZONE STRUCTURE 6.3.1. Geophysics and Subsurface Architecture—Macro Viewpoint Noninvasive geophysical methods designed to investigate the CZ at hillslope scales have improved our understanding of controls on the architecture of the deep CZ (Holbrook et al., 2014; Olyphant et al., 2016; St Clair et al., 2015). Of particular importance is the information inferred from seismic refraction and electrical resistivity data sets on the distribution of porosity, water, and weathering products (e.g., clay minerals) in the deep subsurface (Riebe et  al., 2017). Revealing the distribution and range of porosity in the CZ has helped interpret vertical and lateral weathering extents observed in geochemical and mineralogical data, depths of soil formation, conduits available for hydrologic partitioning, and mass and energy transfer and dissipation into the subsurface (Riebe et al., 2017). Porosity distribution varies considerably from one site to another and even within short distances within a single watershed, and this variability is difficult to extrapolate to the greater watershed scale with traditional point data (i.e., soil pits and drill cores) (Parsekian et  al., 2015). However, geophysical techniques such as seismic refraction and electrical resistivity tomography are proving to be useful tools to extrapolate point data to the watershed scale, improving the understanding of spatial patterns and heterogeneity of water distribution and biogeochemical processes often encountered in the CZ (Binley et al., 2015; Daily et  al., 1992; Flinchum, Holbrook, Grana, et al., 2018; Holbrook et al., 2014). 6.3.2. Drilling to Understand Vertical Architecture and Morphology Historically, most CZ science has been conducted in the upper 1–2 m below the surface, providing a very good understanding of soil production, erosion, weathering,

and biogeochemical cycles (Riebe & Chorover, 2014). However, the CZ below these shallow depths has been much less explored, in part because of its poor accessibility, and yet it plays a vital role in CZ function and profoundly influences the near surface (Holbrook et  al., 2014; Riebe & Chorover, 2014). Near‐surface geophysics have provided an important macro‐viewpoint of deep CZ structure (Flinchum, Holbrook, Grana, et  al., 2018; Holbrook et al., 2014; Parsekian et al., 2015), but these techniques are much more powerful when coupled with subsurface interrogation via drilling (Flinchum, Holbrook, Rempe, et  al., 2018). Recently, drilling in established CZOs has provided vertical profiles of CZ architecture and morphology (Brantley et  al., 2013; Flinchum, Holbrook, Rempe, et  al., 2018; Salve et  al., 2012). Additionally, drilling, sampling, and instrumentation (e.g., soil‐moisture probes and pressure transducers) coupled with near‐surface and downhole geophysics provide ground truthing to complex data sets, expanding the reach of subsurface investigation to watershed scales and has the potential to add to a deeper understanding of biogeochemical processes governing CZ function (Brantley et al., 2013; Flinchum, Holbrook, Grana, et al., 2018; Flinchum, Holbrook, Rempe, et  al., 2018; Olshansky et al., 2018; Rempe & Dietrich, 2018; Riebe & Chorover, 2014; Riebe et  al., 2017; Salve et  al., 2012). However, more work needs to be done to understand the role of lithologic heterogeneity, geologic structure, and legacy in driving deep CZ processes and ultimately landscape evolution. 6.4. PORE‐SCALE PROCESSES The variation that is observed in biogeochemical processes and composition with depth across the entire regolith cross‐section begins at the pore scale. It is at this scale that root‐tissues, microbial cells, biomolecules, solutes, and grain surfaces interact to give rise to the porous geofabric that transmits water and gases in three dimensions. Hence, an understanding of the multicomponent materials that comprise porous media as a function of depth in the CZ helps to better predict overall CZ function. In surface soils, the primary pore‐forming structures are soil aggregates. At depth, as soil grades into saprolite, pore domains are observed to form along primary mineral grain boundaries weakened by a combination of physical and chemical weathering. In the underlying bedrock, porosity is highly heterogeneous over relatively large spatial scales, and dominated by the presence or absence of fractures. In upland hillslope systems, formation of the CZ profile begins at the interface between impermeable and porous bedrock, and then propagates upward through bedrock fractures, saprock or saprolite, the soil zone, and finally into surface soils at

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the upper limit of the weathering profile. These regions of the CZ profile all differ in respect to their pore‐scale structure and processes. These porous media domains are discussed in turn below. 6.4.1. Fractured Bedrock At lower limits of the CZ, bedrock that has been fractured by local or regional tectonic forces undergoes incipient weathering focused on freshly exposed fracture surfaces. The partially weathered surfaces contain some inherent porosity of their own, making them permeable to water or gas, as a result of physical alteration (e.g., fracturing along primary mineral grain boundaries), as well as pre‐ or post‐depositional fracturing and geochemical weathering (Worthington et al., 2016). Investigations of weathering in fractured bedrock have incorporated direct and indirect observations to unravel the role of fracture surfaces in water–rock interaction, secondary mineral and biofilm formations, and regolith development above. Some studies have focused on the progressive replacement of corestones with secondary mineralogy, which is surface area, lithology, and diffusion rate dependent (Behrens et  al., 2015; Buss et  al., 2017). Geophysical surveys coupled with drilling have shown that fractured bedrock can provide most of the water storage in the CZ (Flinchum, Holbrook, Grana, et  al., 2018) and shallow fractured bedrock moisture may play a primary role in plant‐accessible water, analogous to soil moisture (Rempe & Dietrich, 2018; Salve et  al., 2012). Indirect observation of CZ processes also points towards a deep groundwater store within fractured bedrock that is displaced by pressure pulse propagation driven by hydrologic inputs to the CZ surface (McIntosh et  al., 2017; Olshansky et  al., 2018; Zapata‐Rios et  al., 2015). Such hydrologic events, therefore, give rise to stream water concentration–discharge patterns that have geochemical signatures of long‐residence‐time water, despite the overall system having a flashy response to meteoric inputs (McIntosh et al., 2017). Microbial communities within the reduced‐C‐limited and commonly suboxic environment of fractured bedrock in the deep subsurface are primarily low‐density colonies of chemolithoautotrophic microbes with some co‐ associated heterotrophic bacteria (Akob & Kuesel, 2011). Microbial diversity comprises chemolithoautotrophic bacteria and archaea that utilize S, Fe, or C as the primary electron acceptor and hence derive their energy from lithogenic materials, and their C from CO2 (Bomberg et al., 2013, 2016; Schlegel et al., 2011). However, opportunistic heterotrophic microorganisms, obtaining C and energy from biomass, are frequently in co‐association. These microbial cells and their extracellular polymeric substances (EPS) are not only planktonic (and hence

­ obile with water), but also line the surfaces of bedrock m fractures, utilizing transported solutes, and both actively and passively driving mineral dissolution and precipitation. As a result, the near surfaces of fractures are often composed of secondary phyllosilicates and metal (oxyhydr)oxides intermixed with microbial cells, necromass, and EPS (Banwart et  al., 1999; McKay et  al., 2002; Purkamo et al., 2013, 2016). Moreover, these studies indicate that weathering byproducts are more likely to be isolated on fracture surfaces, altering CZ biogeochemical processes from a bulk process near the surface to microscale surface processes within the weathered interfaces of fractured bedrock. Hence, pore‐scale processes in weathering fractured bedrock systems can be conceptually modeled from the perspective of a dual porosity system. The fractures represent large pores whose conductivity to advected fluids is dependent on their connectivity, whereas the weathered fracture surfaces and low‐porosity bedrock represent a porous domain of their own, with an internal porosity subjected to diffusion that limits interchange of solutes with the mobile fluids in the fractures (Lipson et al., 2007; Worthington, 2015). 6.4.2. Saprolite Whereas the fracture morphology and distribution dominate the time evolution of nutrient poor, low‐microbial‐ biomass structures in the interfaces of fractured bedrock, and the interfacial area of surficial soils is often dominantly composed of aggregated secondary mineral assemblages and their complexes with organic matter, the transition zone between these two represents the grading of fractured bedrock into saprolite at depth. Saprolite is characterized by significant geochemical weathering of bedrock, but unlike surface soil it retains the massive lithologic structural fabric of underlying bedrock (Frazier & Graham, 2000). Globally, saprolite is an important reservoir for water and moisture (Flinchum, Holbrook, Grana, et al., 2018; Rempe & Dietrich, 2018; Salve et al., 2012). A recent study revealed saprolite and saprock stored sufficient moisture to sustain forest ecosystems during a multiyear drought (Bales et  al., 2018). Rempe and Dietrich (2018) concluded that water storage in unsaturated saprolite and saprock likely mediates the timing and magnitude of groundwater recharge and runoff and may store up to 27% of annual precipitation. As such, saprolite plays an underappreciated but important hydrologic role for biogeochemical processes in the CZ, providing a deep‐water store and baseflow to support deeply rooted surface and aquatic ecosystems particularly during periods of water stress and drought. Biogeochemical processes that occur in saprolite are limited by nutrient and water availability. Unlike ­aggregates above and fractures below, pore domains in

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s­aprolite are observed to form along primary mineral grain boundaries, thereby increasing surface area to weathering, yet retaining much of the primary mineral assemblage and structure of the protolith. Nutrient and water availability may be seasonally plentiful in the near‐ surface organic soil resulting in a diverse microbial distribution, but the region below organic soil represents a transition from organotrophic islands (comprising hot spots and hot moments, defined here as locations and periods in time, respectively, where rates of biogeochemical reactions are higher than those occurring in the time‐averaged matrix (McClain et al., 2003)) to oligotrophic deserts promoting chemo‐autotrophic microbial processes. Plant‐derived soil organic matter (SOM) is higher near the surface than at depth and into saprolite, and it has been hypothesized SOM observed in saprolite is primarily derived from microbial sources (e.g., cellular components) rather than vertical downward migration of plant‐derived SOM (Gabor et  al., 2014). Furthermore, the composition of the microbial community has been shown to change with depth (Eilers et  al., 2012), with trends postulated to be the result of changes in the chemistry of soluble organic C content (Gabor et al., 2014). In fractured bedrock, biogeochemical processes are isolated on fracture surfaces, with little weathering occurring within the rock matrix. In contrast, saprolite is the zone where weathering occurs within intragranular voids, thereby increasing the spatial distribution of porosity and weathering potential. The transition from bedrock to saprolite is often marked by an increase in solid‐phase weathering products (phyllosilicate clays, carbonates, and hydroxides) and a decrease in intact rock. This transition is visible as a movement away from zero chemical alteration in chemical depletion profiles that utilize the dimensionless mass‐transfer coefficient (τ) (Brimhall & Dietrich, 1987; Brimhall et  al., 1992; Chadwick et  al., 1990; Rasmussen, Brantley, de Richter, et  al., 2011) or, similarly, by a divergence of weathering indices away from the chemical signature of unweathered rock (Dethier & Bove, 2011). Water movement through the profile in saprolite is dominated by matrix flow, thereby increasing water– mineral surface interaction along tortuous flowpaths with high solid–water interface area, potentially promoting further weathering and pore filling with secondary minerals (Dethier & Bove, 2011; Navarre‐Sitchler et al., 2011, 2015). This transition may coincide with the presence of steady‐state groundwater elevations or a rapid change in the elemental depletion profile coinciding with the lower limits of vertical reaction fronts (Brantley et al., 2013). Preferential flow paths, either inherited from underlying fractured bedrock, or formed as a result of deep root penetration, likely remain an important aspect of saprolite fluid flow dynamics. However, saprolite is comprised of a more hydrologically homogeneous porous

medium, and fluid flow is characteristically modeled as dominated by Darcy flow through microscale grains within a fabric that is nonetheless macroscopically similar to bedrock. These pores, and the dissolving primary mineral grains surrounding them, become progressively filled by secondary mineral precipitates that set a trajectory for further soil formation and aggregation processes occurring above. 6.4.3. Soil Aggregates Soil aggregates, composed of organo‐mineral complexes (micro‐aggregates) hierarchically assembled into larger order (macro‐aggregate) structures, provide permeability, physical stability, moisture retention, and play an important role in C stabilization and greenhouse‐gas consumption/emission in soils (Wang et  al., 2019). As discussed in Section 6.2.1, the emerging model for SOC stocks proposes that small C molecules self‐assemble into  variably sized “supramolecular” assemblies, whose association with metals, soil‐particle surfaces, and occlusion within aggregates, can make them relatively inaccessible to soil microbes (Lehmann & Kleber, 2015). Hence,  soil aggregate structure often limits physical contact between SOC stocks and microbial communities or extracellular enzymes, inhibiting SOC degradation based on soil architectural features (Gupta & Germida, 2015; Kleber et al., 2007; Verchot et al., 2011). Conversely, where microbial communities and available SOC coincide, islands of biogeochemical reaction (i.e., hot spots) exist. Soil aggregates are dynamic, physical habitats critical to SOC and nutrient cycling and may represent the vast biological heterogeneity in soil systems (Gupta & Germida, 2015). Spatial and seasonal variability in soil conditions, such as pH, water content, and temperature impact biogeochemical processes in aggregates—i.e., hot moments. Likewise, microbial community composition and biologic symbiosis (e.g., plant/mycorrhizal relationships) can shape aggregate biogeochemical processes and timing. Importantly, because soil aggregates are composed of tightly associated organo‐mineral complexes assembled into a higher order aggregate structure, they tend to exhibit sharp biogeochemical gradients. For example, whereas the exterior of soil aggregates interfaces with macroporous (interaggregate) domains, their interior domains tend to be microporous and biogeochemically distinct (Zachara et  al., 2016). For example, O2 may become depleted within aggregates, which leads to suboxic or anoxic conditions, limiting the energetic availability of particular organic substrates contained within (Keiluweit et al., 2016, 2017). This type of redox‐ limitation may indeed be an additional and underappreciated chemical mechanism of soil C stabilization, on top of that contributed by physical occlusion within aggregates, and bonding interaction with metals or surfaces.

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6.5. CHALLENGES IN UPSCALING FROM PORE TO CATCHMENT 6.5.1. From Pore to Continuum Scale (Reactive Transport Modeling) Several models of varying complexity have been developed to quantify biogeochemical processes in the CZ (Li et al., 2017). Most of these models have been developed to understand regolith forming processes and weathering based on elemental composition, erosion, and other parameters important to soil formation (Steefel et  al., 2005). However, scaling from pore processes to watershed‐ and landscape‐scale processes has presented challenges in both generalizing heterogeneity inherent in natural systems as well as simplifying multiple parameters into quantifiable packets that may be measured or estimated (Brantley & White, 2009). Continuum models generally utilize contemporary processes such as solute chemistry, weathering byproducts, and ion specific depletion and enrichment in order to quantify soil‐forming processes (Brantley & White, 2009). Regolith‐scale models utilize ion mass transfer enrichment/depletion profiles as a function of depth to quantify mineral weathering reactions, weathering front propagation, and regolith characteristics. Recent advances in modeling continuum scale reactive transport has led to the development of reactive transport models (Fletcher & Brantley, 2010; Lebedeva & Brantley, 2013; Lebedeva et  al., 2010; Maher & Chamberlain, 2014; Maher et  al., 2009; Steefel et  al., 2005, 2015). This approach utilizes equilibrium and kinetic chemical information coupled with fluid flow modeling to estimate weathering front propagation as well as regolith thickness. An important distinction of reactive transport models is that, by coupling fluid flow through complex porous media with estimates of chemical reaction rates, they provide a means to develop mechanistic predictions of kinetic limitations observed in natural settings relative to laboratory weathering experiments. That is, reactive transport models can help us to determine hydrologic and lithologic conditions for which systems become transport‐rate controlled; they tend to comprise fluids closer to equilibrium based on, e.g., fast water–rock geochemical interaction relative to slow fluid migration. Conversely, such models also reveal systems that may be chemical‐reaction‐rate controlled, with transport rates being high relative to rates of geochemical approach to equilibrium (Maher & Chamberlain, 2014). Such models have been very useful to interpretation of solute concentration—discharge (C/Q) relations for surface waters emanating from the CZ (Maher, 2010, 2011). For example, chemostatic C/Q relations (i.e., lithogenic solute concentrations are unaffected by surface water ­discharge rate) are typically interpreted as a feature of

transport‐limited systems, where solutes are generated at sufficient rate to maintain a constant concentration irrespective of fluid flux. Conversely, those catchments that show chemodilution behavior (i.e., decreasing lithogenic solute concentration with increasing discharge) are assumed to indicate transport‐rate control (Ibarra et al., 2016). Moreover, reactive transport models allow for chemical reactions involving active secondary mineral precipitation (clays) that may drive dissolution reactions of particular mineral species in the soil/rock solution (e.g., albite and K‐feldspars) (Maher et al., 2009). 6.5.2. Other Modeling Approaches Modeling of reactions controlling regolith development and biogeochemical processes in the CZ have focused on the vertical, one‐dimensional CZ continuum, and the development of three‐dimensional models of CZ evolution are more limited, in part because of the computational requirements. However, to better resolve the development of CZ structure in three dimensions, such process‐based coupled flow and reaction models are needed. Coupling of geochemical reactive transport models to hillslope hydrologic (saturated and unsaturated flow) models is a promising approach that can be used to develop and test numerically hypotheses related to the long‐term evolution of CZ structure (Beisman et  al., 2015; Dontsova et  al., 2009). Such an approach, which naturally involves lateral as well as vertical components of fluid transport coupled with geochemical reactions along hydrologic flow paths, presents an interesting challenge to quantifying weathering processes and regolith development. However, the validity of such models requires a thorough understanding CZ structure, hydrologic flow, in situ rates of biogeochemical reactions, and how these are distributed spatially across a watershed to give rise to time and space variation in aqueous geochemistry. Developing such a characterization and model parameter inputs for the complex CZ subsurface remains a challenge for interdisciplinary CZ scientists, who are called upon by society to predict how this life‐sustaining zone is expected to change in the future. 6.6. FUTURE DIRECTIONS Most CZ research to date has focused on measuring CZ architecture or dynamics, with fewer studies focused on making direct linkages between the two. Future work should emphasize that linkage with the goal of developing foundational relationships that can then be used for conceptual and quantitative modeling of CZ evolution, and CZ response to perturbation (e.g., land‐use change, climate change, etc.). Observatory settings are especially valuable, in that they provide an opportunity to

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invest in detailed characterization of CZ architecture (e.g., by combining surface geophysics with core extraction and analysis), and then to probe that structure to discover the underlying cause for real‐time behavior, such as water, solute, gas, and energy flux during hydrologic events. Future CZ investigations will also need to expand into systems of greater complexity and anthropogenic impact relative to the upland hillslope systems that have been the focus of much of the current work. Systems with greater geologic and land‐use complexity will pose challenges for predictive models. Anthropogenically altered systems, such as agro‐ecosystems or built environments, make it difficult to separate past versus present effects on CZ structure. Societal needs will require CZ scientists to extrapolate from specific observatory locations to larger watershed and regional scales. The use of common measurement schemes across the global CZO network would enable the testing of hypotheses across a larger climate, geology, and land‐use parameter space, and will constrain sophisticated models that better represent subsurface heterogeneities. For example, legacy effects from past geologic events and paleoclimatic conditions present an interesting challenge for CZ scientists to: (a) deconvolve past from present CZ processes to better quantify modern CZ form and function; and (b) develop an understanding of contemporary CZ evolution within a preexisting, weathered, geologic template. By expanding our reach into more complex terrains, we can provide greater contextual understanding of landscape evolution in a rapidly changing world. REFERENCES Abney, R.B., & Berhe, A.A. (2018). Pyrogenic carbon erosion: Implications for stock and persistence of pyrogenic carbon in  soil. Frontiers in Earth Science, 6(26). doi: 10.3389/ feart.2018.00026 Ahmed, E., & Holmström, S.J.M. (2015). Microbe–mineral interactions: The impact of surface attachment on mineral weathering and element selectivity by microorganisms. Chemical Geology, 403, 13–23. doi: https://doi.org/10.1016/j. chemgeo.2015.03.009 Akob, D.M., & Kuesel, K. (2011). Where microorganisms meet rocks in the Earth’s Critical Zone. Biogeosciences, 8(12), 3531–3543. doi: 10.5194/bg‐8‐3531‐2011 Amundson, R., Richter, D.D., Humphreys, G.S., Jobbagy, E.G., & Gaillardet, J. (2007). Coupling between biota and earth materials in the Critical Zone. Elements, 3(5), 327–332. doi: 10.2113/gselements.3.5.327 Andersen, R.S., Chapman, S.J., & Artz, R.R.E. (2013). Microbial communities in natural and disturbed peatlands: A review. Soil Biology and Biochemistry, 57, 979–994. Anderson, S.P., Anderson, R.S., & Tucker, G.E. (2012). Landscape scale linkages in critical zone evolution. Comptes Rendus Geoscience, 344(11–12), 586–596.

Anderson, S.P., Hinckley, E.‐L., Kelly, P., & Langston, A. (2014). Variation in critical zone processes and architecture across slope aspects. In J. Gaillardet (Ed.), Geochemistry of the Earth’s surface (pp. 28–33). Tenth International Symposium, 18–22 August, Institut de Physique du Globe de Paris. Bales, R.C., Goulden, M.L., Hunsaker, C.T., Conklin, M.H., Hartsough, P.C., O’Geen, A.T., Hopmans, J.W., & Safeeq, M. (2018). Mechanisms controlling the impact of multi‐year drought on mountain hydrology. Nature Scientific Reports, 8(690). Balland, C., Poszwa, A., Leyval, C., & Mustin, C. (2010). Dissolution rates of phyllosilicates as a function of bacterial metabolic diversity. Geochimica et Cosmochimica Acta, 74(19), 5478–5493. doi: https://doi.org/10.1016/j.gca.2010.06.022 Balogh‐Brunstad, Z., Keller, C.K., Dickinson, J.T., Stevens, F., Li, C.Y., & Bormann, B.T. (2008). Biotite weathering and nutrient uptake by ectomycorrhizal fungus, Suillus tomentosus, in liquid‐culture experiments. Geochimica et Cosmochimica Acta, 72(11), 2601–2618. doi: 10.1016/j.gca.2008.04.003 Balogh‐Brunstad, Z., Keller, C.K., Shi, Z., Wallander, H., & Stipp, S.L.S. (2017). Ectomycorrhizal fungi and mineral interactions in the rhizosphere of Scots and Red pine seedlings. Soils, 1(1), 5. Banwart, S.A., Bernasconi, S.M., Bloem, J., Blum, W., Brandao, M., Brantley, S., Chabaux, F., et al. (2011). Soil Processes and functions in critical zone observatories: Hypotheses and experimental design. Vadose Zone Journal, 10(3), 974–987. doi: 10.2136/vzj2010.0136. Banwart, S.A., E. Gustafsson, & M. Laaksharju, (1999). Hydrological and reactive processes during rapid recharge to fracture zones ‐ The Aspo large scale redox experiment. Applied Geochemistry, 14(7), 873–892. Banwart, S. A., Menon, M., Bernasconi, S.M., Bloem, J., Blum, W.E.H., Maia de Souza, D., et al. (2012). Soil processes and functions across an international network of Critical Zone Observatories: Introduction to experimental methods and initial results. Comptes Rendus Geoscience, 344(11–12), 758– 772. doi: 10.1016/j.crte.2012.10.007 Baran, R., Brodie, E.L., Mayberry‐Lewis, J., Hummel, E., Da Rocha, U.N., Chakraborty, R., et  al. (2015). Exometabolite niche partitioning among sympatric soil bacteria. Nature Communications, 6. Behrens, R., Bouchez, J., Schuessler, J.A., Dultz, S., Hewawasam, T., & von Blanckenburg, F. (2015). Mineralogical transformations set slow weathering rates in low‐porosity metamorphic bedrock on mountain slopes in a tropical climate. Chemical Geology, 411, 283–298. doi: 10.1016/j.chemgeo.2015.07.008 Beisman, J.J., Maxwell, R.M., Navarre‐Sitchler, A.K., Steefel, C.I., & Molins, S. (2015). ParCrunchFlow: an efficient, parallel reactive transport simulation tool for physically and chemically heterogeneous saturated subsurface environments. Computational Geosciences, 19(2), 403–422. Berhe, A.A., Harden, J.W., Harte, J., & Torn, M.S. (2007). The significance of the erosion‐induced terrestrial carbon sink. BioScience, 57(4), 337–346. doi: 10.1641/b570408. Berner, R.A., (1992). Weathering, plants, and the long‐term carbon‐cycle. Geochimica et Cosmochimica Acta, 56(8), 3225– 3231. doi: 10.1016/0016‐7037(92)90300‐8 Binley, A., Hubbard, S.S., Huisman, J.A., Revil, A., Robinson, D.A., Singha, K., & Slater, L.D. (2015). The emergence of

Critical Zone Biogeochemistry: Linking Structure and Function  145 hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water Resources Research, 51(6), 3837–3866. doi: 10.1002/2015wr017016 Bomberg, M., Lamminmäki, T., & Itävaara, M. (2016). Microbial communities and their predicted metabolic characteristics in deep fracture groundwaters of the crystalline bedrock at Olkiluoto, Finland. Biogeosciences, 13(21), 6031–6047. doi: 10.5194/bg‐13‐6031‐2016 Bomberg, M., Nyyssönen, M., Itävaara, M., Purkamo, L., Kukkonen, I., Ahonen, L., & Kietäväinen, R. (2013). Dissecting the deep biosphere: retrieving authentic microbial communities from packer‐isolated deep crystalline bedrock fracture zones. FEMS Microbiology Ecology, 85(2), 324–337. doi: 10.1111/1574‐6941.12126 Brantley, S.L., Buss, H., Lebedeva, M., Fletcher, R.C. & Ma, L. (2011). Investigating the complex interface where bedrock transforms to regolith. Applied Geochemistry, 26(Supplement), S12–S15. doi: 10.1016/j.apgeochem.2011.03.017 Brantley, S.L., Holleran, M.E., Jin, L., & Bazilevskaya, E. (2013). Probing deep weathering in the Shale Hills Critical Zone Observatory, Pennsylvania (USA): the hypothesis of nested chemical reaction fronts in the subsurface. Earth Surface Processes and Landforms, 38(11), 1280–1298. doi: 10.1002/esp.3415 Brantley, S.L., & Lebedeva, M. (2011). Learning to read the chemistry of regolith to understand the Critical Zone. Annual Review of Earth and Planetary Sciences, 39, 387–416. Brantley, S.L., McDowell, W.H., Dietrich, W.E., White, T.S., Kumar, P., Anderson, S.P., et al. (2017). Designing a network of critical zone observatories to explore the living skin of the terrestrial Earth. Earth Surface Dynamics, 5(4), 841–860. Brantley, S.L., & White, A.F. (2009). Approaches to modeling weathered regolith. Reviews in Mineralogy and Geochemistry, 70, 435–484. Brimhall, G.H., Chadwick, O.A., Lewis, C.J., Compston, W., Williams, I.S., Danti, K.J., et al., (1992). Deformational mass‐ transport and invasive processes in soil evolution. Science, 255(5045), 695–702. doi: 10.1126/science.255.5045.695 Brimhall, G.H., & Dietrich, W.E. (1987). Constitutive mass balance relations between chemical‐composition, volume, density, porosity, and strain in metasomatic hydrochemical ­systems—results on weathering and pedogenesis. Geochimica et Cosmochimica Acta, 51(3), 567–587. doi: 10.1016/0016‐ 7037(87)90070‐6 Brooks, P.D., Chorover, J., Fan, Y., Godsey, S.E., Maxwell, R.M., McNamara, J.P., & Tague, C. (2015). Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics. Water Resources Research, 51(9), 6973–6987. doi: 10.1002/2015wr017039 Bui, E.N. (2016). Data‐driven Critical Zone science: A new paradigm. Science of the Total Environment, 568, 587–593. Buss, H.L., Lara, M.C., Moore, O.W., Kurtz, A.C., Schulz, M.S., & White, A.F. (2017). Lithological influences on contemporary and long‐term regolith weathering at the Luquillo Critical Zone Observatory. Geochimica et Cosmochimica Acta, 196, 224–251. doi: 10.1016/j.gca.2016.09.038 Buss, H.L., Luttge, A., & Brantley, S.L. (2007). Etch pit formation on iron silicate surfaces during siderophore‐

promoted dissolution. Chemical Geology, 240(3–4), 326–342. doi: 10.1016/j.chemgeo.2007.03.003 Buss, H.L., Sak, P.B., Webb, S.M., & Brantley, S.L. (2008). Weathering of the Rio Blanco quartz diorite, Luquillo Mountains, Puerto Rico: Coupling oxidation, dissolution, and fracturing. Geochimica et Cosmochimica Acta, 72(18), 4488–4507. doi: 10.1016/j.gca.2008.06.020 Chadwick, O.A., & Chorover, J. (2001). The chemistry of pedogenic thresholds. Geoderma, 100(3–4), 321–353. Chadwick, O.A., Brimhall, G.H., & Hendricks, D.M. (1990). From a black to a gray box—a mass balance interpretation of pedogenesis. Geomorphology, 3, 369–390 pp. Chorover, J., Kretzschmar, R., Garcia‐Pichel, F., & Sparks, D.L. (2007). Soil biogeochemical processes within the Critical Zone. Elements, 3(5), 321–326. doi: DOI 10.2113/gselements.3.5.321 Chorover, J., Troch, P.A., Rasmussen, C., Brooks, P., Pelletier, J., Breshars, D.D., et al. (2011). How water, carbon, and energy drive Critical Zone evolution: The Jemez‐Santa Catalina Critical Zone Observatory. Vadose Zone Journal, 10(3), 884– 899. doi: 10.2136/vzj2010.0132 Czarnes, S., Hallett, P.D., Bengough, A.G., & Young, I.M. (2000). Root‐ and microbial‐derived mucilages affect soil structure and water transport. European Journal of Soil Science, 51(3), 435–443. doi: 10.1046/j.1365‐2389.2000.00327.x Daily, W., Ramirez, A., Labrecque, D., & Nitao, J. (1992). Electrical‐resistivity tomography of vadose water‐movement. Water Resources Research, 28(5), 1429–1442. doi: 10.1029/ 91wr03087 Dethier, D.P., & Bove, D.J. (2011). Mineralogic and geochemical changes from alteration of granitic rocks, Boulder Creek Catchment, Colorado. Vadose Zone Journal, 10(3), 858–866. doi: 10.2136/vzj2010.0106 Dontsova, K., Steefel, C.I., Desilets, S., Thompson, A., & Chorover, J. (2009). Solid phase evolution in the Biosphere 2 hillslope experiment as predicted by modeling of hydrologic and geochemical fluxes. Hydrology and Earth System Sciences, 13(12), 2273–2286. Drever, J.I. (1994). The effect of land plants on weathering rates of silicate minerals. Geochimica et Cosmochimica Acta, 58(10), 2325–2332. doi: 10.1016/0016‐7037(94)90013‐2 Drigo, B., Pijl, A.S., Duyts, H., Kielak, A.M., Gamper, H.A., Houtekamer, M.J., et  al. (2010). Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proceedings of the National Academy of Sciences of the United States of America, 107(24), 10938–10942. Dutta, A., Gupta, S.D., Gupta, A., Sarkar, J., Roy, S., Mukherjee, A., & Sar, P. (2018). Exploration of deep terrestrial subsurface microbiome in Late Cretaceous Deccan traps and underlying Archean basement, India. Nature: Scientific Reports, 8(17459). Eilers, K.G., Debenport, S., Anderson, S.P., & Fierer, N. (2012). Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biology and Biochemistry, 50, 58–65. Field, J.P., Breshears, D.D., Law, D.J., Villegas, J.C., Lopez‐ Hoffman, L., Brooks, P.D., et  al. (2015). Critical zone services: Expanding context, constraints, and currency ­

146  BIOGEOCHEMICAL CYCLES beyond ecosystem services. Vadose Zone Journal, 14(1). doi: 10.2136/vzj2014.10.0142 Finzi, A.C., Cole, J.J., Doney, S.C., Holland, E.A. & Jackson, R.B. (2011). Research frontiers in the analysis of coupled biogeochemical cycles. Frontiers in Ecology and the Environment, 9(1), 74–80. doi: 10.1890/100137 Fletcher, R.C., & Brantley, S.L. (2010). Reduction of bedrock blocks as corestones in the weathering profile: observations and model. American Journal of Science, 310(3), 131–164. doi: 10.2475/03.2010.01 Flinchum, B.A., Holbrook, W.S., Grana, D., Parsekian, A.D., Carr, B.J., Hayes, J.L., & Jiao, J. (2018). Estimating the water holding capacity of the critical zone using near‐surface geophysics. Hydrological Processes, 32(22), 3308–3326. doi: 10.1002/hyp.13260 Flinchum, B.A., Holbrook, W.S., Rempe, D., Moon, S., Riebe, C.S., Carr, B.J., et al., (2018). Critical Zone structure under a granite ridge inferred from drilling and three‐dimensional seismic refraction data. Journal of Geophysical Research— Earth Surface, 123(6), 1317–1343. doi: 10.1029/2017jf004280 Frazier, C.S., & Graham, R.C. (2000). Pedogenic transformation of fractured granitic bedrock, southern California. Soil Science Society of America Journal, 64(6), 2057–2069. Gabet, E.J., Reichman, O.J., & Seabloom, E.W. (2003). The effects of bioturbation on soil processes and sediment transport. Annual Review of Earth and Planetary Sciences, 31, 249–273. doi: 10.1146/annurev.earth.31.100901.141314 Gabor, R.S., Eilers, K., McKnight, D.M., Fierer, N., & Anderson, S.P. (2014). From the litter layer to the saprolite: Chemical changes in water‐soluble soil organic matter and their correlation to microbial community composition. Soil Biology and Biochemistry, 68, 166–176. doi: 10.1016/j. soilbio.2013.09.029 Gadd, G.M. (2013). Microbial roles in mineral transformations and metal cycling in the Earth’s Critical Zone. In J. Xu, D.L. Sparks (Eds.), Molecular environmental soil science (pp. 115– 165). Dordrecht, Netherlands: Springer. Giardino, J.R., & Houser, C. (2015). Introduction to the Critical Zone. In J.R. Giardino, C. Houser (Eds.), Developments in earth surface processes (pp. 1–13). Amsterdam: Elsevier. Grant, G.E., & Dietrich, W.E. (2017). The frontier beneath our feet. Water Resources Research, 53(4), 2605–2609. Gupta, V.V.S.R., & Germida, J.J. (2015). Soil aggregation: Influence on microbial biomass and implications for biological processes. Soil Biology and Biochemistry, 80, A3– A9. doi: https://doi.org/10.1016/j.soilbio.2014.09.002 Hasenmueller, E.A., Gu, X., Weitzman, J.N., Adams, T.S., Stinchcomb, G.E., Eissenstat, D.M., et al. (2017). Weathering of rock to regolith: The activity of deep roots in bedrock fractures. Geoderma, 300, 11–31. doi: 10.1016/j.geoderma.2017.03.020 Heimsath, A.M., Dietrich, W.E., Nishiizumi, K., & Finkel, R.C. (1997). The soil production function and landscape equilibrium. Nature, 388(6640), 358–361. Hirmas, D.R., Amrhein, C., & Graham, R.C. (2010). Spatial and process‐based modeling of soil inorganic carbon storage in an arid piedmont. Geoderma, 154(3–4), 486–494. Holbrook, W.S., Riebe, C.S., Elwaseif, M., Hayes, J.L., Basler‐ Reeder, K., Harry, D.L., et al. (2014). Geophysical ­constraints on deep weathering and water storage potential in the Southern

Sierra Critical Zone Observatory. Earth Surface Processes and Landforms, 39(3), 366–380. doi: 10.1002/esp.3502 Holmstrom, S.J.M., Lundstrom, U.S., Finlay, R.D., & Van Hees, P.A.W. (2004). Siderophores in forest soil solution. Biogeochemistry, 71(2), 247–258. Hutchens, E. (2009). Microbial selectivity on mineral surfaces: possible implications for weathering processes. Fungal Biology Reviews, 23(4), 115–121. doi: https://doi.org/10.1016/j. fbr.2009.10.002 Ibarra, D.E., Caves, J.K., Moon, S., Thomas, D.L., Hartmann, J., Chamberlain, C.P. & Maher, K. (2016). Differential weathering of basaltic and granitic catchments from concentration‐discharge relationships. Geochimica et Cosmochimica Acta, 190, 265–293. Ice, G.G., Neary, D.G., & Adams, P.W. (2004). Effects of wildfire on soils and watershed processes. Journal of Forestry, 102(6), 16–20. doi: 10.1093/jof/102.6.16 Itavaara, M., Salavirta, H., Marjamaa, K., & Ruskeeniemi, T. (2016). Geomicrobiology and metagenomics of terrestrial deep subsurface microbiomes. Advances in Applied Microbiology, 94, 1–77. Jenny, H. (1941). Factors of soil formation: A system of quantitative pedology. New York: Dover Publications, Inc. Johnson, M.S., & Lehmann, J. (2006). Double‐funneling of trees: Stemflow and root‐induced preferential flow. Ecoscience, 13(3), 324–333. doi: 10.2980/i1195‐6860‐13‐3‐324.1 Kallenbach, C.M., Grandy, A.S., Frey, S.D., & Diefendorf, A.F. (2015). Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biology and Biochemistry, 91, 279–290. Keiluweit, M., Nico, P.S., Kleber, M. & Fendorf, S. (2016). Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils? Biogeochemistry, 127(2–3), 157–171. Keiluweit, M., Wanzek, T., Kleber, M., Nico, P., & Fendorf, S. (2017). Anaerobic microsites have an unaccounted role in soil carbon stabilization. Nature Communications, 8(1771). Kleber, M., Sollins, P., & Sutton, R. (2007). A conceptual model of organo‐mineral interactions in soils: self‐assembly of organic molecular fragments into zonal structures on mineral surfaces. Biogeochemistry, 85(1), 9–24. doi: 10.1007/s10533‐007‐9103‐5 Kleber, M., Eusterhues, K., Keiluweit, M., Mikutta, C., Mikutta, R., & Nico, P.S. (2015). Mineral‐organic associations: Formation, properties, and relevance in soil environments. Advances in Agronomy, 130, 1–140. Lebedeva, M.I., & Brantley, S.L. (2013). Exploring geochemical controls on weathering and erosion of convex hillslopes: beyond the empirical regolith production function. Earth Surface Processes and Landforms, 38(15), 1793–1807. doi: 10.1002/esp.3424. Lebedeva, M.I., Fletcher, R.C., & Brantley, S.L. (2010). A mathematical model for steady‐state regolith production at constant erosion rate. Earth Surface Processes and Landforms, 35(5), 508–524. doi: 10.1002/esp.1954 Lehmann, J., & Kleber, M. (2015). The contentious nature of soil organic matter. Nature, 528(7580), 60–68. doi: 10.1038/ nature16069 Li, L., Maher, K., Navarre‐Sitchler, A., Druhan, J., Meile, C., Lawrence, C., et  al. (2017). Expanding the role of reactive

Critical Zone Biogeochemistry: Linking Structure and Function  147 transport models in critical zone processes. Earth‐Science Reviews, 165, 280–301. doi: 10.1016/j.earscirev.2016.09.001 Lipson, D.S., McCray, J.E., & Thyne, G.D. (2007). Using PHREEQC to simulate solute transport in fractured bedrock. Ground Water, 45(4), 468–472. Lohse, K.A., Brooks, P.D., McIntosh, J.C., Meixner, T., & Huxman, T.E. (2009). Interactions between biogeochemistry and hydrologic systems. Annual Review of Environment and Resources, 34, 65–96. doi: 10.1146/annurev.environ.33.031207. 111141 Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth and Planetary Science Letters, 294(1–2), 101–110. Maher, K. (2011). The role of fluid residence time and topographic scales in determining chemical fluxes from landscapes. Earth and Planetary Science Letters, 312(1–2), 48–58. Maher, K., & Chamberlain, C.P. (2014). Hydrologic regulation of chemical weathering and the geologic carbon cycle. Science, 343(6178), 1502–1504. doi: 10.1126/science.1250770 Maher, K., Steefel, C.I., White, A.F., & Stonestrom, D.A. (2009). The role of reaction affinity and secondary minerals in regulating chemical weathering rates at the Santa Cruz Soil Chronosequence, California. Geochimica et Cosmochimica Acta, 73(10), 2804–2831. doi: 10.1016/j.gca.2009.01.030 McClain, M.E., Boyer, E.W., Dent, C.L., Gergel, S.E., Grimm, N.B., Groffman, P.M., et  al. (2003). Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems, 6(4), 301–312. doi: 10.1007/ s10021‐003‐0161‐9. McIntosh, J.C., Schaumberg, C., Perdrial, J., Harpold, A., Vázquez‐Ortega, A., Rasmussen, C., et al. (2017). Geochemical evolution of the Critical Zone across variable time scales informs concentration‐discharge relationships: Jemez River Basin Critical Zone Observatory. Water Resources Research, 53(5), 4169–4196. doi: 10.1002/2016wr019712 McKay, L.D., Harton, A.D., & Wilson, G.V. (2002). Influence of flow rate on transport of bacteriophage in shale saprolite. Journal of Environmental Quality, 31(4), 1095–1105. Monger, H. C., Kraimer, R.A., Khresat, S., Cole, D.R., Wang, X.J., & Wang, J.P. (2015). Sequestration of inorganic carbon in soil and groundwater. Geology, 43(5), 375–378. Moulton, K.L., West, A.J., & Berner, R.A. (2000). Solute flux and mineral mass balance approaches to the quantification of plant effects on silicate weathering. American Journal of Science, 300(7), 539–570. doi: 10.2475/ajs.300.7.539 Navarre‐Sitchler, A., Brantley, S.L., & Rother, G. (2015). How  porosity increases during incipient weathering of crystalline silicate rocks. In C.I. Steefel, S. Emmanuel, L.M. Anovitz (Eds.), Pore‐scale geochemical processes. Reviews in Mineralogy and Geochemistry, 80, 331–354. Navarre‐Sitchler, A., Steefel, C.I., Sak, P.B., & Brantley, S.L. (2011). A reactive‐transport model for weathering rind formation on basalt. Geochimica et Cosmochimica Acta, 75(23), 7644–7667. doi: 10.1016/j.gca.2011.09.033. NRC (2001). Basic research opportunities in the Earch sciences. Washington, DC: National Academies Press. O’Geen, A., Safeeq, M., Wagenbrenner, J., Stacy, E., Hartsough, P., Devine, S., et  al. (2018). Southern Sierra Critical Zone Observatory and Kings River experimental watersheds:

A  synthesis of measurements, new insights, and future ­directions. Vadose Zone Journal, 17(1), 180081. Olshansky, Y., White, A., Moravec, B., McIntosh, J. & Chorover, J. (2018). Subsurface pore water contributions to stream concentration–discharge relations across a snowmelt ­hydrograph. Frontiers in Earth Science, 6. doi: 10.3389/ feart.2018.00181 Olyphant, J., Pelletier, J.D., & Johnson, R. (2016). Topographic correlations with soil and regolith thickness from shallow‐ seismic refraction constraints across upland hillslopes in the Valles Caldera, New Mexico. Earth Surface Processes and Landforms, 41(12), 1684–1696. doi: 10.1002/esp.3941. Parsekian, A.D., Singha, K., Minsley, B.J., Holbrook, W.S., & Slater, L. (2015). Multiscale geophysical imaging of the ­critical zone. Reviews of Geophysics, 53(1), 1–26. doi: 10.1002/2014rg000465 Pawlik, L., Phillips, J.D., & Samonil, P. (2016). Roots, rock, and  regolith: Biomechanical and biochemical weathering by  trees and its impact on hillslopes—a critical literature review. Earth‐Science Reviews, 159, 142–159. doi: 10.1016/j. earscirev.2016.06.002 Pelletier, J.D., & Rasmussen, C. (2009a). Quantifying the climatic and tectonic controls on hillslope steepness and erosion rate. Lithosphere, 1(2), 73–80. doi: 10.1130/l3.1 Pelletier, J.D., & Rasmussen, C. (2009b). Geomorphically based predictive mapping of soil thickness in upland watersheds. Water Resources Research, 45. https://doi.org/10.1029/2008 WR007319 Pelletier, J.D., Barron‐Gafford, G.A., Breshears, D.D., Brooks, P., Chorover, J., Durcik, M., et  al. (2013). Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona. Journal of Geophysical Research‐Earth Surface, 118(2), 741–758. Pelletier, J.D., Barron‐Gafford, G.A., Gutiérrez‐Jurado, H., Hinckley, E.L.S., Istanbulluoglu, E., Mcguire, L.A., et  al. (2018). Which way do you lean? Using slope aspect variations to understand Critical Zone processes and feedbacks. Earth Surface Processes and Landforms, 43(5), 1133–1154. doi: 10.1002/esp.4306 Perdrial, J., Brooks, P., Swetnam, T., Lohse, K.A., Rasmussen, C., Litvak, M., et al. (2018). A net ecosystem carbon budget for snow dominated forested headwater catchments: linking water and carbon fluxes to critical zone carbon storage. Biogeochemistry, 138(3), 225–243. Purkamo, L., Bomberg, M., Kietavainen, R., Salavirta, H., Nyyssonen, M., Nuppunen‐Puputti, M., et  al. (2016). Microbial co‐occurrence patterns in deep Precambrian bedrock fracture fluids. Biogeosciences, 13(10), 3091–3108. Purkamo, L., Bomberg, M., Nyyssonen, M., Kukkonen, I., Ahonen, L., Kietavainen, R., & Itavaara, M. (2013). Dissecting the deep biosphere: retrieving authentic microbial communities from packer‐isolated deep crystalline bedrock fracture zones. FEMS Microbiology Ecology, 85(2), 324–337. Rasmussen, C., Brantley, S., de Richter, D., Blum, A., Dixon, J. & White, A.F. (2011). Strong climate and tectonic control on  plagioclase weathering in granitic terrain. Earth and Planetary Science Letters, 301(3–4), 521–530. doi: 10.1016/j. epsl.2010.11.037

148  BIOGEOCHEMICAL CYCLES Rasmussen, C., Heckman, K., Wieder, W., Keiluweit, M., Lawrence, C., Asefaw‐Berhe, A., et  al. (2018). Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry, 137(3), 297–306. Rasmussen, C., & Tabor, N.J. (2007). Applying a quantitative pedogenic energy model across a range of environmental gradients. Soil Science Society of America Journal, 71(6), 1719– 1729. doi: 10.2136/ssaj2007.0051 Rasmussen, C., Troch, P.A., Chorover, J., Brooks, P., Pelletier, J.D., & Huxman, T.E. (2011). An open system framework for integrating critical zone structure and function. Biogeochemistry, 102(1–3), 15–29. doi: 10.1007/s10533‐010‐9476‐8 Reichard, P.U., Kretzschmar, R., & Kraemer, S.M. (2007). Dissolution mechanisms of goethite in the presence of siderophores and organic acids. Geochimica et Cosmochimica Acta, 71(23), 5635–5650. Rempe, D.M., & Dietrich, W.E. (2014). A bottom‐up control on fresh‐bedrock topography under landscapes. Proceedings of the National Academy of Sciences of the United States of America, 111(18), 6576–6581. Rempe, D.M., & Dietrich, W.E. (2018). Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2664–2669. doi: 10.1073/ pnas.1800141115 Reynolds, J.F., Virginia, R.A., Kemp, P.R., de Soyza, A.G., & Tremmel, D.C. (1999). Impact of drought on desert shrubs: Effects of seasonality and degree of resource island development. Ecological Monograph, 69(1), 69–106. Riebe, C.S., & Chorover, J. (2014). Report on drilling, sampling, and imaging the depths of the critical zone. NSF Workshop Report. Arlington, VA: National Science Foundation. Riebe, C.S., Hahm, W.J., & Brantley, S.L. (2017). Controls on deep critical zone architecture: a historical review and four testable hypotheses. Earth Surface Processes and Landforms, 42(1), 128–156. doi: 10.1002/esp.4052 Saccone, L., Gazze, S.A., Duran, A.L., Leake, J.R., Banwart, S.A., Ragnarsdottir, K.V., Smits, M.M., & McMaster, T.J. (2012). High resolution characterization of ectomycorrhizal fungal–mineral interactions in axenic microcosm experiments. Biogeochemistry, 111(1–3), 411–425. Salve, R., Rempe, D.M., & Dietrich, W.E. (2012). Rain, rock moisture dynamics, and the rapid response of perched groundwater in weathered, fractured argillite underlying a steep hillslope. Water Resources Research, 48. doi: 10.1029/2012wr012583 Schlegel, M. E., McIntosh, J.C., Bates, B.L., Kirk, M.F. & Martini, A.M. (2011). Comparison of fluid geochemistry and microbiology of multiple organic‐rich reservoirs in the Illinois Basin, USA: Evidence for controls on methanogenesis and microbial transport. Geochimica et Cosmochimica Acta, 75(7), 1903–1919. doi: https://doi.org/10.1016/j.gca.2011.01.016 Schmidt, M.W.I., Torn, M.S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I.A., et al. (2011). Persistence of soil organic matter as an ecosystem property. Nature, 478(7367), 49–56. doi: 10.1038/nature10386 Simpson, A.J., Song, G., Smith, E., Lam, B., Novotny, E.H., & Hayes, M.H.B. (2007). Unraveling the structural components of soil humin by use of solution‐state nuclear magnetic

­resonance spectroscopy. Environmental Science and Technology, 41(3), 876–883. doi: 10.1021/es061576c St Clair, J., Moon, S., Holbrook, W.S., Perron, J.T., Riebe, C.S., Martel, S.J., et  al. (2015). Geophysical imaging reveals ­topographic stress control of bedrock weathering. Science, 350(6260), 534–538. Steefel, C.I., Appelo, C.A.J., Arora, B., Jacques, D., Kalbacher, T., Kolditz, O., et al. (2015). Reactive transport codes for subsurface environmental simulation. Computational Geosciences, 19(3), 445–478. doi: 10.1007/s10596‐014‐9443‐x Steefel, C.I., DePaolo, D.J., & Lichtner, P.C. (2005). Reactive transport modeling: An essential tool and a new research approach for the Earth sciences. Earth and Planetary Science Letters, 240(3–4), 539–558. doi: 10.1016/j.epsl.2005.09.017 Stinchcomb, G.E., Kim, H., Hasenmueller, E.A., Sullivan, P.L., Sak, P.B., & Brantley, S.L. (2018). Relating soil gas to weathering using rock and regolith geochemistry. American Journal of Science, 318(7), 727–763. doi: 10.2475/07.2018.01 Sutton, R., & Sposito, G. (2005). Molecular structure in soil humic substances: The new view. Environmental Science and Technology, 39(23), 9009–9015. doi: 10.1021/es050778q Troch, P.A., Lahmers, T., Meira, A., Mukherjee, R., Pedersen, J.W., Roy, T., & Valdes‐Pineda, R. (2015). Catchment coevolution: A useful framework for improving predictions of hydrological change?. Water Resources Research, 51(7), 4903–4922. Uroz, S., Calvaruso, C., Turpault, M., & Frey‐Klett, P. (2009). Mineral weathering by bacteria: ecology, actors and mechanisms. Trends in Microbiology, 17(8), 378–387. doi: 10.1016/j. tim.2009.05.004 Uroz, S., Kelly, L.C., Turpault, M., Lepleux, C., & Frey‐Klett, P. (2015). The Mineralosphere Concept: Mineralogical control of the distribution and function of mineral‐associated bacterial communities. Trends in Microbiology, 23(12), 751– 762. doi: https://doi.org/10.1016/j.tim.2015.10.004 Uroz, S., Oger, P., Lepleux, C., Collignon, C., Frey‐Klett, P., & Turpault, M. (2011). Bacterial weathering and its contribution to nutrient cycling in temperate forest ecosystems. Research in Microbiology, 162(9), 820–831. doi: https://doi. org/10.1016/j.resmic.2011.01.013 Verchot, L.V., Dutaur, L., Shepherd, K.D., & Albrecht, A. (2011). Organic matter stabilization in soil aggregates: Understanding the biogeochemical mechanisms that determine the fate of carbon inputs in soils. Geoderma, 161(3–4), 182–193. doi: 10.1016/j.geoderma.2010.12.017 Vitousek, P.M. (2004). Nutrient cycling and limitation: Hawaii as a model system. Princeton University Press. Wagener, T., Sivapalan, M., Troch, P.A., McGlynn, B.L., Harman, C.J., Gupta, H.V., et  al. (2010). The future of hydrology: An evolving science for a changing world. Water Resources Research, 46. doi: 10.1029/2009wr008906 Wang, B., Brewer, P.E., Shugart, H.H., Lerdau, M.T., & Allison, S.D. (2019). Soil aggregates as biogeochemical reactors and implications for soil–atmosphere exchange of greenhouse gases—a concept. Global Change Biology, 25(2), 373–385. doi: 10.1111/gcb.14515 White, T., Brantley, S., Banwart, S., Chorover, J., Dietrich, W., Derry L., et al. (2015). The role of Critical Zone Observatories

Critical Zone Biogeochemistry: Linking Structure and Function  149 in critical zone science. Developments in Earth Surface Processes, 19, 15–78. doi: 10.1016/B978‐0‐444‐63369‐9. 00002‐1 Wilkinson, M.T., Richards, P.J., & Humphreys, G.S. (2009). Breaking ground: Pedological, geological, and ecological implications of soil bioturbation. Earth‐Science Reviews, 97(1–4), 257–272. doi: 10.1016/j.earscirev.2009.09.005 Worthington, S.R.H. (2015). Diagnostic tests for conceptualizing transport in bedrock aquifers. Journal of Hydrology, 529, 365–372. Worthington, S.R.H., Davies, G.J., & Alexander, E.C. (2016). Enhancement of bedrock permeability by weathering. Earth‐ Science Reviews, 160, 188–202.

Zachara, J., Brantley, S.L., Chorover, J., Ewing, R., Kerisit, S., Liu, C.X., et al. (2016). Internal domains of natural porous media revealed: critical locations for transport, storage, and chemical reaction. Environmental Science and Technology, 50(6), 2811–2829. Zamanian, K., Pustovoytov, K., & Kuzyakov, Y. (2016). Pedogenic carbonates: Forms and formation processes. Earth‐Science Reviews, 157, 1–17. Zapata‐Rios, X., McIntosh, J., Rademacher, L., Troch, P.A., Brooks, P.D., Rasmussen, C., & Chorover, J. (2015). Climatic and landscape controls on water transit times and silicate mineral weathering in the critical zone. Water Resources Research, 51(8), 6036–6051. doi: 10.1002/2015wr017018

7 Tracking the Fate of Plagioclase Weathering Products: Pedogenic and Human Influences Scott W. Bailey ABSTRACT Catchment mass balance demonstrates the role of hydrologic, pedogenic, and human influences on cycling of Ca, Al, Na, and Si, derived from plagioclase dissolution. As Na is little taken up by vegetation, does not accumulate in secondary soil pools, and is primarily in plagioclase, net Na output is a measure of plagioclase dissolution. Net Na flux is proportional to streamflow, illustrating hydrologic control of weathering. Rates of Ca, Al, and Si release from mineral dissolution are more difficult to evaluate as they are variably taken up by vegetation or stored in secondary soil pools. However, plagioclase stoichiometry provides a benchmark by which the net storage or release of these elements may be gauged. Net export of Ca relative to Na shows that Ca export was enhanced in the 1960s, peaking in the 1970s, taken as an indication of acid deposition effects on the soil exchange pool, which were further exacerbated by harvesting treatments. Net export of Si and Al relative to Na suggest dynamic biotic Si pools and provide a measure of the podzolization soil development process. Net catchment export ratios provide a tool for comparing biogeochemical processes across catchments and a basis for investigating processes controlling pool accumulation.

7.1. INTRODUCTION Element mass balance is a well‐known experimental technique that has been used to elucidate the cycling of elements at the catchment scale in both undisturbed, reference catchments, as well as in response to various disturbances in both natural settings and in experimentally treated or manipulated catchments. Catchment element cycling studies began at the Hubbard Brook Experimental Forest (HBEF), NH, United States in 1963, facilitated by advances in technology to electronically measure water solute concentrations quickly and inexpensively. This record at HBEF has continued uninterrupted since 1963 (Likens & Bailey, 2014). This catchment‐scale mass balance technique was pioneered as a method for estimating rates of release of base cations such as Ca2+, Na+, etc., by dissolution of minerals US Forest Service, Northern Research Station, North Woodstock, New Hampshire, USA

in field settings (Johnson et al., 1968). As this approach evolved and was applied at other sites, a growing appreciation of dynamics in biomass and soil pools of some elements released by mineral dissolution was taken into account, thus refining estimates of mineral weathering and highlighting dynamics in response to disturbances in biotic and soil pools (Likens et al., 1996; Price, Rice, et  al., 2013; Velbel & Price, 2007). Thus, the mass balance for an element x at a catchment scale is determined by inputs of atmospheric deposition (Px) and mineral weathering flux (Wx), stream output (Sx) and changes in ecosystem pools, including net biomass uptake (Bx), secondary mineral formation (Mx), and change in available soil pools (ΔAx) as expressed by

Px Wx

Sx

Bx

Mx

Ax

(7.1)

Note that at this site, typical of relatively undisturbed forested catchments of the region, erosion rates are very low. Catchment export of mineral‐derived materials is

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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primarily in a dissolved form; particulate export is relatively small (Likens et al., 1977). Net catchment export of sodium (Na), calculated simply as streamwater export minus atmospheric input in bulk precipitation, has been interpreted as an estimate of Na+ release by primary mineral dissolution, and has been shown to be attributable to plagioclase weathering in catchments underlain by soils and bedrock of a variety of lithologies (Price, Peresolak, et  al., 2013; Price, Rice, et al., 2013; Stutter et al., 2002). The limited uptake of Na by plants (Kronzucker & Britto, 2011), resulting in minimum storage in biomass pools, and large ionic radius of Na+, which makes it a poor competitor for soil ion exchange sites, and its absence as an essential component in secondary minerals, contribute to the simpler mass balance of this element (S.W. Bailey et al., 2003), facilitating the use of net Na export as an indicator of mineral dissolution, and in particular plagioclase weathering. While the relatively conservative behavior of Na may facilitate its use as a weathering index, the other elements derived from plagioclase dissolution are critical to a number of ecosystem processes and functions. Calcium (Ca) and silicon (Si) are important nutrients, actively taken up by plants (McLaughlin & Wimmer, 1999; Ronchi et  al., 2013). Limitations in Ca supply lead to health impacts in sensitive species, which include red spruce and sugar maple, two of the most ecologically and commercially important species in NE North America (Long et  al., 2009). Silica drives productivity in marine environments, where its supply is derived from runoff from terrestrial sources (Ronchi et al., 2013). Aluminum (Al) is not a nutrient, and can be toxic to terrestrial and aquatic life in its free ionic form Al3+ (Driscoll et  al., 2001). However, in humid, temperate to subtropical portions of the world where podzolization is an important soil forming process, Al is responsible for mineral horizon carbon (C) sequestration, the dominant C store in Spodosols (Lundström et al., 2000). Thus indices of Ca, Si, and Al loss normalized to Na export may provide an important tool for understanding biogeochemical processing of these elements including dynamics in storage in vegetation and soil pools. S. W. Bailey et al. (2003) proposed the use of a net ecosystem Ca/Na ratio, or the net loss of Ca from a catchment divided by the net Na loss, as a tool for evaluating temporal dynamics in ecosystem Ca pools, which may include net storage in aggrading vegetation or changes in weakly bound pools on the soil cation exchange complex. Unlike Na, ionic Ca pools in biomass and in soil are typically large compared to atmospheric inputs and streamwater outputs, and are potentially dynamic, responding to disturbances such as air pollution and vegetation loss, including biomass harvest during forest management. By normalizing net Ca loss to net Na loss, the effect of

annual variability in mineral weathering flux can be removed, highlighting dynamics in ecosystem Ca pools. Elevation of net Ca flux relative to net Na flux, and subsequent declines have been interpreted as evidence for depletion of Ca from available soil pools, which peaked in the 1970s in response to anthropogenic acid deposition (S.W. Bailey et  al., 2003; Watmough et  al., 2016). Catchments at the HBEF that received various forest disturbance treatments, which included clearfelling trees, herbicide applications, and removal of forest biomass in tree boles or whole trees, showed further elevated net Ca/ Na flux ratios relative to reference or unmanipulated catchments, suggesting additional losses of Ca in excess of weathering inputs, with treatment effects lasting decades and continuing through the record reported on by S.W. Bailey et al. (2003). As plagioclase feldspar is also a dominant mineral source for Al and Si, there is potential to interpret net ecosystem Al/Na and Si/Na export ratios relative to the stoichiometry of plagioclase similar to the approach that has been applied with Ca. The objectives of the current study are to update the analysis of net ecosystem Ca and Na loss at the HBEF (S.W. Bailey et al., 2003) to determine if net Ca loss continues to decline relative to net Na loss in response to acid deposition, and to determine if experimentally treated catchments continue to follow a distinct trajectory from reference catchments, or if these trajectories have merged, suggesting that treatment effects on Ca export have ceased. The current study was also designed to expand this technique to evaluate net Si and Al loss from the same suite of catchments. This will elucidate processes affecting the export of these additional elements and infer potential dynamics in Si and Al ecosystem pools. 7.2. METHODS 7.2.1. Study Area Hubbard Brook Experimental Forest is located in west‐central New Hampshire, USA (43°56’N, 71°45’W; Figure 7.1) within the White Mountain National Forest. The climate is humid continental with a mean annual precipitation of 140 cm and stream runoff of 90 cm (A.S. Bailey et al., 2003). Much of the research is centered on nine headwater catchments located in the northern (W1–W6) and southern (W7–W9) portions of the experimental forest (Figure  7.1) where water and solute input and output is measured at a series of meteorological and stream gauging stations. This study focuses on catchments W3, W6, and W9, which are maintained in a natural, unmanaged condition and serve as reference catchments, and W2, W4, and W5, which have had various vegetation manipulation treatments, described below.

TRACKING THE FATE OF PLAGIOCLASE WEATHERING PRODUCTS  153

73°0’0”W

71°0’0”W

45°0’0”N

N 44°0’0”N

Key Stream gauge Meteorologic station Streams 10m Contour 0 Catchments

43°0’0”N

0.5

1

2

3

4 Kilometers

Figure 7.1  Locus map of Hubbard Brook Experimental Forest.

Land cover at the HBEF is forest with northern hardwoods dominated by Acer saccharum Marsh. (sugar maple), Betula alleghanensis Britt. (yellow birch), and Fagus grandifolia Ehrh. (American beech) on deeper and better‐drained soils; mixed conifers dominated by Picea rubens Sarg. (red spruce), Abies balsamea (L.) Mill. (balsam fir), and Betula papryifera var. cordifolia (Regel) Fern. (mountain paper birch) occupy wetter sites and areas where soils are shallow to bedrock. Forests on the reference catchments are mature, mostly second growth following partial cutting in the period of 1890–1920, and severe blowdown during a hurricane in 1938. Reference catchments (W3, W6, W9) have had no direct human disturbance or management since prior harvesting which ended in the 1910s. Treated catchments (W2, W4, W5) have aggrading forest following recent harvesting or deforestation experiments. Catchment W2 was clearfelled during the winter of 1965–1966 and herbicided to prevent regrowth during the growing seasons of 1966–1968; W4 was harvested in strips 25 m wide along contour during fall/winter 1970, 1972, and 1974, with one‐third of the catchment harvested during each of the three years;

and W5 was whole‐tree harvested, removing all tops and stems > 5 cm diameter at breast height during the winter of 1983–1984. Bedrock in the portion of the HBEF considered in this study is the Silurian Rangeley Formation, a sillimanite‐ grade metapelite consisting of mica schist with minor amounts of calc‐silicate granulite (Burton et  al., 2000). Bedrock is poorly exposed, cropping out mostly along ridges and in some stream channels, and is covered by a veneer of late Wisconsinan glacial drift. In the study watersheds, drift is thin and interspersed with exposed bedrock in the uppermost portion of the catchments, particularly along catchment divides, while it is up to 10 m thick in central to lower portions of the catchments. Catchment W9 is distinct in having much more bedrock outcrops while the majority of the catchment is underlain by thin drift, with bedrock less than 1 m deep. Glacial drift is dominated by granitic lithologies, transported from the north and west of the study catchments, with lesser contributions from local bedrock (S.W. Bailey et  al., 2003), and is the parent material for soil development. Based on a review of published bedrock

154  BIOGEOCHEMICAL CYCLES Table 7.1  Mineralogic contributions to Ca, Na, Al, and Si content (percent) of soil parent material Mineral

Ca

Na

Al

Si

Plagioclase Minor silicatesa Apatite Stable silicatesb

75 15 10 0

98 2 0 0

73 3 0 24

24 1 0 75

a

 Includes hornblende, diopside, epidote, actinolite, biotite.  Includes quartz, K‐feldspar, muscovite.

b

mineralogy, till lithologic sources, and direct microprobe analyses of soil minerals (S.W. Bailey et al., 2003, 2019; Hyman et  al., 1998), plagioclase feldspar is of oligoclase composition (Ca0.2Na0.8Al1.2Si2.8O8) and a dominant source of Ca and Na, as well as the most easily weathered source of Al and Si in the catchments (Table  7.1). Normalized to Na, the stoichiometry of oligoclase is 0.25Ca:1.5Al:3.5Si:Na. Thus, dissolution of this mineral is expected to produce 0.25 mol Ca, 1.5 mol Al, and 3.5 mol Si per mole of Na, resulting in index ratios that are useful for tracking the fate of solutes released by dissolution. Where not confined by shallow bedrock, soils average 0.7 m to the top of the C‐horizon, corresponding to the depth of major alteration of glacial drift by soil‐forming processes, as well as the limit of the rooting zone (S.W. Bailey et al., 2014). Podzolization is a dominant soil‐ forming process, with organic acids leaching iron (Fe) and Al from surficial mineral soil layers (eluviation) and depositing them as organic Fe and Al complexes in lower mineral soil layers (illuviation). Soils vary in their expression of eluvial horizons, mineral soil low in organic matter, and illuvial horizons, with mineral surfaces coated by organic matter complexed by Al and Fe. Presence and thickness of eluvial and illuvial horizons depends on thickness of the soil parent material, subsurface drainage limitations, and upslope drainage area (S.W. Bailey et al., 2014). 7.2.2. Mass Balance The small catchment approach was used to calculate water flux and element mass balance on an annual basis using a 1st June water year (S.W. Bailey et al., 2003). The experimental catchments are numbered consecutively by the date that monitoring began, with W1 established in 1956 to W9 established in 1995. Water inputs of rain and snow were measured at a series of meteorologic stations, with the two to four closest collectors to each catchment used to calculate an area‐weighted water input. Water output via streamflow was measured at weir‐controlled gauging stations at the base of each catchment. Gauging stations were installed on stream reaches with bedrock outcrops with a cement basin capturing all of the streamflow

and directing it over a calibrated steel v‐notch weir. Due to low density, nonintersecting orientation of fractures aligned primarily along foliation of tightly folded crystalline metamorphic bedrock, negligible water flux occurs through deep seepage (< 1% of atmospheric inputs) (Likens et  al., 1977). Rain and snow were sampled for major solutes in bulk precipitation collectors open to wet and dry deposition and retrieved weekly. Streamwater major solute composition was measured from a weekly sample at the catchment outlet. Instrumentation and analytical procedures for water chemical analyses are described fully in Buso et al. (2000). Chemical analyses were performed at the Cary Institute, Millbrook, NY through 2012 and then at the US Forest Service, Durham, NH from 2012 to the present. Similar methods were used at both laboratories and a year of overlap in the analyses was conducted in order to insure the integrity of the long‐term record. Calcium, Na, and Si were measured on an ICP optical emission spectrometer (OES). Total dissolved Al was also measured by ICP‐OES from 2012 onwards at the US Forest Service laboratory. Prior total dissolved Al measurements were made spectrometrically using the wet‐chemical Ferron method (Buso et  al., 2000). Inputs of dissolved Al and Si in rain and snow are negligible; although routinely measured, samples yield concentrations below analytical detection limits. Errors incorporated in the catchment mass‐balance approach include error in annual water flux measured by gauges, estimated as ≤ 5% by Winter (1981) and determination of cation concentrations, estimated at ≤ 5% for precipitation and ≤ 1% for streamwater (Buso et  al., 2000). Water flux and water chemical data used in this study are available in Campbell (2017a, 2017b) and Likens (2017). 7.3. RESULTS 7.3.1. Net Na Flux Net Na flux from the reference catchments showed no long‐term trend (Figure 7.2), with interannual variation largely reflecting wetness conditions (annual net Na flux vs. streamflow for W3, W6, and W9, r = 0.93, 0.89, 0.77, respectively). The reference catchments, as well as the experimentally manipulated catchments outside of the immediate post‐treatment periods, follow a consistent order with W4 and W3 having the greatest net Na flux while W9 had the least flux. The other three catchments, W2, W5, and W6 had Na flux similar to each other and intermediate to the other two groups of catchments (Figure 7.2). The reference and treated catchments follow similar trends and interannual variation (outside of the treatment periods). Thus, with regards to Na flux, there appears to be no difference in behavior between the reference and treatment catchments.

Net sodium flux (mol ha–1 year–1)

W2 - clearfelled and herbicide W3 - reference W4 - strip cut harvest W5 - whole tree harvest W6 - reference W9 - reference

W5 treatment

600

W4 treatment

700

W2 treatment

TRACKING THE FATE OF PLAGIOCLASE WEATHERING PRODUCTS  155

500

400

300

200

100 1960

1970

1980

1990 Water year

2000

2010

Figure 7.2  Annual net Na flux calculated as stream export minus precipitation input. Reference catchments are shown with black lines while treated catchments are shown with colored lines. Symbols and colors for each catchment are consistent among all figures. Dashed vertical lines show the start of treatments in the experimentally manipulated catchments.

Compared to the other catchments, net Na flux was elevated following treatment with W2, which was devegetated for 3 years, showing the strongest response with increased flux for 5 years following treatment, and a maximum increase of about seven times over pretreatment conditions for the first 2 years following treatment. Whole‐tree harvested W5 had an increase of net Na flux of about 40% for 2 years following treatment. Strip‐cut W4 was harvested over a 6 year period, with a buffer strip left along the central stream. Thus, in contrast to the other two experiments, it maintained partial vegetative cover throughout the treatment and recovery period. This less severe treatment, coupled with W4’s tendency to have the highest net Na flux throughout the record, make it somewhat more difficult to delineate a treatment response. However, net Na flux from W4 in 1973 of 603 mol ha−1 year−1 was the highest flux measured in any of the catchments except for W2 in the first 2 years of its treatment. Overall, the deviation in net Na flux in the treated compared to the reference watersheds was proportional to the severity of the disturbance. 7.3.2. Net Ca/Na Flux The net Ca/Na flux ratio was always higher than that expected from stoichiometric plagioclase dissolution, with a minimum of 0.42 measured in W3 in 2011 and

2012, compared to a ratio of 0.25 in plagioclase (Figure 7.3). In contrast to the net Na flux, the net Ca/Na flux ratio showed a distinct long‐term pattern with lesser interannual variation. The three reference catchments tracked each other closely, with net Ca/Na relatively low at 0.5 (W6) to 0.8 (W3) at the beginning of the record, increasing to a peak of 1.3 (W3) to 1.5 (W6) in the early 1970s, then decreasing steadily to 0.5 (W3) to 0.6 (W6) in 2000. The record in W9 began in 1995 with the net Ca/Na flux in W9 tracking the other reference catchments. Since 2000, which is beyond the limit of the record previously reported by Bailey et al. (2003), the net Ca/Na flux has been relatively steady in the reference catchments at values between 0.4 and 0.6 (Figure 7.3). The treated watersheds followed a similar trend of net Ca/Na flux increasing to the early 1970s followed by a decrease, with deviations from the reference catchments following treatment, and continuing on trajectories distinct from the reference catchments until about 2011 (Figure 7.3). Similar to the relative treatment effects seen in net Na flux, the increase in net Ca/Na flux was greatest in W2, with lesser treatment effect seen in W5 and the least response in W4. In W2 and W5, the increase in net Ca/Na flux relative to the other catchments appeared immediately after initial treatment while the response in W4 was not apparent until about 1977, 7 years after treatment began and 3 years after

W2 - clearfelled and herbicide W3 - reference W4 - strip cut harvest W5 - whole tree harvest W6 - reference W9 - reference

W5 treatment

W4 treatment

3.5

W2 treatment

156  BIOGEOCHEMICAL CYCLES

3.0

Ecosystem Ca/Na ratio

2.5

2.0

1.5

1.0

0.5

Ecosystem Ca/Na ratio minimum = 0.41 Plagioclase release = 0.25

1960

1970

1980

1990

2000

2010

Year

Figure 7.3  Net Ca/Na flux ratio calculated as the net Ca flux divided by the net Na flux. The minimum value observed was 0.41, compared to a stoichiometric value for plagioclase of 0.25.

treatment was complete. In contrast to the shorter‐lived, 5 year or less, response seen in net Na flux following treatment, net Ca/Na flux followed an elevated trajectory relative to the reference catchments until 2011. Net Ca/Na flux in the treated catchments tracked parallel but still slightly higher than the reference catchments in the last 4 years of the record. Taking 2011 as the end of treatment response in net Ca flux relative to Na, this suggests that elevated Ca following treatment lasted 45, 34, and 27 years in W2, W4, and W5, respectively (Figure 7.3). 7.3.3. Net Si/Na Flux The net Si/Na flux showed no apparent long‐term trends and perhaps the greatest interannual variation of any of the fluxes or flux ratios considered in this study

(Figure 7.4). The catchments were all distinct from each other, with the exception of W3 and W6, the two reference catchments dominated by deeper soils and northern hardwood forests, which tracked each other closely in net Si/ Na. The other reference catchment, W9, with shallow to bedrock soils and dominantly coniferous vegetation had distinctly higher Si/Na ratio. The net Si/Na ratio in W3 and W6 was generally less than the ratio of 3.5 expected from stoichiometric plagioclase dissolution, varying mostly in the range of 2.0–3.0. In contrast, the higher ratios observed in W9 were mostly greater than the ratio expected from plagioclase dissolution, varying mostly in the range of 3.5–4.5. Outside of the treatment periods, the treated catchments (W2, W4, and W5) fell generally between the Si/Na ratio of the reference catchments, with W9 at the upper end and W3 and W6 at the lower end.

TRACKING THE FATE OF PLAGIOCLASE WEATHERING PRODUCTS  157

W2 - clearfelled and herbicide

W5 treatment

W4 treatment

5.0

W2 treatment

5.5 W3 - reference W4 - strip cut harvest W5 - whole tree harvest W6 - reference W9 - reference

4.5

Ecosystem Si/Na ratio

4.0

3.5

Plagioclase release = 3.5

3.0

2.5

2.0

1.5 1960

1970

1980

1990

2000

2010

Year

Figure 7.4  Net Si/Na flux ratio calculated as Si output in streamwater divided by the net Na flux. Si input in atmospheric deposition was negligible. The measured values vary about the stoichiometric value for plagioclase of 3.5.

The Si/Na ratio in the treated catchments was similar to the reference catchments before treatment and then showed complicated deviations compared to the patterns seen in the reference catchments (Figure 7.4). In W2, Si/ Na decreased sharply to the lowest levels exhibited in the study for 2 years following treatment. This was followed by a sharp increase, with Si/Na ratios greater than that expected from plagioclase dissolution for the following 12 years. In whole‐tree harvested W5, Si/Na also decreased sharply for 1 year following treatment, followed by a sharp increase. Net Si/Na flux in W5 fluctuated about the plagioclase value for most of the remaining record. The response in W4 was more subtle, without strong swings in the ratio following treatment. However, W4, which had exhibited Si/Na ratios less than the reference catchments W3 and W6 until treatment, has shown Si/Na ratios

greater than those reference catchments since treatment (Figure 7.4). 7.3.4. Net Al/Na Flux Routine monitoring of total dissolved Al in all HBEF catchments commenced in 2013. During 2013–2014, net Al/Na in two of the reference catchments, W3 and W6, as well as in all three of the treated catchments was very low, ranging from 0.08 to 0.40 (Figure 7.5). In contrast, W9, the reference catchment with shallow soils and coniferous forest had ratios of 1.2 and 1.4, much higher than any of the other measurements of net Al/ Na except for W2 during the treatment period, and approaching the ratio of 1.5 expected from plagioclase dissolution.

158  BIOGEOCHEMICAL CYCLES 1.6

1.4

W2 treatment

W2 - clearfelled and herbicide W3 - reference W4 - strip cut harvest

Plagioclase release = 1.5

W5 - whole tree harvest W6 - reference W9 - reference

1.2

Ecosystem Al/Na ratio

1.0

0.8

0.6

0.4

0.2

0.0 1960

1970

1980

1990

2000

2010

Year

Figure 7.5  Net Al/Na flux ratio calculated as Al output in streamwater divided by the net Na flux. The Al input in atmospheric deposition was negligible. The measured values are substantially less than the stoichiometric value for plagioclase of 1.5 except for W2 following treatment, and for reference catchment W9, where the measured values approach the plagioclase value.

The record of net Al/Na flux is limited by available data and is most complete for reference catchment W6. The Al/Na at W6 showed a slightly decreasing trend, at least from the late 1970s to the present (Figure 7.5). The measured flux ratio ranged from 0.78 in 1978 to 0.27 in 2013, all substantially less than a ratio of 1.5 expected from stoichiometric plagioclase dissolution. The only treated catchment with a record around a treatment period is clearfelled and herbicided W2, which showed Al/Na ratios slightly less than the W6 reference catchment in two pretreatment years, followed by strong increases during the year treatment commenced and the two post‐treatment years for which data are available. The ratio of 1.5 measured in 1968 at the end of the

­ erbicide applications equals that expected from plagioh clase dissolution. The level of analysis of Al/Na ratio is limited by available data. Monitoring of future trends will confirm and extend the findings presented here. 7.4. DISCUSSION 7.4.1. Long‐Term Variation in Plagioclase Weathering Shortly after recognition of acid deposition as an air pollution phenomenon, Johnson et al. (1972) questioned whether a switch from a carbonic acid buffering system to a dominance of sulfate as the major anion would result in

TRACKING THE FATE OF PLAGIOCLASE WEATHERING PRODUCTS  159

a change in mineral weathering rates. Lack of trends in net Na export (Figure  7.2), with Na fluxes correlating with streamflow (section 7.3.1) suggest a hydrologic driver of plagioclase weathering that has been insensitive to dynamics in acid deposition levels and water solute composition. Over the period of the HBEF record, there has been a sevenfold reduction in hydrogen ion concentrations in precipitation, with concomitant increases in surface‐ water pH and reductions in surface‐water concentrations of base cations and Al (Likens & Bailey, 2014). These changes may counteract each other with respect to influencing weathering rates, with decreases in pH and dissolved Al, for example, expected to have opposing effects on weathering rates. Over the medium term, at decadal timescales, weathering of plagioclase shows no trends, responding only to interannual variation in wetness. Differences in net Na flux between catchments appear to vary with soil depth. Bedrock outcrops are most common in W9, with intervening areas largely covered by glacial drift‐based soils less than 1 m deep. Catchment W9 clearly has the shallowest soils, corresponding to the lowest net Na flux (Figure  7.2). The other catchments have bedrock outcrops mostly confined to catchment divides and existing soil surveys are insufficient to ­quantitatively compare soil depth between catchments. However, as W3 and W4 are wider relative to their length compared to the other catchments, it seems likely that soil depth could be deeper in these two catchments, especially in interior portions farther from a bedrock‐controlled catchment divide. Better determination of soil thickness across the catchments, coupled with soil textural analyses, could be used to express net Na flux on a mineral surface area basis, rather than just a catchment area basis, and would improve our ability to compare weathering rate estimates measured in this field setting with those measured in laboratory experiments, which are routinely expressed on a mineral‐surface‐area basis. Differences in plagioclase dissolution would also be expected from climatic differences related to temperature or moisture gradients (Dere et al., 2013; Williams et al., 2010). However, as the study catchments are in close proximity and within the same elevation range (Figure  7.1), they receive nearly identical precipitation amounts and have similar air temperature (A.S. Bailey et  al., 2003). Thus it does not seem likely that climate accounts for differences between catchments in the relationship between net Na flux and streamflow. Differences in groundwater composition between the catchments could also account for differences in weathering flux. Catchments with groundwater of lower pH and higher dissolved organic carbon (DOC) would be expected to have greater dissolution rates (Hausrath et  al., 2009). Groundwater chemistry at the HBEF has been shown to vary by soil type, with lower pH and higher DOC concentrations in groundwater in shallow soils associated with

bedrock outcrops (Gannon et  al., 2015; Zimmer et  al., 2013), suggesting that dissolution rates might be expected to be highest in W9 with its distinctly shallower soils. However, W9 shows the lowest net Na export suggesting that limitations in soil depth and mineral surface area may outweigh increases in weathering rate due to acidity and organic acid complexation. 7.4.2. Soil Calcium Depletion Dynamics in net Ca export relative to net Na flux, and in comparison to plagioclase stoichiometry, was a central basis that Likens et al. (1996) used to infer that acid deposition had depleted Ca soil exchange pools at the HBEF. This approach was modified by S.W. Bailey et al. (2003) who recognized that weathering of other minerals, such as hornblende and apatite was likely to contribute to catchment mass balance, and would release more Ca relative to Na than plagioclase. At the time of the S.W. Bailey et al. (2003) analysis, net Ca/Na flux was declining in all study catchments. Since 2011, net Ca/Na ratios have stabilized (Figure  7.3). This may signify that exchangeable soil pools of Ca have reached a new steady state, or have started to rebuild. In order to differentiate between these two possibilities, new studies of primary mineral depletion and contributions of minor minerals such as hornblende and apatite, would have to be undertaken to determine more quantitatively the Ca flux relative to plagioclase dissolution. Watmough et al. (2016). also reported declining net Ca export coupled with steady net Na export. That net Ca/ Na export has stabilized at a level slightly above that suggested by stoichiometric plagioclase dissolution is consistent with plagioclase being the primary weathering source of Ca, but with some contributions from dissolution of higher Ca/Na minerals such as hornblende, and/or non‐ Na bearing Ca minerals such as apatite (S.W. Bailey et al., 2003; Blum et al., 2002). 7.4.3. Ecosystem Silicon Dynamics Net Si/Na flux from the catchments was mostly less than that expected from plagioclase dissolution, consistent with storage of Si in secondary pools. Both formation of clay minerals or their precursors, as well as net storage of biogenic Si (Table 7.2; Ronchi et al., 2013) may contribute to lower Si export than expected based on plagioclase stoichiometry. Export from reference catchment W9 was an exception to this pattern with greater Si export than expected from plagioclase dissolution. Presumably secondary clay and biogenic minerals are accumulating in W9 as well, adding to an unexpectedly high net Si flux in this catchment. Plagioclase accounts for only about a quarter of the Si in primary minerals at the HBEF (Table  7.1), with the

160  BIOGEOCHEMICAL CYCLES

remainder in minerals considered to be stable in weathering environments, such as muscovite, K‐feldspar, and quartz. There has been some controversy as to whether quartz may be subject to enhanced weathering in the presence of higher concentrations of organic acids (Bennett, 1991; Drever & Stillings, 1997), based mostly on laboratory studies or theoretical considerations. Drainage waters from W9 stand out as being more acidic and with much higher DOC concentrations than the other catchments (Wellington & Driscoll, 2004), which is typical of shallow to bedrock soils (Gannon et al., 2015). A distinct Si cycling process in W9 is suggested by the net Si/Na ratio and provides an opportunity to investigate enhanced weathering of quartz and other silicates in a field setting. The treated catchments show a complex response of net Si/Na ratio following disturbance, with an initial reduction in Si export relative to Na for 1 or 2 years, followed by enhanced export for an extended post‐treatment period. In general, the enhanced export is consistent with Conley et  al. (2008) who reported increased dissolved Si loss in streamwater following all three harvesting experiments, with the magnitude of the response varying with the amount of detrital vegetation remaining after cutting. These results coupled with no detectable loss of amorphous Si soil pool, suggest that treatment response may be, at least partially, due to increased loss of biogenic Si from decomposing vegetation following treatment. Derry et al. (2005) showed that the Ge/Si ratio in drainage waters is sensitive to biogenic versus mineral weathering sources of Si export. This tool might be applied to archived samples of streamwater from the HBEF treated catchments to further elucidate mechanisms controlling treatment response in Si export. If stored pools of biogenic Si were contributing to Si export differentially to primary mineral dissolution, the Ge/Si ratio of drainage waters would be expected to reflect this dynamic.

7.4.4. Aluminum and Soil Development Total dissolved Al was not measured routinely at the HBEF until 2012. Since that time, net flux of Al relative to Na is very low compared to plagioclase stoichiometry except for W9, where net Al flux approaches that expected from plagioclase dissolution. Shallow to bedrock soils in W9 are dominated by eluvial processes, with thick E horizons low in spodic materials or Al–organic‐matter complexes (S.W. Bailey et  al., 2014), whereas high concentrations of dissolved Al and dissolved organic carbon are exported downstream (Wellington & Driscoll, 2004). At the other five catchments considered here, net Al/Na flux is about 0.2 compared to a value of 1.5 for stoichiometric plagioclase dissolution. Given a typical value of net Na flux of 300 mol ha−1 year−1, this is equivalent to a net storage of Al of about 60 mol ha−1 year−1. To the extent that this storage of Al is due to spodic Al accumulation, a primary soil forming process in podzols, this flux is related to ongoing accumulation of C in mineral soil horizons. Other sources of Al accumulation include formation of secondary aluminosilicate minerals (Table 7.2). Bourgault et al. (2017) show concentrations of oxalate extractable Al < 1000 mg kg−1 in shallow eluviated soils in the vicinity of bedrock outcrops, rising to 15,000 – 25,000 mg kg−1 in deeper soils. These results suggest an opportunity to further evaluate mineral soil C storage. Quantification of Al stored as spodic organic– metal complexes, and as secondary minerals would provide a tool to evaluate long‐term soil development processes that sequester Al. Comparison of secondary pools with net Al/Na catchment export may provide a tool to evaluate trends in C storage and dynamics as climate continues to change. Although routine monitoring of streamwater Al and calculation of catchment fluxes was not instituted until 2012 (with the exception of W6), well after treatments applied to W2, W4, and W5, a four‐ to sixfold increase in

Table 7.2  Catchment annual fluxes and pools. Fluxes are the range for the six study catchments for 2013–2014 Flux/pool

Ca

Na

Al

Si

Atmospheric deposition (mol ha−1 year−1)a Streamwater flux (mol ha−1 year−1)a Biomass pool (mol ha−1)b Soil exchange pool (mol ha−1)b Other soil pools (mol ha−1)

20–28

79–133

Trace

Trace

98–196

214–424

22–166

514–904

18450 6700 None

Trace Trace None

Trace Substantial Spodic materials, secondary minerals and precursers

579 None Biogenic opal, secondary minerals and precursers

Note: Adapted from Likens et al. (1998).  Flux range calculated for all study catchments for the 2013 water year. b  Ca pools were quantified by Likens et al. (1998); Si biomass pool was quantified by Clymans et al. (in preparation). a

TRACKING THE FATE OF PLAGIOCLASE WEATHERING PRODUCTS  161

total dissolved Al concentration in streamwater following whole‐tree harvesting of W5 was reported by synoptic sampling conducted by Lawrence et  al. (1987). Likens et al. (1970) reported an eightfold increase in streamwater Al export in the 2 years following harvest of W2 (Figure 7.5). Enhanced Al export as an impact of forest harvesting has been found to be most prevalent when > 40–68% of the tree basal area in a catchment is harvested (Siemion et  al., 2011), as is certainly the case with the clearfelling/herbicide treatment at W2 and whole‐tree harvesting treatment at W5. As such harvesting intensities in operational systems are rarely practiced at the catchment scale, the Al response due to the nonsilvicultural treatments at the HBEF may have limited applicability in a management context. 7.5. CONCLUSION Although catchment‐scale mass balance is a commonly used technique to understand the biogeochemistry of elements, this method is typically applied in studies of single elements. Utilizing the stoichiometry of an important ecosystem component, in this case the primary mineral plagioclase, allowed the coupled evaluation of multiple elements at once. Normalizing the biologically and pedogenically active elements Ca, Si, and Al to the relatively conservative Na enhanced the detection of dynamics in ecosystem pools in response to long‐term trends in acid deposition as well as to pulse disturbance events such as harvesting experiments. These results highlight uncertainties in understanding of ecosystem processes, or, in other words, opportunities for new studies to test hypotheses of catchment dynamics suggested by the net ecosystem flux ratio approach. In the last 2–3 years of record at the HBEF, net Ca/Na ratios appear to have stabilized relative to long‐term declines in response to acid deposition and harvest treatment effects. Whether Ca soil exchange pools have reached a new steady state or are rebuilding following decades of depletion depends on the exact ratio of Ca/Na released from mineral weathering processes. More detailed studies of mineral weathering, perhaps aided by native isotopic or minor element tracers may determine the potential for soils to recover and the time frame involved. Similarly, net Si flux relative to Na is highly dynamic, especially in the first few years after vegetation disturbance, a response that is likely mediated by dynamics in biogenic Si pools. The net flux ratio tool also highlights the role of disturbance and pedogenic processes in Al dynamics, suggesting new methods to track rates of soil formation, including subsurface C storage dynamics. Thus, this tool to evaluate catchment input–output dynamics provides a framework for opening the black box with detailed studies of internal element pools and their controlling processes.

REFERENCES Bailey, A.S., Hornbeck, J.W., Campbell, J.L., & Eagar, C. (2003). Hydrometeorological database for Hubbard Brook Experimental Forest: 1955–2000. US Department of Agriculture, Forest Service, Northeastern Research Station. http://www.fs.fed.us/ne/newtown_square/publications/ technical_reports/pdfs/2003/gtrne305.pdf Bailey, S.W., Brousseau, P.A., McGuire, K.J., & Ross, D.S. (2014). Influence of landscape position and transient water table on soil development and carbon distribution in a steep, headwater catchment. Geoderma, 226–227, 279–289. Bailey, S.W., Buso, D.C., & Likens, G.E. (2003). Implications of sodium mass balance for interpreting the calcium cycle of a forested ecosystem. Ecology, 84(2), 471–484. Bailey, S.W., Ross, D.S., Perdrial, N. Jercinovic, M., Webber, J., & Bourgault, R. (2019). Determination of primary mineral content and calcium sources in forest soils using electron probe microanalysis mapping and cluster analysis. Soil Science Society of America Journal. doi:10.2136/sssaj2019.07.0231 Bennett, P.C. (1991). Quartz dissolution in organic‐rich aqueous systems. Geochimica et Cosmochimica Acta, 55, 1781–1797. Blum, J.D., Klaue, A., Nezat, C.A., Driscoll, C.T., Johnson, C.E., Siccama, T.G., & Eagar, C. (2002). Mycorrhizal weathering of apatite as an important calcium source in base‐ poor forest ecosystems. Nature, 417, 729–731. Bourgault, R.R., Ross, D.S., Bailey, S.W., Bullen, T.D., & McGuire, K.J. (2017). Redistribution of soil metals and organic carbon via lateral flowpaths at the catchment scale in a glaciated upland setting. Geoderma, 307, 238–252. Burton, W.C., Walsh, G.J., & Armstrong, T.R. (2000). Bedrock geologic map of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire. Reston, VA: US Geological Survey. Buso, D.C., Likens, G.E., & Eaton, J.S. (2000). Chemistry of precipitation, streamwater, and lakewater from the Hubbard Brook Ecosystem Study: a record of sampling protocols and analytical procedures. General Technical Report No. NE‐275 (p. 52). Newtown Square, PA. Campbell, J.L. (2017a). Hubbard Brook Experimental Forest (US Forest Service): Total daily precipitation by watershed, 1956–present (Database). http://data.hubbardbrook.org/ data/dataset.php?id=14 Campbell, J.L. (2017b). Hubbard Brook Experimental Forest (USDA Forest Service): Daily streamflow by watershed, 1956–present. http://data.hubbardbrook.org/data/dataset. php?id=2 Conley, D.J., Likens, G.E., Buso, D.C., Saccone, L., Bailey, S.W., & Johnson, C.E. (2008). Deforestation causes increased dissolved silicate losses in the Hubbard Brook Experimental Forest. Global Change Biology, 14(11), 2548–2554. Dere, A.L., White, T.S., April, R.H., Reynolds, B., Miller, T.E., Knapp, E.P., et  al. (2013). Climate dependence of feldspar weathering in shale soils along a latitudinal gradient. Geochimica et Cosmochimica Acta, 122, 101–126. https://doi. org/10.1016/j.gca.2013.08.001 Derry, L.A., Kurtz, A.C., Ziegler, K., & Chadwick, O.A. (2005). Biological control of terrestrial silica cycling and export fluxes to watersheds. Nature, 433(7027), 728–731.

162  BIOGEOCHEMICAL CYCLES Drever, J.I., & Stillings, L.L. (1997). The role of organic acids in mineral weathering. Colloids and Surfaces, 120, 167–181. Driscoll, C.T., Lawrence, G.B., Bulger, A.J., Butler, T.J., Cronan, C.S., Eagar, C., et al. (2001). Acidic deposition in the northeastern United States: sources and inputs, ecosystem effects, and management strategies. BioScience, 51(3), 180–198. Gannon, J.P., Bailey, S.W., McGuire, K.J., & Shanley, J.B. (2015). Flushing of distal hillslopes as an alternative source of stream dissolved organic carbon in a headwater catchment. Water Resources Research, 51(10), 8114–8128. https://doi. org/10.1002/2015WR016927 Hausrath, E.M., Neaman, A., & Brantley, S.L. (2009). Elemental release rates from dissolving basalt and granite with and without organic ligands. American Journal of Science, 309(8), 633–660. Hyman, M.E., Johnson, C.E., Bailey, S.W., Hornbeck, J.W., & April, R.H. (1998). Chemical weathering and cation loss in a base‐poor watershed. Geological Society of America Bulletin, 110(1), 85–95. Johnson, N., Likens, G.E., Bormann, F.H., & Pierce, R.S. (1968). Rate of chemical weathering of silicate minerals in New Hampshire. Geochimica et Cosmochimica Acta, 32, 531–545. Johnson, N., Reynolds, R.C., & Likens, G.E. (1972). Atmospheric sulfur: its effect on the chemical weathering of New England. Science, 177, 514–516. Kronzucker, H.J., & Britto, D.T. (2011). Sodium transport in plants: a critical review: Tansley review. New Phytologist, 189(1),54–81.https://doi.org/10.1111/j.1469‐8137.2010.03540.x Lawrence, G.B., Fuller, R.D., & Driscoll, C.T. (1987). Release of aluminum following whole‐tree harvesting at the Hubbard Brook Experimental Forest, New Hampshire. Journal of Environment Quality, 16(4), 383–390. Likens, G.E. (2017). Fifty years of continuous precipitation and stream chemistry data from the Hubbard Brook ecosystem study (1963–2013). Ecology, 98(8), 2224. Likens, G.E., & Bailey, S.W. (2014). The Discovery of Acid Rain at the Hubbard Brook Experimental Forest: A Story of Collaboration and Long‐term Research. In USDA Forest Service Experimental Forests and Ranges (pp. 463–482). New York: Springer‐Verlag. Likens, G.E., Bormann, F.H., Johnson, N.M., Fisher, D.W., & Pierce, R.S. (1970). Effects of forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook watershed‐ecyosystem. Ecological Monographs, 40(1), 23–47. Likens, G.E., Bormann, F.H., Pierce, R.S., Eaton, J.S., & Johnson, N. (1977). Biogeochemistry of a forested ecosystem. New York: Springer‐Verlag. Likens, G.E., Driscoll, C.T., & Buso, D.C. (1996). Long‐term effects of acid rain: Response and recovery of a forest ecosystem. Science, 172, 244–246. Likens, G.E., Driscoll, C.T., Buso, D.C., Siccama, T.G., Johnson, C.E., Lovett, G.M., et al. (1998). The biogeochemistry of calcium at Hubbard Brook. Biogeochemistry, 41(2), 89–173.

Long, R.P., Horsley, S.B., Hallett, R.A., & Bailey, S.W. (2009). Sugar maple growth in relation to nutrition and stress in the northeastern United States. Ecological Applications, 19(6), 1454–1466. Lundström, U.S., van Breemen, N., & Bain, D. (2000). The podzolization process. A review. Geoderma, 94(2), 91–107. McLaughlin, S.B., & Wimmer, R. (1999). Tansley Review No. 104. Calcium physiology and terrestrial ecosystem processes. New Phytologist, 142(3), 373–417. Price, J.R., Peresolak, K., Brice, R.L., & Tefend, K.S. (2013). Temporal variability in the chemical weathering of Ca2+‐ bearing phases in the Loch Vale watershed, Colorado, USA: a mass‐balance approach. Chemical Geology, 342, 151–166. Price, J. R., Rice, K.C., & Szymanski, D.W. (2013). Mass‐ balance modeling of mineral weathering rates and CO2 consumption in the forested metabasaltic Hauver Branch watersed, Catoctin Mountain, Maryland, USA. Earth Surface Processes and Landforms, 38, 859–875. Ronchi, B., Clymans, W., Barao, A.L.P., Vandevenne, F., Struyf, E., Batelaan, O., et al. (2013). Transport of dissolved Si from soil to river: a conceptual mechanistic model. Silicon, 5, 115–133. Siemion, J., Burns, D.A., Murdoch, P.S., & Germain, R.H. (2011). The relation of harvesting intensity to changes in soil, soil water, and stream chemistry in a northern hardwood forest, Catskill Mountains, USA. Forest Ecology and Management, 261(9), 1510–1519. https://doi.org/10.1016/j. foreco.2011.01.036 Stutter, M., Smart, R., & Cresser, M.S. (2002). Calibration of the sodium base cation dominance index of weathering for the River Dee catchment in north‐east Scotland. Applied Geochemistry, 17, 11–19. Velbel, M.A., & Price, J.R. (2007). Solute geochemical mass‐ balances and mineral weathering rates in small watersheds: Methodology, recent advances, and future directions. Applied Geochemistry, 22(8), 1682–1700. Watmough, S.A., Eimers, C., & Baker, S. (2016). Impediments to recovery from acid deposition. Atmospheric Environment, 146, 15–27. Wellington, B.I., & Driscoll, C.T. (2004). The episodic acidification of a stream with elevated concentrations of dissolved organic carbon. Hydrological Processes, 18(14), 2663–2680. https://doi.org/10.1002/hyp.5574 Williams, J.Z., Bandstra, J.Z., Pollard, D., & Brantley, S.L. (2010). The temperature dependence of feldspar dissolution determined using a coupled weathering–climate model for Holocene‐aged loess soils. Geoderma, 156(1–2), 11–19. https://doi.org/10.1016/j.geoderma.2009.12.029 Winter, T.C. (1981). Uncertainties in estimating the water balance of lakes. Journal of the American Water Resources Association, 17(1), 82–115. Zimmer, M.A., Bailey, S.W., McGuire, K.J., & Bullen, T.D. (2013). Fine scale variations of surface water chemistry in an ephemeral to perennial drainage network. Hydrological Processes, 27, 3438–3451.

8 Small Catchment Scale Molybdenum Isotope Balance and its Implications for Global Molybdenum Isotope Cycling Thomas Nägler1, Marie‐Claire Pierret2, Andrea Voegelin1, Thomas Pettke1, Lucas Aschwanden1, and Igor Villa1,3 ABSTRACT The mass balance of molybdenum (Mo) was studied in the Strengbach catchment. Monitoring of rainfall, vegetation, and soil characteristics in this 0.8 km2 catchment was started decades ago. We present Mo concentrations and isotope compositions of about 60 samples including bedrock types, perennial springs, soil profiles, roots and leaves, and the outflowing brook. Both streamwaters and bedrock have Mo concentrations at least one order of magnitude lower than global averages. The Mo isotope composition of topsoils, foliage, litter, and roots is rather homogeneous. Net biological fractionation is thus subordinate to differences in the Mo sources. Efficient Mo recycling from organic litter to plants keeps Mo bioavailable. The Mo and Sr isotope data are used to identify the source(s) of Mo and Sr and their (transient) storage within the catchment. The resulting best model identifies rock weathering and seawater‐derived aerosol as the principal Mo sources. Moreover, soil in the Strengbach catchment has reached steady state for Mo (the time constant to achieve soil steady state is calculated to be in the order of 50 years) where the Mo isotope compositions of fluxes to and from the catchments soil are identical.

8.1. INTRODUCTION Molybdenum (Mo) is the most abundant transition metal in the ocean and is essential for nitrogen metabolism of organisms (Dellwig et al., 2007; Stiefel, 2002). In oxic ocean water, the predominant Mo species is soluble molybdate oxyanion, MoO42−. Its low chemical reactivity results in a comparatively long oceanic residence time of several hundred thousand years (Colodner et  al., 1995; Emerson & Huested, 1991; Miller et al., 2011). This rendered Mo isotope systematics increasingly important in reconstructing the oceanic and atmospheric redox history  Institute of Geological Sciences, University of Bern, Bern, Switzerland 2  Laboratory of Hydrology and Geochemistry of Strasbourg, EOST, Strasbourg University, CNRS, Strasbourg, France 3  University Center for Dating and Archaeometry, University of Milan Bicocca, Milano, Italy 1

(Arnold et al., 2004; Baldwin et al., 2013, Czaja et al., 2012; Dickson et  al., 2017; Duan et  al., 2010; Eroglu et  al., 2015;  Kendall et  al., 2009, 2010, 2011; Kurzweil et  al., 2016; Pearce et al., 2008; Pearce, Coe, et al., 2010; Siebert et al., 2003; Voegelin et al., 2010; Wille et al., 2007, 2008). The main Mo input to the ocean is continental runoff (McManus et al., 2002, 2006; Morford & Emerson, 1999). Early studies (Barling et  al., 2001; Siebert et  al., 2003) assumed that continental runoff had a roughly constant long‐term Mo isotope composition of δ98/95Mo ≈ 0, based on the initially available Mo isotope composition of molybdenites and crustal igneous rocks as the principal continental Mo sources. (NB: Mo isotope data are reported as permil deviation from NIST SRM 3134 defined to have δ98/95Mo = 0.25‰ (Nägler et  al., 2014); cited data are renormalized where necessary). This assumption was challenged by Archer and Vance (2008), who reported that river waters have variable Mo concentrations of between 2 and 511 nmol and variable

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

163

164  BIOGEOCHEMICAL CYCLES

δ98/95Mo between 0.2 and 2.3‰, averaging 0.7‰. Subsequent papers confirmed the large spread in Mo concentrations and Mo isotope composition in rivers (Pearce, Burton, et al., 2010; Neubert et al., 2011; Voegelin et al., 2012). One argument made by Archer and Vance (2008) was that the Mo isotopic heterogeneity of river water was due to pedogenesis, whereby isotopically light Mo was hypothesized to be permanently trapped in secondary phases in soils, an argument that Pearce, Burton, et  al. (2010) and Wang et  al. (2015) embraced. In contrast, Neubert et  al. (2011) documented an overlap between river water Mo isotope composition and the respective bedrocks of the catchments they studied. They concluded that Mo isotope fractionation during weathering and pedogenesis was a minor effect. Even if light Mo isotopes initially are preferentially retained in soil, erosion will eventually liberate them again. Once steady state between soil formation and soil erosion is attained, the net isotope fractionation is zero. Consequently a bias of the runoff relative to the bedrock in a catchment can only be maintained on time scales shorter than the establishment of a steady‐state soil thickness, and thus cannot be considered as a long‐term geological process. Voegelin et al. (2012) investigated a small catchment in the Massif Central (France) underlain exclusively by igneous crustal rocks and documented that incongruent weathering was a more efficient cause of Mo isotope fractionation than soil formation. Laboratory leaching of bedrock basalt yielded a leach solution enriched in heavy Mo isotopes to the same extent as the natural river waters. These authors proposed that the source of isotopically heavy Mo was magmatic sulfides, which have Mo concentrations up to 250 μg g−1. As sulfides are much more soluble than magmatic silicates, they are expected to vastly dominate the dissolved Mo river load, whereas silicates remained mostly in the particulate load. Along‐stream profiles (Archer & Vance, 2008; Pearce, Burton, et al., 2010; Neubert et al., 2011) show that the Mo isotope composition of river waters is mainly established in the source area. Intrafluvial processes, such as precipitation of secondary minerals in the river bed and/or in lakes (Villa et  al., 2017) or scavenging by suspended matter (Wang et  al., 2015), appear to be of subordinate importance in most rivers. Pearce, Burton, et al. (2010), Marks et al. (2015), and King et  al. (2016) have investigated atmospheric Mo input. Pearce, Burton, et al. (2010) observed the heaviest fluvial δ98/95Mo of their study area, 1.8‰, in a river supplied by glacial meltwater. This heavy Mo isotope composition was interpreted as deriving from sea‐spray advection to the glacier (either by direct aerosol transport or by incorporation into rain/snow clouds). Seawater has a heavy δ98/95Mo of 2.3‰ and thus is a plausible source of

isotopically heavy Mo. Marks et  al. (2015) studied a forest ecosystem and observed that atmospheric Mo influx supplied one order of magnitude more Mo than litterfall. There is strong evidence that the binding of molybdenum to organic matter reduces the loss of micronutrient Mo from soils (Wang et al., 2015; Wichard et al., 2009). Marks et  al. (2015) showed that Mo bound to organic matter (OM) accounts for an average of one‐ third of the bulk Mo of soils in their study area. Analyses of the speciation of molybdenum in Arizona and New Jersey forest soils (Wichard et al., 2009) indicate that Mo forms strong complexes with plant‐derived tannins and tannin‐like compounds. Anthropogenic aerosols are a potential additional source of Mo of variable isotope composition in heavily industrialized areas (Chappaz et al., 2012; Lane et al., 2013). Of special relevance for the present study is the characterization of 87 Sr/86Sr isotope ratio in bedrock, spring‐ and river water. Since 87Sr is the radiogenic daughter of 87Rb, whereas 86Sr is nonradiogenic, the 87Sr/86Sr ratio depends on the age of a rock and on its parent/daughter (Rb/Sr) element ratio, and is thus highly variable amongst terrestrial rocks. Probst et al. (2000) used Sr isotope compositions to model the relative contributions of weathering and atmospheric dust influx to the Ca mass balance of the Strengbach catchment studied here. They attributed net losses of Sr from the catchment to exchange and weathering processes in soils and saprolite. Consequently this catchment was not in a steady state at least for Ca and Sr at the sampling period of the respective waters (late 1980s to early 1990s.). The neutralization of acid atmospheric inputs led to strong depletion of the exchangeable pool of cations in the soils and accounts for the base cation losses from the catchment (2.8 keq ha−1 year−1; Probst, Fritz, et al., 1992; Probst, Viville, et al., 1992). In order to assess the relative importance of these processes, we selected a well‐studied catchment, remote from direct industrial or agricultural activities so as to minimize anthropogenic sources of Mo. The mass balance of river waters and of the surface processes within the catchment has been documented over several years of continuous monitoring. Extrapolating this knowledge of the catchment to the Mo mass balance endeavors to quantify the Mo budget of bedrock erosion, atmospheric input, and pedogenesis, both inorganic and biologically controlled. Within our study on the Mo sources and budgets of the entire catchment, we also address internal Mo cycling. 8.1.1. The Strengbach Catchment It is self‐evident that large rivers average the chemical composition of the sum of their tributaries, hence they smooth out potential scatter of the Mo isotopic composition. This is the opposite of what is required for a

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  165

fine‐scale understanding of surface processes. We therefore chose the comparatively small (0.8 km2) Strengbach catchment in the Vosges Mountains (France). This catchment has been studied and regularly monitored during 32 years by the Observatoire Hydrogéochimique de l’Environnement (OHGE; http://ohge.unistra.fr). It is located at altitudes between 880 and 1150 m a.s.l. Forest covers 90% of the area and is composed of about 80% spruce (mainly Picea abies L.) and 20% beech (Fagus sylvatica). Eleven perennial springs (Figure  8.1) feed the Strengbach streamlet (Pierret et al., 2014). Bedrock lithology of the catchment is rather homogeneous and consists only of crystalline silicate rocks: sillimanite gneiss, cordierite‐bearing granite, and aplitic microgranite, including a dated, alkali‐feldspar‐rich Hercynian granite (Boutin et al., 1995). Hydrothermal alteration (180 Ma; Fichter et al., 1998) transforming K‐feldspar and muscovite into illite/sericite and quartz and almost completely consuming biotite and albite (El Gh’mari, 1995) is strongest for the granite from the northern slope. A sandy saprolite separates soil and granite. Its thickness varies between 1 and 9 m. On the rainier southern

Gneiss

slope it is generally thicker (El Gh’mari, 1995), and is thickest in a depression zone near the four springs labeled CS in Figure 8.1. The overlying soil system is up to 1 m thick. Acidity of the soils results both from natural (podsolization) and anthropogenic causes. Soil solutions close to the surface (0–5 cm) are also acidic (pH 4.0; Prunier et  al., 2016). In deeper soils the acidity is progressively neutralized by mineral dissolution and H+‐cation exchanges. Annual precipitation was 1442 mm and annual runoff was 723 mm year–1, or 22.8 L s−1 km−2 for the study period of 2 years (OHGE data). From the early 1980s symptoms of forest decline have been recognized. Spruce yellowing has been related to magnesium deficiencies (Landmann et  al., 1987) and early studies addressed the impact of acid rain on the forested ecosystem (Dambrine et  al., 1991; Dambrine, Carisey, et al., 1992; Dambrine, Pollier, et al., 1992; Probst et  al., 1990; Probst, Viville, et  al., 1992; Probst, Fritz, et  al., 1992). Since 1986, annual sulfur deposition has decreased from 2 t of S in 1986 to 260 kg in 2015 at the Strengbach catchment (Pierret et al., 2019), corresponding to a drop of 87%. For this period, average annual pH

1107

Hydrothermally altered granite

Granite Microgranite

France

N

0

200m

1142

1146

Experimental plot sites: vegetation, soil profiles

Figure 8.1  Location and geological overview of the Strengbach catchment. Triangles represent springs and stream water locations. Inset: sketch map of France with the position of the Strengbach catchment.

166  BIOGEOCHEMICAL CYCLES

levels changed from 4.4 to 5.1 for rain and from 6.1 to 6.5 for the stream at the catchment outlet. Similarly, average annual sulfate concentrations decreased from 0.028 to 0.007 and from 0.102 to 0.040 mmol L‐1 for rain and stream at the outlet respectively (Pierret et al., 2018). In line, the average pH values of Strengbach rain and stream waters were 5.1 and 6.5, respectively, for the sampling period of the present study (2009–2010). 8.1.2. Sample Description and Analytical Techniques About 60 samples were collected in the Strengbach catchment. These include samples from the trunk stream and different springs in the catchment, bedrocks, soil profiles, and organic samples (see Figures 8.1–8.3). The springs SG, ARG, RH, BH, CS3, and CS4 are located on the northern slope and the springs CS1, CS2, SH, and RUZS emerge at the southern slope (Figure 8.1); spring RUZS is situated in the humid zone at the bottom of the catchment near the outlet (saturated area, Figure 8.1) and covered by dense grass vegetation; RS corresponds to the Strengbach stream at the outlet of the catchment and represents the sum of all measured spring waters; RAZS is a sampling site located along the Strengbach upstream of the humid zone.

The soil profile sampled at the spruce plot is located at 1070 m altitude on the northern slope (Figures  8.1 and 8.4) and belongs to the Alocrisol type (brown acidic soil). Acid mull/moder humus of about 2 cm is above the Ah dark brown horizon (0–5 cm depth) that contains a very large number of fine roots and polyhedric and microcrumbly structure, and represents a sandy clay loam soil. The AE horizon (5–1 cm depth) is brown/yellow sandy clay loam soil, with many roots and about 10% gravel. The B horizon (11–60 cm depth) is a grey‐reddish‐brown, tight and compact soil with a sandy clay loam texture with 10–20% gravel and many roots extending to about 50 cm depth. The C horizon (60–125 cm) is a grey‐red soil with much higher gravel content and fewer roots. The soil profile sampled at the beech plot located at 1060 m altitude on the southern slope belongs to the ochreous podzol type (ochreous brown podzolic soil). The moder humus is about 34 cm thick. The Ah blackish brown horizon (0–10 cm) is very porous, contains a very large number of fine roots, has a microcrumbly structure and is classified as a sandy clay loam soil. The AE horizon (10–25 cm depth) is beige‐grey sandy clay loam soil, poorly compact, very porous, and with many roots. The B/BPs horizon (25–55 cm depth) is a yellowish

Waters

Springs and streams Gneiss

1107

Hydrothermally altered granite

δ98Mo

δ98Mo = 1.1 ± 0.1‰

= 1.3 ± 0.1‰

[Mo] = 0.16 nmol L–1

[Mo] = 0.05 – 0.1 nmol L–1

δ98Mo = 0.6 ± 0.1‰ [Mo] = 0.19 nmol L–1

Granite

Microgranite

δ98Mo = 0.8 ± 0.1‰ [Mo] = 0.08 nmol L–1

δ98Mo = 1.0 ± 0.2‰

[Mo] = 0.009 nmol L–1

δ98Mo = 1.1 ± 0.1‰ [Mo] = 0.05 nmol L–1 0

200m

1142

1146 Figure 8.2  Locations of sampled waters as well as δ98/95Mo and Mo concentration data (full data in Table 8.1).

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  167

orange compact and friable soil, with polyedric structure, a loamy sand texture, with 25% gravels and many roots. The C horizon (55140 cm) is a light yellowish‐ orange, compact and friable soil with a loamy sand texture, particle structure, fewer roots, and up to 50% of gravel. Organic material was sampled from both soil profile locations, including leaf litter, roots, and spruce needles, to obtain an overview of potential Mo isotope fractionation in the biological cycle. Moreover nine rock samples (six fresh and three weathered rocks or rims) were collected at different sites of the Strengbach catchment in order to have a representative sampling of the different granite types. 8.1.3. Methods 8.1.3.1. Sampling and Sample Preparation Water samples for Mo isotope analyses were filtered through a 0.45 μm Nalgene® membrane filter and collected in low‐density polyethylene bottles and acidified immediately to pH < 2 with distilled nitric acid. Water samples for elemental analyses were collected in high‐

density polyethylene bottles and filtered the same day through a 0.45 μm pore diameter membrane. Rock samples were cleaned and subsequently crushed in a hydraulic press. Bulk rock samples were treated with concentrated HCl and H2O2 and HF + HNO3 dissolution steps to ensure complete digestion of the silicate matrix (Siebert et al., 2001; Wille et al., 2007). Soil material was dried at 60°C and subsequently sieved below 2 mm to separate it from gravel and root material. All the data on soil were obtained on the fraction < 2 mm. Roots were cleaned in an ultrasonic bath and rinsed repeatedly with high purity H2O to remove any residual soil particles. Soil and root samples were digested using multiple HNO3 and HF steps closely following the procedure described in Cenki‐Tok et al. (2009). 8.1.3.2. Chemical Mo Purification (all Samples) Mo was purified using the separation procedure described in Siebert et al. (2001) and Wille et al. (2007), modified for low concentration samples (see also Voegelin et al., 2009). Only an outline is given here: a 100Mo–97Mo

Rocks δ98Mo = 0.5‰

Gneiss

[Mo] = 0.2 ppm

Hydrothermally altered granite

δ98Mo = 0.1–0.2‰ [Mo] = 0.2 ppm

Granite

Microgranite

δ98Mo = 0.4‰ [Mo] = 0.2 ppm

δ98Mo = 0.2–0.3‰

N

[Mo] = 0.1 ppm

0

200m

1142

1146 Figure 8.3  Locations of rock samples as well as whole‐rock δ98/95 Mo and Mo concentration data (full data in Table 8.5).

168  BIOGEOCHEMICAL CYCLES

double spike was added to the sample prior to chemical purification. H2O2 was added to keep Mo in its highly soluble Mo6+ state. Dried sample solutions were redissolved in 4 M HCl and passed through an anion exchange column (1 mL Dowex™ 1X8 resin). Mo was eluted with 6 mL 2 M HNO3. A subsequent cation exchange column (Dowex 50WX8 resin) was additionally used depending on the sample matrix. Procedural Mo blanks (< 1 ng) are small compared to the typical total amount of sample Mo processed (mostly > 60 ng, minimum 20 ng). The dried and purified Mo fraction was redissolved in 0.5 M HNO3 for measurement using a Nu Instruments™ multiple collector ICP‐MS. The protocol followed Siebert et al. (2001) except for use of an Apex Q desolvating nebulizer for sample introduction. Mo isotope data are reported as permil deviation from a reference material, whereby the NIST SRM 3134 is defined to have δ98/95Mo = 0.25‰ (Nägler et al., 2014). The long‐term external reproducibility of SRM 3134 and in‐house reference solution is better than 0.1‰ (two standard deviations; Greber et al., 2012; Neubert et al., 2011; Siebert et  al., 2001; Voegelin et  al., 2009). This uncertainty was used for all water results in the figures unless limits on individual measurements were larger. Table 8.1 lists δ98/95Mo of the water samples with 2σ errors of the measurements. Replicates of higher concentration samples indicate a better reproducibility for the measurement period. Eluted matrix solutions from the Mo anion column separation were evaporated and Sr was separated on miniaturized SrSpec™ columns. Sr isotope compositions were measured at the University of Bern on a Thermo Fisher TRITON thermal ionization mass spectrometer. The external reproducibility of the NIST SRM 987 standard was 0.710235 ± 0.000029 (2σ; v = 6) during the 2 day measuring period of the present analyses. 8.1.3.3. Element Analyses of Waters Analytical procedures are described in Pierret et  al. (2014), so only a brief description is given here. Major element concentrations were determined by ion chromatography and ICP‐AES and the trace element concentrations were determined by ICP‐MS. The analytical uncertainty is ±2% for the major elements and ±5% for the trace element concentrations. The DOC was determined using an organic carbon analyser with an uncertainty of 5–10 %. The uncertainty of the pH measurement was ±0.02 units. Complementary water samples were analyzed for major ions Na+, K+, Ca2+, Mg2+, F−, Cl−, NO3−, and SO42− in Bern using a Metrohm™ 861 Advanced Compact Ion Chromatograph. Soil parameters (cation exchange capacity (CEC), pH, OM, Table 8.2) were determined in the national soil analysis service from INRA (French National Institute for

Agricultural Research/LAS Laboratoire d’Analyse des Sols). Soil pH was measured in a solution of 10 g of dried soils in 50 mL of deionized water (NF ISO 10390). Mn or Fe concentrations (Table 8.2a) correspond to the chemical concentration of whole soils after calcination and fusion with lithium‐tetraborate in the Laboratoire d’ Hydrologie et de Géochimie de Strasbourg (LHYGES). Exchangeable cation concentrations (Table  8.2) have been determined after shaking 2.5 g of soil with 100 mL of acetate ammonium solution at 1 mol L−1. The filtered solution is then measured and concentration determined using ICP‐ AES (AFNOR NF X 31‐108) in LAS of INRA at Arras (Ciesielski et al., 1997). 8.1.3.4. Laser Ablation ICP‐MS Analyses of Bedrock Minerals LA‐ICP‐MS analyses were performed at the University of Bern on minerals of one selected granite using a GeoLas‐Pro 193 nm ArF excimer laser system in combination with a Perkin Elmer Elan DRC‐e quadrupole mass spectrometer. Instrumental conditions were similar to those reported in Pettke et al. (2012). Bracketing standardization with NIST SRM 610 was used for instrument sensitivity calibration and drift correction. Internal standardization was carried out by summing major element oxides to 100 wt% minus water. Data quantification used the SILLS software package (Guillong et al., 2008). 8.2. RESULTS AND DISCUSSION AT THE PLOT SCALE 8.2.1. Soil Profiles and Organic Matter Mo concentrations are highest in the topsoil. For both soil profiles, under spruce or beech, the δ98/95Mo tends to decrease with increasing depth (see Figure 8.4; Table 8.2) except for the highest value (HP at 75 cm depth) δ98/95Mo correlates positively with CEC, OM, and C content (Figure  8.5). Figure  8.6 also indicates a correlation of δ98/95Mo with exchangeable Ca. Samples from intermediate depth can be interpreted as a mixture between organic matter and weathered bedrock, as C and OM contents decrease with depth. The positive correlation of δ98/95Mo with total OM supports this hypothesis. In a first systematic study of Mo isotope fractionation in soils covering a wide range in climate and degree of weathering, Siebert et  al. (2015) found both light and heavy δ98/95Mo from ‐0.41‰ to +1.5‰ in bulk soil samples. Soils consistently showed lighter Mo isotope ratios associated with net loss of Mo. These authors reported a positive correlation of Mo gains and C contents of soils, supporting the importance of Mo retention via interactions of Mo with organic matter (see also McManus

Table 8.1  Mo and Sr isotope data and other parameters of the water samples Sample

Date

Type

RS (H09) RS (H10) RAZS (H10) RUZS (H09) CS (H10) CS3 (H09) RH (F10) RH3 (H09) RH3 (F10) BH (H09) BH (F10) Open field precipitation

October 2009 October 2010 October 2010 October 2009 October 2010 October 2009 May 2010 October 2009 May 2010 October 2009 May 2010 September 2017

Outlet Outlet Stream Spring Spring Spring Spring Spring Spring Spring Spring Rain

Cover

δ98/95Moa (‰) 2σ

Mo (ppb) Mo (nmol L–1)

87

2σb

Beech Beech Spruce Spruce Spruce Spruce Spruce Spruce

– 1.14 1.13 0.80 1.00 – 1.13 1.42 1.37 0.69 0.46

0.011 0.005 0.005 0.007 0.001 0.005 0.003 0.016 0.003 0.028 0.009 0.001

0.724434 0.724775 0.725372 0.726051 0.725705 0.723134

0.000064 6.80 0.049 0.000053 0.051c 0.000057 0.048 0.000045 6.76 0.026 0.000042 0.039 0.000058 6.35 0.055

0.721546 0.723308 0.722282 0.722234 0.713419

0.000047 6.35 0.053 0.000010 0.052c 0.000060 6.97 0.045 0.000014 0.000014

– 0.08 0.09 0.13 0.14 – 0.08 0.10 0.07 0.06 0.02

0.12 0.05 0.05 0.08 0.01 0.05 0.04 0.16 0.03 0.29 0.10

Sr/86Sr

pH

Cl− (mmol L–1) SO42− (mmol L–1) 0.054 0.059c 0.063 0.045 0.055 0.058 0.064d 0.070 0.072c 0.045

a 98/95  δ Mo data are reported as deviation in ‰ from a reference material, whereby the NIST SRM 3134 is defined to have δ98/95Mo = 0.25‰, following Nägler et al. (2014). b  2σ error of the mean of the measurement. The long term external reproducibility of standard measurements is better than 0.1‰ (2 SD). c  Anions measured in Bern, all other anions measured in Strasbourg. d  Interpolation from long‐term trend.

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Table 8.2 (a) Beech experimental plot: Mo isotope data and other parameters of the soil profile Depth (cm)

Mean (cm)

Humus 0–5 5–8 5–10 8–13 10–20 13–20 20–25 25–30 30–35 30–40 43–48 50–60 60–65 70–75 70–80 75–80 80–85 85–90 90–100 105–115 110–120 115–130 130–145

0.0 2.5 6.5 7.5 10.5 15.0 16.5 22.5 27.5 32.5 35.0 45.5 55.0 62.5 72.5 75.0 77.5 82.5 87.5 95.0 110.0 115.0 122.5 137.5

0004534003.INDD 170

OM (%)

pH

Ctot (g 100 g–1)

CEC (cmol kg–1)

19.7 16.1

3.14 3.14

10.4 7.84

24.3 19.9

10.0

3.18

4.58

14.8

7.72 8.36 7.88 6.83

3.24 3.31 3.40 3.48

3.40 3.37 2.91 2.56

16.1 16.3 16 14.5

6.86 6.55 6.22 4.86

3.58 3.65 3.68 3.72

2.65 2.29 2.03 1.93

15.1 14 12.5 10.4

5.40 6.81 5.39 4.60 2.64

3.75 3.78 3.80 3.84 3.83

1.94 2.09 1.79 1.41 0.60

12.8 12.4 12.1 9.35 6.0

1.82 2.05

4.17 4.18

0.31 0.33

3.87 4.52

δ98/95Moa (‰)

2σb

Moc (ppm)

0.42 0.54

0.08 0.05

1.66 0.82

0.39

0.05

0.37

0.42

0.07

0.33

0.37

0.05

0.27

0.35

0.05

0.27

0.74

0.06

0.48

0.30

0.04

0.37

0.26

0.05

0.45

Caex (g kg –1)

Fe (g 100 g–1)

Mn (g 100 g–1)

0.279 0.159

0.342 0.387

0.547 0.260

0.0555

0.942

0.108

0.0276 0.0188 0.0164 0.0113

1.74 2.29 1.84 1.38

0.067 0.078 0.145 0.179

0.0118 0.00848 0.00842 0.00692

0.979 0.548 0.208 0.245

0.232 0.143 0.099 0.065

0.00658 0.00688 0.00748 0.00716 0.00574

0.289 0.531 0.348 0.243 0.109

0.053 0.031 0.027 0.019 0.018

0.0121 0.0232

0.109 0.088

0.242 0.240

2/8/2020 1:48:45 PM

(b) Spruce experimental plot: Mo isotope data and other parameters of the soil profile Depth (cm)

Mean (cm)

OM (%)

Ctot (g 100 g–1)

CEC (cmol kg–1)

0.0 0.0 3–13 13–16 16–21 Rep 21–30 30–40 Rep 40–45 45–50 Rep 50–56 56–61 61–67 61–67 70–75 75–80 81–90 90–96 96–105 115–130 130–140

0.0 0.0 8.0 14.5 18.5

9.86 28.4 19.8

3.30 3.21 3.57

4.66 14.2 9.51 3.24 1.77

17 33.7 25.5 14.6 12.7

5.20

25.5 35.0

5.00 4.52

3.73 3.86

1.62 1.30

13.5 13.5

42.5 47.5

3.99 3.90

3.92 3.94

1.06 0.91

12.3 11.4

53 58.5 64 64 72.5 77.5 84.5 93 100.5 122.5 135

3.95 3.90 3.08 3.08 3.12 3.31 3.07 2.90 2.96

3.92 3.92 3.92 3.92 4.14 4.14 4.16 4.15 4.14

0.74 0.82 0.66 0.66 0.46 0.53 0.49 0.44 0.43 0.51 0.49

11.5 11.5 10.4

pH

10.8 9.65 10.2 9.75 9.96 8.46

δ98/95Moa (‰)

2σb

Moc (ppm)

0.50 0.38 0.18 0.20 0.28 0.18 0.25 0.28 0.37 0.33 0.27 0.24 0.25

0.08 0.05 0.08 0.04 0.04 0.05 0.06 0.05 0.04 0.06 0.04 0.05 0.06

0.7 0.45 0.6 0.6 0.41 0.42 0.42 0.42 0.41 0.41 0.4 0.4 0.41

0.34 0.26 0.28 0.32 0.25 0.15

0.04 0.05 0.04 0.04 0.05 0.05

0.42 0.41 0.42 0.39 0.45 0.44

Caex (g kg–1)

Mnexd (ppm)

Alexd (ppm)

Feexd (ppm)

0.0535 0.194 0.127 0.02 < 0.02

23.6 71.6 53.6 13.2 43.3

1890 1790 1770 1860 2040

3400 2430 2440 3450 4930

< 0.02 < 0.02

73.1 39.5

2570 2840

5050 5080

< 0.02 < 0.02

31.0 29.7

2950 3110

4790 4980

< 0.02 < 0.02 < 0.02

22.0 70.1 17.4

3040 2630 2720

4610 4100 4060

< 0.02 < 0.02 < 0.02 < 0.02 < 0.02

13.2 11.8 13.9 12.1 13.7

2740 2880 2950 2600 2960

3900 4180 4020 3460 4160

< 0.02

9.66

2920

2210

OM, organic matter; Ctot, total Carbon (no significant carbonate C present); CEC, cation‐exchange capacity; Caex, exchangeable Ca (see methods for details); Rep, second measurement. a 98/95  δ Mo data reported as ‰ deviation relative to NIST SRM 3134 = 0.25‰ (Nägler et al., 2014). b  2σ error of the measurement; c  Mo measurements are made on equivalent but not identical samples. d  Xex, exchangeable cations (see methods for details)

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172  BIOGEOCHEMICAL CYCLES δ98/95Mo/‰

Humus 0

Ah

0.1

0.2

0.3

0.4

0.5

0.6 0.1

0.2

0.3

0.4

0.5

0.6

Humus Ah

AE

Depth/cm

B

20

AE

40

B/BPs

60

75 cm 0.74 ‰

80

C

100

C 120

(a)

(b)

140 Bedrock

Bedrock

Figure 8.4  δ98/95Mo variations in two soil profiles investigated: (a) δ98/95Mο in the soil profile at the spruce plot; (b) δ98/95Mο in the soil profile at the beech plot. Deep soil samples fall within the range of fresh bedrock granites. Topsoils have δ98/95Mο values in the range of organic material (see below), but lower than δ98/95Mο values of surface waters.

et al., 2006; Wichard et al., 2009). Based on soils formed on Hawaiian basalts, King et  al. (2016) suggested that Mo increases result from precipitation, volcanic fog, and potentially anthropogenic Mo and that the retention of Mo depends strongly on binding Mo to organic matter. Topsoil samples of the Strengbach catchment yield δ98/95Mo in the range of Strengbach biological litter (spruce needles [Mo] = 0.02 ppm δ98/95Mo 0.5 ± 0.1‰; beech leaf litter ([Mo] = 0.05 ppm δ98/95Mo 0.7 ± 0.1‰, see Figure 8.7, Table 8.3). Evidently the high amount of OM dominates the Mo budget (Figure 8.8) and hence the isotope composition in soil. Deep soil samples fall within the range of fresh bedrock granites. Samples from intermediate depth can be interpreted as mixtures of these sources, as Carbon and OM content decrease with depth. The correlation of δ98/95Mo with total organic matter (Figure 8.5) supports this hypothesis. Similarly, Marks et al. (2015) found a positive correlation of exchangeable Mo with soil C indicating OM as the source of readily exchangeable Mo. In their study they examined soil, bedrock, and plant Mo variations across 24 forests in the Oregon Coast Range. Mo adsorption on

OM was shown to result in significant Mo isotope fractionation in laboratory experiments by King et al. (2018) who investigated Mo isotope fractionation during adsorption onto insolubilized humic acid as a proxy for OM. At the pH range of the Strengbach soils (pH ≈4), the liquid–solid Δ98/95Mo was found to be about 0.8‰. For the Strengbach catchment Pierret et al. (2014) suggested that physico‐chemical soil processes have a subordinate impact on the chemical balance of the main cations of waters at the outlet. As an example, they reported that the mean annual flux of Ca in soil solution at 60 cm depth represents 5–20% of the annual flux at the outlet, depending on the type of vegetation or soil (Cenki‐Tok et al., 2009). In addition, on the basis of spatial and temporal variations of Sr and U isotopes in spring and stream water, Pierret et al. (2014) concluded that the compositions of superficial waters are mainly controlled by interactions occurring with the saprolite and bedrock, along preferential pathways. Fe concentrations vary by a factor of two between beech plot and spruce plot soils (ranging between 1.2 and 2.4‰ and between 3.5 and 4.5‰, respectively), whereas the Mo concentrations are similar in both profiles.

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  173 30

25

20

20 OM/%

CEC/cmol kg–1

25

15

15

H; 75 cm 10 10 H; 75 cm 5

5

0

0.1

0.2

0.4

0.3

0.5

0.6

0.7

0

0.8

0.1

0.2

0.3

δ98/95Mo

0.4

0.5

0.6

0.7

0.8

δ98/95Mo

12

4.5

10 4.0

H; 75 cm pH

C/%

8

6

3.5

4 H; 75 cm

2

0

3.0

2.5 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

δ98/95Mo

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

δ98/95Mo

Figure 8.5  Diagrams showing δ98/95Mο versus soil parameters. Symbols as in Figure 8.4. δ98/95Mο correlates positively with cation exchange capacity (CEC), organic matter (OM) and carbon (C) content. Organic carbon content appears to be a main factor controlling Mo isotope composition in soils.

Below organic‐rich topsoils, Mo concentrations in the two Strengbach soil profiles are very homogeneous, even though pH values vary with depth (Figure 8.8). In acidic soils, Mo is adsorbed onto mineral surfaces (Wichard et al., 2009), which should be more efficient at pH 3 (upper soils) than at pH 4 (deeper soils). However, the pH range my just be too small to result in significant net effects. Mo in soils is generally associated with Fe‐oxyhydroxides and/or OM. Binding to iron oxides should lead to lighter δ98/95Mo ratios (Goldberg et  al., 2009). Results of our profiles, however, do not show a significant net effect of Fe or Mn concentration on δ98/95Mo (Figure 8.6). As a consequence the main factor controlling Mo isotope composition

appears to be the organic carbon content. In its correlation with organic matter, Mo mimics exchangeable Ca (Figure  8.6c), which is highly biologically recycled in this environment. For mass balance reasons, the δ98/95Mo of the initial Mo source would have to fall between OM and dissolved loads. As the observed δ98/95Mo in soil of the Strengbach catchment is always above bedrock and below dissolved riverine load, the initial Mo source cannot be purely bedrock. An additional source with higher δ98/95Mo is required. As already noted by Siebert et  al. (2015) and King et  al. (2016), Mo in soil can also have an atmospheric origin. This reinforces the similarity with Ca, which shows a significant atmospheric contribution in

174  BIOGEOCHEMICAL CYCLES (a)

(b)

Fe/(g/100 g) 0.50

0.0

1.0

1.5

2.0

2.5

0.0

(c)

Mn/(g/100 g) 0.10

0.20

0.30

0.40

0.50

0.60

0.00

0

0

30

30

30

60

60

60

90

90

90

120

120

120

Depth/cm

0

0.3

0.4

0.5

0.6

0.7

0.3

0.4

δ98/95Mo

0.5

0.6

Exchangeable Ca/(g/kg) 0.05

0.10

0.15

0.20

0.25

0.30

0.7

δ98/95Mo

Figure 8.6  (a,b) Soil profiles illustrating Fe and Mn concentrations versus depth for the soil profile of the beech plot (lines and symbols in grey: δ98/95Mο data plotted for reference). There is no significant net effect of Fe or Mn concentration on δ98/95Mo. (c) Exchangeable Ca versus depth for both soil profiles. The enrichment of Ca in the topsoils indicates that it is highly biologically recycled in this environment.

the litter, soil horizons, and soil solutions of the Strengbach catchment. The annual Ca flux from throughfall is higher than the annual Ca flux in soil solutions at 60 cm depth, on the spruce stand as well as on beech stand (Pierret et al., 2019). The atmospheric Ca contribution at the catchment scale represents about 30% of the annual flux at the outlet (Pierret et al., 2019). 8.2.2. Litter, Foliage, and Roots We observe that the Mo isotope composition of foliage, litter, roots, humus, and topsoil are similar. This points to efficient recycling of Mo in the biological cycle, similar to findings for Ca. It is also in line with the conclusion of the importance of organically bound Mo to significantly reduce Mo loss from the system (King et al., 2016; Marks et al., 2015; see above). King et al. (2018) reported that, for 10 of the 12 forested sites in the Oregon Coast Range, δ98/95Mo of foliage were identical within 2σ uncertainty to δ98/95Mo in the soil. Throughfalls under spruce or beech have Mo concentrations ranging between 0.04 and 0.12 ppb. Mo concentrations in soil solutions are quite constant: 0.03 ppb at 60 cm depth and 0.08 ppb at 5 cm depth at the spruce plot; and 0.01 ppb at 70 cm depth and 0.03 ppb at 10 cm depth at the beech plot (unpublished date from Strengbach data base).

The fact that Mo concentrations in deeper soil solution are lower than in throughfalls implies that a significant fraction of Mo is recycled between topsoil and 60 cm depth by biological uptake and/or is supplied by atmospheric input. The Mo mass balance, including soil, OM (foliage, litter, and roots) and atmospheric inputs at the catchment scale are discussed below.

8.3. RESULTS AND DISCUSSION AT THE CATCHMENT SCALE 8.3.1. Waters The Mo concentrations of springs and surface waters of the Strengbach catchment range from 0.01 to 0.29 nmol (Table  8.1), 5–50 times lower than small rivers from the small Entlebuch catchment, Switzerland (0.53–2.95 nmol; Neubert et  al., 2011) and the “pre‐ anthropogenic” estimated concentration of average global dissolved riverine Mo has been estimated by Martin & Meybeck (1979) as 5 nmol. More recently, Miller et al. (2011) published a world river average Mo concentration of 8.0 nmol Mo kg‐1, based on 38 rivers equivalent to 37% of total global water discharge. Although Mo concentrations are unusually low, the Mo

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  175 SW site Beech

NE site Spruce

Spruce needles

δ98/95Mo: 0.5‰

Beech leaf litter δ98/95Mo: 0.7‰

Humus δ98/95Mo: 0.4‰

Soil < 20 cm δ98/95Mo: 0.4‰ Beech root:

Soil < 20 – 85 cm δ98/95Mo: 0.3‰

δ98/95Mo: 0.3‰

Spruce root: δ98/95Mo: 0.4‰

Soil > 85 cm δ98/95Mo: 0.26‰ Fresh granite δ98/95Mo: 0.2‰ Figure 8.7  Diagram showing Mo isotope cycle in vegetation and soils of the Strengbach catchment. The Mo isotope composition of foliage, litter, roots, humus, and topsoil is rather homogeneous. Apparently, there is an efficient Mo recycling from plants to organic litter to plants, limiting the Mo loss from the ecosystem. Net biological fractionation is subordinate to the differences in the Mo sources. Samples from intermediate depth can be interpreted as a mixture between organic matter and weathered bedrock, as carbon and organic matter content decrease with depth. Table 8.3  Mo isotope data of plant parts and litter from the experimental plots Plot

Sampled material

δ98/95Moa (‰)

2σb

Mo (ppm)

Beech

Root Leaf litter Root Needles

0.31 0.65 0.40 0.51

0.08 0.05 0.07 0.10

0.035 0.015 0.022 0.020

Spruce

 δ98/95Mo data are reported as deviation in ‰ from a reference material, whereby the NIST SRM 3134 is defined to have δ98/95Mo = 0.25‰ (Nägler et al., 2014). b  2σ error of the mean of the measurement. a

isotope compositions of springs and surface waters of the Strengbach catchment (0.69–1.42‰, Figure 8.2) are in the “normal” range when compared to the dissolved riverine δ98/95Mo data of Neubert et  al. (2011; 0.14– 1.60‰) and Archer & Vance (2008; 0.2–2.3‰) at an average of 0.7‰.

The Sr and Mo isotope signatures from the water analyses presented here (Figure  8.9; Table  8.1) show a clear (nonlinear) trend, but with an outlier (BH). Qualitatively it can be stated that δ98/95Mo ratios increase with increasing Cl− and SO42−, (Figures 8.10 and 8.11), with spring BH again being an outlier in Figure 8.10. Chlorine is deposited globally via marine aerosol, and thus it is taken as a proxy for this source (Adriaens et al., 2013). Marine aerosol forms in different ways, resulting in salt particles of different size, totaling around 10–30 Pg of sea salt in aerosols each year (Brimblecombe, 2003). Pupier et al. (2016) studied an experimental beech forest located near Montiers‐sur‐Saulx, NE France (< 200 km west of the Strengbach catchment) and concluded that the observed Cl concentrations dominantly result from marine aerosols. Previously, Junge and Werby (1958) monitored Cl, Na, K, Ca, and SO42− concentrations in rainwater across the United States and noted a significant influence of marine aerosol as far as 800 km from the coast. The Strengbach catchment is less than 500 km from the Channel in the north or the Mediterranean in

176  BIOGEOCHEMICAL CYCLES (a) 0.40

0.80

1.2

1.6

(c)

pH

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

OM/%

0.0

0

0

0

30

30

30

60

60

60

90

90

90

120

120

120

5.0

10

15

20

25

Figure 8.8  Soil profile illustrating Mo concentrations, pH, and organic matter (OM) versus depth: symbols as in Figure 8.4. Mo mimics exchangeable Ca (Figure 8.6), which is highly biologically recycled in this environment. This supports the hypothesis of efficient recycling of Mo in the biological cycle, Further, organically bound Mo appears to be important to reduce Mo loss from the system.

1.6

1.4

H09

RH3

F10 RAZS H10

1.2

δ98/95Mo

Depth/cm

(b)

Mo/ppm 0.0

outlet H10 CS1 H10

1.0

RUZS H09

0.8 H09 0.6

BH F10

0.4 0.720

0.721

0.722

0.723

0.724

0.725

0.726

0.727

0.728

87Sr/86Sr

Figure 8.9  δ98/95Mo versus 87Sr/86Sr diagram showing waters from the Strengbach catchment (Table 8.1). With the exception of one sample (BH), the data show a clear (curved) negative correlation. Error bars represent 2σ uncertainty of 0.1% (external reproducibility of water samples). If a particular in‐run uncertainty is higher, then the latter is shown. H09 and H10, autumn 2009 and 2010 respectively; F10, spring 2010.

30

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  177 1.60

RH3 F10

RH3 H09

1.40

1.20 outlet H10

δ98/95Mo

RAZS H10 CS1 H10

1.00

RUZS H09

0.800

BH H09 0.600 0.02

0.025

0.03

0.035

0.04

0.045

0.05

0.055

0.06

Cl– (mmol L–1)

Figure 8.10  δ98/95Mo versus Cl− diagram showing waters of the Strengbach catchment. Again sample BH is an outlier, the other samples are positively correlated. Uncertainties and labels as in Figure 8.9.

1.6 RH3 F10 1.4 outlet H10

RH3 H09

δ98/95Mo

1.2

1.0

RAZS H10 RUZS H09

0.8

RH F10

CS1 H10

BH H09 0.6 0.040

0.045

0.050

0.055

0.060

0.065

0.070

0.075

SO42– (mmol L–1)

Figure 8.11  δ98/95Mo versus SO42− diagram showing waters of the Strengbach catchment. Uncertainties and labels as in Figure 8.9.

178  BIOGEOCHEMICAL CYCLES

the south, and less than 700 km from the Atlantic in the west (i.e., the prevalent wind direction). An influence of sea salt on the precipitation is thus plausible. In effect, Probst et al. (2000) described an airborne contribution to the Strengbach catchment that carries the Sr isotope signature of seawater. The marine contribution to the rain composition can be calculated using the enrichment factor (EF) of element i according to the formula: EFi = (Xi/Na)rain/(Xi/Na)seawater where Xi is the concentration and Na is considered as a seasalt tracer (Laouli et al., 2012; Singh et  al., 2007). In the Strengbach catchment, the average EF was 1.1 and 24 for Cl and SO42−, respectively (Table  8.4), during the sampling period (2009–2010), illustrating that a large part of sulfate has a nonmarine origin (Migliavacca et al., 2005). The time series of rain data in Table 8.4 with EF of Cl− and SO42− indicates that the marine contribution to the precipitation in the Strengbach catchment is quite constant as reflected by Na and Cl concentrations. In contrast SO42− decreases over time. SO42− has additional significant nonmarine sources, as oxidative sulfide weathering and acid rain from anthropogenic SO42− emissions. Oxidative sulfide weathering was shown to result in a correlation of δ98/95Mo with SO42− in surface waters (Neubert et  al., 2011; Voegelin et al., 2012). This correlation is also observed in our data (Figure 8.11). However, in the Strengbach catchment, sulfides were not present in surface granites. Only some small sulfide grains (pyrite, arsenopyrite, and CuS) were observed in association with ankerite in fractures from northern slope granites. Pierret et  al. (2014) proposed that the different geochemical and isotope (i.e., Sr and U) signatures of the waters from the single springs from the Strengbach catchment are controlled by different and independent water pathways in fractured granite. Thus, the linear relation between δ98/95Mo and SO42− concentration can be explained by a variable contribution of sulfide dissolution representing one mixing end‐ member, while a second end‐member can be atmospheric,

with sulfate at least partly reflecting anthropogenic sources (Smith et al., 2001, 2011; Sudalma et al., 2015). In contrast to Mo isotope compositions, Mo concentrations do not correlate with SO42− concentrations (Figure 8.12). As noted by Pierret et al. (2014), dissolution/precipitation processes of secondary minerals also significantly affect elemental concentrations of the individual source waters. Atmospheric pollution in open field bulk precipitation is indicated by NH4, NO3, and SO42− (Probst et al., 2000). Mo isotope compositions from industrial contaminants are extremely limited. However, findings of Lane at al. (2013) and Chappaz et  al. (2012) point to a predominance of values similar to continental crust and, thus, similar to the Strengbach bedrock. Thus anthropogenic Mo may represent a Mo source to the Strengbach catchment with a δ98/95Mo value similar to bedrock, but with a different [Mo]/SO42−. 8.3.2. Bedrock Whole‐rock analyses of six unweathered (but more or less hydrothermally altered) rocks and two weathered rims, as well as strongly weathered material collected at the outlet from the Strengbach catchment (Figure  8.3) also have a low Mo concentrations compared to similar rocks from other regions and the global average. The Mo concentrations of the 52 Palaeozoic granitic rocks reported by Yang et  al. (2017; A‐, I‐, and S‐type) from SE Australia (Lachlan Fold Belt and New England Batholith), and the Southern Uplands of Scotland (Loch Doon, Criffell, and Fleet plutons) vary from 0.1 ppm to 8.1 ppm with 48 granites falling into the narrower range of 0.4 to 2.4 ppm. The average of 1.1 ppm is consistent with estimates for average continental crust (Rudnick & Gao, 2003). Thus, Mo concentrations of the granites from the Strengbach area (0.12‐0.35 ppm; Table 8.5) are about an order of magnitude lower, probably due to prior fluid Mo removal during magmatic hydrothermal fluid saturation (KD(Mo)fluid/melt ~10; Audédat & Pettke, 2003). Moreover, minerals in these

Table 8.4  Open field precipitation of selected periods Period

n

pH

Na+ K+ Mg2+ Ca2+ Cl− NO3− SO42− –1 –1 –1 –1 –1 –1 (mmol L ) (mmol L ) (mmol L ) (mmol L ) (mmol L ) (mmol L ) (mmol L–1) EFCla

1998–1992b 2004–2006b 2009b 2010b 2014b 27 September 2017c

156 76 25 25 25 1

4.75 4.87 5.11 5.12 5.14 5.99

0.014 0.013 0.010 0.007 0.008 0.008

0.007 0.006 0.004 0.004 0.005 0.009

0.003 0.002 0.004 0.003 0.003 0.003

0.009 0.006 0.009 0.010 0.012 0.004

0.018 0.015 0.011 0.010 0.011 0.011

0.032 0.033 0.028 0.029 0.023 0.014

0.023 0.015 0.012 0.012 0.009 0.007

 Enrichment factor: EFi = (Xi/Na)rain/(Xi/Na)seawater (Laouli et al., 2012; Millero et al., 2008; Singh et al., 2007).  OHGE data base (http://bdd‐ohge.u‐strasbg.fr; Probst et al 2000; Pierret et al 2014). c  This study. a

b

1.1 0.9 1.0 1.2 1.2 1.2

EFSO4a 27 18 20 28 20 15

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  179 0.030 BH H09 0.025

Mo (ppb)

0.020

RH3 H09

0.015

0.010

outlet H09 RUZS H09

outlet H10

0.005

0.000 0.040

0.045

0.050

RH F10

CS1 H10

CS3 H09 RAZS H10

0.055

0.060

0.065

RH3 F10

0.070

0.075

SO42– (mmol L–1)

Figure 8.12  Mo concentration of waters of the Strengbach catchment relative to SO42− (Table 8.1). Open symbols: samples with no δ98/95Mo data.

Table 8.5  Mo concentration and isotopic compositions of rocks Sample

Sample type

Moa (ppm)

HP2‐F HP2‐F HP2‐W HP2‐W RH‐1R‐F RH‐1R‐W RH‐1R‐W AU2‐F AU2‐F RS‐1Rc (outlet) RS‐1Rc (outlet) HP1‐F R1‐RAZS‐F R1‐RAZS‐F GN2‐F

Granitic aplite Granitic aplite Granitic aplite Granitic aplite Granite Granite Granite Granite Granite Granitic gravel Granitic gravel Granite Granite Granite Gneiss

0.131 0.134 0.119 0.134 0.140 0.190 0.230 0.200 0.208 0.140 0.209 0.127 0.330 0.350 0.226

δ98/95Mo (‰)

2σb

0.31 0.27 0.22 0.07 0.08 0.07

0.04 0.07 0.05 0.05 0.03 0.05

0.24

0.08

0.43 0.18 0.22 0.19 0.47

0.06 0.07 0.04 0.04 0.06

Note. W, weathered outer part; F, inner part. a  Replicates of concentration measurements indicate a pooled standard deviation of 0.04 ppm (2SD). b  Uncertainty given as 2σ error of the measurement. The results of the replicates indicate a pooled standard deviation of 0.04‰ (2SD). c  Granitic gravel trapped at the catchment outlet after thunderstorms.

rocks analyzed by LA‐ICP‐MS are all below 0.2 ppm with the only exception of oxyhdroxides, which are > 1 ppm (Table 8.6). We thus propose that the low Mo concentrations in the Strengbach waters (in particular springs) mirror the low Mo concentrations in the bedrock. All analyzed rocks have δ98/95Mo between 0.1 and 0.5‰. For samples HP2 (aplitic granite) and RH‐1R (granite), both the weathered outer part and the fresh inner part were sampled. No resolvable difference was observed for Mo concentration or δ98/95Mo. Also, the total averages of all weathered ([Mo] = 0.18 ppm, δ98/95Mo = 0.22 ± 0.13‰ 2σ) and fresh samples ([Mo] = 0.21 ppm, δ98/95Mo = 0.24 ± 0.09‰ 2σ) do not differ significantly. These Mo isotope ratios are typical for continental crust (Voegelin et  al., 2014). To address the debate regarding the δ98/95Mo variability of the continental crust these authors studied high‐ temperature Mo isotope fractionation in a volcano‐plutonic system (Kos, Aegean Arc, Greece). These authors determined an average continental δ98/95Mo of +0.3 to +0.4‰ based on their results and previously published data. 8.3.3. Foliage, Litter, and Humus Mo is taken up by the root network of trees, and incorporated into the organic matter. In the leaf litter layer, Mo forms strong complexes with tannins and humins

180  BIOGEOCHEMICAL CYCLES Table 8.6  Average Mo concentrations in magmatic and weathering phases of rock HP‐2 Rock sample

Mineral

Mo (ppm)

na

SDb

HP‐2

Quartz Plagioclase Biotite K‐feldspar Oxy‐hydroxides: light beige orange dark brown dark brown Strongly weathered Feldspar Quartz Biotite Oxy‐hydroxides: light beige light brown brown dark brown

< 0.003 0.04 0.12 0.13

3 1 2 2

0.06 0.01

0.10 0.12 0.31 5.29 < 0.005

1 2 2 1 1

< 0.003 0.10

2 2

0.05 0.13 0.33 0.91

1 4 1 3

RH‐1R

a

0.01 0.10

0.11 0.03 0.08

 n, number of measured spots.  One standard deviation where multiple spots are measured.

b

(Wichard et  al., 2009). However, as the biological Mo cycle is not completely isolated in nature, the probable loss of a minor fraction of organic bound Mo needs to be balanced by a Mo flux from surface waters or groundwater, in the reasonable assumption that biomass is in steady state. In experimental setups (Wasylenki et  al., 2008; Zerkle et  al., 2011) biological uptake from abiogenic Mo sources was found to disproportionate Mo isotopes between an isotopically light organic matter and isotopically heavier residual nutrient liquid. The bacterial Mo isotope fractionation was estimated to range between Δ98/95Mocell‐solution = −0.2‰ and Δ98/95Mocell‐solution = −1.0‰. It has not yet been quantified in soils because the mass balance between organic bound and abiogenic Mo sources is unknown. However, the low Δ98/95Mocell‐solution precludes biological Mo isotope fractionation as a significant component of the Δ98/95Mo between soils and waters in the Strengbach catchment. 8.3.4. First‐Order Models Assuming disproportionation between isotopically light soils and isotopically heavy waters requires an input with an intermediate Mo isotopic composition, in contrast, the Strengbach catchment has bedrock with a lighter Mo isotopic composition than soil or water. Thus, catchment outcrop weathering alone cannot explain riverine δ98/95Mo because the median of granitic bedrocks (δ98/95MoG = 0.2‰) is significantly lower than the median of stream and spring waters (δ98/95MoW = 1.1‰) and also

lower than soil samples, saprolite, and organics (Tables 8.2 and 8.3). The isotopically heavy stream waters thus require either a "hidden sink" of isotopically light Mo in the catchment, or a heretofore unrecognized, additional “heavy source.” Even though the processes and observations discussed on a plot scale are all significant for local geochemical signatures, Pierret et  al. (2014) concluded that the physico‐chemical processes in soil do not dominate the chemical balance of waters in the Strengbach catchment. King et  al. (2018) hypothesized that Mo adsorption onto OM also significantly influences Mo fluxes to the oceans, in order to explain why the global riverine Mo flux appears to be isotopically heavy (Archer & Vance, 2008). However, King et  al. (2018) also admit that Mo isotope compositions of OM within catchments are strongly influenced by Mo from atmospheric inputs. The relative proportions of these two effects and their influences on the total Mo output from the catchment remained unconstrained. Any global Mo‐isotope‐based model that reconstructs the redox history of the atmosphere and hydrosphere, however, is only as good as the assumption on the δ98/95Mo of the continental runoff. This in turn, can only be approached/improved if we understand the recent Mo cycle and related processes. A simple calculation can be used to evaluate the expected effect of OM on the outflow of a catchment. The total volume of organic‐rich upper soil can be approximated by its average depth (20 cm) multiplied by the catchment surface area. Given the measured Mo concentrations in soil and outlet as well as the outgoing Mo flux, the total amount of Mo present in organic rich soils can be compared to the total annual outgoing flux of dissolved Mo. Using the parameters given in Table 8.7, the total amount of Mo in organic soils is calculated to be equal to ~20 years of outflow. To maintain Mo disequilibrium in the catchment between total input and total output by assuming permanent retention of light Mo isotopes in soils, the soil volume would have to be doubled every 20 years irrespective of the Mo source involved. Failing this large increase, Mo uptake and release would reach steady state in the soil reservoir. For the case of the Strengbach, this implies that the total input has to be δ98/95Mo = 1.1‰. Extending the "light Mo sink" into the deep soil (by postulating a significant additional Mo retention by clays) only increases the time needed to reach steady state, but not its ultimate achievement. Removing the "light Mo sink" via insoluble organic particulate loads, does not solve the paradox of Mo in bedrock being isotopically lighter than both soil and waters. The same accounts for “light Mo” removal via suspended loads. Under normal erosion the contribution of suspended load to the total removal of Mo is negligibly small in the Strengbach catchment. During storm events larger

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  181 Table 8.7  Parameters of first order mass balance calculation of the Mo budget Water flux

Inventory

Variable

In: precipitation

Volume/time (mm year ) Volume/time (L s–1) Mo (ppb) Mo total (g year–1) –1

1400 35b 0.0011 1214

Out: outlet (RS)a

Variable

20 0.008 5046

Volume (m ) Densityd (gcm–3) Mo (ppm) Mo total (g) c

3

Soil (0–100 cm)

OM‐rich soil (0–20 cm)

800 000 1.6 0.5 640 000

160 000 1.0 0.7 112 000

Note. Mo from precipitation equals 24% of total Mo outflux of the catchment. Assuming all Mo would be exchangeable the mean residence time in soils (inventory divided by flux out) would be 22 years (only 20 cm of organic soils considered) or 127 years (considering the upper meter). Assuming a smaller fraction of exchangeable Mo reduces these residence times. Steady state would be closely approached within decades, i.e., δ98/95Mo of the input equals δ98/95Mo of the total output, which is measured (RS) as 1.1‰. a  Loss due to evaporation is not relevant for Mo cycle. b  Calculated from long‐term average precipitation (1400 mm year–1) and surface area. c  Calculated based on a catchment surface of 0.8km2. d  Average bulk density based on https://de.wikipedia.org/wiki/Lagerungsdichte (accessed 22.3.2018).

amounts of erosion products leave the catchment. A coarser grained fraction of this material trapped at the catchments outlet after intense thunderstorms yields δ98/95Mo = 0.43‰ (Table  8.5). Finer fractions were not trapped, but all fine‐grained materials in the soils have δ98/95Mo values above bedrock. It is not plausible to assume a significant drain of particulate Mo with isotopic composition below any potential source component measured in the catchment, including bedrock. As a consequence it is necessary to explore the possibility of an additional isotopically heavy Mo input. Such an additional isotopically heavy Mo input could be provided by a marine component in precipitations. Probst et  al. (2000) described an airborne contribution to the catchment that retains the Sr isotope signature of seawater. These authors reported that ca. 50% of dissolved Sr in stream water was atmospherically derived. This value was calculated based on throughfall (which contains 5–10 times more Sr than open field precipitation) as representative of bulk atmospheric input because 90% of the area is covered by forest. The total mass of airborne input could be affected by dust derived from local continental sources with high 87Sr/86Sr (such as loess in the nearby Alsace plain). However, the 87Sr/86Sr signature points to an airborne source compatible with sea salt. The marine aerosol hypothesis is further supported by reports of marine aerosols as prominent sources of atmospheric Mo, besides continental dust, volcanic fog, and fuel combustion (Marks et al., 2015; Mather et al., 2012; Sansone et al., 2002; Tsukuda et al., 2005). Dust from the application of phosphate fertilizer of marine origin is another potential anthropogenic source of Mo and Sr with seawater signatures (see below). Siebert et al. (2015) and King et  al. (2016) report significant atmospheric input of Mo to soils along these lines. King et al. (2016) found up to +139% net accumulation of Mo across

Hawaiian soils, which is positively correlated with increasing mean annual precipitation. Also, δ98/95Mo values were lowest at dry sites and increased with increasing precipitation. Mo isotope ratios in local rainwater were δ98/95Mo = +1.36‰ on average. The conclusion of King et  al. (2016) was that in Hawaiian soils, Mo is substantially augmented by additions from precipitation, volcanic fog, and potentially anthropogenic inputs. Moreover, Marks et  al. (2015) concluded that atmospheric input may be a significant source of Mo to forest ecosystems. Thus, atmospheric input with seawater‐like Sr and Mo isotope compositions must be taken into account in the quest to find additional Mo influx to sources for the Mo in the Strengbach catchment. Based on isotope dilution concentration measurements (Table 8.1), the dissolved Mo concentrations at the outlet in autumn 2009 and autumn 2010 were 0.011 and 0.005 ppb, respectively. Further the streamlet samples RAZS (H09 = 0.009 ppb, H10 = 0.005 ppb) or the average of all dissolved load spring and outlet results (0.008 ppb) are all similar. The multiyear average water outflow is 20 L s–1. In a near steady‐state situation of the hydrological system, the influx (precipitation) should be of the same order. The balance is achieved when taking into account loss due to evapotranspiration (precipitation: 1400 mm year–1 or 35 L s–1; outflow equals 800 mm year–1 or 20 L s–1). The question than becomes, what percentage of the total Mo outflux corresponds to the atmospheric influx. Precise Mo concentration data of precipitation are scarce. We analyzed one open field precipitation and measured [Mo] = 0.001 ppb (Table  8.1). In order to substantiate this result, we took the available Sr concentration average (Pierret et al., 2014) and assumed a seawater Sr/Mo ratio to calculate [Mo]. The result is also 0.001 ppb. Thus we feel confident using this concentration as a first order approach: as 0.001 ppb Mo × 35 L s–1 is about 20% of

182  BIOGEOCHEMICAL CYCLES

0.008 ppb Mo × 20 L s–1 about one‐fifth of the Mo in the dissolved load can be from precipitation, without additional input from dry deposits (see Table  8.7 for model parameters). 8.3.5. Mo and Sr Isotope Models Heavy element isotope ratio diagrams are excellent for identifying mixing end‐members in surface processes, as their “source signature” is not affected by dilution or evaporation. The radiogenic 87Sr/86Sr ratio (as long as it is normalized to the fixed 86Sr/88Sr ratio of 0.1194) is not even subject to isotope fractionation problems during sorption or desorption, processes that might affect δ98/95Mo. In the following section, we evaluate if a simple two‐component mixing model with rock weathering and marine aerosol as its two end‐ members can successfully describe the 87Sr/86Sr and δ98/95Mo of the dissolved loads of the Strengbach catchment. As a first step, the end‐members have to be constrained as closely as possible. ••Sea spray/marine end‐member. Both, Sr and Mo, with their long residence time in the open ocean (Sr, 2.4 Ma Ravizza & Zachos, 2003); Mo, 450 ka (Miller et al. 2011) or ~800 ka (Collier, 1985; Firdaus et  al., 2008; Sohrin et  al., 1987)), have constant isotope compositions and salinity normalized concentrations in the modern open ocean (Table 8.8). Thus, if sea spray is a significant source to the Strengbach system in terms of Mo and Sr, seawater isotope composition should be used (δ98/95Mo = 2.3 (Nakagawa et  al., 2012) and 87Sr/86Sr = 0.70917

(Krabbenhöft et  al., 2009; McArthur et  al., 2006); see Table 8.8 for details). ••Weathering end‐member. Unweathered bedrock samples from the Strengbach catchment area are scarce, as are Sr isotope ratios of such material. Probst et al. (2000) report one 87Sr/86Sr value of 0.83838 ± 1 from a bedrock granite in the southern part of the catchment. Other granite whole rocks (Data Base Strengbach) range from 0.75950 to 0.84492 ± 2 and 1.86091 ± 2 for one strongly hydrothermally altered granite. However, Sr isotope compositions of bulk bedrock might not provide an adequate weathering end‐member because the different weathering rates of individual minerals control the isotope budget of weathering solutions, particularly in early, pre‐steady‐state weathering stages (Clow et  al., 1997). Another approach in identifying weathering end‐member signatures utilizes laboratory leaching. This, however, does not necessarily reflect field conditions. In particular, the application of organic acids (e.g., Wickman & Jacks, 1992) mainly reflects conditions of the upper layers of the soils whereas bedrock weathering is mainly an interaction between rock and mineral acid. Consequently, modeling the isotope ratios of the weathering end‐member to account for the specific dissolution and volumetric abundance of each mineral has so far been preferred (Probst et al., 2000). Simulating the interaction between the minerals of the bedrock granite with acid solutions (open field precipitation, throughfall, or soil solution) generated a 87Sr/86Sr ratio of the potential bedrock weathering end‐member of 0.736– 0.742. However, formation of the weathering solution is

Table 8.8  Parameters of mixing models based on Mo and Sr isotope data Variable Sr/ Sr δ98/95Mo Sr (ppb) Mo (ppb) Srswa (%) Moswa (%) 87

86

End‐Member 1 ’seawater / marine aerosol’ (EM1) 0.70917 2.30c 7658d 10.3c

b

End‐Member 2 ’Weathering’ Model 1 (M1EM2)

Model 2 (M2EM2)

Model 3 (M3EM2)

0.72605 0.80 11.0e 0.007 8 23

0.72801 0.63f 4.6e 0.003 17 31

0.73629g 0.23 4.0g 0.005 42 44

e

Note. Model 1: M1EM2 measured data from RUZS. RUZS is chosen as first‐order approach to the weathering solution as it is low in δ98/95Mo and has the highest 87Sr/86Sr measured here. Model 2: M2EM2 Sr data are taken from the spring (SH) with the highest 87Sr/86Sr in Pierret et al. (2014). δ98Mo is deduced via the δ98/95Mo SO42− correlation (see Figure 8.10). Model 3: M3EM2 Sr data modeled weathering solution from Probst et al. (2000) (see text). δ98/95Mo taken from average rock; Mo concentration adjusted so the model fits to the outlet composition. a Percentage of element in outlet flow that originates from seawater source. b  McArthur et al. (2006) and Krabbenhöft et al. (2009). c  Siebert et al. (2003) and Nakagawa et al. (2012); recalculated following Nägler et al. (2014). d  de Villiers (1999). e  Pierret et al. (2014). f  From correlation with SO42−. g  Modeled value, Probst et al. (2000).

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  183

of course more complex than just primary mineral dissolution and may include formation and alteration of secondary mineral phases like clays, that incorporate apatite‐derived Sr during their formation (Pierret et  al., 2014). Thus a plausible alternative is to select the highest 87 Sr/86Sr value in the Strengbach dissolved loads and the corresponding concentration, as this is the observed value of dissolved load that is the farthest away from the other assumed end‐member, seawater. It represents the best approximation of indigenous Sr, minimally imparted by marine aerosol. The δ98/95Mo and Mo concentrations of the weathering end‐member are the least constrained input parameters. The approaches of the three models used are presented below. 1. Measured Sr and Mo isotope ratios and concentrations from the sample showing the least potential marine aerosol influence (i.e., with low δ98/95Mo, lowest Mo concentration, and highest 87Sr/86Sr (RUZS)) are used. This approach reduces the number of assumptions, but the sample Sr and Mo isotope ratios and concentrations do not represent the ultimate weathering end‐member.

δ98/95Mo

2.0

2. Sr data are taken from spring SH, which has the highest 87Sr/86Sr in Pierret et al. (2014). Because no measured δ98/95Mo is available for this sample, it is deduced independently via the δ98/95Mo versus SO42− correlation (see Figure 8.11). The model fit does not depend on the precise Mo concentration, but rather on the Sr/Mo ratio of the sources. 3. 87Sr/86Sr and Sr concentration are taken from one modeled weathering end‐member from Probst et al. (2000), representing interaction of all minerals of one granite with soil solution. The bedrock δ98/95Mo of 0.23 is used as approximation for the Mo end‐member (see Table 8.5 for details). The good fit to all models (Figures 8.13–8.15) indicates that marine aerosol is a significant source of dissolved Mo in this catchment. The most important result is that the catchment outlet lies on the model lines defined by independent constraints. This also validates the assumption of steady‐state condition, namely that total Mo input equals total output for Mo at the catchment scale. This conclusion is not challenged by the one spring within the catchment, spring BH that is offset relative to all others, most likely due to local Mo fractionation.

EM1: marine aerosol

1.5

outlet

1.0

M1EM2: RUZS

0.5 0.710

0.715

0.720

0.725

0.730

87Sr/86Sr

Figure 8.13  δ98/95Mo versus 87Sr/86Sr diagram showing the results of model 1 together with the measured water samples of the Strengbach catchment. Model 1 assumes a binary mixing of a marine aerosol end member (EM1), and the second end member M1EM2 representing a weathering source. In model 1 the unknown M1EM2 is approached by the measured data of the water sample showing the least potential marine aerosol influence, i.e., with low δ98/95Mo, lowest Mo concentration, and highest 87Sr/86Sr (RUZS). This approach reduces the number of assumptions compared to models 2 and 3, but the sample values do not represent the ultimate weathering end‐ member. The points shown are the same as in Figure  8.9; labels are omitted for clarity. Error bars are as in Figure 8.9. Model parameters are listed in Table 8.8.

184  BIOGEOCHEMICAL CYCLES

2.0

EM1: marine aerosol

δ98/95Mo

1.5

outlet

1.0

M2EM2: SH

0.5

0.710

0.715

0.720

0.725

0.730

87Sr/86Sr

Figure 8.14  δ98/95Mo versus 87Sr/86Sr diagram showing the results of model 2 together with the measured water samples of the Strengbach catchment. Model 2 assumes the same end member (EM1) as in model 1. In model 2 the unknown weathering source M2EM2 is approached by 87Sr/86Sr data taken from the spring (SH) with the highest 87Sr/86Sr in Pierret et al. (2014), i.e., a sample potentially being closer to the true weathering solution. The unmeasured δ98/95Mo of this sample is deduced independently via the δ98Mo–SO42− correlation (Figure  8.11). Solid line: calculation assumes a ratio M2EM2 Sr/Mo of 1500 identical to the ratio in model 1. Dashed line: model calculated with a M2EM2 Sr/Mo of 2245, slightly above the value of the outlet (2200). Error bars are as in Figure 8.9. Model parameters are listed in Table 8.8.

Quantification of the amount of marine aerosol is not straightforward because absolute concentrations of Mo and Sr are underconstrained. The isotope ratios, however, allow for calculating Sr and Mo fractions from sea spray relative to local sources. For the outlet, the relative amount of marine aerosol Mo is 23–44% and Sr is 8–42%, depending on the choice of weathering components (Figures 8.13– 8.15 and Table  8.6). The δ98/95Mo of soils and OM are 0.3–0.4‰ lower than the lowest δ98/95Mo of these springs (0.63‰). On the other hand none of these reservoirs is below the bedrock δ98/95Mo range of unweathered granite (0.07–0.5‰). Also, Mo concentration measurements of minerals by LA‐ICP‐MS did not identify a single (fresh) mineral with significantly enriched Mo that could cause an erratic δ98/95Mo in the weathering solution by incongruent weathering. The weathering solution δ98/95Mo can thus be bracketed between 0.2‰ and 1000. Error bars are as in Figure 8.9. Model parameters are listed in Table 8.8.

carbonate‐rich loess is not expected to host significant Mo and sulfur species concentrations, except for possibly fixing aerosol Mo via adsorption on airborne particles. To our knowledge this factor is currently unexplored. Additional Mo (Charter et al., 1995) and Sr could also be sourced from phosphate fertilizers of anthropogenic origin. Due to the marine origin of the vast majority of commercial phosphate, the Sr and Mo from this source are expected to have marine isotopic compositions. While Mo isotope fractionation during phosphate deposition has remained unexplored, some indication of only limited fractionation in marine phosphate formation is given by Wen et al. (2011). Given the intense corn culturing along with phosphate fertilization, an airborne marine Sr and Mo input to the Strengbach catchment is plausible. Mo and Sr isotope compositions in this contribution would merge with the “marine aerosol” end‐member in the above mixing models. It must be stressed that the relative importance of the various Mo sources and isotopic fractionation controls was made clearer by the exceptionally low Mo content of the bedrock in the Strengbach catchment. However, it is plausible that in catchments with mainly carbonate bed-

rock, that show even lower Mo contents (Voegelin et al., 2009), marine aerosol Mo can represent a prominent contribution to the Mo budget. What has emerged from our observations is that the "heavy source" marine aerosol has a greater importance than an assumed reservoir of light Mo in soils. At present, quantification of the global mass balance of marine aerosol input would be premature. However, the inequality between the permanent effects of a hidden reservoir in soils and those of the heretofore neglected marine aerosol input remains. 8.4. CONCLUSIONS 8.4.1. At the Plot Scale In soils, OM is the main parameter that governs Mo concentrations. Apparently, there is an efficient Mo recycling from plants to organic litter to plants. This limits Mo loss from the ecosystem and keeps Mo bioavailable, especially in an environment with very low Mo concentrations in rocks and rivers waters. The Mo isotope composition of topsoils and OM is rather homogeneous. Net biological fractionation is again less relevant than differences in the isotope composition of

186  BIOGEOCHEMICAL CYCLES

the Mo sources. Organic matter contents are observed to be much more important than pH or Fe oxyhydroxide abundance for controlling Mo concentration differences in soil. 8.4.2. At the Catchment Scale 1. Marine aerosol and rock weathering are both prominent sources of Mo in the Strengbach catchment. Our 87Sr/86Sr versus δ98/95Mo isotope correlation and model calculations indicate a 23–44% Mo fraction of seawater origin in the dissolved load at the outlet of the Strengbach system. A simple mass balance calculation indicated a contribution of 20%. Both independent approaches thus give consistent and robust estimates. Further sources (e.g., fertilizer dust) cannot be excluded, however. 2. Despite the distance to ocean water of > 600 km along predominantly westerly winds, marine aerosol is identified as an important component to the Strengbach Mo budget and, by inference, to the global Mo cycle. 3. Mass balance calculations are inconsistent with significant differences between δ98/95Mo of input and output to the catchment (e.g., due to a hypothetical long‐ lasting retention of light Mo isotopes in soils). Therefore, soil formation is most likely not causing permanent isotope shifts in Mo continental runoff. 4. Successful δ98/95Mo and 87Sr/86Sr mixing models imply that the catchment has reached steady state for Mo and Sr. Therefore, the δ98/95Mo of the outflow corresponds to the average input signature. The largest uncertainty is caused by the limited constraints on the Mo isotope composition and concentration of the weathering solution. However, the fact that the models offer a solution for both Mo and Sr isotope ratios indicates that the assumptions made for the bedrock‐weathering component are robust. 5. A prominent fraction of Mo in surface waters of the Strengbach catchment is derived from marine aerosols, even hundreds of kilometers away from the seashore. To date, all global Mo isotope cycling models have assumed a continental runoff value based on inferred average continental crust signatures and/or river water averages, where the latter are taken as representing a weathering product of the former. Our findings imply that future Mo isotope global cycling models need to account for the recoupling of airborne Mo. The exact mass balance of contributing Mo sources to the bulk airborne load is unknown at present. Additional contributions to the aerosol input (volcanic exhalations, fixation of aerosols onto atmospheric dust particles, and anthropogenic sources) are possible but not evident in case of the Strengbach catchment.

ACKNOWLEDGMENTS The authors are particularly grateful to U. Linden for his substantial technical support in the field. Two thoughtful reviews are also gratefully acknowledged. We thank S. Benarioumlil, S. Cotel, P. Friedmann, C. Fourtet, S. Gangloff and R. Boutin for providing technical laboratory assistance and field management. This study was supported by the Swiss National Science Foundation Grant: 200021_126759 to TFN. Some data collection was funded by the Observatoire Hydro‐Géochimique de l’Environnement, which is financially supported by the CNRS/INSU France and Université de Strasbourg. The OHGE is part of the French RBV watershed network (French watershed network) and of the OZCAR research infrastructure (http://www.ozcar‐ri.org). REFERENCES Adriaens, P., Gruden, C., & McCormick, M.L. (2013). Biogeochemistry of halogenated hydrocarbons. In H.D. Holland, K.K. Turekian (Eds.), Treatise on geochemistry, 2nd edn (Vol. 11, pp. 511–533). Amsterdam: Elsevier. Archer, C., & Vance, D. (2008). The isotopic signature of the global riverine molybdenum flux and anoxia in the ancient oceans. Nature Geoscience, 1(9), 597–600. Arnold, G.L., Anbar, A.D., Barling, J., & Lyons, T.W. (2004). Molybdenum isotope evidence for widespread anoxia in mid‐ proterozoic oceans. Science, 304(5667), 87–90. Audétat, A. & Pettke, T. (2003). The magmatic‐hydrothermal evolution of two barren granites: A melt and fluid inclusion study of the Rito del Medio and Canada Pinabete plutons in Northern New Mexico (USA). Geochimica et Cosmochimica Acta, 67(1), 97–122. Baldwin, G.J., Nägler, T.F., Greber, N.D., Turner, E.C., & Kamber, B.S. (2013). Mo isotopic composition of the mid‐ Neoproterozoic ocean: An iron formation perspective. Precambrian Research, 230, 168–178. Barling, J., Arnold, G.L., & Anbar, A.D. (2001). Natural mass‐ dependent variations in the isotopic composition of molybdenum. Earth and Planetary Science Letters, 193(3–4), 447–457. Boutin, R., Montigny, R., & Thuizat, R. (1995). Chronologie K–Ar et 39Ar/40Ar du métamorphisme et du magmatisme des Vosges. Comparaison avec les massifs varisques avoisinants et determination de l’âge de la limite Viséen inférieur  –  viséen supérieur. Géologie de la France, 1, 3–25. Brimblecombe, P.,(2003). The global sulfur cycle. In H.D. Holland, K.K. Turekian (Eds.), Treatise on geochemistry (Vol. 8, pp. 645–682). Oxford: Pergamon. doi. 10.1016/ B0‐08‐043751‐6/08134‐2. Cenki‐Tok, B., Chabaux, F., Lemarchand, D., Schmitt, A.‐D., Pierret, M.‐C., Viville, D., Bagard, M.‐L., & Stille, P. (2009). The impact of water–rock interaction and vegetation on calcium isotope fractionation in soil‐ and stream waters of a small forested catchment (the Strengbach case). Geochimica et Cosmochimica Acta, 73, 2215– 2228.

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  187 Chappaz, A., Lyons, T.W., Gordon, G.W., & Anbar A.D. (2012). Isotopic fingerprints of anthropogenic molybdenum in lake sediments. Environmental Science and Technology, 46(20), 10934–10940. Charter, R.A., Tabatabai, M.A., & Schafer, J.W. (1995) Arsenic, molybdenum, selenium, and tungsten contents of fertilizers and phosphate rocks. Communications in Soil Science and Plant Analysis, 26, 17–18, 3051–3062. doi: 10.1080/00103629509369508 Ciesielski, H., Sterckeman, T., Santerne, M., & Willery, J.P. (1997). A comparison between three methods for the determination of cation exchange capacity and exchangeable cations in soils. Agronomie, 17, 9–16. Clow, D.W., Mast, A., Bullen, T.D., & Turk, J.T. (1997). Strontium 87/strontium 86 as a tracer of mineral weathering reactions and calcium sources in an alpine/subalpine watershed, Loch Vale, Colorado. Water Resource Research. 33(6) 1335–1351. Collier, R.W. (1985) Molybdenum in the Northeast Pacific Ocean. Limnology and Oceanography, 30, 1351–1354. Colodner, D., Edmond, J., & Boyle, E. (1995). Rhenium in the Black Sea: comparison with molybdenum and uranium. Earth and Planetary Science Letters, 131, 1–15. Czaja, A.D., Johnson, C.M., Roden, E.E., Beard, B. L., Voegelin, A.R., Nägler, T.F., Beukes, N.J., & Wille, M. (2012). Evidence for free oxygen in the Neoarchean ocean based on coupled iron–molybdenum isotope fractionation. Geochimica et Cosmochimica Acta, 86, 118–137. Dambrine, E., Carisey, N., Pollier, B., & Granier, A. (1992a). Effects of drought on the yellowing status and the dynamic of mineral elements in the xylem sap of a declining spruce stand (Picea abies Karst.). Plant Soil, 150, 303–306. Dambrine, E., Le Goaster, S., & Ranger, J. (1991). Croissance et nutrition minérale d’un peuplement d’épicéa sur sol pauvre, II Prélève‐ ment racinaire et transferts internes d’éléments minéraux au cours de la croissance. Acta Oecologica‐ International Journal of Ecology, 12(6), 791–808. Dambrine, E., Pollier, B., Poszwa, A., Ranger, J., Probst, A., Viville, D., Biron, P., & Granier, A. (1992b). Evidence of current soil acidification in spruce (Strengbach catchment, Vosges mountains, North‐ Eastern France). Water, Air, and Soil Pollution, 105, 43–52 de Villiers, S. (1999). Seawater strontium and Sr/Ca variability in the Atlantic and Pacific oceans). Earth and Planetary Science Letters, 171, 623–634. Dellwig, O., Beck, M., Lemke, A., Lunau, M., Kolditz, K., Schnetger, B., & Brumsack, H.J. (2007). Non‐conservative behaviour of molybdenum in coastal waters: Coupling geochemical, biological, and sedimentological processes. Geochimica et Cosmochimica Acta, 71(11), 2745–2761. Dickson, A.J., Gill, B.C., Ruhl, M., Jenkyns, H.C., Porcelli, D., Idiz, E., Lyons, T.W., & van den Boorn, S.H.J.M. (2017), Molybdenum‐isotope chemostratigraphy and paleoceanography of the Toarcian oceanic anoxic event (Early Jurassic). Paleoceanography, 32, 813–829, doi:10.1002/2016PA003048 Duan, Y., Anbar, A.D., Arnold, G.L., Lyons, T.W., Gordon, G.W., & Kendall, B. (2010). Molybdenum isotope evidence for mild environmental oxygenation before the Great Oxidation Event. Geochimica et Cosmochimica Acta, 74(23), 6655–6668. El Gh’Mari, A. (1995). Etude minéralogique, pétrophysique et géochim‐ ique de la dynamique d’altération d’un granite soumis

au dépôts atmosphériques acides (Bassin versant du Strengbach, Vosges, France) mécanismes, bilans et modélisations. PhD Thesis, University Strasbourg, Strasbourg, 202 pp. Emerson, S.R., & Huested, S.S. (1991). Ocean anoxia and the concentrations of molybdenum and vanadium in seawater. Marine Chemistry, 34(3–4), 177–196. Eroglu, S., Schoenberg, R., Wille, M., Beukes, N., & Taubald, H. (2015). Geochemical stratigraphy, sedimentology, and Mo isotope systematics of the ca. 2.58–2.50 Ga‐old Transvaal Supergroup carbonate platform, South Africa. Precambrian Research, 266, 27–46. Fichter, J., Turpault, M.P., Dambrine, E., & Ranger, J. (1998). Mineral evolution of acid forest soils in the Strengbach catchment (Vosges mountains, N‐E France). Geoderma, 82(4), 315–340. Firdaus, M. L., Norisuye, K., Nakagawa, Y., Nakatsuka, S., & Sohrin, Y. (2008). Dissolved and labile particulate Zr, Hf, Nb, Ta, Mo and W in the western North Pacific Ocean. Journal of Oceanography, 64(2), 247–257. Goldberg, T., Archer, C., Vance, D., & Poulton, S.W. (2009). Mo isotope fractionation during adsorption to Fe (oxyhydr) oxides. Geochimica et Cosmochimica Acta, 73, 6502–6516. Greber, N., Siebert, C., Nägler, Th.F., & Pettke, Th. (2012). δ98/95Mo values and molybdenum concentration data for NIST SRM 610, 612 and 3134: Towards a common protocol for reporting Mo data. Geostandards and Geoanalytical Research, 36, 3, 291–300. doi: 10.1111/j.1751‐908X.2012.00160.x Guillong, M., Meier, D.L., Allan, M.M., Heinrich, C.A., & Yardley, B.W.D. (2008). SILLS: A MATLAB‐based program for the reduction of laser ablation ICP–MS data of homogeneous materials and inclusions. In Sylvester, P. (Ed.), Laser ablation ICP‐MS in the Earth Sciences: Current practices and outstanding issues (Vol. 40, pp. 328–333). Short Course Series, Mineralogical Association of Canada. Junge, C.E., & Werby, R.T. (1958). The concentration of chloride, sodium, potassium and sulfate in rain water over the United States. Journal of Meteorology, 15, 417–425. Kendall, B., Creaser, R.A., Gordon, G.W., & Anbar, A.D. (2009). Re–Os and Mo isotope systematics of black shales from the Middle Proterozoic Velkerri and Wollogorang Formations, McArthur Basin, northern Australia. Geochimica et Cosmochimica Acta, 73(9), 2534–2558. Kendall, B., Gordon, G.W., Poulton, S.W., & Anbar, A.D. (2011). Molybdenum isotope constraints on the extent of late Paleoproterozoic ocean euxinia. Earth and Planetary Science Letters, 307(3–4), 450–460. Kendall, B., Reinhard, C.T., Lyons, T.W., Kaufman, A.J., Poulton, S.W., & Anbar, A.D. (2010). Pervasive oxygenation along late Archaean ocean margins. Nature Geoscience, 3, 647. King, E.K., Perakis, S.S., &, Pett‐Ridge, J.C. (2018). Molybdenum isotope fractionation during adsorption to organic matter. Geochimica et Cosmochimica Acta, 222, 584–598. King, E.K., Thompson, A., Chadwick, O.A., & Pett‐Ridge, J.C. (2016). Molybdenum sources and isotopic composition during early stages of pedogenesis along a basaltic climate transect. Chemical Geology, 445, 54–67. Krabbenhöft A., Fietzke J., Eisenhauer A., Liebetrau V., Böhm F., & Vollstaedt H. (2009). Determination of radiogenic and stable

188  BIOGEOCHEMICAL CYCLES strontium isotope ratios (87Sr/86Sr; δ88/86Sr) by thermal ionization mass spectrometry applying an 87Sr/84Sr double spike. Journal of Analytical Atomic Spectrometry, 24, 1267–1271. Kurzweil, F., Wille, M., Gantert, N., Beukes, N. J., & Schoenberg, R. (2016). Manganese oxide shuttling in pre‐GOE oceans— evidence from molybdenum and iron isotopes. Earth and Planetary Science Letters, 452, 69–78. Landmann, G., Bonneau, M., & Adrian, M., (1987). Le dépérissement du sapin pectiné et de l’épicéa commun dans le massif vosgien est‐il en relation avec l’état nutritionnel des peuplements? Revue Forestière Française, 39, 5–11. Lane, S., Proemse, B.C., Tennant, A., & Wieser, M.E. (2013). Concentration measurements and isotopic composition of airborne molybdenum collected in an urban environment. Analytical and Bioanalytical Chemistry, 405(9), 2957–2963. Laouali, D., Galy‐Lacaux, C., Diop, B., Delon, C., Orange, D., Lacaux, J. P., et  al. (2012). Long term monitoring of the chemical composition of precipitation and wet deposition fluxes over three Sahelian savannas. Atmospheric Environment, 50, 314–327. Marks, J.A., Perakis, S.S., King, E.K., & Pett‐Ridge, J. (2015). Soil organic matter regulates molybdenum storage and mobility in forests. Biogeochemistry, 125(2), 167–183. Martin, J.‐M., & Meybeck, M. (1979). Elemental mass‐balance of material carried by major world rivers. Marine Chemistry, 7(3), 173–206. Mather, T.A., Witt, M.L.I., Pyle, D.M., Quayle, B.M., Aiuppa, A., Bagnato, E., et al. (2012). Halogens and trace metal emissions from the ongoing 2008 summit eruption of Kilauea volcano, Hawai’i. Geochimica et Cosmochimica Acta, 83, 292–323. doi: 10.1016/j.gca.2011.11.029 McArthur J.M., Rio, D., Massari, F., Castradori, D., Bailey, T., Thirlwall, M. & Houghton, S. (2006). A revised Pliocene record for marine‐87Sr/86Sr used to date an interglacial event recorded in the Cockburn Island Formation, Antarctic Peninsula. Palaeogeography Palaeoclimatology Palaeoecology, 242, 126–136. McManus, J., Berelson, W.M., Severmann, S., Poulson, R. L., Hammond, D.E., Klinkhammer, G. P., & Holm, C. (2006). Molybdenum and uranium geochemistry in continental margin sediments: Paleoproxy potential. Geochimica et Cosmochimica Acta, 70(18), 4643–4662. McManus, J., Nägler, T.F., Siebert, C., Wheat, C.G., & Hammond, D.E. (2002). Oceanic molybdenum isotope fractionation: Diagenesis and hydrothermal ridge‐flank alteration. Geochemistry Geophysics Geosystems, 3(12), 1078. doi:10.1029/2002GC000356 Migliavacca, D., Teixeira, E.C., Wiegand, F., Machado, A.C.M., & Sanchez, J. (2005). Atmospheric precipitation and chemical composition of an urban site, Guaiba hydrographic basin, Brazil. Atmospheric Environment, 39(10), 1829–1844. Miller, C.A., Peucker‐Ehrenbrink, B., Walker, B.D., & Marcantonio, F. (2011). Re‐assessing the surface cycling of molybdenum and rhenium. Geochimica et Cosmochimica Acta, 75(22), 7146–7179. Millero, F.J., Feistel, R., Wright, D.G., & McDougall, T.J. (2008). The composition of standard seawater and the definition of the reference‐composition salinity scale. Deep‐Sea Research Part I, 55, 50–72. doi: 10.1016/j.dsr.2007.10.001

Morford, J.L., & Emerson, S.R. (1999). The geochemistry of redoxsensitive trace metals in sediments. Geochimica et Cosmochimica Acta, 63, 1735–1750. Nägler, T.F., Anbar, A.D., Archer, C., Goldberg, T., Gordon, G.W., Greber, N.D., et al. (2014). Proposal for an International Molybdenum Isotope Measurement Standard and Data Representation. Geostandards and Geoanalytical Research, 38(2), 149–151. Nakagawa, Y., Takano, S., Firdaus, M.L., Norisuye, K., Hirata, T., Vance, D., & Sohrin, Y. (2012). The molybdenum isotopic composition of the modern ocean. Geochemical Journal, 46(2), 131–141. Neubert, N., Heri, A.R., Voegelin, A. R., Nägler, T.F., Schlunegger, F., & Villa, I.M. (2011). The molybdenum isotopic composition in river water: Constraints from small catchments. Earth and Planetary Science Letters, 304(1–2), 180–190. Pearce, C.R., Burton, K.W., Pogge von Strandmann, P.A.E., James, R.H., & Gislason, S.R. (2010). Molybdenum isotope behaviour accompanying weathering and riverine transport in a basaltic terrain. Earth and Planetary Science Letters, 295(1–2), 104–114. Pearce, C.R., Coe, A.L., & Cohen, A.S. (2010). Seawater redox variations during the deposition of the Kimmeridge Clay Formation, United Kingdom (Upper Jurassic): Evidence from molybdenum isotopes and trace metal ratios. Paleoceanography, 25, PA4213. Pearce, C.R., Cohen, A.S., Coe, A.L., & Burton, K.W. (2008). Molybdenum isotope evidence for global ocean anoxia coupled with perturbations to the carbon cycle during the Early Jurassic. Geology, 36(3), 231–234. Pettke, T., Oberli, F., Audetat, A., Guillong, M., Simon, A.C., Hanley, J.J., & Klemm, L.M., (2012). Recent developments in element concentration and isotope ratio analysis of individual fluid inclusions by laser ablation single and multiple collector ICP‐MS. Ore Geology Reviews 44, 10–38. Pierret, M.C., Cotel, S., Ackerer, P., Beaulieu, E., Benarioumlil, S., Boucher, M., et al. (2018). The Strengbach Catchment: a multidisciplinary environmental sentry for 30 years. Vadose Zone Journal, 17, 180090. doi:10.2136/vzj2018.04.0090 Pierret, M.C., Stille, P., Prunier, J., Viville, D., & Chabaux, F. (2014). Chemical and U–Sr isotopic variations in stream and source waters of the Strengbach watershed (Vosges mountains, France). Hydrology and Earth System Sciences, 18(10), 3969–3985. Pierret, M.C., Viville, D., Dambrine, E., Cotel, S., & Probst, A. (2019). Long‐term record of open field precipitation and throughfalls in a medium altitude forested environment (Strengbach catcment—NE France): Response to atmospheric pollution trend. Atmospheric Environment, 202, 296– 314. doi:/10.1016/j.atmosenv.2018.12.026]. Probst, A., Dambrine, E., Viville, D., & Fritz, B. (1990). Influence of acid atmospheric inputs on surface water chemistry and mineral fluxes in a declining spruce stand within a small granitic catchment (Vosges Massif—France). Journal of Hydrology, 116, 101–124, 1990. Probst, A., El Gh’Mari, A., Aubert, D., Fritz, B., & McNutt, R. (2000). Strontium as a tracer of weathering processes in a silicate catchment polluted by acid atmospheric inputs, Strengbach, France. Chemical Geology, 170, 203–219. Probst, A., Fritz, B., & Stille, P. (1992). Consequence of acid deposition on natural weathering processes: field studies and

SMALL CATCHMENT SCALE MOLYBDENUM ISOTOPE BALANCE AND ITS IMPLICATIONS  189 modelling. In Y.K. Kharaka, A.S. Maest (Eds.), Water rock interaction (pp. 581–584). Rotterdam: Balkema. Probst, A., Viville, D., Fritz, B., Ambroise, B., & Dambrine, E. (1992). Hydrochemical budgets of a small forested catchment exposed to acid deposition: the Strengbach catchment case study (Vosges Massif, France). Water Air and Soil Pollution, 62, 337–347. Prunier, J., Chabaux, F., Stille, P., Gangloff, S., Pierret, M.C., Viville D. & Aubert, A., (2016). Geochemical and isotopic (Sr, U) monitoring of soil solutions from the Strengbach catchment (Vosges Mountains, France): Evidence for recent weathering evolution. Chemical Geology, 417, 289–305 Pupier, J., Benedetti, L., Bouchez, Bourlès, D., Leclerc E., Thiry, Y., & Guillou, V. (2016). Monthly record of the Cl and 36Cl fallout rates in a deciduous forest ecosystem in NE France in 2012 and 2013. Quaternary Geochronology, 35, 26–35. Ravizza, G.E., & Zachos, J.C. (2003). Records of Cenozoic ocean chemistry. In H.D. Holland, K.K. Turekian (Eds.), Treatise on geochemistry (Vol. 6, pp. 551–581). Oxford: Pergamon. Rudnick, R. L. & Gao, S. (2003). Composition of the continental crust. In H.D. Holland, K.K. Turekian (Eds.), Treatise on geochemistry (Vol. 3, pp. 1–64). Amsterdam: Elsevier. Sansone, F.J., Benitez‐Nelson, C.R., Resing, J.A., DeCarlo, E.H., Vink, S.M., Heath, J.A., & Huebert, B.J. (2002). Geochemistry of atmospheric aerosols generated from lava‐ seawater interactions. Geophysical Research Letters, 29, 2–5. doi: 10.1029/ 2001GL013882 Siebert, C., Nägler, T.F., & Kramers, J.D. (2001). Determination of molybdenum isotope fractionation by double‐spike multicollector inductively coupled plasma mass spectrometry. Geochemistry Geophysics Geosystems, 2, art. no.‐2000GC000124. Siebert, C., Nägler, T.F., von Blanckenburg, F., & Kramers, J.D. (2003). Molybdenum isotope records as a potential new proxy for paleoceanography, Earth and Planetary Science Letters, 211(1–2), 159–171. Siebert, C., Pett‐Ridge, J.C., Opfergelt, S., Guicharnaud, R.A., Halliday, A.N., & Burton, K.W. (2015). Molybdenum isotope fractionation in soils: Influence of redox conditions, organic matter, and atmospheric inputs. Geochimica et Cosmochimica Acta, 162, 1–24. Singh, K.P., Singh, V.K., Malik, A., Sharma, N., Murthy, R.C. and Kumar, R. (2007). Hydrochemistry of wet atmospheric precipitation over an urban area in Northern Indo‐ Gangetic Plains. Environmental Monitoring and Assessment, 131(1–3), 237. Smith, S.J., Pitcher H. & Wigley, T.M. (2001). Global and regional anthropogenic sulfure dioxides emissions. Global Planetary Change, 29, 99–119. Smith, S.J., van Aardenne, J., Klimont, Z., Andres, R., Volke, A.C. & Delgado Arias, S. (2011). Anthropogenic sulfur dioxide emissions: 1850–2005. Atmospheric Chemistry and Physics, 11, 1101–1116. Sohrin, Y., Isshiki, K., Kuwamoto, T. & Nakayama, E. (1987). Tungsten in north Pacific waters. Marine Chemistry, 22, 95–103. Stiefel, E.L. (2002). The biogeochemistry of molybdenum and tungsten. In A. Sigel, H. Sigel (Eds.), Molybdenum and tungsten: their roles in biological processes (Vol. 39, pp. 1–29). Boca Raton, FL: CRC Press. Sudalma, S., Purwanto, P., & Santoso, L.W., (2015). The effect of SO2 and NO2 from transportation and stationary emis-

sions sources to SO42− and NO3− in rain water in Semarang. Procedia Environmental Sciences, 23, 247–252. Tsukuda, S., Sugiyama, M., Harita, Y., & Nishimura, K. (2005). Atmospheric bulk deposition of soluble phosphorus in Ashiu Experimental Forest, Central Japan: source apportionment and sample contamination problem. Atmospheric Environment, 39, 823–836. doi: 10.1016/j.atmosenv.2004.10.028 Villa, I.M., Nägler, T.F., & Voegelin A.R. (2017). Mo isotopic fractionation in Tso Morari lake waters. Paper presented at 32nd Himalaya–Karakorum–Tibet International Workshop, Chinese Academy of Sciences, Kunming, China. Voegelin, A.R., Nägler, T.F., Pettke, T., Neubert, N., Steinmann, M., Pourret, O., & Villa, I.M. (2012). The impact of igneous bedrock weathering on the Mo isotopic composition of stream waters: Natural samples and laboratory experiments. Geochimica et Cosmochimica Acta, 86, 150–165. Voegelin, A.R., Nägler, T.F., Samankassou, E., & Villa, I.M. (2009) Molybdenum isotopic composition of modern and Carboniferous carbonates. Chemical Geology, 265(3–4), 488–498. Voegelin, A.R., Pettke, T., Greber, N., von Niederhäusern, B. & Nägler, Th.F. (2014). Magma differentiation fractionates Mo isotope ratios: Evidence from the Kos Plateau Tuff (Aegean Arc). Lithos, 190/191, 440–448. doi: 10.1016/j.lithos.2013.12.016 Wang, Z., Ma, J., Li, J., Wei, G., Chen, X., Deng, W., et  al. (2015). Chemical weathering controls on variations in the molybdenum isotopic composition of river water: evidence from large rivers in China. Chemical Geology, 410, 201–212. doi: 10.1016/j.chemgeo.2015.06.022. Wasylenki, L.E., Anbar, A.D., Liermann, L.J., Mathur, R., Gordon, G.W., & Brantley S.L. (2007). Isotope fractionation during microbial metal uptake measured by MC‐ICP‐MS. Journal of Analytical Atomic Spectrometry, 22, 905–910. Wen, H.J., Carignan, J., Zhang, Y.X., Fan, H.F., Cloquet, C., & Liu, S.R. (2011). Molybdenum isotopic records across the Precambrian–Cambrian boundary. Geology, 39, 775–778. Wichard, T., Mishra, B., Myneni, S.C.B., Bellenger, J.P., & Kraepiel, A.M.L. (2009). Storage and bioavailability of molybdenum in soils increased by organic matter complexation. Nature Geoscience, 2(9), 625–629. Wickman, T., & Jacks, G. (1992). Strontium isotopes in weathering budgeting. In Y.K. Kharaka , A.S. Maest (Eds.), Water rock interaction (pp. 611–614). Rotterdam: Balkema. Wille, M., Kramers, J.D., Nägler, T.F., Beukes, N.J., Schröder, S., Meisel, T., Lacassie, J.P., & Voegelin, A.R. (2007). Evidence for a gradual rise of oxygen between 2.6 and 2.5 Ga from Mo isotopes and Re–PGE signatures in shales. Geochimica et Cosmochimica Acta, 71(10), 2417–2435. Wille, M., Nägler, T.F., Lehmann, B., Schröder, S., & Kramers, J.D. (2008). Hydrogen sulphide release to surface waters at the Precambrian/Cambrian boundary. Nature, 453, 767. Yang, J., Barling, J., Siebert, C., Fietzke, J., Stephens, E., & Halliday, A.N. (2017). The molybdenum isotopic compositions of I‐, S‐ and A‐type granitic suites. Geochimica et Cosmochimica Acta, 205, 168–186. Zerkle, AL., Scheiderich, K., Maresca, J.A., Liermann, L.J., & Brantley, S.L. (2011). Molybdenum isotope fractionation by cyanobacterial assimilation during nitrate utilization and N2 fixation.Geobiology,9,94–106.http://dx.doi.org/10.1111/j.1472‐ 4669.2010.00262.x

9 Trace Metal Legacy in Mountain Environments: A View from the Pyrenees Mountains Gaël Le Roux1, Sophia V. Hansson1,2, Adrien Claustres1, Stéphane Binet1,3, François De Vleeschouwer1,4, Laure Gandois1, Florence Mazier5, Anaelle Simonneau3, Roman Teisserenc1, Deonie Allen1, Thomas Rosset1, Marilen Haver1, Luca Da Ros1, Didier Galop5,6, Pilar Durantez1, Anne Probst1, Jose Miguel Sánchez-Pérez1, Sabine Sauvage1, Pascal Laffaille1, Séverine Jean1, Dirk S. Schmeller1, Lluis Camarero7, Laurent Marquer1,5, and Stephen Lofts8 ABSTRACT The mineral reserves of mountain environments have been exploited since the beginning of metallurgy and legacy contamination from activities such as mining persist to this day. This is particularly the case in the soils of the European mountains where potential harmful trace elements (such as Pb, Sb, As, and Hg) of anthropogenic origin have accumulated since Antiquity. The French Pyrenees are no exception to this, as many mine sites in the region date back to the Bronze Age, resulting in landscape alternations and anthropogenic environmental impacts on a millennial scale. The mountain critical zone is sensitive both to human‐induced environmental changes (e.g., agriculture, mining, clear‐cutting) as well as to climate‐induced rapid environmental fluctuations. The legacy of trace metal contamination in other environments has been documented at individual sites in Europe and around the world, however, the fate of such legacy metals over time, in particular within mountainous regions, is poorly understood. This is despite the fact that a large proportion of metals was deposited and stored before 1800 CE in these areas. Using a case study from the Central French Pyrenees as a specific example, we here show that legacy metal (e.g., Pb) contamination in mountain environments is still persistent and a potential threat to mountain ecosystem health. We emphasize methods that aim to understand, in an interdisciplinary and coordinated way, the fate of legacy metals in the Central Pyrenees and beyond. We highlight the importance of research in the mountain critical zone for the whole of Europe, as mountains are the source of water and provide regional economic and socio‐ecological resources. The goal of this chapter is, therefore, to draw attention to and provide fellow researchers with, the background information and methodologies needed to address the problem of legacy metal accumulation, transport, storage, remobilization, and redeposition in mountain watersheds, as well as potential subsequent environmental impacts downstream.

1   Laboratory of Functional Ecology and Environment, EcoLab, University of Toulouse, CNRS, INPT, UPS, Toulouse, France 2   Department of Bioscience  –  Arctic Research Centre, Aarhus University, Aarhus, Denmark 3  Institute of Earth Sciences, ISTO, University of Orléans, BRGM, Orléans, France 4  Franco-Argentine Institute for the Study of Climate and its Impacts, University of Buenos Aires, Argentina

5  GEODE, Geography of the Environment, CNRS, University of Jean‐Jaurès Toulouse, France 6  LabEx DRIIHM (ANR-11-LABX-0010), INEE-CNRS, Paris, France 7  Center for Advanced Studies of Blanes, CSIC, Blanes, Girona, Spain 8  Centre for Ecology and Hydrology, Lancaster Environment Centre, Bailrigg, Lancaster, UK

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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192  BIOGEOCHEMICAL CYCLES

9.1. METAL LEGACY IN MOUNTAIN ENVIRONMENTS The advent of copper (Cu) mining approximately 7000 years ago resulted in the commencement of environmental metal contamination (Pompeani et al., 2013). Humans have been modifying the biogeochemical cycles of metals since the onset of the use of iron (Fe) and Cu deposits for metallurgical purposes (Hong et al., 1994). Due to dust and fume emissions from these mining and metallurgical activities, the environments close to the mines have been contaminated (Cooke & Bindler, 2015). Technological developments allowing the separation of metals from ores (i.e., cupellation, amalgamation; Craddock, 2000) have increased the likelihood of dispersion of fine particles, especially due to the use of high‐temperature furnaces. Adoption of such technologies heralded the start of transboundary trace‐metal contamination and dispersion to the most remote areas (De Vleeschouwer et  al., 2014, 2010; Rosman et  al., 1997). Contamination related to lead (Pb) and silver (Ag) metallurgy during Antiquity is documented by elevated concentrations of Pb in Greenland ice cores (Rosman et  al., 1997; Zheng et  al., 2007). During the past 200 years, there have been major emissions of trace metal particles marking a clear “acceleration” (Gałuszka et  al., 2014, 2017; Waters et  al., 2016) in the environmental archives of trace metals. In recent decades, numerous efforts have been made to reduce anthropogenic metal emissions. Consequently, the atmospheric deposition of many elements has significantly declined in many regions. A prominent example of this is the reduction of Pb emission and atmospheric deposition due to the ban on leaded fuel worldwide (Le Roux et al., 2005). Legislation such as the Clean Air Acts in Europe or the United States reduced emissions of most trace metals as a whole. The impacts of these measures on the deposition levels of trace metals have been observed at the European scale in mosses for Pb, As, and Hg (Harmens et al., 2010, 2015). Despite these efforts and the reductions of trace‐metal emissions, issues remain concerning the contamination by emerging metals. Sen and Peucker‐Ehrenbrink (2012) show that the cycle of 62 chemical elements is still dominated by human emissions. There is an increasing demand and use of metals and metalloids in industrial products and production, and thus an associated emerging demand for trace elements such as antimony (Amarasiriwardena & Wu, 2011), or rare earths elements (REE) (Migaszewski & Gałuszka, 2015; Suzuki et al., 2011), or platinum group elements (PGE). The PGE have been found in snow and rain including in the Pyrenees Mountains, primarily as a result of road traffic emissions from the use of platinum‐ based catalysts (Chen et  al., 2009; Moldovan et  al.,

2007). Correspondingly, studies on ice cores have revealed an increase of PGE since the 1980s in Antarctic snow (Soyol‐Erdene et al., 2011). Whereas the history of metal contamination across Europe seems to follow a chronologically similar trend (Cooke & Bindler, 2015), differences in contamination intensity and trends over time are found close to local metal production centers. This is especially the case for European mountain areas where geological features present valuable metallic ores that have been mined since the Bronze Age. Indeed, many European mountain soils were impacted by metallic contamination with a similar intensity during both ancient and present times (Camarero et al., 2009; Hansson, Claustres, et al., 2017). In addition to enrichment of local trace elements, either due to local mining or industrial activities, highlands are specifically impacted by long range transboundary air pollution (LRTAP). Compared to neighboring valleys, higher precipitation occurs on alpine areas due to several orographic effects (Le Roux et al., 2008; Roe, 2005), including the feeder–seeder effect and canopy interception of low‐ altitude clouds and fog. The deposition is thus very variable depending on controlling mechanisms such as topography, predominant wind directions, and vegetation composition and type (Likuku, 2006; Lovett & Kinsman, 1990; Stankwitz et  al., 2012; Weathers et  al., 2000). The accumulation of trace metals in mountain soils is shown by many examples in the scientific literature (Le Roux et al., 2010; Stankwitz et al., 2012; Weathers et al., 2000). The mountain critical zone (Figure 9.1) is highly sensitive to environmental changes (Catalan et al., 2006; Le Roux et al., 2016), both human‐induced (e.g., agriculture, mining, clear‐cutting) and natural impacts of long‐ term climate change and rapid environmental changes. The fate of these potential harmful trace elements (PHTE; such as Pb, Ag, Bi, Sb, Hg, As) in relation to long‐term climate change or rapid environmental changes is poorly understood, similarly in the boreal and arctic areas (Klaminder et al., 2010). For example, flooding can remobilize trace metals stored in soils and mining heaps (Brunel et  al., 2003) or from the natural bedrock (Zaharescu et al., 2009). This is especially the case where abandoned mines were exploited until the early to mid‐ 20th century (Brunel et  al., 2003; Byrne et  al., 2010). Once these PHTE are released from surrounding soils to the catchment, they can become highly enriched in bioavailable forms (exceeding the recommended guideline values for ecosystem or human health). These metals can also be bio‐accumulated in a range of different biological species, including fish and other river biota (Monna et al., 2011), and impact the quality of domestic water (Delpla et al., 2009) with far‐reaching impacts on human health and well‐being.

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  193 Intense rain and snow storms

Fire intensities

Droughts Tree rings

Soil erosion

Peat cores

Ice & snow cores

Droughts Otoliths

Lake sediments

Speleothems ice caves

Watershed monitoring Environmental archives and monitoring Environmental threats related to climate change

Extreme floods

Figure 9.1  Main environmental features of the mountain critical zone. Clocks indicate environmental archives— environmental records of past trends. The arrows indicate transport of different potentially harmful trace elements (PHTE) with different chemical fates, depending on their time and manner of deposition. Microtopography also plays a role in PHTE accumulation by concentrating snow in winter (white arrow). Threats to the temperate mountainous areas are also shown (environmental threats related to climate change). The aquatic living organisms that can be viewed as bio‐indicators and sentinels of environmental changes are shown in green. [Adapted from Le Roux et al. (2016).]

9.2. LEGACY OF PB IN EUROPEAN MOUNTAINS Mountain lakes and peatlands reveal local and regional contamination of the mountain critical zones around former mining and industrial sites. Combination of palynological and geochemical analyses reveals a link between deforestation and intensification of contamination. This is the case in the Morvan, the Lozère, the Basque Country, and the Vosges Mountains (Jouffroy‐Bapicot et  al., 2007; Mariet et  al., 2018; Monna et  al., 2004); where trees have been cleared to provide space for agriculture/pastoralism and to feed forges either directly or by being processed into charcoal. The intensification of investigated environmental archives in a specific area makes it possible to discuss more specifically the geographic origin of the recorded signals. Forel et al. (2010) compared four records from the Vosges and Switzerland and concluded that ancient contamination from Iron Age/Roman Period corresponded both to a global signal, likely originating from metallurgical activities in the Iberian Peninsula, and to local contributions from the same period. Figure  9.2 summarizes briefly the accumulation of Pb from several western European Mountain studies. Each of these studies (except for Misten in Belgium) is located in mountainous areas

(Black Forest, Jura, Massif Central, Pyrenees, Sierra de Xistral, and Vosges) and is based on Pb in peat records. The Pb inventories and proportions are presented by “time of contamination,” i.e., Roman/late Antiquity (400 BCE to 500 CE), Middle Ages to preindustrial (500–1800 CE) and industrial (1800 CE to present). The results show that 31–70% of the Pb inputs were accumulated before the industrial era (i.e., 1800 CE) depending on the mountain range. European mountains with a long mining history may have been impacted by ancient contamination as evidenced recently in the Balkans (Longman et al., 2018). Not only is there a need to investigate the current socio‐ecological trajectory of the mountain critical zone, but there is also a need to assess past environmental pollution in European mountains. 9.3. ANCIENT METAL POLLUTION IN CENTRAL PYRENEES: THE BASSIÈS CASE STUDY 9.3.1. Previous and Current Remobilizations of Pb in the Watershed In the Pyrenees, a large number of mines has been registered according to the first known census of them (de Dietrich, 1786). For the 18th century, hundreds of

194  BIOGEOCHEMICAL CYCLES Pb inventory 3 gm–2 Period: 1800 CE to present 500 to 1800 CE 400 BCE to 500 CE

Carpathians Alps Balkans

Figure 9.2 Total Pb inventories, industrial and preindustrial inventories and proportions calculated with peat cores from several European mountain ranges, i.e. Vosges (Forel et al., 2010), Jura (Shotyk, 1996; Shotyk et al., 1998), Massif Central (De Vleeschouwer, unpublished data), Black Forest (Le Roux et al., 2005), Pyrenees (Enrico, unpublished data) and Sierra do Xistral (Martinez Cortiza et al., 2002); original data in Hansson, Claustres, et al. (2017). Absence of a specific time period means that this was not assessed in the original peat study. The whole of Europe is represented in order to also show the importance of mountains in the east and around the Mediterranean Sea. Ongoing studies include Carpathian Mountains (Fiałkiewicz‐Kozieł et  al., 2018), Balkans (Longman et al., 2018), and the Alps. [Adapted from Forel et al. (2010), Shotyk (1996), and Shotyk et al. (1998).]

mine records exist, largely Fe to the east of Vicdessos (from Vicdessos to the Mediterranean Sea) and polymetallic mines in the west, comprising Pb, Cu, Zn, Sb, Ag, and other metallic elements, from the Couserans to the Basque Country. The Haut‐Vicdessos watershed in the Ariège Department, where the Bassiès Valley (N42.46; E1.26, Pyrenees, France) is located (Figure 9.3), has long been subject to anthropogenic pressure via agro‐pastoral, mining, and industrial activities. This combination of several types of human pressure on the environment is one of the focuses of the study by the Observatoire Hommes et Environnement, a long‐term socio‐ecological monitoring institution funded by the CNRS and the LABEX DRIIHM (http://w3.ohmpyr.univ‐tlse2.fr/). Pastoralism and metallurgical (Fe, Pb, Ag, Cu) activities (Dubois, 1999) have placed pressure on forest resources since the early Middle Ages and thus have heavily altered the surrounding landscape through deforestation for pastoralism, firewood, and charcoal (Davasse & Galop, 1990; Galop & Jalut, 1994; Galop et al., 2013). Over the past 50 years, human impact on the Bassiès Valley has decreased and pressure from tourism and pastoralism is

low. Today Bassiès is a remote valley with low human impacts. The upper Bassiès Valley is flat, extending from 1550 to 1750 m a.s.l., with lakes and sphagnum peatlands that formed following glacial retreat. Due to increasing deforestation up to the 19th century, the valley is mainly covered by grasslands, pastures, and heathlands (67%), and bare rocks (23%). The percentage of bare rock increases with increasing altitude. Average annual precipitation is 1640 mm year−1, with one‐third being snow, and the area has an annual average temperature of 7°C. The snow cover starts around December and ends in April– May. However, snow patches (névés: snow beds) can remain until August. The Pb contamination chronology in Bassiès Valley, based on several peat cores collected from the area, is described in detail by Hansson, Claustres, et  al. (2017) and summarized in Figure 9.4a. A good simplified summary is the low‐resolution record of the Orri de Theo peatland (Figure 9.4b), which clearly shows a shift in Pb accumulation rate since the Bronze Age with a concomitant Pb isotope signature characteristic of a larger anthropogenic contribution. A large Pb peak occurs in

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  195 Upper Vicdessos Valley

20 km

N

Haute - Garonne Aude

Ariège

Bassiès catchment

Former mining districts Iron Galena

Vicdessos Altitude 500 m > 2000 m Streams

SPAIN ANDORRA

Bassies catchment

Figure 9.3  Location of Bassies Valley in the Central Pyrenees, together with location of Orri de Théo peatland and sphagnum samples within the Bassiès Valley (orange circles) as well as two altitudinal transects (T and H) and Lake Legunabens (Bacardit et al., 2012). The red area represents the Largentière mining area with potential soil and dust remobilization impacting Pb concentrations in Sphagnum mosses.

the Pyrenees around 1000–1500 CE. We show that a large proportion of Pb stored in Haut Vicdessos soils (> 50%) is from this period of intensive landscape occupation, mining, and metallurgical activities (Hansson, Claustres, et  al., 2017; Figure  9.2). The first remobilization of Pb stored in these soils occurred because of intense erosion due to pastoralism and deforestation in the 17 and 18th centuries as evidenced by dated peat layers from minerotrophic mires influenced by detrital inputs (Hansson, Claustres, et  al., 2017). Pb peaked again during the industrial period, with the Pb coming from remote sources including leaded gasoline, as fingerprinted by a  lower radiogenic signature in the 1990s. The Pb

accumulation rate then decreased but remained above natural levels measured in prehistoric peat layers from the same area (Hansson, Claustres, et  al., 2017; Le Roux et al., unpublished data). Concentrations of Pb up to 300 mg kg−1 in peat layers and 150 mg kg−1 in soils can be found in the Bassiès Valley. Currently, human activities are relatively limited in this mountain valley and we think that direct remobilization in the watershed through erosion is negligible. However, based on a mass balance approach between the different environmental compartments of the critical zone, Bacardit et  al. (2010) showed that Pb is still remobilized in the watershed from soils to lakes in the Bassiès Valley.

196  BIOGEOCHEMICAL CYCLES (a) Ancient atmospheric & local contamination

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–500

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Figure 9.4  (a) Graphical representation of Pb accumulation chronology in peatlands from the Bassiès Valley (Central Pyrenees). [Hansson, Claustres, et al., 2017; Reproduced with permission of Elsevier.] (b) Pb accumulation and Pb isotopes as recorded by a low‐resolution peat core from Orri de Theo peatland dated by 210Pb and radiocarbon ages.

Atmospheric, lake water, and sediment (using sediment traps) Pb fluxes were measured in Legunabens Lake (Figure  9.3) in 2004. The Pb inputs from the catchment into the lake are now largely dominant (> 98%) compared to the present atmospheric flux. Approximately 1500 μg of Pb per day enters Legunabens Lake via the catchment, of which only ~200 μg is derived daily from atmospheric inputs. The current direct atmospheric input to the lake is ~20 μg of Pb per day. This demonstrates that soils of the Bassiès Valley are net sources of lead stored in the Middle Ages and during the pre‐industrial period. 9.3.2. Legacy and Persistence of Pb as Shown by Moss Bio‐indicators We investigated an additional dispersion pathway of PHTE including Pb in the watershed using mosses as bio‐ indicators (Harmens et  al., 2004, 2010, 2015; Kempter et al., 2017). The monitoring of direct metal fallout in precipitation in the long term is challenging due to the low

concentrations for some trace elements, which can make the monitoring both time and cost consuming (Adriano, 2001). This is particularly the case when the objective is to measure several points in the same watershed to assess spatial representation. The use of bio‐indicator organisms such as lichens and mosses has been demonstrated to be a good alternative to direct measurements (Agnan et  al., 2013, 2015; Szczepaniak & Biziuk, 2003; Wolterbeek, 2002). These monitoring techniques have been used as contamination indicators in different contexts such as boreal zones close to oil‐sand fields (Shotyk et al., 2014), forests (Berg & Steinnes, 1997) or mountains (Gandois, 2014; Meyer et al., 2015). A total of 24 Sphagnum moss samples were collected at Bassiès Valley (Figure 9.3) and analyzed using ICP‐OES and Q‐ICP‐MS after full acid digestion. Direct mercury (Hg) analysis (DMA) was undertaken using 50 mg of freeze‐dried sample. The Pb isotopes were measured using HR‐ICP‐MS according to Krachler et  al. (2004) and Hansson, Claustres, et  al. (2017). Trace elements in the Sphagnum mosses are ranked

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  197

in order: Ti (56 ± 20 mg kg−1) > Zn > Pb > Cu > Cr > As > Sb > Hg (33 ± 7 μg kg−1) as illustrated in Figure 9.5. The spatial variability of metal contents is relatively low for Ti, Cr, Cu, Zn, As, and Hg (2–4× between extreme values); Sb, Pb (15–20×), and U (75×) exhibit higher variability, an order of magnitude greater in range. For comparison, Kempter et al.’s (2010) study of 10 peatlands in southern Germany showed intrasite variability in the order of 1.8– 2.5× for Ti and 2.3–4×for Pb, which is much less variable. There is a direct relationship between altitude and contents in Pb, Ce, Al, Sr, and Zn (p < 0.05, n = 22), the coefficient of determination, however, is not strong (R2 = 0.44

Pb

for Pb and R2 = 0.09 for Zn). The highest concentrations appear on transects T and H for Zn, whereas for Pb at equal altitude concentrations are higher on transect T than H (Figure  9.5). The enrichment factor (EF; Figure 9.6) values are very variable according to the elements, reflecting their diverse origins. Aluminum, Sn, Cr, and V have EF values of 0.6 ± 0.2, 1.0 ± 0.2, 1.4 ± 0.7, and 1.8 ± 0.2, respectively, which illustrate a lithogenic origin. Uranium, Pb, Se, and As are slightly enriched, with EF values of 4.7 ± 7.6, 5.3 ± 5.1, 5.5 ± 2.1, and 7.6 ± 6.1 respectively. EF values of 18 ± 7, 32 ± 13, 32 ± 15, 40 ± 28, and 101 ± 132 were observed for Zn, Hg, Cu, Cd, and Sb,

Ti

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Figure 9.5  Potential harmful trace element (PHTE) concentration (Pb, Ti, Sb, As, Cr, Hg, Zn, and Cu) in Sphagnum samples from Bassiès Valley (see Figure 9.3 for the transect identification). Note the higher concentrations of Pb with increasing altitude.

198  BIOGEOCHEMICAL CYCLES

Boxplot color Sampling area B

H

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10 Enriched by anthropogenic sources

Substantially enriched

1

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Hg* Cd* Cu

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Cr

Sn

Ce

Al

Th

Figure 9.6  Boxplots of enrichment factors (EF) in moss samples in Bassiès Valley calculated with Ti and local bedrock (*except Hg, Cd, and Br which are calculated with the upper continental crust). Colors represent the different transects as plotted on Figure 9.3.

respectively, indicating enrichment by anthropogenic sources for these elements in corroboration of results from other remote areas (Agnan et  al., 2015; Gandois et al., 2014; Meyer et al., 2015). The enrichment in Pb, As, U, Cd, and Sb shows high variability over the entire valley. Pb isotope ratios are shown in Figure  9.7. The mean values for all samples taken together are 1.164 ± 0.003 for 206 Pb/207Pb and 2.099 ± 0.004 for 208Pb/206Pb. They are comparable to the values observed in peat for the past 10 years at the Bassiès Valley site (1.163 ± 0.005 for 206 Pb/207Pb and 2.100 ± 0.005 for 208Pb/206Pb; (Hansson, et  al., 2017) and are slightly more radiogenic than the values obtained for dissolved Pb in atmospheric deposition (1.156 ± 0.003 for 206Pb/207Pb and 2.105 ± 0.003 for 208 Pb/206Pb), although this is not significant for 208Pb/206Pb. The main source of Pb appears to be predominantly anthropogenic. Based on Pb isotopes, the sources of Pb in mosses are similar to the “European Standard Lead Pollution” (ESLP; Figure 9.7; Haack et al., 2003), but the differences in Pb concentrations may indicate variability in capture efficiency of Pb within mosses. Bassiès Valley can be considered remote from any current sources of metal contaminants. Tarascon, the largest city nearby, contains only 3000 inhabitants and is about 15 km away.

However, the high EF values for Sb, Hg, Se, Pb, Cu, Cd, Zn, and As, and the isotopic signatures observed for Pb indicate a clear anthropogenic origin for these elements. This suggests that the elements were transported over intermediate (between 10 and 100 km) or long (> 100 km) distances (Gombert et al., 2004; Zannetti, 1990). However, for the specific case of Pb, its altitudinal distribution with a strong NW enrichment and its potential link with aluminum oxides (Claustres, 2016) also suggest a resuspension of soil particles on the western crest. The presence of former Ag–Pb mines on the opposite mountain slope and thus locally Pb‐enriched soils (Figure 9.3) may act as a secondary source of Pb. The Pb isotope signature of those mines (ranging from 1.17 to 1.18 for the 206Pb/207Pb ratio) could explain the shift of the Pb isotopic signature found in the mosses compared to the rain signature. 9.4. FUTURE DIRECTIONS IN PHTE GEOCHEMISTRY OF THE MOUNTAIN CRITICAL ZONE Research on the mountain critical zone is essential (Drexler et al., 2016; Price, 2016), since mountains provide a large part of the water to the connecting lowland

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  199 1.20

2σ = 95% CI

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206Pb

/ 207Pb

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2013-2015 CE

1.14

1.12 2.06

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2.12

2.10 208Pb

Sample

/

Sources

Rain

Aerosols Europe

Sphagnum

Coal Europe Gasoline Europe

Peat

2.14

206Pb

ESLP

Figure 9.7  Pb isotopes ratios (206Pb/207Pb vs. 208Pb/206Pb) in Sphagnum mosses, recent peat layers (2000–2015 CE) and pre‐anthropogenic peat layers (1000–900 BCE) (Hansson, Claustres, et al., 2017), and rainwaters (dissolved phase) from Bassiès Valley (Le Roux & Claustres, unpublished). ESLP, European Standard Lead line (Haack et al., 2003); Pb potential sources are from an unpublished literature compilation database (Claustres & Le Roux, unpublished).

valleys, in addition to providing regional economic and socio‐ecological resources (hydroelectricity, timber, recreational activities, etc.). Mountains have a long mining history that has impacted their landscape in the past. In addition, mountains are sinks of long‐range transported pollutants including trace elements. Mining imprints are still present and it is necessary to understand the fate of these legacy metals. In the Vicdessos area, we showed that medieval Pb stocked in the soil is now the predominant Pb source to the watershed, whereas Pb from high‐altitude soils located close to the mines may act as a secondary atmospheric source. Our results suggest a “metallic ambiance” in Bassiès Valley with soil trace‐metal persistence from local and remote contaminations from various ages. Peatlands, soils, and lake sediments are shown to store trace metals, especially Pb because of its affinity to organic matter. Based on our soil and peatland results, and using a Pb inventory of 5 g m−2 (Hansson, Claustres, et al., 2017), we estimate that more than 300 t of anthropogenic Pb is stored in soils, lakes, and peatlands in the Bassiès and neighboring valleys area (approximately 6000 ha) additional to the lithogenic Pb contained in local soils and rocks.

9.4.1. The Trace Metal Legacy on Mountain Aquatic Ecogeochemistry Project The legacy of trace metal stocks in the mountain critical zone is poorly understood and the Pyrenees provide a perfect mountain range for further detailed scientific investigations of the fate and the impact of those PHTE on the ecological functioning of mountain catchments. The project “Trace Metal legacy on Mountain Aquatic Ecogeochemistry” (TRAM; www.summits.cnrs.fr) aims to draw upon interdisciplinary expertise in soil and catchment modeling, PHTE chemistry, isotope tracing, and biotic impacts, in order to tackle the challenges of understanding and predicting the impacts of PHTEs in mountain critical zones under changing global conditions. 1. TRAM is assessing changes in the introduction and transfer of PHTE over millennia using innovative geochemical and isotopic tracers. 2. TRAM further combines a geochemical approach with ecological analyses to define the impact of PHTE on biodiversity and ecosystem services. 3. TRAM is developing a range of indicators to make the impact of PHTE on the mountain critical zone clearer

200  BIOGEOCHEMICAL CYCLES

Proxies vs. mo de

Mon itor ing

v

to decision‐makers and stakeholders, considering also hydrological, biogeochemical modelling, and GIS analyses. We are investigating the intimate relationship between organic matter and PHTE in the mountain critical zone in an area that (a) has been repeatedly subjected to human impacts including mining, as well as an area without any known mining history, and (b) is now protected. Transfer of PHTE via stream water to the biota will be investigated by comparing streams with different trophic webs. By comparing the outputs of hydrogeochemical models with the data obtained from environmental archives, the robustness of models will be improved for longer timescales and to pinpoint the technical barriers for understanding the future of the mountain critical zone. One of the greatest obstacles to understanding PHTE dynamics in the mountain critical zone is the integration of all the factors that can explain the variability of the geochemical signal. To achieve that, all relevant compartments will be monitored as a continuum from the atmosphere to the stream and from the forest to the fish: the mountain critical zone. As illustrated in the TRAM project framework (Figure  9.8), we believe that there is a need for more integrative projects on the fate of contaminants in

g lin

mountain environments. In the following sections we suggest possible scientific solutions to solve the fate and impact of trace elements derived from legacy pollution in the mountain critical zone. 9.4.2. Environmental Archives and Isotopes to Investigate the Fate of Trace Elements in the Mountain Critical Zone A number of projects focused on the biochemical state of lakes during the 1990s (Catalan et  al., 2013), including Pyrenean lakes (Broder & Biester, 2015). Among these, the most studied lake is Lake Redon in the Spanish Pyrenees (Catalan et  al., 2006). However, few of them focused on paleoperspectives of PHTE contaminants (Aries et  al., 2001; Camarero et al., 1998; Monna et al., 2004) and even less on spatial variability among the different Pyrenean valleys (Bacardit et  al., 2012). As Catalan et  al. (2013) noted, sediments and wetlands are “system recorders but also sinks of materials. Determining how stable this sink is for metals is a matter of considerable interest.” To understand this issue, it is useful to have a paleoperspective on the baseline, transfer, and impact of PHTE in the environment.

eling od m s.

Present TE ecogeochemistry

• Present atmospheric input • TE reactivity in the critical zone • TE export to the watersheds • Impact of sudden landscape changes • Fire impacts • Others • Aquatic ecogeochemistry

TE Holocene biogeochemistry TE future biogeochemistry

• Integrated environmental archives • Mountain land-cover changes • Aquatic habitats: biological proxies

• Mountain hydrological models • Including sediment and organic matter transport

• Trace element transport and ecotoxicological models

Figure 9.8  Circular flow chart of the integrative projects necessary to investigate the fate of trace elements in mountain environments: example of the TRAM project describing the links between the different fields (TE for trace elements) and tasks.

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  201

Combining geochemical tracers of PHTE from various environmental archives (peat, lakes, fish otoliths, tree rings; Figure 9.1) will allow not only the deciphering of the different sources of PHTE relative to time in the mountain critical zone but also the assessment of the magnitude of the impact on the past environment. In the case of Pb, for example, a paleoperspective is necessary since literature results show that ancient anthropogenic Pb may remain stored in the catchment for centuries or millennia. In extremely contaminated regions, however, a portion of the incoming Pb may have a potentially shorter residence time in the catchment, no longer than a few decades (Bohdalkova et al., 2014). In their study on Pb export in Czech catchments, Bodhalkova et  al. (2014) emphasize the need for longer time‐series than are provided by present monitoring studies. These authors show that a consensus on the long‐ term potential for Pb soil remobilization to surface water does not exist, despite the fact that the following concerns have been raised: (a) an export of legacy Pb via runoff may have a negative effect on the drinking water supply; (b) the Pb origin can be traced using its isotopic signature (Shotyk & Le Roux, 2005; Stille et al., 2011); and (c) some integrative studies already exist (Johnson et al., 1995). Similar conclusions can be drawn for Hg, for which study of the Hg biogeochemical cycle is now assisted by possible measurements of its different isotopes (Blum et  al., 2014). The legacy Hg impact on the present global Hg cycle is not well understood (Amos et al., 2015). With regard to long‐term behaviors of other PHTEs, there are even fewer data despite the fact that some are highly toxic (e.g., antimony and arsenic) or may have association effects that will affect the “quality” of the mountain critical zone. More specifically, beyond obvious impacts such as mining activities, mountain drivers involving strong feedbacks between organic matter and PHTE cycle must be implemented in present monitoring studies, models, and future scenarios. Unlike boreal regions (Teisserenc et al., 2014), the impact of dams and/or deforestation on PHTE is not well studied in mountainous areas. 9.4.3. Using Different Isotope Tracers A perspective to better understand the cycle of trace elements is to use their different isotopic systems. We have already mentioned the isotopes of Pb. Another promising isotopic system is that of Hg that can be used to trace both the sources and the processes specific to that element (Blum et  al., 2014). In particular, Enrico et  al. (2016, 2017) have recently demonstrated in mountain peatlands that it is possible to reconstruct gaseous elemental mercury (GEM) concentrations and wet deposits from Hg isotopes measured in peat deposits. Enrico et al. (2017) also showed the unprecedented increase in Hg in the Pyrenean air since the beginning of the Anthropocene.

Not only are Hg isotopes used to reconstruct the history of this element in mountain areas but they also help to understand the processes that involve a greater accumulation of Hg in mountain soils. For example, using the traditional EF approach combined with Hg isotopes, Zhang et al. (2013), showed that Tibetan soils are enriched in Hg with increasing altitude. Altitudinal effects linked with temperature, precipitations (including occult deposition), and change in vegetation type have been hypothesized as the main drivers of the Hg concentrations in mountains soils. Like Arctic foodchains, which are studied for Hg using isotopes, mountain ecosystems can also be further studied using Hg isotopes and thus provide an understanding of the (bio)accumulation mechanisms specific to the mountain watersheds (Hansson, Sonke, et al., 2017; Xu et al., 2016). Other isotopic systems are available: for example, Nd isotopes have been used to trace the sources of urban dust (Lahd Geagea et  al., 2008). They also help trace dust sources in more rural environments (Le Roux et al., 2012). It is conceivable that soon the isotopes of Nd may be used to trace anthropogenic dust sources in mountain environments due to the increasing industrial use and recycling of REE. The isotopes of antimony (Wen et al., 2017) and osmium (Rauch et al., 2004) are also used in environmental archives (peat and sediments) and in urban areas and will be useful in the future to trace the sources of these elements in the mountain environment. 9.4.4. Combining with Other Proxies Despite a clear impact of trace metals on biological organisms, there are few environmental archive studies on the paleo‐impact of trace metals in mountains. The long‐term (> 10 years) consequences of accumulations of metals and metalloids in the environment are still unknown. Acute effects are becoming increasingly clear, however, the chronic, long‐term effects on the functioning of an ecosystem, in general, are difficult to ascertain. Because of the numerous environmental archives, it is also possible to utilize chemical and biological proxies for the proper functioning (e.g., biodiversity, the presence of sentinel species) or the malfunctioning (e.g., anoxia, the presence of many pathogens, etc.) of ecosystems. Korosi et al. (2017) reviewed the potential to combine paleolimnology with other approaches to develop a new field: paleoecotoxicology. Mountain ecosystems with their different environmental archives (lakes, peatlands, trees, fish otoliths) are clearly a perfect setting to test the paleoecotoxicology hypothesis. Prehistoric pristine conditions can be found followed by acute contamination during periods such as the Middle Ages, and by the present “chronic” long‐range transported pollution.

202  BIOGEOCHEMICAL CYCLES

Not only have humans modified metal concentrations and speciation in the environment but, as shown by Hansson, Claustres, et al. (2017), they have also modified the dispersion of these elements through the environment by heavily modifying the landscape. Because of the steep slopes, landscape‐altering activities such as mining, logging, dams, or fires in mountains (Figures  9.1 and 9.9) can have a profound impact on soil erosion and particle transport, which may also impact trace‐element transport. Proxies of sediment sources, erosion, and transport intensity have been developed in the past decade by paleolimnologists (Arnaud et  al., 2016; Simonneau et  al., 2013) that now allow us to combine organic‐matter transport with trace‐element budgets for recent centuries in the mountain catchments (Bacardit & Camarero, 2010; Bacardit et al., 2012).

9.4.5. Confront the Past to Better Understand the Future of PHTE in the Mountain Critical Zone Figure 9.9 shows potential past and future, local abrupt and continuous environmental changes in the upper Vicdessos Valley. There is now a good overview of the main human and environmental changes on mountain environments of the Central Pyrenees. We hypothesize, for example, that continuous mire development over lakes has modified PHTE export through the Holocene, buffering and modifying PHTE‐organic matter relationships. We expect that strong environmental changes on soils and mires due to grazing and deforestation during the Middle Ages, coupled with mining activities, have hugely impacted the mountain environment. From a prospective point of view, the Pyrenees can expect a significant reduction in

Landscape trajectories and PHTE release in the Pyrenees mountain critical zone

o

N ow m sn 800 1

er

nd

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Soil thickness

m

is

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at

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g in m in lis M tora s Pa

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Increasing “green” energy demand

Lakes development ro

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da

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PHTE release

M

g

g

in

in

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1000 In the past

100

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2020 In the future

Figure 9.9  Main environmental shifts and knowledge gaps that have and will impact the Haut‐Vicdessos area. The past evolution of the snowpack and snowfields is an imaginary scenario in the absence of robust proxies (*). Prospective scenarios are based speculatively on an economic reappropriation of mountain territories. We hypothesize that PHTE released to the mountain watersheds declined because of soil and wetlands formation, increased retention in forested soils and disappearance of easily weathered minerals after the Last Glacial Maximum. In the future, we may expect further PHTE release both because of exposed bare rocks in high altitude areas and because of reopening of mines.

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  203

snow cover and a reopening of mines previously exploited in the 19th and 20th centuries. Bare rock surfaces after the retreat of glaciers and snowfields could revive rock weathering and the export of metals such as As. The reopening of mines can also be accompanied by local emission of PHTE to aquatic, terrestrial, and atmospheric environments. Long‐term PHTE monitoring provided by environmental archives (lake, peat, tree rings, and otoliths) is, therefore, necessary on a long timescale to understand and also to validate hydro‐biogeochemical critical zone models that will evaluate responses of the mountain critical zones to climate change and to local socio‐ecological changes. ACKNOWLEDGMENTS This research is supported by a Young Researcher Grant of the Agence National de la Recherche (ANR), project ANR‐15‐CE01‐0008 “TRAM” (summits.cnrs.fr). A. Claustres PhD was funded by a University of Toulouse grant. S.V. Hansson was funded by AXA Research Fund (14‐AXA‐PDOC‐030). D. Allen and S.V. Hansson were complementary supported by the program Prestige/ Campus France—co‐funded by Marie Curie EU program (PCOFUND‐GA‐2013‐609102 for D. Allen and PRESTIGE‐2014‐1‐0037 for S.V. Hansson). Generous support by Labex DRIIHM and Observatoire Hommes et Milieux Haut Vicdessos as well as ADEME (BioGeoSTIB project) is also acknowledged. D. Schmeller and G. Le Roux are also generously supported by the project “People, Pollution, and Pathogens” which is financed through the  call “Mountains as Sentinels of Change” by the Belmont‐Forum (ANR‐15‐MASC‐0001 ‐ P3, DFG‐ SCHM 3059/6‐1, NERC‐1633948, NSFC‐41661144004). J. M. Sanchez‐Perez and G. Le Roux are also regionally funded by the European Union through the Interreg‐ POCTEFA territorial cooperation program through the OPCC projects: REPLIM and PIRAGUA Interreg V-A Spain-French-Andorra (POCTEFA 2014–2020). REFERENCES Adriano, D.C. (2001). Trace elements in terrestrial environments: biogeochemistry, bioavailability, and risks of metals, 2nd edn. New York: Springer‐Verlag Inc. Agnan, Y., Séjalon‐Delmas, N., Claustres, A., & Probst, A. (2015). Investigation of spatial and temporal metal atmospheric deposition in France through lichen and moss bioaccumulation over one century. Science of the Total Environment, 529, 285–296. doi:10.1016/j.scitotenv.2015.05.083 Agnan, Y., Séjalon‐Delmas, N., & Probst, A. (2013). Comparing early twentieth century and present‐day atmospheric pollution in SW France: A story of lichens. Environmental Pollution, 172, 139–148. doi:10.1016/j.envpol.2012.09.008

Amarasiriwardena, D., & Wu, F. (2011). Antimony: Emerging toxic contaminant in the environment. Microchemistry Journal, 97, 1–3. doi:http://dx.doi.org/10.1016/j.microc.2010.07.009 Amos, H.M., Sonke, J.E., Obrist, D., Robins, N., Hagan, N., Horowitz, H.M., et  al. (2015). Observational and modeling constraints on global anthropogenic enrichment of mercury. Environmental Science and Technology, 49(7), 4036–4047. Aries, S., Motelica‐Heino, M., Freydier, R., Grezes, T., & Polvé, M. (2001). Direct determination of lead isotope ratios by laser ablation‐inductively coupled plasma‐quadrupole mass spectrometry in lake sediment samples. Geostandards and Geoanalytical Research, 25, 387–398. doi:10.1111/j.1751‐908X.2001.tb00613.x Arnaud, F., Poulenard, J., Giguet‐Covex, C., Wilhelm, B., Révillon, S., Jenny, J.‐P., et al. (2016). Erosion under climate and human pressures: An alpine lake sediment perspective. Quaternary Science Reviews, 152, 1–18. Bacardit, M., & Camarero, L. (2010). Modelling Pb, Zn and As transfer from terrestrial to aquatic ecosystems during the ice‐free season in three Pyrenean catchments. Science of the Total Environment, 408, 5854–5861. doi:10.1016/j. scitotenv.2010.07.088 Bacardit, M., Krachler, M., & Camarero, L. (2012). Whole‐ catchment inventories of trace metals in soils and sediments in mountain lake catchments in the Central Pyrenees: Apportioning the anthropogenic and natural contributions. Geochimica et Cosmochimica Acta, 82, 52–67. doi:10.1016/j.gca.2010.10.030 Berg, T., & Steinnes, E. (1997). Use of mosses (Hylocomium splendens and Pleurozium schreberi) as biomonitors of heavy metal deposition: From relative to absolute deposition values. Environmental Pollution, 98, 61–71. Blum, J.D., Sherman, L.S., & Johnson, M.W. (2014). Mercury isotopes in Earth and environmental sciences. Annual Review in Earth and Planetary Sciences, 42, 249–269. doi:10.1146/ annurev‐earth‐050212‐124107 Bohdalkova, L., Novak, M., Stepanova, M., Fottova, D., Chrastny, V., Mikova, J., et al. (2014). The fate of atmospherically derived Pb in central European catchments: Insights from spatial and temporal pollution gradients and Pb isotope ratios. Environmental Science and Technology, 48, 4336–4343. doi:10.1021/es500393z Broder, T., & Biester, H. (2015). Hydrologic controls on DOC, As and Pb export from a polluted peatland—the importance of heavy rain events, antecedent moisture conditions and hydrological connectivity. Biogeosciences 12, 4651–4664. doi:10.5194/bg‐12‐4651‐2015 Brunel, C., Munoz, M., & Probst, A. (2003). Remobilisation of Zn and Pb in a mountain stream contaminated by mining wastes during a moderate flood event (Ariège, France). Journal de Physique IV (Proceedings), 107, 233–236. doi: 10.1051/jp4:20030285 Byrne, P., Reid, I., & Wood, P.J. (2010). Sediment geochemistry of streams draining abandoned lead/zinc mines in central Wales: the Afon Twymyn. Journal of Soils and Sediments, 10, 683–697. doi:10.1007/s11368‐009‐0183‐9 Camarero, L., Botev, I., Muri, G., Psenner, R., Rose, N., & Stuchlik, E. (2009). Trace elements in alpine and arctic lake sediments as a record of diffuse atmospheric contamination across Europe. Freshwater Biology, 54, 2518–2532. doi: 10.1111/j.1365‐2427.2009.02303.x

204  BIOGEOCHEMICAL CYCLES Camarero, L., Masqué, P., Devos, W., Ani‐Ragolta, I., Catalan, J., Moor, H., et al. (1998). Historical variations in lead fluxes in the Pyrenees (Northeast Spain) from a dated lake sediment core. Water, Air and Soil Pollution, 105, 439–449. Catalan, J., Camarero, L., Felip, M., Pla, S., Ventura, M., Buchaca, T., et al. (2006). High mountain lakes: extreme habitats and witnesses of environmental changes. Limnetica, 25, 551–584. Catalan, J., Pla‐Rabés, S., Wolfe, A.P., Smol, J.P., Rühland, K.M., Anderson, N.J., et al. (2013). Global change revealed by palaeolimnological records from remote lakes: a review. Journal of Paleolimnology, 49, 513–535. doi:10.1007/s10933‐013‐9681‐2 Chen, C., Sedwick, P.N., & Sharma, M. (2009). Anthropogenic osmium in rain and snow reveals global‐scale atmospheric contamination. Proceedings of the National Academy of Sciences of the United States of America, 106, 7724–7728. Claustres, A. (2016). Répartition des éléments traces potentiellement toxiques dans les zones de montagne: Rôle et part des facteurs naturels et anthropiques des temps pédologiques. Toulouse: Université Paul Sabatier. Cooke, C.A., & Bindler, R. (2015). Lake sediment records of preindustrial metal pollution. In J.M. Blais, M.R. Rosen, J.P. Smol (Eds.), Environmental contaminants (pp. 101–119). Developments in Paleoenvironmental Research Series. Springer. Craddock, P.T. (2000). From hearth to furnace: Evidence for the earliest metal smelting technologies in the Eastern Mediterranean. Paléorient, 26, 151–165. Davasse, B., & Galop, D. (1990). Le paysage forestier du haut Vicdessos (Ariège): évolution d’un milieu anthropisé. Revue géographique des Pyrénées et du Sud‐Ouest, 61, 433–457. de Dietrich, P.‐F. (1786). Description des gîtes de minerai, des forges et des salines des Pyrénées. Didot fils aîné. De Vleeschouwer, F., Le Roux, G., & Shotyk, W. (2010). Peat as an archive of atmospheric pollution and environmental change: A case study of lead in Europe. PAGES News, 18, 20–22. https://doi.org/10.22498/pages.18.1.20 De Vleeschouwer, F., Vanneste, H., Mauquoy, D., Piotrowska, N., Torrejón, F., Roland, T., et al. (2014). Emissions from pre‐ Hispanic metallurgy in the South American atmosphere. PLOS One, 9, e111315 Delpla, I., Jung, A.‐V., Baures, E., Clement, M., & Thomas, O. (2009). Impacts of climate change on surface water quality in relation to drinking water production. Environment International, 35, 1225–33. doi:10.1016/j.envint.2009.07.001 Drexler, C., Braun, V., Christie, D., Claramunt, B., Dax, T., Jelen, I., et al. (2016). Mountains for Europe’s future—a strategic research agenda. www.mountainresearchinitiative.org Dubois, C. (1999). Les mines de plomb argentifère et zinc d’Aulus‐les‐Bains (Ariège). Archéologie Midi Médiév, 17, 187–211. Enrico, M., Le Roux, G., Heimburger, L.‐E., Van Beek, P., Souhaut, M., Chmeleff, J., et  al. (2017). Holocene atmospheric mercury levels reconstructed from peat bog mercury stable isotopes. Environmental Science and Technology, 51(11), 5899–5906. doi:10.1021/acs.est.6b05804 Enrico, M., Le Roux, G., Marusczak, N., Heimbürger, L.‐E., Claustres, A., Fu, X., et  al. (2016). Atmospheric mercury transfer to peat bogs dominated by gaseous elemental mercury

dry deposition. Environmental Science and Technology, 50, 2405–2412. Fiałkiewicz‐Kozieł, B., De Vleeschouwer, F., Mattielli, N., Fagel, N., Palowski, B., Pazdur, A., Smieja‐Król B. (2018). Record of Anthropocene pollution sources of lead in disturbed peatlands from Southern Poland. Atmospheric Environment, 179, 61–68. Forel, B., Monna, F., Petit, C., Bruguier, O., Losno, R., Fluck, P., et al. (2010). Historical mining and smelting in the Vosges Mountains (France) recorded in two ombrotrophic peat bogs. Journal of Geochemical Exploration, 107, 9–20. Galop, D., & Jalut G. (1994). Differential human impact and vegetation history in two adjacent Pyrenean valleys in the Ariège basin, southern France, from 3000 BP to the present. Vegetation History and Archaeobotany, 3, 225–244. Galop, D., Rius, D., Cugny, C., & Mazier, F. (2013). A history of long‐term human–environment interactions in the French Pyrenees inferred from the pollen data. In L.R. Lozny (Ed.), Continuity and change in cultural adaptation to mountain environments (pp. 19–30). Studies in Human Ecology and Adaptation Series. Springer. Gałuszka, A., Migaszewski, Z.M., & Namieśnik, J. (2017). The role of analytical chemistry in the study of the Anthropocene. Trends in Analytical Chemistry, 97, 146–152. doi:10.1016/j. trac.2017.08.017 Gałuszka, A., Migaszewski, Z.M., & Zalasiewicz, J. (2014). Assessing the Anthropocene with geochemical methods. Geological Society of London Special Publication, 395, 221– 38. doi:10.1144/SP395.5 Gandois, L., Agnan, Y., Leblond, S., Séjalon‐Delmas, N., Le Roux, G., & Probst A. (2014). Use of geochemical signatures, including rare earth elements, in mosses and lichens to assess spatial integration and the influence of forest environment. Atmospheric Environment, 95, 96–104. doi:10.1016/j. atmosenv.2014.06.029 Gombert, S., Traubenberg, C.R.D., Losno, R., Leblond, S., Colin, J.L., & Cossa, D. (2004). Biomonitoring of element deposition using mosses in the 2000 French survey: Identifying sources and spatial trends. Journal of Atmospheric Chemistry, 49, 479–502. doi:10.1007/s10874‐004‐1261‐4 Haack, U.K., Heinrichs, H., Gutsche, F.H., & Plessow, K. (2003). The isotopic composition of anthropogenic Pb in soil profiles of northern Germany: evidence for pollutant Pb from a continent‐wide mixing system. Water, Air and Soil Pollution, 150, 113–134. Hansson, S.V., Claustres, A., Probst, A., De Vleeschouwer, F., Baron, S., Galop, D., et al. (2017). Atmospheric and terrigenous metal accumulation over 3000 years in a French mountain catchment: Local vs distal influences. Anthropocene, 19, 45–54. doi:10.1016/j.ancene.2017.09.002 Hansson, S.V., Sonke, J., Galop, D., Bareille, G., Jean, S., & Le Roux, G. (2017). Transfer of marine mercury to mountain lakes. Nature: Scientific Reports, 7, 12719. doi:10.1038/ s41598‐017‐13001‐2 Harmens, H., Buse, A., Büker, P., Norris, D., Mills, G., Williams, B., et  al. (2004). Heavy metal concentrations in European mosses: 2000/2001 survey. Journal of Atmospheric Chemistry, 49, 425–436. doi:10.1007/s10874‐004‐1257‐0 Harmens, H., Norris, D.A., Sharps, K., Mills, G., Alber, R., Aleksiayenak, Y., et  al. (2015). Heavy metal and nitrogen

TRACE METAL LEGACY IN MOUNTAIN ENVIRONMENTS  205 concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environmental Pollution, 200, 93–104. doi:10.1016/j.envpol.2015.01.036 Harmens, H., Norris, D.A., Steinnes, E., Kubin, E., Piispanen, J., Alber, R., et al. (2010). Mosses as biomonitors of atmospheric heavy metal deposition: Spatial patterns and temporal trends in Europe. Environmental Pollution, 158, 3144–3156. doi:10.1016/j.envpol.2010.06.039 Hong, S., Candelone, J.‐P., Patterson, C.C., & Boutron, C.F. (1994). Greenland ice evidence of hemispheric lead pollution two millennia ago by Greek and Roman civilizations. Science, 265, 1841–1843. Johnson, C.E., Siccama, T.G., Driscoll, C.T., Likens, G.E., & Moeller, R.E. (1995). Changes in lead biogeochemistry in response to decreasing atmospheric inputs. Ecological Applications, 5, 813–22. Jouffroy‐Bapicot, I., Pulido, M., Baron, S., Galop, D., Monna, F., Lavoie, M., et  al. (2007). Environmental impact of early palaeometallurgy: pollen and geochemical analysis. Vegetation History and Archaeobotany, 16, 251–258. Kempter, H., Krachler M, & Shotyk, W. (2010). Atmospheric Pb and Ti Accumulation Rates from Sphagnum Moss: Dependence upon Plant Productivity. Environ Sci Amp Technol 44:5509–15. doi:10.1021/es100366d. Kempter, H., Krachler M, Shotyk, W., & Zaccone, C. (2017). Validating modelled data on major and trace element deposition in southern Germany using Sphagnum moss. Atmospheric Environment, 167, 656–664. doi:10.1016/j.atmosenv.2017.08.037. Klaminder, J., Hammarlund, D., Kokfelt, U., Vonk, J.E., & Bigler, C. (2010). Lead contamination of subarctic lakes and its response to reduced atmospheric fallout: can the recovery process be counteracted by the ongoing climate change? Environmental Science and Technology, 44, 2335–2340. doi:10.1021/es903025z Korosi, J., Thienpont, J.R., Smol, J.P., & Blais, J.M. (2017). Paleo‐ ecotoxicology: what can lake sediments tell us about ecosystem responses to environmental pollutants? Environmental Science and Technology, 51(17), 9446–9457. Krachler, M., Le Roux, G., Kober, B., & Shotyk, W. (2004). Optimising accuracy and precision of lead isotope measurement (206Pb, 207Pb, 208Pb) in acid digests of peat with ICP‐SMS using individual mass discrimination correction. Journal of Analytical Atomic Spectrometry, 19, 354–361. Lahd Geagea, M., Stille, P., Gauthier‐Lafaye, F., & Millet, M. (2008). Tracing of industrial aerosol sources in an urban environment using Pb, Sr, and Nd isotopes. Environmental Science and Technology, 42, 692–698. Le Roux, G., Aubert, D., Stille, P., Krachler, M., Kober, B., Cheburkin, A., et al. (2005). Recent atmospheric Pb deposition at a rural site in southern Germany assessed using a peat core and snowpack, and comparison with other archives. Atmospheric Environment, 39, 6790–6801. Le Roux, G., Duffa, C., Vray, F., & Renaud, P. (2010). Deposition of artificial radionuclides from atmospheric Nuclear Weapon Tests estimated by soil inventories in French areas low‐impacted by Chernobyl. Journal of Environmental Radioactivity, 101, 211–218. doi:10.1016/j.jenvrad.2009.10.010 Le Roux, G., Fagel, N., De Vleeschouwer, F., Krachler, M., Debaille, V., Stille, P., et al. (2012). Volcano‐ and climate‐driven

changes in atmospheric dust sources and fluxes since the Late Glacial in Central Europe. Geology, 40, 335–338. Le Roux, G., Hansson, S.V., and Claustres, A. (2016). Inorganic chemistry in the mountain critical zone: Are the mountain water towers of contemporary society under threat by trace contaminants? Developments in Earth Surface Processes, 21, 131–155. Le Roux, G., Pourcelot, L., Masson, O., Duffa, C., Vray, F., & Renaud, P. (2008). Aerosol deposition and origin in French mountains estimated with soil inventories of 210Pb and artificial radionuclides. Atmospheric Environment, 42, 1517– 1524. doi:10.1016/j.atmosenv.2007.10.083. Likuku, A.S. (2006). Wet deposition of 210Pb aerosols over two areas of contrasting topography. Environmental Research Letters, 1, 014007. doi:10.1088/1748‐9326/1/1/014007 Longman, J., Veres, D., Finsinger, W., & Ersek, V. (2018). Exceptionally high levels of lead pollution in the Balkans from the Early Bronze Age to the Industrial Revolution. Proceedings of the National Academy of Sciences of the United States of America, 115, E5661–E5668. doi:10.1073/ pnas.1721546115 Lovett, G.M., & Kinsman, J.D. (1990). Atmospheric pollutant deposition to high‐elevation ecosystems. Atmospheric Environment, Part A, General Topics, 24, 2767–86. doi: 10.1016/0960‐1686(90)90164‐I Mariet, A.‐L., Walter‐Simonnet, A.‐V., Gimbert, F., Cloquet, C., & Bégeot, C. (2018). High‐temporal resolution landscape changes related to anthropogenic activities over the past millennium in the Vosges Mountains (France). Ambio, 47, 893– 907. doi:10.1007/s13280‐018‐1044‐9 Martinez Cortiza, A., García‐Rodeja, E., Pontevedra Pombal, X., Nóvoa Muñoz, J.C., Weiss, D., & Cheburkin, A. (2002). Atmospheric Pb deposition in Spain during the last 4600 years recorded by two ombrotrophic peat bogs and implications for the use of peat as archive. Science of the Total Environment, 292, 33–44. Meyer, C., Diaz‐de‐Quijano, M., Monna, F., Franchi, M., Toussaint, M.‐L., Gilbert, D., et  al. (2015). Characterisation and distribution of deposited trace elements transported over long and intermediate distances in north‐eastern France using Sphagnum peatlands as a sentinel ecosystem. Atmospheric Environment, 101, 286–293. doi:10.1016/j.atmosenv.2014.11.041 Migaszewski, Z.M., & Gałuszka, A. (2015). The characteristics, occurrence, and geochemical behavior of rare earth elements in the environment: a review. Critical Reviews in Environmental Science and Technology, 45, 429–471. Moldovan, M., Veschambre, S., Amouroux, D., Bénech, B., & Donard, O.F. (2007). Platinum, palladium, and rhodium in fresh snow from the Aspe Valley (Pyrenees Mountains, France). Environmental Science and Technology, 41, 66–73. Monna, F., Camizuli, E., Revelli, P., Biville, C., Thomas, C., Losno, R., et al. (2011). Wild brown trout affected by historical mining in the Cévennes National Park, France. Environmental Science and Technology, 45, 6823–6830. doi:10.1021/es200755n Monna, F., Galop, D., Carozza, L., Tual, M., Beyrie, A., Marembert, F., et al. (2004). Environmental impact of early Basque mining and smelting recorded in a high ash minerogenic peat deposit. Science of the Total Environment, 327, 197–214.

206  BIOGEOCHEMICAL CYCLES Pompeani, D.P., Abbott, M.B., Steinman, B.A., & Bain, D.J. (2013). Lake sediments record prehistoric lead pollution related to early copper production in North America. Environmental Science and Technology, 47, 5545–5552. Price, M.F. (2016). Mountains move up the European agenda. Mountain Research Developments, 36, 376–379. Rauch, S., Hemond, H.F., & Peucker‐Ehrenbrink, B. (2004). Source characterisation of atmospheric platinum group element deposition into an ombrotrophic peat bog. Journal of Environmental Monitoring, 6, 335–343. Roe, G.H. (2005). Orographic precipitation. Annual Review in Earth and Planetary Sciences, 33, 645–71. doi:10.1146/ annurev.earth.33.092203.122541 Rosman, K.J., Chisholm, W., Hong, S., Candelone, J.‐P., &Boutron, C.F. (1997). Lead from Carthaginian and Roman Spanish mines isotopically identified in Greenland ice dated from 600 BC to 300 AD. Environmental Science and Technology, 31, 3413–3416. Sen, I.S., & Peucker‐Ehrenbrink, B. (2012). Anthropogenic disturbance of element cycles at the Earth’s surface. ­ Environmental Science and Technology, 46, 8601–9. doi: 10.1021/es301261x Shotyk, W. (1996). Natural and anthropogenic enrichments of As, Cu, Pb, Sb, and Zn in ombrotrophic versus minerotrophic peat bog profiles, Jura Mountains, Switzerland. Water, Air and Soil Pollution, 90, 375–405. doi:10.1007/BF00282657 Shotyk, W., Belland, R., Duke, J., Kempter, H., Krachler, M., Noernberg, T., et  al. (2014). Sphagnum mosses from 21 ombrotrophic bogs in the Athabasca Bituminous Sands Region show no significant atmospheric contamination of “heavy metals.” Environmental Science and Technology, 48, 12603–12611. doi:10.1021/es503751v Shotyk, W., & Le Roux, G. (2005). Biogeochemistry and cycling of lead. In H.Sigel, R. Sigel (Eds.), Metal ions in biological systems (Vol. 43, 239–275). Biogeochemical Cycles of Elements. Taylor & Francis. Shotyk, W., Weiss, D., Appleby, P.G., Cheburkin, A.K., Frei, R., Gloor, M., et  al. (1998). History of atmospheric lead deposition since 12,370 C‐14 yr BP from a peat bog, Jura  Mountains, Switzerland. Science, 281, 1635–1640. doi:10.1126/science.281.5383.1635 Simonneau, A., Chapron, E., Courp, T., Tachikawa, K., Le Roux, G., Baron, S., et al. (2013). Recent climatic and anthropogenic imprints on lacustrine systems in the Pyrenean Mountains inferred from minerogenic and organic clastic supply (Vicdessos valley, Pyrenees, France). The Holocene. https://doi.org/10.1177/0959683613505340 Soyol‐Erdene, T.‐O., Huh, Y., Hong, S., & Hur, S.D. (2011). A 50‐year record of platinum, iridium, and rhodium in Antarctic snow: volcanic and anthropogenic sources. Environmental Science and Technology, 45, 5929–5935. Stankwitz, C., Kaste, J.M., & Friedland, A.J. (2012). Threshold increases in soil lead and mercury from tropospheric deposition across an elevational gradient. Environmental Science and Technology, 46, 8061–8068. doi:10.1021/es204208w

Stille, P., Pourcelot, L., Granet. M., Pierret, M.‐C., Guéguen, F., Perrone T, et al. (2011). Deposition and migration of atmospheric Pb in soils from a forested silicate catchment today and in the past (Strengbach case): Evidence from 210Pb activities and Pb isotope ratios. Chemical Geology, 289, 140–53. doi:10.1016/j.chemgeo.2011.07.021 Suzuki, Y., Hikida, S., & Furuta, N. (2011). Cycling of rare earth elements in the atmosphere in central Tokyo. Journal of Environmental Monitoring, 13, 3420–3428. Szczepaniak, K., & Biziuk, M. (2003). Aspects of the biomonitoring studies using mosses and lichens as indicators of metal pollution. Environmental Research, 93, 221–230. Teisserenc, R., Lucotte, M., Canuel, R., Moingt, M., & Obrist D. (2014). Combined dynamics of mercury and terrigenous organic matter following impoundment of Churchill Falls Hydroelectric Reservoir, Labrador. Biogeochemistry, 118, 21–34. doi:10.1007/s10533‐013‐9902‐9 Waters, C.N., Zalasiewicz, J., Summerhayes, C., Barnosky, A.D., Poirier, C., Gałuszka, A., et  al. (2016). The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science, 351(6269). Weathers, K.C., Lovett, G.M., Likens, G.E., & Lathrop, R. (2000). The effect of landscape features on deposition to  Hunter Mountain, Catskill Mountains, New York. Ecological Applications, 10, 528–540. doi:10.1890/1051‐0761 (2000)010[0528,TEOLFO]2.0.CO;2 Wen, B., Zhou, J., Zhou, A., Liu, C., & Li, L. (2017). A review of antimony (Sb) isotopes analytical methods and application in environmental systems. International Biodeterioration and Biodegradation, 128, 109–116 Wolterbeek, B. (2002). Biomonitoring of trace element air pollution: principles, possibilities and perspectives. Environmental Pollution, 120, 11–21. Xu, X., Zhang, Q., & Wang, W.‐X. (2016). Linking mercury, carbon, and nitrogen stable isotopes in Tibetan biota: Implications for using mercury stable isotopes as source tracers. Nature: Scientific Reports, 6. doi:10.1038/srep25394 Zaharescu, D.G., Hooda, P.S., Fernandez, J., Soler, A.P., & Burghelea, C.I. (2009). On the arsenic source mobilisation and its natural enrichment in the sediments of a high mountain cirque in the Pyrenees. Journal of Environmental Monitoring, 11, 1973–1981. Zannetti, P. (1990). Air pollution modeling. Boston, MA: Springer. Zhang, H., Yin, R., Feng, X., Sommar, J., Anderson, C.W.N., Sapkota, A., et  al. (2013). Atmospheric mercury inputs in montane soils increase with elevation: evidence from mercury isotope signatures. Nature: Scientific Reports, 3. doi:10.1038/ srep03322. Zheng, J., Shotyk, W., Krachler, M., & Fisher, D.A. (2007). A 15,800‐year record of atmospheric lead deposition on the Devon Island Ice Cap, Nunavut, Canada: Natural and anthropogenic enrichments, isotopic composition, and predominant sources. Global Biogeochemical Cycles, 21. doi: 10.1029/2006GB002897

10 Poised to Hindcast and Earthcast the Effect of Climate on the Critical Zone: Shale Hills as a Model Pamela L. Sullivan1, Li Li2, Yves Goddéris3, and Susan L. Brantley4 ABSTRACT One goal in critical zone (CZ) science is to project the response of Earth’s near‐surface fluxes of water, sediments, and nutrients to perturbations in climate and human actions, an approach that is increasingly known as earthcasting. However, earthcasting requires knowledge of the present and a deep understanding of the past and, more importantly, a validation through collection of data and simulations of past processes. This so‐called hindcasting pairs past climate and present‐day critical zone structure to understand how the function of Earth’s living skin has evolved with time. The combined approach of hindcasting and earthcasting illuminates strategies for managing the critical zone and generates new hypotheses to be tested in the field and laboratory. Here we: (a) present a road map to earthcasting and hindcasting, (b) review several examples of these projections, and (c) explore a recent earthcast and hindcast of the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) where aspect was used to explore the effect of increased temperature and enhanced evapotranspiration rates on soil water solute fluxes. We conclude by discussing opportunities, challenges, and future directions for earthcasting and hindcasting, with the intent to inspire more simulations that simultaneously project water, sediments, and solute fluxes and their effect on CZ architecture.

10.1. HINDCASTING AND EARTHCASTING 10.1.1. What is Hindcasting and Earthcasting? Hindcasting reconstructs the evolution of the critical zone by pairing knowledge about past climate with the present‐day regolith record and solute/sediment fluxes. Earthcasting is the projection of Earth’s near‐surface fluxes (e.g., water, solutes, and sediments), architecture 1  College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, Oregon, USA 2   Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA 3  Environmental Geosciences Toulouse, CNRS— Midi ‐ Pyrénées Observatory, Toulouse, France 4   Earth and Environmental Systems Institute and Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania, USA

(e.g., soil thickness and hydrologic properties), and biotic processes (e.g., plant community structure and microbiomes) in order to examine how human and climatic perturbations will drive the evolution and resource availability of terrestrial Earth (Brantley et  al., 2011; Duffy et  al., 2014; Goddéris & Brantley, 2013; Sullivan et al., 2019). In essence, earthcasts aim to project the state of a pedon, hillslope, watershed, or landscape in the future (Perignon et al., 2016). It is this mechanistic understanding of past and present that will allow for better projections of critical zone processes into the future (Figure 10.1). 10.1.2. Why Do We Do It? The idea of an “earthcast” was coined by Chris Paola at the National Center for Earth Dynamics and was discussed by Brantley et  al. (2006), Murray et  al. (2009), and Goddéris and Brantley (2013). Here, we emphasize

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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Figure 10.1  A hindcast (red arrow) for Earth’s surface is a simulation (light blue line) of such components as water, solutes, soils, biota, and sediments from the past (gray arrows) to the present, and an earthcast (green arrow) is a projection of the Earth’s surface and its fluxes and reservoirs across scales (royal blue) into the future. To hindcast and earthcast successfully requires mechanistic understanding of how climate and human actions affect the critical zone.

the use of earthcasting as a quest to answer questions such as: (a) Can we earthcast the response of the critical zone—Earth’s terrestrial living skin—to both gradual changes and extreme events? (b) How does earthcasting depend on spatial and temporal scales? Now that we have entered the so‐called “Anthropocene,” i.e., the modern era in which humans are the largest geologic force on Earth system processes (Steffen et  al., 2015; Lewis & Maslin, 2015), earthcasting can be a powerful tool that allows for better management and adaptation strategies. Human impacts have led to an acceleration (or change in rates) of: (a) land conversion (urban and agriculture systems now rival forest and grassland systems); (b) atmospheric CO2 concentrations (concentrations increasingly rise each year); (c) average global annual air temperatures (global average temperatures are increasing); (d) sea‐levels (levels are now reaching new heights in areas that experience isostatic equilibrium and tectonic inactivity); and (e) the nitrogen and phosphorus cycle (reactive species have more than doubled in concentration since the industrial revolution). These changes to the external forcing of the critical zone alter hydrologic, biologic, and weathering (physical and chemical) processes. We illustrate the complex and coupled effect of just one  of these changes, the rise in atmospheric CO2 concentration, on critical zone services by using the interdisciplinary approach of integrating observations of vegetation, soil, bedrock, and fluxes of water and weathering products. Evidence suggests that elevated atmospheric CO2 will potentially impact vegetation by affecting stomatal conductance, photosynthesis, evapotranspiration (ET), plant growth rates, nutrient cycling, and uptake of

mineral nutrients by biota. One plant response may be stomata closure to reduce water loss through transpiration, inherently making them more efficient at using soil moisture (e.g., Woodward, 2002) and allowing a greater water flux through the soil profile (see Beaulieu et  al., (2010) for a detailed explanation). Here alterations to the feedback between water residence time and chemical weathering arises—as water residence times decrease, some mineral–water reactions can switch to kinetic control, while as water residence times increase, solubility controls dominate chemical weathering (Maher, 2010). Another response of vegetation to elevated atmospheric CO2 can be changes in root growth and respiration rates (root or microbial) (Drigo et  al., 2010; Iversen, 2010; Rogers et  al., 1992). When deeper roots grow, they can help promote macropore density and this in turn can result in water drainage to greater depths (Viglizzo et al., 2014), while increased respiration may elevate soil CO2 concentrations and promote mineral dissolution (Gulley et al., 2013, 2014). If we then scale up from soil–plant– atmosphere interactions it is possible to see that these changes have the potential to not only alter chemical transformation rates but also stream water solute behavior (e.g., Sullivan et al., 2018). Lastly, increased soil moisture content can also help to promote runoff and, therefore, the potential power of erosion in environments. This example highlights the complexity of feedbacks between multiple processes in the critical zone. The question that remains elusive is to what degree will future environmental changes alter access to clean air, potable water, fertile soil, and food (Brantley, McDowell, et al., 2017), and specifically, how will these changes impact critical zone services (Field et al., 2015)?

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10.1.3. How Do We Hindcast and Earthcast? To successfully earthcast will require large datasets from an extensive network of environmental observatories such as the Critical Zone Observatories (CZOs). This is because projections of the future Earth’s surface requires knowledge of climatic and land‐cover conditions today and in the future, but also knowledge of the past conditions in order to tune models. These datasets include domain characteristics and properties (e.g., lithology and vegetation cover), external conditions (e.g., climate), and measures of processes for parameter calibration (e.g., soil chemistry, stream flow, solute concentrations, erosion rate). In addition, we need the ability to link or couple external conditions and domain characteristics to multiple Earth surface processes (Goddéris & Brantley, 2013). It requires the models to be flexible and modular (Duffy et al., 2014) so that only the necessary mechanistic processes are being simulated over the appropriate time periods (Sullivan, Ma, et al., 2016). These models must be designed at multiple scales. For example, we cannot probe the mechanistic relationship between roots, micro‐organisms, and soil fabric at continental scales. Instead we must gain a finer understanding using pedon‐scale models, synthesize the outcomes and generate a basic relationship from fine‐scale models that can be used to inform larger‐scale models. This example highlights a challenge that persists in earthcasting and hindcasting models, namely the lack of integration between physical processes such as erosion, chemical processes such as mineral dissolution, and biotic processes such as changing plant and microbial community structure. Recently, a one‐dimensional integrated critical zone model (1D‐ICZ) has been developed with the goal of understanding how management of soils (e.g., land use and land cover) influences soil  functions, biogeochemical fluxes, and structure (Giannakis et  al., 2017), but such concepts need to be embraced at larger scales. Similar challenges were highlighted in a survey of Earth system dynamic modelers and researchers operating within the USA and international environmental networks (e.g., CZO and Long Term Ecological Research program (LTER)) where: (a) Earth system models (ESM) need to include integrated processes that represent the critical zone and data across multiple disciplines such that the interactions of processes can be explored, and (b) existing data catalogs need to be more discoverable and the measured variables and their spatial extent made more explicit for easier data–model integration (Baatz et al., 2018). Model integration is another large barrier to encouraging more scientists to earthcast and truly link disciplines. Currently, codes from different communities are  written in different programming languages and use

different operating systems, limiting access to the users who can operate over multiple platforms. With concepts such as the Basic Model Interface (BMI; Jiang et  al., 2017), which wrap codes with a standard model interface through web‐based applications, we may be able to expand ability to earthcast to the larger scientific community. 10.2. ADVANCES IN HINDCASTING AND EARTHCASTING Over the past decade, great strides have been made in hindcasting Earth surface systems as thinking has evolved about the critical zone response to climate and human perturbations. Yet significant hurdles still remain for integrating codes across disciplines, and scaling across space and time. In contrast to hindcasting, the number of earthcasts has been much more limited, focused on projecting weathering (as demonstrated below), and many more challenges remain. Below we highlight some of the advances in earthcasting and hindcasting. 10.2.1. Hindcasts All hindcasts rely on information about past processes which are recorded in regolith profiles. By reading depth‐ profile records of stacked reaction fronts that can separate or remain colocated in the regolith, we can deduce past water fluxes and temperature patterns (Brantley & White, 2009; Brantley et al., 2013; Brantley & Lebedeva, 2011; Brantley, Lebedeva, et al., 2017). If a regolith profile has developed from the underlying parent material, these patterns in chemical reaction fronts yield important clues about water–mineral interactions and geochemical evolution (Brantley & White, 2009; Lichtner, 1988; Steefel & Lichtner, 1998; White et al., 2001). These earlier papers showed that the depth of weathering is mostly controlled by the flow rate of the weathering fluid, the solubility of reacting minerals, the duration of weathering, and the initial amount of the reacting mineral. At the same time, the width of the reaction front increases with the rate of advection and decreases with the rate of mineral reaction. We hindcast the evolution of these profiles by pairing this information with estimates of regolith age/ residence time using isotopes of elements such as uranium and beryllium (e.g., Granger & Riebe, 2007; Ma et  al., 2010; West et al., 2013) to constrain weathering rates and the influence of past climate. To constrain uncertainty in age and residence time for hindcasting, the use of multiple proxies should be employed (Sullivan, Ma, et  al., 2016) and deep‐time hindcasts (e.g., 10–1000 ka) must be discussed in terms of high order governing mechanisms and trends. Hindcasts of climosequences (Goddéris et  al., 2010) and chronosequences (Maher et al., 2009; Moore et al.,

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2012) of pedons have demonstrated that: (a) reactive transport modeling can simulate mineral depth profiles in soils successfully; (b) these modeling efforts yield constraints on soil pCO2 and clay–water reaction rates; (c) the dissolution of one mineral, such as a carbonate, impacts dissolution and precipitation of other minerals, sometimes resulting in large impacts on primary silicate weathering and the stability of secondary clays. For example, hindcasts of soil formation from Marcellus Shale (Heidari et al., 2017), a black shale that is rich in organic matter and pyrite, highlighted the interplay between pyrite oxidative dissolution, chlorite dissolution, and iron hydroxide precipitation over the past 10,000 years. Specifically, chlorite dissolution was facilitated by carbonic and organic acids, but was slower than pyrite oxidation, and resulted in the precipitation of secondary minerals (e.g., vermiculite). If O2 was excluded in the model, pyrite remained in the soil even after 10,000 years, while if CO2 was excluded, chlorite remained abundant and porosity remained small in the soil profile. The simulation was run for that past ~10,000 years, a period when O2 concentration does not change significantly so the model was based on the assumption of a constant O2 atmospheric concentration over time. Although the model accounted for the changes in atmospheric concentration of CO2 by using the level before and after the industrial revolution, the influence of this small change was insignificant. This is likely because the soil CO2 level is more than an order of magnitude higher than that of the atmospheric CO2 level (Hasenmuller et  al., 2015). This example also demonstrates how we can quantify and predict how the base of the critical zone, where fresh bedrock is first weathered will respond to changing climate conditions or human actions. This hindcasting model required various model input data. The parent rock mineralogy, including mineral volume abundance and surface area, was used to set up the domain. The rainwater chemistry constrains one boundary condition. The difference between meteoric precipitation (P) and ET was used to set the water infiltration rates. Modern‐day pore‐water chemistry and soil mineralogical depth profiles were used to contrain the model. The model was also parameterized with mineral dissolution and precipitation reaction stoichiometry, and kinetic and thermodynamic parameters including rate constants and equilibrium constants. Due to the large number of processes and parameters, large uncertainties exist. These uncertainties have been partially addressed by sensitivity analysis. Such analysis has indicated that hindcast results are highly sensitive to the presence or absence of reactive gases (O2, CO2), water flow rates, and surface area. This implies that the largest uncertainties may be caused by these parameters whereas other parameters may not matter as much.

Hindcasts of soil profiles can also be used to illuminate processes controlling depth distributions of soil organic carbon (SOC) and soil profile C exchange with the atmosphere. For example, hindcasts of erosion controls on SOC production and oxidation examined in the SOrCERO model (Soil Organic Carbon, Erosion, Replacement and Oxidation; Billings et al., 2010) show that anthropogenically enhanced erosion over the past 150 years at the Calhoun CZO may have generated a net sequestration of carbon from the atmosphere of up to ~10 kg C m−2. The extent of sequestration depends on changes in SOC oxidation in formerly deeper horizons upon erosion, the degree to which SOC production was maintained at the eroding site, and the fate of the eroded SOC. Here, pre‐erosion, initial mineralization rates were prescribed based on mean residence time of the SOC determined from radiocarbon analysis. When solved over an annual time‐step, the model captures SOC’s general oxidative potential and CO2 fluxes without accounting for seasonal mechanisms  driving oscillations in mineralization, although SOrCERO accommodates user‐prescribed variable time‐steps. 10.2.2. Earthcasts The forward projection of Earth surface systems— earthcasts—can include projections of such processes as  biogeochemical fluxes from soils to rivers, sediment fluxes across landscapes, and changing regolith architecture. Below we highlight earthcasts that have explored how continued increases in atmospheric CO2 concentrations and temperature, and changes in biological activity and soil‐water residence, govern weathering and solute fluxes. These studies have spanned tropical (Roelandt et al., 2010), temperate (Goddéris & Brantley, 2013), and arctic climates (Beaulieu et  al., 2010, 2012) and have demonstrated several key relationships: (a) the estimated flux of dissolved species from soil profiles is strongly controlled by clay mineral abundances (Roelandt et  al., 2010); (b) sulfuric‐acid‐driven mineral weathering reduces the consumption of atmospheric CO2 through weathering by roughly a factor of two (Beaulieu et al., 2010); (c) in terrestrial tropical environments, plants exert control on base cation concentrations by governing soil hydrology rather than by controlling the partial pressure of CO2 through root respiration (Roelandt et  al., 2010); (d) the weathering of carbonates is more important than that of silicates with respect to CO2 on short geologic timescales (e.g., Goddéris & Brantley, 2013); and (e) in arctic climates weathering processes may increase by more than 50% under atmospheric conditions where CO2 is doubled in concentration (Beaulieu et al., 2010).

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10.3. USING ASPECT TO INFORM FUTURE EARTHCASTS SOLUTE FLUXES FROM SHALE: SSHCZO Variations in aspect, and thus incident solar radiation, can create important differences in microclimate (temperature and ET) and hydrologic regimes (soil moisture and water fluxes) across relatively small areas (Barry, 1982; Thornthwaite, 1961), providing a platform where variability attributed to lithological changes can be ­ ­minimized (e.g., Burnett et  al., 2008; Hinckley et  al., 2012). Solar radiation and temperature regimes may significantly differ between north‐ and south‐facing ­ ­hillslopes within a single catchment, and these differences can illuminate how energy and water fluxes govern the nonlinear behavior of the complex processes that occur within critical zone (e.g., weathering, erosion, primary production) and thus future critical zone dynamics. For example, at the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), a well‐studied, east–west oriented 7.9 ha watershed, soil bulk chemistry has revealed differences in both elemental release rates (i.e., the rate of release of weathered elements to solution from soil pedons) and weathering extent (i.e., the fractional mass loss of weathered elements in a soil pedon compared to the parent material; Ma et  al., 2010) as a function of aspect. Ma et  al. (2010) defined the aspect of the hillslopes relative to the sun position (i.e., sunny and shaded hillslopes). Three main phenomena were observed at SSHCZO: (a) soils on the shaded hillslope are more chemically depleted from weathering compared to the sunny hillslope (Ma et al., 2010, 2013); (b) the sunny hillslope shows greater elemental release rates (rate of loss of weathered elements to solution) compared to the shaded hillslope (Ma et al., 2010, 2013); (c) solute concentrations in soil water are similar on the two hillslopes (Herndon, 2012). Sullivan et al. (2019) explored the puzzling nature of these chemical weathering processes by hindcasting the soil chemistry on each side of the catchment. Specifically, Sullivan et al. (2019) cascaded hydrologic (Flux‐PIHM; Shi et al., 2013) and geochemical (WITCH; Goddéris et al., 2006) models to inform future earthcasts of climate‐driven shale weathering through exploring the effects of aspect. The effect of a warmer‐drier climate was evaluated by investigating the sunny hillslope—the sunny side experiences a 0.8°C warmer temperature and 100 mm less recharge. Simulations that included both aspect and “biolifting”—uptake and return of inorganic nutrients from depth to the soils surface—(aspect + vegetation) were best able to reproduce today’s observed soil water solute fluxes. Likewise, weathering and elemental release rates were well simulated if inherited differences in clay content were included and different soil residence times (sunny side = 10 wt% and 12 ka; shaded side = 3 wt%,

33 ka) were assumed for the two hillslopes. The different residence times have been estimated for the two hillslopes based on uranium disequilibrium measurements. The cascade of models was able to adequately reproduce fluxes of Mg, Si, and Ca. Under those conditions, model results produced an elemental release rate that was 110% higher and an extent of weathering that was 39% lower for the sunny versus the shaded hillslope—results which are evocative of the observed trends of 150% higher and 70% lower, respectively, on the sunny versus the shaded hillslope. Most surprisingly, the aspect + vegetation simulations showed that (Figure 10.2): (a) shale weathering rates were reduced by 10% when vegetation cycling and biolifting were included, and that these shale weathering rates were not sensitive to the small, expected changes in temperature (increase) and recharge (decrease); (b) the weathering fluxes—i.e., the mass of weathering‐released solutes exported from each soil pedon per unit time—from shale environments increased up to 13% for the warmer‐drier conditions of the sunny as compared to the shaded side of the catchment; and (c) fluxes and extent of weathering of soil with low clay content are more sensitive to a warming climate than those with a high clay content. Finally, this experiment sheds light on the time scales of different critical‐zone processes contributing to present‐ day phenomena and future scenarios. Most obviously, the modeling highlighted that differences in the effects of erosion as a function of aspect, and their influence on residence times of weathering materials over geologically long time periods, must be considered in order to model weathering extent. 10.4. OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS 10.4.1. Opportunities for Continental‐Scale Earthcasts ESMs allow us to examine the feedbacks between changing atmospheric conditions and critical‐zone processes, especially in terms of water and energy budget predictions (Clark et al., 2015). Such models thus are a means to earthcast at the continental scale (e.g., Beaulieu et al., 2010, 2012). The movement and storage of water and energy underlies many of the biological, physical, and chemical processes that give rise to the resources upon which humanity depends (e.g., food security). Recent advances in hillslope hydrology and hydrologic connectivity research have illuminated the importance of accurately distributing water across the landscape to predict hydrologic flow paths (e.g., Good et al., 2015; Jencso et  al., 2009; McGuire& McDonnell, 2010) and its feedback to architecture of the subsurface (e.g., van der Meij et  al., 2018). This has challenged the ESM

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Figure 10.2  Simulated solute concentrations in pore waters in soil profiles (0–60 cm deep) over 1 year (2008–2009) using WITCH as described in the text. Each panel shows the plotted variable contoured (see legends) as calculated for each depth as shown on the y axis. The panels thus show changes in the variables as a function of depth and time: (a, b) pH, (c, d) Ca concentrations, (e, f) Mg concentrations, and (g, h) illite saturation state for aspect (left) and aspect + vegetation model (right) on the sunny side of the catchment. Warmer colors indicate higher pH, elevated Ca concentrations, elevated Mg concentrations, and greater potential for mineral dissolution. [Sullivan et al. (2019). Reproduced with permission of John Wiley & Sons.]

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community to consider updating the current one‐dimensional representation of hydrology to a two‐dimensional representation that allows lateral flow at the grid‐cell level (latitude–longitude: 1–5°) using a model that can account for topographic variability, and, thus include hillslope hydrology, variability in ET rates, and lateral drainage (Clark et al., 2015; Fan et al., 2017; Freund & Kirchner, 2017; Pelletier et al., 2015). The response is the ongoing development of the community land model that represents hillslope hydrology (CLM 5.0‐CH, D. Lawrence, personal communication) that can function as a land surface model within ESMs. This advance was fostered in part by the intensive data collected by Earth observing networks, especially the Critical Zone Observatories, a more mechanistic understanding of water flow on hillslopes (e.g., Brantley, Lebedeva, et al., 2017; McGuire & McDonnell, 2010) and how these subsurface flow paths can potentially govern landscape evolution (e.g., Rempe & Dietrich, 2014; Sullivan, Ma, et al., 2016), and enhanced communication between field scientists and modelers. The ESM community has now challenged watershed scientists and pedon‐to‐watershed scale modelers to rank the importance of critical‐zone processes that might be needed for continental‐scale earthcasts and help to formulate simple numerical representations that can account for these processes within the low‐resolution spatial framework that ESMs represent.

Chadwick et  al., 2013; Jenny, 1994; Jin et  al., 2010). In general, numerical studies of weathering along hillslopes have not explicitly coupled physical erosion with chemical weathering. For example, Lebedeva and Brantley (2013) used a previously published treatment of soil erosion to explore chemical weathering within a convex‐up hillslope. The chemical weathering reactions were not explicitly coupled to physical erosion: therefore, the shape of the hillslope was dictated by the erosion rate while the geometric distributions of the internal reaction fronts created by weathering were dictated by the mineral composition of the rock, the reactivity of the dissolving minerals, the duration of weathering, and the rate of advection of water through the hill. It was assumed that erosion created the shape of the hill and chemical weathering within that hill shape dictated the geometry of the reaction fronts within the hill. Explicit coupling between chemical weathering and physical erosion has been explored by others, but always in somewhat simplified regimes. With such coupling, downslope movement occurs simultaneously with chemical weathering of the eroding material, affecting the chemistry of surficial material across the hilltop. Much remains to be explored to understand how the faster rates of physical erosion caused by humans (Hooke et  al., 2012; Wilkinson & McElroy, 2007) are impacting rates of weathering and solute loss to streams and rivers.

10.4.2. Challenges in Capturing Nonlinear Feedbacks of Landscape and Critical Zone Evolution

10.4.2.2. Example 2: Rapid Soil Structural Changes May Alter Subsurface Hydrologic and Biogeochemical Fluxes Emerging evidence at the plot, hillslope, and continental scales indicates that soil structure (i.e., the arrangement of soil particles and pores) is changing faster than previously thought—potentially on decadal timescales—in response to shifts in precipitation regimes (Caplan et al., 2018; Hirmas et al., 2018; Robinson et al., 2016). These structural changes alter soil macroporosity and saturated hydraulic conductivity (Ksat)—properties in soil that control water storage and flux and, thus, the water cycle. Here evidence suggests that more humid climatic conditions appear to promote a reduction in both macroporosity and Ksat, while drier climatic conditions promote an increase in these properties. The mechanisms governing these rapid responses of soil structure to changing precipitation regimes remain elusive. Biotic processes are the most likely explanation for changes in soil structure on such short timescales, but given the suite of biotic dynamics governing the depth distribution of water, organic carbon, CO2 fluxes (root and microbial), organic acid production, oxygen availability, mineral weathering processes (primary and secondary), and physical mixing via bioturbation by macrofauna, it is difficult to know which specific biologically controlled processes are most

One of the large gaps that remains is the need to couple or link biological, physical, chemical, and tectonic processes to elucidate the nonlinear feedbacks of landscape evolution and critical‐zone processes (e.g., Murray et al., 2009; St. Clair et al., 2015). This gap exists within models at all scales. Below, we provide just three examples of how linking biotic, physical, and chemical models is needed to help us earthcast the response to climate and land‐use change, and thus illuminate what processes should be potentially included at continental scales. 10.4.2.1. Example 1: Coupling of Chemical and Physical Weathering May Alter Our Projections of Landscape Evolution Along a Hillslope Many investigations have used weathering models in order to understand critical zone processes, but only a few have tried to explicitly address weathering along hillslopes where physical erosion must be explicitly considered (e.g., Brantley, Lebedeva, et al., 2017; Braun et al., 2016; Kirkby, 2002; Lebedeva & Brantley, 2013; Mudd & Furbish, 2006; Yoo & Mudd, 2008). In contrast, pedologists have long undertaken studies of soils along a hillslope (catenas;

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responsible for these changes and how they are coupled to alterations of soil fabric. Interactions between these processes are complex, requiring rigorous mathematical descriptions to advance our understanding of the link between soil structure and function and predict changes to soil systems on “human timescales.” Such models, not yet developed, will transform our ability to forecast how a changing environment (e.g., precipitation and land‐ cover changes) will govern future soil resources and associated ecosystem resilience. Mechanistic representations of these complex feedbacks can be incorporated into pedon‐ and watershed‐scale models, but at the continental scale these concepts generally must be simplified into pedotransfer functions that reduce the reliance on unconstrained parameters. The deepening of root systems is only one potential response of plants to lower soil moisture conditions that might accompany drier climates (Fan et al., 2017; Jackson et al., 2000; Joslin et al., 2001; Schenk & Jackson, 2005; Schlesinger et al., 2016). In well‐drained upland systems, rooting depth has also been shown to be sensitive to the depth to which meteoric water infiltrates and to the water table position (Fan et al., 2017), suggesting that changes in these hydrologic depth distributions will alter rooting depth. Changes in root architecture (size class, density, and depth distribution) can alter subsurface hydrologic flow paths as: (a) roots help to promote macropores (Beven & Germann, 1982, 2013), which in turn can be responsible for ~70% of soil‐water flow (Watson & Luxmoore, 1986); (b) fine roots promote soil aggregation that helps to move and store water (Oades, 1993); and (c) during dormant periods, water can be transported along the root–soil interface to depth as evidenced by recent geophysical measurements (Pawlik & Kasprzak, 2018). In addition, deep root penetration results in greater CO2 inputs from root respiration and organic acids at depth, potentially increasing the partial pressure of CO2 and the propensity for chemical weathering in deep soil environments (e.g., Billings et al., 2018). If deeper roots help promote greater water flow to depth and accelerate chemical weathering by increasing the concentrations of organic acid and the pCO2 at depth, the phenomena of deepening roots may have a large impact on hydrologic and geochemical response of the critical zone to climatic perturbations or human actions. Stream, soil, and groundwater geochemical evidence from woody encroached grasslands in the central United States support the hypothesis that deepening roots can alter hydrologic flowpaths and watershed biogeochemical behavior (Macpherson & Sullivan, 2018; Sullivan et  al., 2018). To earthcast the interaction between roots, subsurface porosity, and biogeochemical processes in a warmer future will require that reactive transport models (RTMs) be linked to or cascaded with codes that simulate dynamic vegetation

processes and representations of subsurface structure. Models that could be used include Biome‐BGC, the Terrestrial Ecosystem Process Model, or NOAH‐MP, a land surface model. Such models can be used to project root system depth and density into the future for scenarios of climate. 10.4.2.3. Example 3. Climate‐driven Changes in ­Subsurface Oxidation, Channel Incision, and Hillslope Evolution Recent work by Manning et al. (2013), Murphy et al. (2016), and Sullivan, Hynek, et al. (2016) demonstrates a potential connection between the depth to which oxygen‐ rich groundwater penetrates into the subsurface and the generation of secondary porosity and conversion of fresh bedrock to weathered bedrock as reduced minerals (e.g., pyrite) undergo oxidative dissolution, which can penetrate to large depths, along fracture in crystalline (Spiessl et al., 2008; MacQuarrie et al., 2010) models. In upland watersheds, these studies surmise that the depth of this oxidative boundary controls where porosity is first generated under the channel, and that over time this porosity generation helps to drain water from the hillslopes and allow for the upslope advance of chemical weathering fronts to greater depths. Increased ratios of ET to P or groundwater pumping (through changing flow paths) can also drive penetration of oxygen‐rich water to greater depths, mobilizing metals that are found in compounds that are insoluble except under reduced conditions (e.g., arsenic and selenium) in to river systems or irrigation waters (e.g., western Kansas/eastern Colorado). The implication of these changes on subsurface structure and critical zone evolution could be explored through the coupling of models that allow for channel incision, porosity development, reactive transport, and response of lands surface to changing climatic conditions and their effects on ET/P. These examples illustrate a small sampling of how combinations of water, solutes, and sediment fluxes might respond to climate and human perturbations but we still lack a fundamental, integrated model of physical, chemical, and biotic processes that can fully project critical‐zone response to climate and human perturbations. 10.4.3. Challenges with Scaling In addition to the development of models that couple many different types of processes, scaling (both spatial and temporal) remains a central challenge in earthcasting and hindcasting. As spatial and temporal scales change, dominant processes might change due to the need on the part of models for “averaging” and “aggregation” at larger scales. Scaling models to different spatiotemporal

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conditions will require different mathematical formulations and constitutive relationships. 10.4.3.1. Spatial Scaling The spatial scaling issue arises primarily from the difference in measurement scale and the scale of model projection, complicated by the ubiquitous presence of spatial heterogeneities in conditions and properties in natural surface and subsurface systems. A good example is the long‐standing puzzle of quantifying chemical weathering rates: the three to six orders of magnitude discrepancy between mineral dissolution rates measured in well‐mixed laboratory systems representing small‐scale processes without mass transport limitation versus those observed in the field where weathering represents an integration of the effects of flow, transport, and multiple reactions in heterogeneous subsurface systems (Blum & Stillings, 1995; Drever & Clow, 1995; Richards & Kump, 2003; Sverdrup et  al., 1995; White, 1995; White & Brantley, 1995, 2003). This rate discrepancy between laboratory and field has been attributed to two major categories of factors that operate at different spatial scales. One category is intrinsic to the mineral in that it includes the amount and “reactivity” of mineral surfaces without considering the interactions with the reacting fluid. Examples include mineral surface roughness or fractalness (White & Brantley, 2003), passivation or armoring layers as a result of clay coating or secondary mineral precipitation (Helgeson, 1971; Maher et al., 2009; Nugent et al., 1998), leached layers that act as diffusion barriers (Luce, 1972), or water films between grains that vary under different temperature and pressure conditions (Renard et  al., 1997). The second category includes factors that are extrinisic to the mineral and are related to fluid compositions. For example, water flow controls reaction rates by regulating the extent of deviation from equilibrium (Maher, 2010; Salehikhoo & Li, 2015). The amount of wetted surface area under unsaturated conditions, as well as concentrations of catalyzing or inhibiting aqueous species, also plays a significant role (Lawrence et al., 2014; Oelkers et al., 1994). A more nuanced treatment of the laboratory–field discrepancy than the intrinsic–extrinisic paradigm (White & Brantley, 2003) is now emerging. Specifically, recent explorations of chemical weathering rates in heterogeneous media have revealed that the key controls of the rates at the domain scale depend on the relative time scales of water interacting with reacting minerals relative to the overall time scales of water crossing the whole domain (Wen & Li, 2017, 2018). This dimensionless ratio of time scales ultimately dictates how much reactive surface area “effectively” dissolves, and often this is orders of magnitude lower than the total reactive surface area. Such differences have been observed, for example, by

comparing rates at different scales (Navarre‐Sitchler & Brantley, 2007). These differences inferred from data and reactive transport modeling work are further supported by reactive transport modeling of weathering profiles (at meter scale) that indicate that the total surface area often needs to be artificially lowered by two to four orders of magnitude to reproduce field data (Heidari et al., 2017; Moore et al., 2012). Because these effective surface areas depend on flow conditions and heterogeneity characteristics, they are emergent and dynamic. As such, they can only be estimated through modeling, in contrast to the conventionally measured surface area that is static and measurable using various laboratory techniques (White & Brantley, 2003). These effective surface areas ultimately set the pace for the long‐term chemical weathering rates. Existing models of continental weathering typically have large grids (e.g., spatial resolution of 0.5° × 0.5° ­latitude–longitude in some treatments (Beaulieu et  al., 2012)), whereas our understanding and measurements of multicomponent reactive transport processes currently focus on much smaller scales (micrometers to meters). The key questions are then: What is the effective surface area at the grid scale of interest? How can this effective surface area be estimated for continental‐scale projections? How do above‐ and below‐ground conditions, including climate, topography, plants, and lithology, combine to influence rates and extent of chemical weathering? How can existing small‐scale (10−3 to 101 m) measurements be used to parameterize continental and global scale (103 to 105 m) projections? Although active research is ongoing into these questions, the answers remain largely unknown. Answers to these questions are critical to understand and quantify the negative feedback chemical weathering offers to dampen the rates of climate change (Berner, 1999), and therefore advance not only our capability of hindcasting and earthcasting, but also the conceptual framework of understanding what factors are important at different scales and how complex our models should be (Höge et al., 2018; Li et al., 2017). Such understanding will reduce computational cost and facilitate explicit incorporation of two‐way feedback schemes among processes. Interestingly, theoretical frameworks from ecology and hydrogeology often predict that heterogeneity effects decrease as spatial scales increase because dominant processes and properties become simpler at larger scales (Dagan et al., 2003; Fiori et al., 2010; Levin, 1992). Of course, exceptions exist (Dagan et al., 2013). While theoretical upscaling frameworks such as volume averaging have been developed (Whitaker, 1999; Wood & Whitaker, 1998), these approaches fall short when dealing with complex biogeochemical systems that have a large number of species and highly nonlinear reaction thermodynamics and kinetics. A range of multiscale hybrid modeling

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approaches have been reviewed and analyzed recently (Scheibe & Smith, 2015; Scheibe et  al., 2015). Simple averaging is not always possible, however, due to the high nonlinearity and complex process coupling. Massive numbers of process‐based model simulations can be run to derive general principles and performance patterns and can be used to translate small‐scale processes and  characteristics into large‐scale representations (McDonnell et  al., 2007). One option moving forward might be to use models like ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems; Piao et al., 2007) that couple carbon and water cycle dynamics in the critical zone to predict water fluxes at various scales to already existing weathering models. 10.4.3.2. Temporal Scaling Compared to spatial scaling, temporal scaling has been much less frequently explored. (Of course, different spatial scales often are accompanied by differences in time scale.) Hydrological processes operate at time scales of months and years (Duffy et  al., 2014), whereas landscape evolution occurs across millennia (Anderson et al., 2013; Whipple & Tucker, 1999; Zhang et al., 2016). During chemical weathering, highly reactive carbonate and pyrite weather over decades to centuries, whereas silicates and clays weather at time scales that can be orders of magnitude longer. Heidari et  al. (2017) showed that Marcellus Shale weathering likely started with pyrite dissolution and that this reaction may have gone to completion within about 1000 years. This reaction was followed by the dominance of chlorite dissolution in the next ~9000 years. At each stage, the rates evolve nonlinearly as surface area dissolves and flow paths change. This sequence of different reactions leads to the coexistence of multiple weathering fronts—often stacked in two‐dimensions or nested in three‐dimensions—and these fronts may set the permeability structure of the subsurface (Brantley et al., 2013; Riebe et al., 2017). This is possible because mineralogical alteration changes the physical properties of bedrock, increasing or decreasing porosity, and in turn affecting permeability and diffusivity, which further modify weathering and erosion rates by changing the flow paths and available surface area (Buss et al., 2008; Fletcher et al., 2006; Navarre‐Sitchler et al., 2013; Pandey & Rajaram, 2016; Wen et al., 2016). Such coevolution of physical and geochemical processes have rarely been explored quantitatively, although recent conceptual models have suggested that different processes, all mediated by water, coevolve to reach steady state (Brantley, Lebedeva, et al., 2017). In addition, the physical and chemical impact of dynamic changes in vegetation on below‐ground structure, respiration rates, and nutrient demands are often not coupled within these models. Given reoccurring evidence of the coupling of different

processes operating at different time scales during weathering, the following important question arise. How and how much do hydrological variations at short time scales (months to years) influence weathering at the millennia scale? What level of detail of hydrological conditions need to be represented in order to simulate important weathering dynamics? How often should the physical properties of rocks be updated within models of weathering rocks? Morphological scaling factors (MSF), often used by geomorphologists to represent the fact that morphological changes typically occur at orders of magnitude longer temporal scales than hydrological processes, may offer a way of updating rock properties in models (Lesser et al., 2004; Roelvink, 2006; Zhang et al., 2016). Morphological changes during each time‐step therefore can be amplified by multiplying the erosional and depositional flux in a control volume at every hydrological time by a MSF (Zhang et al., 2016). The choice of the MSF values, however, is arbitrary and will need a process‐based understanding of rate evolution to meaningfully capture temporal dynamics of weathering. 10.4.4. Future Directions One important topic in hind‐ and earthcasting weathering is the inclusion of organic matter cycling, especially in shales (Hemingway et al., 2018). Some reactive transport modelers have incorporated organic matter in models of shale weathering (Bolton et al., 2006), while others have not explicitly included organic compounds (Heidari et al., 2017). The effects of biota and decomposition of organic matter play a vital role in how the landscape responds to human and climatic changes. In particular, in areas such as the Arctic that are most vulnerable to climate change, thawing permafrost can release carbon at quantities that can accelerate climate change at alarming rates (Schuur & Abbott, 2011). Thus, a better representation of soil organic carbon dynamics and how it influences weathering is key for moving earthcasting forward as well as strengthening hindcasts. The dynamics of soil organic carbon is complex and is primarily biologically driven. Microbes transform C (e.g., leaves, roots) in the presence of electron acceptors including O2, nitrate, sulfate, as well as solid phases with sorbing surface area such as iron‐containing and manganese‐containing minerals (Dunn et al., 2006; Fierer et al., 2003). Carbon stabilization is typically thought of as abiotically driven through sorption onto solid surfaces (Guggenberger & Kaiser, 2003; Kaiser et al., 2007; Kleber et al., 2015; Mikutta et al., 2009). The competing effects of biotic and abiotic factors therefore can play a key role balancing the soil C decomposition and stabilization. Recent model development has advanced tremendously at

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this front. Riley et al. (2014) present a soil cycling model that includes a comprehensive treatment of microbial and fungal activity, stabilization mechanisms and representation of C via polymeric and monomeric carbon substrate groups. Tang and Riley (2015) show that the reliance on Q10 law (i.e., temperature‐dependent reaction rate used to describe the decomposition of organic matter) and microbial use efficiency for parameterizing soil organic carbon decomposition in ESMs may result in an overestimation in the response of SOC stocks to warming temperatures. The CORPSE (Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment; Sulman et al., 2014) considers the proportion of protected and unprotected soil C pools as a function of clay content, as well as a dynamic microbial biomass pool that controls soil C transformation with rate dependence on the microbial biomass and degradable organic carbon. These models have mostly focused on the soil carbon dynamics itself. The interactions among soil carbon, soil reactive gases, and mineral weathering, however, are rarely explored. Two techniques used to model the effects of soil organic matter on weathering indirectly are to assume a relationship between soil gas composition and primary productivity (Goddéris et al., 2006) or to incorporate assumptions about the rhizosphere (Taylor et al., 2011, 2012). Mechanistic representations of soil‐C– weathering interactions have yet to be explicitly included in global‐scale ESMs such as the Community ESM (CESM) for earthcasting. Highly resolved, mechanistic models are necessary to determine the importance of spatial scale and the level of detail needed to represent the system dynamics at larger scales, and should be viewed as a critical component in the continued development of ESM. Such mechanistic‐based principles hold promise for elucidating the feedbacks between C cycles and climate systems, ultimately facilitating earthcasting at the global scale. 10.5. CONCLUSION We use the term earthcasting to refer to the forward projection of Earth’s near‐surface fluxes (water, solutes, sediments), architecture, and biota using models that couple the relevant systems. Hindcasting is the use of similar models to project today’s critical zone into the past. Together hind‐ and earthcasting provide a powerful set of tools to elucidate how Earth’s critical zone responds to human and climatic perturbations and the potential adaptation and management strategies that can be used to sustain or even enhance critical zone services (e.g., access to potable water). These projections rely on the convergence of ideas and data from multiple disciplines for model validation, and rely on datasets that are often generated at observatories. We summarized how present‐day

simulations that include aspect can be used to inform future earthcasts of climate change on shale weathering at one location (Susquehanna Shale Hills CZO). Several key opportunities and challenges face the hind‐ and earthcasting communities in the decades to come. 1. Capturing nonlinear feedbacks of landscape and critical zone evolution. 2. Coupling physical, chemical, and biological processes at appropriate time scales to represent mechanistic relationships and potential feedback processes. 3. Developing techniques and tools to span spatial (i.e., seconds to millennia) and temporal scales (decimeters to continental scale). 4. Enhancing the process‐based representation of SOC dynamics to better project biogeochemical fluxes and alterations to soil structure in hind‐ and earthcasts. The critical zone science of integrating research across multiple disciplines and across multiple timescales offers an approach to tackle these challenges and opportunities. Solutions to these outstanding challenges will come only from working across disciplines. ACKNOWLEDGMENTS PLS acknowledges NSF’s Konza Prairie Long Term Ecological Research (LTER) grant for support, and support from the University of Kansas Department of Geography and Atmospheric Science and the University of Kansas New Faculty General Research Fund. Research at Shale Hills was conducted in Penn State’s Stone Valley Forest, which is funded by the Penn State College of Agriculture Sciences, Department of Ecosystem Science and Management, and managed by the staff of the Forestlands Management Office. Financial Support was provided by National Science Foundation Grants EAR‐0725019 (C. Duffy), EAR‐1239285 (S. Brantley), and EAR‐1331726 (S. Brantley) for the Susquehanna Shale Hills Critical Zone Observatory and EAR‐1841614 (P.L. Sullivan). PLS also thanks S. Billings and D. Hirmas for thoughtful conversations. REFERENCES Anderson, R.S., Anderson, S.P., & Tucker, G.E. (2013). Rock damage and regolith transport by frost: An example of climate modulation of the geomorphology of the critical zone. Earth Surface Processes and Landforms, 38(3), 299–316. Baatz, R., Sullivan, P.L., Li, L., Weintraub, S., Loescher, H.W., Mirtl, M., et al. (2018). Steering operational synergies in terrestrial observation networks: Opportunity for advancing Earth system dynamics modelling. Earth System Dynamics. 9, 593–609. Barry, R.G. (1992). Mountain weather and climate. Psychology Press.

218  BIOGEOCHEMICAL CYCLES Beaulieu, E., Goddéris, Y., Donnadieu, Y., Labat, D., & Roelandt, C. (2012). High sensitivity of the continental‐ weathering carbon dioxide sink to future climate change. Nature Climate Change, 2(5), 346. Beaulieu, E., Goddéris, Y., Labat, D., Roelandt, C., Oliva, P., & Guerrero, B. (2010). Impact of atmospheric CO2 levels on continental silicate weathering. Geochemistry, Geophysics, Geosystems, 11(7). https://doi.org/10.1029/2010GC003078 Berner, R.A. (1999). A new look at the long‐term carbon cycle. Gsa Today, 9(11), 1–6. Beven, K., & Germann, P. (1982). Macropores and water flow in soils. Water Resources Research, 18(5), 1311–1325. Beven, K., & Germann, P. (2013). Macropores and water flow in soils revisited. Water Resources Research, 49(6), 3071–3092. Billings, S.A., Buddemeier, R.W., deB Richter, D., Van Oost, K., & Bohling, G. (2010). A simple method for estimating the influence of eroding soil profiles on atmospheric CO2. Global Biogeochemical Cycles, 24(2). Billings, S.A., Hirmas, D., Sullivan, P.L., Lehmeier, C.A., Bagchi, S., Min, K., et al. (2018). Loss of deep roots limits biogenic agents of soil development that are only partially restored by decades of forest regeneration. Elementa: Science of the Anthropocene, 6(1), 34. doi: http://doi.org/10.1525/ elementa.287 Blum, A.E., & Stillings, L.L. (1995). Feldspar dissolution kinetics. Reviews in Mineralogy and Geochemistry, 31(1), 291–351. Bolton, E.W., Berner, R.A., & Petsch, S.T. (2006). The weathering of sedimentary organic matter as a control on atmospheric O2: II. Theoretical modeling. American Journal of Science, 306(8), 575–615. Brantley, S.L., Holleran, M. E., Jin, L., & Bazilevskaya, E. (2013). Probing deep weathering in the Shale Hills Critical Zone Observatory, Pennsylvania (USA): the hypothesis of nested chemical reaction fronts in the subsurface. Earth Surface Processes and Landforms, 38(11), 1280–1298. Brantley, S.L., & Lebedeva, M. (2011). Learning to read the chemistry of regolith to understand the critical zone. Annual Review of Earth and Planetary Sciences, 39, 387–416. Brantley, S.L., Lebedeva, M.I., Balashov, V.N., Singha, K., Sullivan, P.L., & Stinchcomb, G. (2017). Toward a conceptual model relating chemical reaction fronts to water flow paths in hills. Geomorphology, 277, 100–117. Brantley, S.L., McDowell, W.H., Dietrich, W.E., White, T.S., Kumar, P., Anderson, S.P., et al. (2017). Designing a network of critical zone observatories to explore the living skin of the terrestrial Earth. Earth Surface Dynamics, 5(4), 841. Brantley, S.L., Megonigal, J.P., Scatena, F.N., Balogh‐Brunstad, Z., Barnes, R.T., Bruns, M.A., et al. (2011). Twelve testable hypotheses on the geobiology of weathering. Geobiology, 9(2), 140–165. Brantley, S.L., & White, A.F. (2009). Approaches to modeling weathered regolith. Reviews in Mineralogy and Geochemistry, 70(1), 435–484. Brantley, S.L., White, T.S., White, A.F., Sparks, D., Richter, D., Pregitzer, K., et al. (2006). Frontiers in exploration of the critical zone. Newark, DE:.Report of a workshop sponsored by the US National Science Foundation. Braun, J., Mercier, J., Guillocheau, F., & Robin, C. (2016). A simple model for regolith formation by chemical weathering.

Journal of Geophysical Research: Earth Surface, 121(11), 2140–2171. Burnett, B.N., Meyer, G.A., & McFadden, L.D. (2008). Aspect‐ related microclimatic influences on slope forms and processes, northeastern Arizona. Journal of Geophysical Research: Earth Surface, 113(F3). Buss, H.L., Sak, P.B., Webb, S.M., & Brantley, S.L. (2008). Weathering of the Rio Blanco quartz diorite, Luquillo Mountains, Puerto Rico: coupling oxidation, dissolution, and fracturing. Geochimica et Cosmochimica Acta, 72(18), 4488–4507. Caplan, J.S., Giménez, D., Hirmas, D.R., Brunsell, N.A., Blair, J.M., & Knapp, A.K. (2019). Decadal‐scale shifts in soil hydraulic properties as induced by altered precipitation. Science Advances, 5(9), eaau6635. Chadwick, O. A., Roering, J. J., Heimsath, A. M., Levick, S. R., Asner, G. P., & Khomo, L. (2013). Shaping post‐orogenic landscapes by climate and chemical weathering. Geology, 41(11), 1171–1174. Clark, M.P., Fan, Y., Lawrence, D.M., Adam, J.C., Bolster, D., Gochis, D.J., et  al. (2015). Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51(8), 5929–5956. Dagan, G., Fiori, A., & Janković, I. (2003). Flow and transport in highly heterogeneous formations: 1. Conceptual framework and validity of first‐order approximations. Water Resources Research, 39(9). doi: 10.1029/2002WR001717 Dagan, G., Fiori, A., & Jankovic, I. (2013). Upscaling of flow in heterogeneous porous formations: Critical examination and issues of principle. Advances in water resources, 51, 67–85. Drever, J.I., & Clow, D.W. (1995). Weathering rates in catchments. Reviews in Mineralogy and Geochemistry, 31(1), 463–483. Drigo, B., Pijl, A.S., Duyts, H., Kielak, A.M., Gamper, H.A., Houtekamer, M.J., et  al. (2010). Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proceedings of the National Academy of Sciences, 107(24), 10938–10942. Duffy, C., Shi, Y., Davis, K., Slingerland, R., Li, L., Sullivan, P. L., … & Brantley, S. L. (2014). Designing a suite of models to explore Critical Zone function. Procedia Earth and Planetary Science, 10, 7–15. Dunn, R.M., Mikola, J., Bol, R., & Bardgett, R.D. (2006). Influence of microbial activity on plant–microbial competition for organic and inorganic nitrogen. Plant and Soil, 289(1–2), 321–333 Fan, Y., Miguez‐Macho, G., Jobbágy, E.G., Jackson, R.B., & Otero‐Casal, C. (2017). Hydrologic regulation of plant rooting depth. Proceedings of the National Academy of Sciences, 114(40), 10572–10577. Field, J.P., Breshears, D.D., Law, D.J., Villegas, J.C., López‐ Hoffman, L., Brooks, P. D., et al. (2015). Critical Zone services: Expanding context, constraints, and currency beyond ecosystem services. Vadose Zone Journal, 14(1). doi:10.2136/ vzj2014.10.0142 Fierer, N., Schimel, J.P., & Holden, P.A. (2003). Variations in microbial community composition through two soil depth profiles. Soil Biology and Biochemistry, 35(1), 167–176. Fiori, A., Boso, F., de Barros, F.P., De Bartolo, S., Frampton, A., Severino, G., et al. (2010). An indirect assessment on the

POISED TO HINDCAST AND EARTHCAST THE EFFECT OF CLIMATE ON THE CRITICAL ZONE  219 impact of connectivity of conductivity classes upon longitudinal asymptotic macrodispersivity. Water Resources Research, 46(8). https://doi.org/10.1029/2009WR008590 Fletcher, RC., Buss, H.L. & Brantley, S.L. (2006). A spheroidal weathering model coupling porewater chemistry to soil thicknesses during steady‐state denudation. Earth and Planetary Science Letters, 244(1–2), 444–457. Freund, E.R., & Kirchner, J.W. (2017). A Budyko framework for estimating how spatial heterogeneity and lateral moisture redistribution affect average evapotranspiration rates as seen from the atmosphere. Hydrology and Earth System Sciences, 21(1), 217. Giannakis, G.V., Nikolaidis, N.P., Valstar, J., Rowe, E.C., Moirogiorgou, K., Kotronakis, M., et al. (2017). Integrated critical zone model (1D‐ICZ): a tool for dynamic simulation of soil functions and soil structure. Advances in Agronomy 142, 277–314. Goddéris, Y., & Brantley, S.L. (2013). Earthcasting the future critical zone. Elemta: Science of the Anthropocene, 1. http:// doi.org/10.12952/journal.elementa.000019 Goddéris, Y., François, L.M., Probst, A., Schott, J., Moncoulon, D., Labat, D., & Viville, D. (2006). Modelling weathering processes at the catchment scale: The WITCH numerical model. Geochimica et Cosmochimica Acta, 70(5), 1128–1147. Goddéris, Y., Williams, J.Z., Schott, J., Pollard, D., & Brantley, S L. (2010). Time evolution of the mineralogical composition of Mississippi Valley loess over the last 10 kyr: Climate and geochemical modeling. Geochimica et Cosmochimica Acta, 74(22), 6357–6374. Good, S.P., Noone, D., & Bowen, G. (2015). Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science, 349(6244), 175–177. Granger, D.E. & Riebe, C.S. (2007). Cosmogenic nuclides in weathering and erosion. In K.K. Turekian, H.D. Holland (Eds.), Treatise on Geochemistry (pp. 1–43). Oxford, UK: Pergamon Press. Guggenberger, G., & Kaiser, K. (2003). Dissolved organic matter in soil: challenging the paradigm of sorptive preservation. Geoderma, 113(3–4), 293–310. Gulley, J., Martin, J. & Moore, P. (2014). Vadose CO2 gas drives dissolution at water tables in eogenetic karst aquifers more than mixing dissolution. Earth Surface Processes and Landforms, 39(13), 1833–1846. Gulley, J.D., Martin, J.B., Moore, P.J., & Murphy, J. (2013). Formation of phreatic caves in an eogenetic karst aquifer by CO2 enrichment at lower water tables and subsequent flooding by sea level rise. Earth Surface Processes and Landforms, 38(11), 1210–1224. Hasenmueller, E.A., Jin, L., Stinchcomb, G.E., Lin, H., Brantley, S.L., & Kaye, J.P. (2015). Topographic controls on the depth distribution of soil CO2 in a small temperate watershed. Applied Geochemistry, 63, 58–69. Hemingway, J.D., Hilton, R.G., Hovius, N., Eglinton, T.I., Haghipour, N., Wacker, L., et al. (2018). Microbial oxidation of lithospheric organic carbon in rapidly eroding tropical mountain soils. Science, 360(6385), 209–212. Heidari, P., Li, L., Jin, L., Williams, J.Z., & Brantley, S.L. (2017). A reactive transport model for Marcellus Shale weathering. Geochimica et Cosmochimica Acta, 217, 421–440.

Helgeson, H.C. (1971). Kinetics of mass transfer among silicates and aqueous solutions. Geochimica et Cosmochimica Acta, 35(5), 421–469. Herndon, E.M. (2012). Biogeochemistry of manganese contamination in a temperate forested watershed. PhD Pennsylvania State University. Hinckley, E.L.S., Ebel, B.A., Barnes, R.T., Anderson, R.S., Williams, M.W., & Anderson, S.P. (2012). Aspect control of water movement on hillslopes near the rain–snow transition of the Colorado Front Range. Hydrological Processes, 28(1), 74–85. Hirmas, D.R., Giménez, D., Nemes, A., Kerry, R., Brunsell, N.A., & Wilson, C.J. (2018). Climate‐induced changes in continental‐scale soil macroporosity may intensify water cycle. Nature, 561(7721), 100. Höge, M., Wöhling, T., & Nowak, W. (2018). A primer for model selection: The decisive role of model complexity. Water Resources Research, 54(3). https://doi.org/10.1002/ 2017WR021902 Hooke, R.L., Martín‐Duque, J.F., & Pedraza, J. (2012). Land transformation by humans: a review. GSA Today, 22(12), 4–10. Iversen, C.M., 2010. Digging deeper: fine‐root responses to rising atmospheric CO2 concentration in forested ecosystems. New Phytologist, 186(2), 346–357. Jackson, R.B., Sperry, J.S., & Dawson, T.E. (2000). Root water uptake and transport: using physiological processes in global predictions. Trends in Plant Science, 5(11), 482–488. Jencso, K.G., McGlynn, B.L., Gooseff, M.N., Wondzell, S.M., Bencala, K.E., & Marshall, L.A. (2009). Hydrologic connectivity between landscapes and streams: Transferring reach‐and plot‐scale understanding to the catchment scale. Water Resources Research, 45(4). https://doi. org/10.1029/2008WR007225 Jenny, H. (1994). Factors of soil formation: a system of quantitative pedology. Courier Corporation. Jiang, P., Elag, M., Kumar, P., Peckham, S.D., Marini, L., & Rui, L. (2017). A service‐oriented architecture for coupling web service models using the Basic Model Interface (BMI). Environmental Modelling and Software, 92, 107–118 Jin, L., Ravella, R., Ketchum, B., Bierman, P.R., Heaney, P., White, T., & Brantley, S.L. (2010). Mineral weathering and elemental transport during hillslope evolution at the Susquehanna/Shale Hills Critical Zone Observatory. Geochimica et Cosmochimica Acta, 74(13), 3669–3691. Joslin, J.D., Wolfe, M.H., & Hanson, P.J. (2000). Effects of altered water regimes on forest root systems. The New Phytologist, 147(1), 117–129. Kaiser, K., Mikutta, R., & Guggenberger, G. (2007). Increased stability of organic matter sorbed to ferrihydrite and goethite on aging. Soil Science Society of America Journal, 71(3), 711–719. Kirkby, M. Modelling the interactions between soil surface properties and water erosion. Catena, 46(2–3), 89–102 Kleber, M., Eusterhues, K., Keiluweit, M., Mikutta, C., Mikutta, R., & Nico, P.S. (2015). Mineral–organic associations: formation, properties, and relevance in soil environments. Advances in Agronomy, 130, 1–140. Lawrence, C., Harden, J., & Maher, K. (2014). Modeling the influence of organic acids on soil weathering. Geochimica et Cosmochimica Acta, 139, 487–507.

220  BIOGEOCHEMICAL CYCLES Lebedeva, M.I., & Brantley, S.L. (2013). Exploring geochemical controls on weathering and erosion of convex hillslopes: beyond the empirical regolith production function. Earth Surface Processes and Landforms, 38(15), 1793–1807. Lesser, G.R., Roelvink, J.V., Van Kester, J.A.T.M., & Stelling, G.S. (2004). Development and validation of a three‐dimensional morphological model. Coastal engineering, 51(8–9), 883–915. Levin, S.A. (1992). The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology, 73(6), 1943–1967. Lewis, S.L., & Maslin, M.A. (2015). Defining the anthropocene. Nature, 519(7542), 171. Lichtner, P.C. (1988). The quasi‐stationary state approximation to coupled mass transport and fluid‐rock interaction in a porous medium. Geochimica et Cosmochimica Acta, 52(1), 143–165. Li, L., Maher, K., Navarre‐Sitchler, A., Druhan, J., Meile, C., Lawrence, C., et  al. (2017). Expanding the role of reactive transport models in critical zone processes. Earth‐Science Reviews, 165, 280–301. Luce, R.W., Bartlett, R.W., & Parks, G.A. (1972). Dissolution kinetics of magnesium silicates. Geochimica et Cosmochimica Acta, 36(1), 35–50. Ma, L., Chabaux, F., Pelt, E., Blaes, E., Jin, L. & Brantley, S. (2010). Regolith production rates calculated with uranium‐ series isotopes at the Susquehanna/Shale Hills Critical Zone Observatory. Earth and Planetary Science Letters, 297, 211–225. Ma, L., Chabaux, F., West, N., Kirby, E., Jin, L., & Brantley, S. (2013). Regolith production and transport in the Susquehanna Shale Hills Critical Zone Observatory, part 1: insights from U‐series isotopes. Journal of Geophysical Research: Earth Surface, 118(2), 722–740. Macpherson, G.L., & Sullivan, P.L. (2018). Watershed‐scale chemical weathering in a merokarst terrain, northeastern Kansas, USA. Chemical Geology. https://doi.org/10.1016/j. chemgeo.2018.12.001 MacQuarrie, K.T.B., Mayer, K.U., Jin, B., & Spiessl, S.M. (2010). The importance of conceptual models in the reactive transport simulation of oxygen ingress in sparsely fractured crystalline rock. Journal of Contaminant Hydrology, 112(1– 4), 64–76. Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth and Planetary Science Letters, 294(1–2), 101–110. Maher, K., Steefel, C.I., White, A.F., & Stonestrom, D.A. (2009). The role of reaction affinity and secondary minerals in regulating chemical weathering rates at the Santa Cruz Soil Chronosequence, California. Geochimica et Cosmochimica Acta, 73(10), 2804–2831. Manning, A.H., Verplanck, P.L., Caine, J.S., & Todd, A.S. (2013). Links between climate change, water‐table depth, and water chemistry in a mineralized mountain watershed. Applied geochemistry, 37, 64–78. McDonnell, J.J., Sivapalan, M., Vaché, K., Dunn, S., Grant, G., Haggerty, R., et al. (2007). Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology. Water Resources Research, 43(7).

McGuire, K. J., & McDonnell, J. J. (2010). Hydrological connectivity of hillslopes and streams: Characteristic time scales and nonlinearities. Water Resources Research, 46(10). https:// doi.org/10.1029/2006WR005467 Mikutta, R., Schaumann, G.E., Gildemeister, D., Bonneville, S., Kramer, M.G., Chorover, J., et al. (2009). Biogeochemistry of mineral–organic associations across a long‐term mineralogical soil gradient (0.3–4100 kyr), Hawaiian Islands. Geochimica et Cosmochimica Acta, 73(7), 2034–2060. Moore, J., Lichtner, P.C., White, A.F., & Brantley, S.L. (2012). Using a reactive transport model to elucidate differences between laboratory and field dissolution rates in regolith. Geochimica et Cosmochimica Acta, 93, 235–261. Mudd, S.M., & Furbish, D.J. (2006). Using chemical tracers in hillslope soils to estimate the importance of chemical denudation under conditions of downslope sediment transport. Journal of Geophysical Research, 111, 1–18. Murphy, B.P., Johnson, J.P., Gasparini, N.M., & Sklar, L.S. (2016). Chemical weathering as a mechanism for the climatic control of bedrock river incision. Nature, 532(7598), 223. Murray, A.B., Lazarus, E., Ashton, A., Baas, A., Coco, G., Coulthard, T., et al. (2009). Geomorphology, complexity, and the emerging science of the Earth’s surface. Geomorphology, 103(3), 496–505. Navarre‐Sitchler, A., & Brantley, S. (2007). Basalt weathering across scales. Earth and Planetary Science Letters, 261(1–2), 321–334. Navarre‐Sitchler, A.K., Cole, D.R., Rother, G., Jin, L., Buss, H.L., & Brantley, S.L. (2013). Porosity and surface area evolution during weathering of two igneous rocks. Geochimica et Cosmochimica Acta, 109, 400–413. Nugent, M.A., Brantley, S.L., Pantano, C.G., & Maurice, P.A. (1998). The influence of natural mineral coatings on feldspar weathering. Nature, 395(6702), 588. Oades, J.M. (1993). The role of biology in the formation, stabilization and degradation of soil structure. In Soil Structure/ Soil Biota Interrelationships (pp. 377–400). Oelkers, E.H., Schott, J., & Devidal, J.L. (1994). The effect of aluminum, pH, and chemical affinity on the rates of aluminosilicate dissolution reactions. Geochimica et Cosmochimica Acta, 58(9), 2011–2024. Pandey, S., & Rajaram, H. (2016). Modeling the influence of preferential flow on the spatial variability and time‐ dependence of mineral weathering rates. Water Resources Research, 52(12), 9344–9366. Pawlik, Ł., & Kasprzak, M. (2018). Regolith properties under trees and the biomechanical effects caused by tree root systems as recognized by electrical resistivity tomography (ERT). Geomorphology, 300, 1–12. Pelletier, J.D., Brad Murray, A., Pierce, J.L., Bierman, P.R., Breshears, D.D., Crosby, B.T., et al. (2015). Forecasting the response of Earth’s surface to future climatic and land use changes: A review of methods and research needs. Earth’s Future, 3(7), 220–251. Perignon, M.C., Griffin, E.R., Tucker, G.E., & Friedman, J.M. (2016). Earthcasting the evolution of riparian landscapes. AGU Fall Meeting Abstracts. Piao, S., Friedlingstein, P., Ciais, P., de Noblet‐Ducoudré, N., Labat, D., et al. (2007). Changes in climate and land use have

POISED TO HINDCAST AND EARTHCAST THE EFFECT OF CLIMATE ON THE CRITICAL ZONE  221 a larger direct impact than rising CO2 on global river runoff trends. Proceedings of the National academy of Sciences, 104(39), 15242–15247. Rempe, D.M., & Dietrich, W.E. (2014). A bottom‐up control on fresh‐bedrock topography under landscapes. Proceedings of the National Academy of Sciences, 111(18), 6576–6581. Renard, F., Ortoleva, P., & Gratier, J. P. (1997). Pressure solution in sandstones: influence of clays and dependence on temperature and stress. Tectonophysics, 280(3–4), 257–266. Richards, P.L., & Kump, L.R. (2003). Soil pore‐water distributions and the temperature feedback of weathering in soils. Geochimica et cosmochimica acta, 67(20), 3803–3815. Riebe, C.S., Hahm, W.J., & Brantley, S.L. (2017). Controls on deep critical zone architecture: a historical review and four testable hypotheses. Earth Surface Processes and Landforms, 42(1), 128–156. Riley, W.J., Maggi, F., Kleber, M., Torn, M.S., Tang, J.Y., Dwivedi, D., & Guerry, N. (2014). Long residence times of rapidly decomposable soil organic matter: application of a multi‐phase, multi‐component, and vertically resolved model (BAMS1) to soil carbon dynamics. Geoscientific Model Development, 7(4). Robinson, D.A., Jones, S.B., Lebron, I., Reinsch, S., Domínguez, M.T., Smith, A.R., et  al. (2016). Experimental evidence for drought induced alternative stable states of soil moisture. Nature: Scientific Reports, 6, 20018. Roelandt, C., Goddéris, Y., Bonnet, M.P., & Sondag, F. (2010). Coupled modeling of biospheric and chemical weathering processes at the continental scale. Global Biogeochemical Cycles, 24(2). https://doi.org/10.1029/2008GB003420 Roelvink, J.A. (2006). Coastal morphodynamic evolution techniques. Coastal Engineering, 53(2–3), 277–287. Rogers, H.H., Peterson, C.M., McCrimmon, J.N. & Cure, J.D. (1992). Response of plant roots to elevated atmospheric carbon dioxide. Plant, Cell and Environment, 15(6), 749–752. Salehikhoo, F., & Li, L. (2015). The role of magnesite spatial distribution patterns in determining dissolution rates: When do they matter? Geochimica et Cosmochimica Acta, 155, 107–121. Scheibe, T.D., Murphy, E.M., Chen, X., Rice, A.K., Carroll, K.C., Palmer, B.J., et al. (2015). An analysis platform for multiscale hydrogeologic modeling with emphasis on hybrid multiscale methods. Groundwater, 53(1), 38–56. Scheibe, T.D., & Smith, J.C. (2015). Multiscale computation. Needs and opportunities for BER science (No. PNNL‐24051). Richland, WA: Pacific Northwest National Laboratory. Schenk, H.J., & Jackson, R.B. (2005). Mapping the global distribution of deep roots in relation to climate and soil characteristics. Geoderma, 126(1), 129–140. Schlesinger, W.H., Dietze, M.C., Jackson, R.B., Phillips, R.P., Rhoades, C.C., Rustad, L.E., & Vose, J.M. (2016). Forest biogeochemistry in response to drought. Global Change Biology, 22(7), 2318–2328. Schuur, E.A., & Abbott, B. (2011). Climate change: High risk of permafrost thaw. Nature, 480(7375), 32. Shi, Y., Davis, K.J., Duffy, C.J., & Yu, X. (2013). Development of a coupled land surface hydrologic model and evaluation at a critical zone observatory. Journal of Hydrometeorology, 14(5), 1401–142

Spiessl, S.M., MacQuarrie, K.T.B., & Mayer, K.U. (2008). Identification of key parameters controlling dissolved oxygen migration and attenuation in fractured crystalline rocks. Journal of Contaminant Hydrology, 95(3–4), 141–153. St. Clair, J., Moon, S., Holbrook, S., Perron, J.T., Riebe, C.S., Martel, S., et  al. (2015). Geophysical imaging reveals topographic stress control of bedrock weathering. Science, 350(6260), 534–538. Steefel, C.I., & Lichtner, P.C. (1998). Multicomponent reactive transport in discrete fractures: I. Controls on reaction front geometry. Journal of Hydrology, 209(1–4), 186–199. Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O., & Ludwig, C. (2015). The trajectory of the Anthropocene: the great acceleration. The Anthropocene Review, 2(1), 81–98. Sullivan, P.L., Goddéris, Y., Shi, Y., Gu, X., Schott, J., Hasenmueller, E.A., et  al. (2019). Exploring the effect of aspect to inform future earthcasts of climate‐driven changes in weathering of shale. Journal of Geophysical Research: Earth Surface, 124(4), 974–993. Sullivan, P.L., Hynek, S.A., Gu, X., Singha, K., White, T., West, N., et  al. (2016). Oxidative dissolution under the channel leads geomorphological evolution at the Shale Hills catchment. American Journal of Science, 316(10), 981–1026. Sullivan, P.L., Ma, L., West, N., Jin, L., Karwan, D.L., Noireaux, J., et  al. (2016). CZ‐tope at Susquehanna Shale Hills CZO: Synthesizing multiple isotope proxies to elucidate Critical Zone processes across timescales in a temperate forested landscape. Chemical Geology, 445, 103–119. Sullivan, P.L., Stops, M.W., Macpherson, G.L., Li, L., Hirmas, D.R., & Dodds, W.K. (2018). How landscape heterogeneity governs stream water concentration–discharge behavior in carbonate terrains (Konza Prairie, USA). Chemical Geology. https://doi.org/10.1016/j.chemgeo.2018.12.002 Sulman, B.N., Phillips, R.P., Oishi, A.C., Shevliakova, E., & Pacala, S.W. (2014). Microbe‐driven turnover offsets mineral‐ mediated storage of soil carbon under elevated CO 2. Nature: Climate Change, 4(12), 1099. Sverdrup, H., Warfvinge, P., Blake, L., & Goulding, K. (1995). Modelling recent and historic soil data from the Rothamsted Experimental Station, UK using SAFE. Agriculture, Ecosystems & Environment, 53(2), 161–177. Tang, J., & Riley, W.J. (2015). Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nature: Climate Change, 5(1), 56. Taylor, L., Banwart, S., Leake, J., & Beerling, D. J. (2011). Modeling the evolutionary rise of ectomycorrhiza on subsurface weathering environments and the geochemical carbon cycle. American Journal of Science, 311(5), 369–403. Taylor, L. L., Banwart, S. A., Valdes, P. J., Leake, J. R., & Beerling, D. J. (2012). Evaluating the effects of terrestrial ­ecosystems, climate and carbon dioxide on weathering over geological time: a global-scale process-based approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1588), 565–582. Thornthwaite, C.W. (1961). The task ahead. Annals of the Association of American Geographers, 51(4), 345–356. van der Meij, W.M., Temme, A.J.A.M., Lin, H.S., Gerke, H.H. & Sommer, M. (2018). On the role of hydrologic processes

222  BIOGEOCHEMICAL CYCLES in soil and landscape evolution modeling: Concepts, ­complications and partial solutions. Earth‐Science Reviews, 185, 1088–1110. Viglizzo, E.F., Nosetto, M.D., Jobbágy, E.G., Ricard, M.F. & Frank, F.C. (2015). The ecohydrology of ecosystem transitions: A meta‐analysis. Ecohydrology, 8(5), 911–921. Watson, K.W., & Luxmoore, R.J. (1986). Estimating macroporosity in a forest watershed by use of a tension infiltrometer 1. Soil Science Society of America Journal, 50(3), 578–582. Wen, H., & Li, L. (2017). An upscaled rate law for magnesite dissolution in heterogeneous porous media. Geochimica et Cosmochimica Acta, 210, 289–305. Wen, H., & Li, L. (2018) An upscaled rate law for mineral ­dissolution in heterogeneous media: the role of residence times and length scales. Geochimica Cosmochimica Acta, 235, 1–20. Wen, H., Li, L., Crandall, D., & Hakala, A. (2016). Where lower calcite abundance creates more alteration: enhanced rock matrix diffusivity induced by preferential dissolution. Energy and Fuels, 30(5), 4197–4208. West, N., Kirby, E., Bierman, P., Slingerland, R., Ma, L., Rood, D., & Brantley, S.L. (2013). Regolith production and transport at the Susquehanna Shale Hills Critical Zone Observatory, Part 2: Insights from meteoric 10Be. Journal of Geophysical Research: Earth Surface, 118, 1877–1896. Whipple, K.X., & Tucker, G.E. (1999). Dynamics of the stream‐ power river incision model: Implications for height limits of mountain ranges, landscape response timescales, and research needs. Journal of Geophysical Research: Solid Earth, 104(B8), 17661–17674.

Whitaker, S. (1999). The method of volume averaging (Vol. 13). Springer Science & Business Media. White, A.F. (1995). Chemical weathering rates of silicate minerals in soils. Reviews in Mineralogy and Geochemistry, 31(1), 407–461. White, A.F., & Brantley, S.L. (1995). Chemical weathering rates of silicate minerals; an overview. Reviews in Mineralogy and Geochemistry, 31(1), 1–22. White, A.F., & Brantley, S.L. (2003). The effect of time on the weathering of silicate minerals: why do weathering rates differ in the laboratory and field? Chemical Geology, 202(3–4), 479–506. White, A.F., Bullen, T.D., Schulz, M.S., Blum, A.E., Huntington, T.G., & Peters, N.E. (2001). Differential rates of feldspar weathering in granitic regoliths. Geochimica et Cosmochimica Acta, 65(6), 847–869. Wilkinson, B.H., & McElroy, B.J. (2007). The impact of humans on continental erosion and sedimentation. Geological Society of America Bulletin, 119(1–2), 140–156. Wood, B.D., & Whitaker, S. (1998). Diffusion and reaction in biofilms. Chemical Engineering Science, 53(3), 397–425. Woodward, F.I. (2002). Potential impacts of global elevated CO2 concentrations on plants. Current Opinion in Plant Biology, 5(3), 207–211. Yoo, K., & Mudd, S.M. (2008). Toward process‐based modeling of geochemical soil formation across diverse landforms: A new mathematical framework. Geoderma, 146(1–2), 248–260. Zhang, Y., Slingerland, R., & Duffy, C. (2016). Fully‐coupled hydrologic processes for modeling landscape evolution. Environmental Modelling and Software, 82, 89–107.

Part III Frontier and Managed Ecosystems

11 Importance of the Collection of Abundant Ground‐Truth Data for Accurate Detection of Spatial and Temporal Variability of Vegetation by Satellite Remote Sensing Shin Nagai1,2, Kenlo Nishida Nasahara3, Tomoko Kawaguchi Akitsu3, Taku M. Saitoh4, and Hiroyuki Muraoka4 ABSTRACT Satellite remote sensing (RS) is useful to indirectly evaluate the spatial and temporal variability of land uses, land‐cover changes, aboveground biomass of vegetation, leaf‐area index of terrestrial vegetation canopies, and plant phenology. These observations allow us to gain a deeper understanding of the spatial and temporal variability of ecosystem structure, ecosystem functions, and biodiversity under climate change. However, satellite RS observations include uncertainties and other issues, and our ecological understanding of satellite RS‐ observed data is still insufficient. To solve these issues and to better understand the ecological meaning, we need to collect abundant ground‐truth data from multiple field sites to validate satellite RS‐observed data and conduct integrated analyses using both types. To enhance the development of phenology observation and land‐use and land‐cover change studies, the Phenological Eyes Network (PEN) and the Site‐based dataset for Assessment of Changing Land cover by JAXA project (SACLAJ) were established. In this chapter, we first introduce some of our findings from these two projects, and then we discuss the usability, other related issues, and the outlook of integrated ecosystem studies that use both in situ and satellite RS‐observed data.

11.1. INTRODUCTION Satellite remote sensing (RS) is a useful tool to indirectly estimate the spatial and temporal variability of ecosystem structure (e.g., plant aboveground density and volume) and ecosystem functions (e.g., photosynthesis and evapotranspiration), which are fundamental factors  Research Institute for Global Change, Japan Agency for Marine‐Earth Science and Technology, Showa‐machi, Kanazawa‐ ku, Yokohama, Kanagawa, Japan 2  Institute of Arctic Climate and Environment Research, Japan Agency for Marine‐Earth Science and Technology, Showa‐ machi, Kanazawa‐ku, Yokohama, Kanagawa, Japan 3  Faculty of Life and Environmental Sciences, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan 4  River Basin Research Center, Gifu University, Yanagido, Gifu, Japan 1

regulating ecosystem dynamics and supporting biodiversity (Muraoka, Ishii, et al., 2013). The unique merits of satellite RS, which have enabled us to conduct long‐term continuous monitoring of ecosystems even in remote areas, have increased both its use and expectations for exploration of Earth systems under climate and anthropogenic changes. Satellite RS has been conducted by using optical sensors, which detect visible and near‐infrared bands, and microwave synthetic aperture radar (SAR), which is not influenced by cloud contamination and atmospheric noise. Optical sensors allow us to estimate the spatial and temporal variability of land use and land cover (Channan et  al., 2014; DeFries et  al., 1998; Tateishi et  al., 2011), land‐cover changes (Hansen et al., 2013; Koh et al., 2014; Langner et al., 2007; Miettinen et al., 2011; Nagai, Ishii, et al., 2014), aboveground biomass of vegetation (Kumar

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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226  BIOGEOCHEMICAL CYCLES

& Mutanga, 2017; Laurin et al., 2017; Pfeifer et al., 2016), leaf‐area index (LAI) of the terrestrial vegetation canopy (Kobayashi et al., 2010; Myneni et al., 2015; Pfeifer et al., 2016; Zhu et al., 2013), and plant phenology (Buitenwerf et  al., 2015; Delbart et  al., 2006, 2008; Garonna et  al., 2014; Myneni et al., 1997). Microwave SAR allows evaluation of the spatial and temporal variability of land use and land cover (L. Li et  al., 2015; Miettinen & Liew, 2011) and aboveground biomass of forests (Avtar et al., 2014; Laurin et al., 2017; Suzuki et al., 2013). Information on land‐use and land‐cover changes, LAI, and plant phenology is fundamental for the evaluation of human impacts on natural systems (Hansen et al., 2013; Nagai, Inoue, et al., 2014; Souza et al. 2013), estimation of ecosystem disturbances (Frolking et  al., 2009; Ishihara & Tadano, 2017), and prediction of the carbon cycle and stock in ecosystems (Ichii et al., 2017; Muraoka, Noda, et al., 2013b). From a biodiversity viewpoint, plant phenology and land‐use and land‐cover changes may be key variables for evaluating the influence of environmental changes on vegetation structure and primary production, which are fundamental factors affecting animal habitats and, hence, biodiversity (Muraoka, Ishii, et al., 2013). The specifications of sensors mounted on Earth observation satellites for monitoring spatial and temporal variability of land use, land‐cover change, aboveground biomass, LAI, and plant phenology are summarized in Table 11.1. From the viewpoint of ecosystem science, an ideal satellite sensor should monitor terrestrial ecosystems with high spatial, temporal, and spectral resolutions. However, at the present time, there is no “perfect” single satellite sensor that can simultaneously observe with high spatial, temporal, and spectral resolutions. The relationships among satellite RS spatial, temporal, and spectral resolutions involve trade‐offs, and therefore sensors (satellites) should be selected according to the specific research focus. For example, to monitor plant phenology, which shows rapid temporal change, daily spectral reflectance data with a coarse spatial resolution (250–1000 m) are required, such as that provided by the MODIS (MODerate resolution Imaging Spectroradiometer) sensor mounted on Terra and Aqua satellites, the VEGETATION sensor mounted on the SPOT (Satellite Pour l’Observation de la Terre) satellite, and the SGLI (Second generation GLobal Imager) sensor mounted on the GCOM‐C (Global Change Observation Mission‐Climate) satellite (however, temporal resolution is 2–3 days). In contrast, to monitor land‐use and land‐cover changes, where temporal change is gradual but spatial changes are fine scale, spectral reflectance data with a high spatial resolution (1–10 m) but low temporal resolution (16 or 46 days) are required. Such data can be obtained by sensors such as the AVNIR‐2 (Advances Visible and Near Infrared Radiometer type 2) sensor mounted on the ALOS (Advanced Land Observing

Satellite) satellite, the MSI (MultiSpectral Instrument) sensor mounted on the SENTINEL‐2 twin satellites (however, temporal resolution is 5 days), and visible and near‐ infrared sensors mounted on the RapidEye satellite. The trade‐offs between spatial and temporal resolution cause spatial and temporal gaps in observations of the spatial and temporal variability of terrestrial ecosystems and result in uncertainties as well as systematic and random noise in observation data. For example, the area per pixel in satellite RS‐observed plant phenology detects the “average” phenology of all plants (i.e., plant types and species composition) within a square that has a side 500– 1000 m long. However, the microtopography and heterogeneity of vegetation within an area of this size cause spatial uncertainty (see figure 3 in Muraoka et al. (2010) for an example of irregular seasonal change in satellite‐derived gross primary production). The timing and pattern of plant life‐cycle events, such as leaf‐flush and leaf‐fall, have been observed to be different among tree species even within a square that has a side 100 m long (Inoue et al., 2014; Nagai, Ishii, et al., 2014; Nasahara et al., 2008). The annual maximum value and seasonal pattern of leaf biomass (i.e., LAI), which mainly reflects seasonal patterns and timing of leaf‐flush and leaf‐fall, are different for each tree species (Nagai et  al., 2017; Nasahara et  al., 2008). These findings suggest that ecological data and in‐depth knowledge are necessary when using and interpreting satellite RS‐observed data for phenology observation. In addition, the advantages of increasing temporal resolution for coarse‐resolution phenological observations are complicated by noise, which is mainly caused by cloud contamination. To avoid cloud contamination in the satellite data, researchers have generally used composite data, which have been synthesized from daily observed data, over a given period of time (e.g., a half year) for analysis of land‐use and land‐cover changes (Koh et al., 2011; Langner et  al., 2007; Miettinen et  al., 2011). However, composite data cannot accurately detect the spatial variability of land uses and land cover because they show dramatic interannual changes due to deforestation and replantation (Hansen et al., 2013; Nagai, Inoue, et al., 2014; Souza et al., 2013). To address the uncertainties resulting from the spatial and temporal resolution of satellite sensors and cloud contamination in satellite data and other related issues in satellite RS observations (e.g., the effects of atmospheric noise and haze, spectral resolution of satellite sensors, and high solar zenith angle in winter over high latitude areas), abundant ground‐truth (in situ) data for multiple field sites are required to validate the satellite RS‐observed data, and integrated analyses using both types of data need to be conducted. Toward this aim, we have established the Phenological Eyes Network (PEN; Nasahara & Nagai, 2015; http://www.pheno‐eye.org, accessed 17 December 2019)

Table 11.1  Satellite sensors for monitoring land use and land cover changes, aboveground biomass, LAI, and plant phenology

Satellite

Sensor namea

Sensor type

Spatial resolution (m)b

NOAA SPOT‐4 Himawari‐8 Terra and Aqua Suomi NPP GCOM‐C Landsat‐series ALOS SENTINEL‐2

AVHRR VEGETATION AHI MODIS VIIRS SGLI AVNIR‐2 MSI

Optical Optical Optical Optical Optical Optical Optical Optical Optical

1100 1165 500 250 375 250 30 10 10

RapidEye ALOS ALOS‐2 SENTINEL‐1

Near‐infrared red PALSAR PALSAR‐2 C‐SAR instrument

Optical SARa SAR SAR

6.5 7–44 3 5

Temporal resolution Daily Daily 10 min Daily Daily 2–3 days 16 days 46 days 5 days (twin satellites) 5.5 days 46 days 14 days 12 days

Land use and land cover

Land‐use and land‐cover changes

Aboveground biomass

LAI

Plant phenology

▵ ▵ ▵ ▵ ▵ ▵ ⚪ ⚪ ⚪

▵ ▵ × ▵ × × ⚪ × ×

▵ ▵ ▵ ▵ ▵ ▵ ⚪ ⚪ ⚪

⚪ ⚪ ⚪ ⚪ ⚪ ⚪ × × ⚪

⚪ ⚪ ⚪ ⚪ ⚪ ▵ × × ▵

⚪ ⚪ ⚪ ⚪

× × × ×

× ⚪ ⚪ ⚪

× × × ×

× × × ×

Note: ⚪, suitable; ▵, not good; ×, unsuitable.  SAR, synthetic aperture radar. b  The highest resolution among the sensor bands is shown. a

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for the development of phenology observation studies and the SACLAJ Project (Site‐based dataset for Assessment of Changing Land cover by JAXA; Kobayashi et  al., 2017; https://eorc‐jaxa.jp/lulc/SACLAJ, please contact [email protected] to access the site; accessed 17 December 2019) for the development of land‐use and land‐cover changes studies. These two projects are being conducted to respond to the challenge of collecting abundant ground‐ truth data at multiple points on a broad scale. Although the project activities are mainly focused on Asia, the scientific evidence obtained and knowledge gained may be applicable to other regions (e.g., Europe and North and South America) and useful for addressing related challenges. The important project findings are introduced in this chapter, and then we discuss the usability of the data, related issues, and the outlook of integrated ecosystem studies that use in situ and satellite RS‐observed data. 11.2. PLANT PHENOLOGY 11.2.1. In Situ Observation To aid in the development and validation of terrestrial ecological satellite RS, a group of RS scientists and ecologists in Japan started PEN in 2003 (Nasahara & Nagai,

2015). PEN consists of three measurement systems. Time‐lapse digital camera systems were installed at 35 sites in the Asia–Pacific region and Europe, spectral radiometer systems were installed at eight sites in Japan, Korea, and interior Alaska, and sun photometer systems were installed at three sites in Japan (Figure  11.1). The time‐lapse digital camera systems were installed on the top of observation towers, on the forest floor, and on building rooftops and are automatically controlled by personal computers and remotely controlled cables. The time‐lapse digital camera systems capture images of individual trees, the canopy surface, the landscape, and whole‐sky images at intervals ranging from 2 min to 24 h (Nagai, Akitsu, et  al., 2018). The spectral radiometers, which were installed on the top of observation towers and on the forest floor, measure the radiance of the canopy surface and the whole sky by rotating up and down every 10 min. The sun photometers were installed on the top of observation towers and rooftops and measure atmospheric conditions by tracking the Sun (Nasahara & Nagai, 2015). The long‐term continuous phenology observations in various ecosystems provided by PEN (Nagai, Akitsu, et  al., 2018) make it possible to test and validate the relationship between vegetation indices observed in

Figure 11.1  Maps of Phenological Eyes Network (PEN) sites in (a) the world and (b) Japan: gray circles, time‐lapse digital camera sites; black circles, time‐lapse digital camera + spectral radiometer sites. [Adapted from Nasahara & Nagai (2015) and Nagai, Akitsu, et al. (2018).]

IMPORTANCE OF THE COLLECTION OF ABUNDANT GROUND-TRUTH DATA  229

situ, which can be calculated from spectral reflectance data on the canopy surface, and plant phenological events such as leaf‐flush, leaf‐coloring, and leaf‐fall (Figure 11.2; Motohka et al., 2010; Nagai et al., 2010, 2012). Whereas satellite RS‐observed data include cloud contamination and atmospheric noise, spectral reflectance and vegetation index data observed in situ are not affected by atmospheric conditions and provide invaluable ground‐truth data. In addition, the time‐lapse digital cameras and spectral radiometers installed on the top of observation towers and on the forest floor capture almost the same as each other (within a radius about 10 m). When vegetation cover is homogeneous per pixel in satellite RS‐observed data, the relationship between seasonal variation of the

We can detect the timing of leaf‐flush and leaf‐fall by analyzing time‐series values for satellite RS‐observed normalized difference vegetation index (NDVI; Tucker, 1979; equation  11.1), enhanced vegetation index (EVI; Huete et al., 2002; equation 11.2), and green‐red vegetation index (GRVI; Tucker 1979; Motohka et al., 2010; equation 11.3).

0.2

0.4

0

0.2

0.4

0.8

–0.2

0

0.6

0.2

0.4

0

0.2

–0.2

0 –0.4

DBF (TKY site) 0

50

100

150 200 250 Day of year in 2017

300

–0.4

ENF (TKC site) –0.2

350

1

0

50

100

150 200 250 Day of year in 2014

300

350

1 0.4

0.4

0.4

0

0.2

–0.2

0

–0.4

Grassland (TGF site) 0

50

100

150

200

250

Day of year in 2016

300

350

0.2

0.6 0.4

0

0.2

–0.2

0 –0.2

Rice paddy (MSE site) 0

50

100

150

200

250

300

350

Day of year in 2017

Figure 11.2  Relationship between plant phenology and seasonal change of vegetation indices calculated from spectral reflectance observed in situ at various ecosystem sites. (Left top) Deciduous broad‐leaved forest (DBF), Takayama (TKY) site; (right top) evergreen needleleaf forest (ENF), Takayama coniferous forest (TKC) site; (bottom left) grassland, TERC grassland field (TGF) site; (bottom right) rice paddy, Mase (MSE) site). Typical images of the canopy surface are presented above each panel. [Adapted from Motohka et al. (2009), Motohka et al. (2010), Nagai et al. (2010), and Nagai et al. (2012).]

–0.4

GRVI

0.2

0.6

NDVI and EVI

0.8

GRVI

NDVI and EVI

0.8

–0.2

GRVI

0.4

0.6

–0.2

NDVI near infrared visible red / (11.1) near infrared visible red



NDVI and EVI

NDVI and EVI

0.8

11.2.2. Integration of In Situ and Satellite Observations

1

NDVI EVI GRVI

GRVI

1

v­ egetation index and plant phenology observed in situ may correspond to the seasonal variation in the satellite RS‐observed vegetation index.

230  BIOGEOCHEMICAL CYCLES EVI G { near infrared visible red / [ near infrared

C1 visible red

C 2 visible blue



L ]}

(11.2)

GRVI visible green visible red / (11.3) visible green visible red

where G = 2.5, C1 = 6, C2 = 7.5, and L = 1 and are constants. Compared with the timing of leaf‐flush, the timing of leaf‐fall includes more uncertainties in the detection algorithm (Delbart et  al., 2005; Liu et  al., 2015). In previous studies, the timing of leaf‐flush and leaf‐fall has been defined in various ways, for example; the date when a reference value of a vegetation index (threshold value) was obtained (Garonna et  al., 2014; Kobayashi et al., 2016; Nagai, Saitoh, et al. 2015; White et al., 1997, 2009); the date with the maximum rate of a vegetation index growth as the timing of leaf‐flush; the date with the maximum rate of a vegetation index reduction as the timing of leaf‐fall (Garonna et  al., 2014; Piao et al., 2006; Studer et al., 2007; White et al., 2009); and the date when inflection points of a smoothed time series in a vegetation index, which was fitted by an approximation function, was obtained (Chen et  al., 2004; Jönsson & Eklundh, 2004; Kobayashi et al., 2016; Zhang et al., 2003). We validated the accuracy and uncertainties of the above‐mentioned satellite RS‐based timings of leaf‐flush and leaf‐fall by using in situ observation data obtained by PEN. For example, the relationship between vegetation indices (NDVI, EVI, and GRVI) and daily phenology images observed in situ was examined in various ecosystems in Japan (Motohka et al., 2010; Nagai et al., 2010, 2012; Nagai, Inoue, et  al., 2014). The first dates when GRVI observed in situ was > 0 in spring and < 0 in autumn showed the respective timing of leaf‐flush and peak timing of leaf‐coloring or leaf‐fall in deciduous coniferous forest, grassland, and rice paddy (Motohka et al., 2010). The timing of leaf‐flush, defined by threshold values of NDVI and EVI in a deciduous broad‐leaved forest, showed almost the same forest canopy status for the observed years. In contrast, the timing of leaf‐fall, defined by the same threshold values as those used to define the timing of leaf‐flush, showed a different forest canopy status for the same period (Nagai et al., 2010). An examination of daily phenology images and leaf litter indicated that GRVI = 0 observed in situ showed the peak timing of leaf‐coloring and leaf‐fall in a deciduous broad‐ leaved forest (Nagai, Inoue, et al., 2014). These facts suggest the possibility that GRVI = 0 can be an indicator (i.e., a threshold value) to detect the spatial pattern of the timing of leaf‐flush and leaf‐fall

and its year‐to‐year variability, and that it is a more accurate indicator than NDVI and EVI threshold values (Nagai, Inoue, et  al., 2014; Nagai, Saitoh, et  al., 2015). With this criterion, the definitions of leaf‐flush and leaf‐ fall by in situ and satellite RS‐observed GRVIs with a coarse spatial resolution (e.g., from MODIS and VEGETATION sensors) do not have the same plant phenology status because the footprints for data observed in situ and the area per pixel in the satellite RS‐observed data are different (10 m versus 500–1000 m). The timing of leaf‐fall detected by analyzing time‐series GRVI data may be considered to indicate the end of the “functional” leafy period (i.e., the end of photosynthesis) but not the end of the “physical” leafy period. This hypothesis is supported by the fact that GRVI = 0 observed in situ shows the peak timing of leaf‐coloring and leaf‐fall (Nagai, Inoue, et al., 2014). Hereafter, in this chapter, we consider the timing of leaf‐flush to correspond to the start of the growing season (SGS) and the timing of leaf‐fall to correspond to the end of growing season (EGS). We validate this choice in section 11.2.3. Figures 11.3–11.5 show the spatial distribution of the timing of SGS and EGS in 2015 in Japan, Russia, and Alaska obtained by analyzing time‐series in GRVI, which was observed daily by MODIS sensors mounted on Terra and Aqua satellites with a 500 m resolution (see Nagai, Saitoh, et al. (2015) for details of the method). In each region, the satellite RS‐based timing of SGS and EGS in high‐latitude regions was later and earlier than that in low‐latitude regions, respectively. Nagai, Saitoh, et  al. (2015) showed the spatial characteristic of satellite RS‐ based timing of SGS and EGS along altitudinal (vertical) and latitudinal (horizontal) gradients. Furthermore, by applying a similar analysis over a long term in various regions, we can accurately evaluate the spatial characteristics of year‐to‐year variability of the timing of SGS and EGS. 11.2.3. Validation of Satellite Analysis We validated the satellite RS‐based timing of SGS and EGS by comparing them with the timing SGS and EGS determined with ground‐truth data from various forest ecosystems. We examined the relationship between satellite RS‐based timing of SGS and EGS and daily phenology images in a deciduous broad‐leaved forest in Japan (Takayama site; 36°8′46′′N, 137°25′23′′E, 1420 m a.s.l.), a deciduous coniferous forest in eastern Siberia (Spasskaya Pad site; 62°15′17′′N, 129°37′10′′E, 214 m a.s.l.), and an evergreen coniferous forest in interior Alaska (Poker Flat Research Range [PFRR] site; 65°7′24′′N, 147°29′15′′W, 210 m a.s.l.; Figure  11.6). In the deciduous broad‐leaved forest (Takayama), the satellite RS‐based timing of SGS and EGS corresponded

IMPORTANCE OF THE COLLECTION OF ABUNDANT GROUND-TRUTH DATA  231 (a)

(b)

Figure 11.3  Spatial distribution of the timing of (a) start (SGS) and (b) end of the growing season (EGS) in 2015 analyzed by daily Terra‐ and Aqua/MODIS‐observed GRVI around Japan. White shows evergreen forests or points where we could not evaluate the timing of SGS and EGS, possibly because of low year‐round GRVI values or an insufficient number of GRVI values for analysis. Analysis was based on three MODIS tiles (h28v04, h28v05, and h29v05).

(a)

(b)

Figure 11.4  Spatial distribution of the timing of (a) start (SGS) and (b) end of the growing season (EGS) in 2015 analyzed by daily Terra‐ and Aqua/MODIS‐observed GRVI in Russia. White shows evergreen forests or points where we could not evaluate the timing of SGS and EGS. Analysis was based on 15 MODIS tiles (h19v02–h26v02 and h19v03–h25v03).

to before leaf‐flush and during or after leaf‐fall, respectively (Figure 11.6a). In the deciduous coniferous forest (Spasskaya Pad), the satellite RS‐based timing of SGS and EGS corresponded to the beginning of leaf‐flush in the larch overstory and during leaf‐coloring in the birch understory, respectively (Figure 11.6b). In the evergreen coniferous forest (PFRR), the satellite RS‐based timing of SGS and EGS corresponded to phenology in understory forest floor vegetation rather than that in overstory forest (Figure 11.6c).

The satellite RS‐based SGS and EGS results were not in agreement with in situ observations in the deciduous broad‐leaved forest (Takayama site, Figure  11.6a), at least in part because the observation site is located in a mountainous region with complex, undulating topography, where a variety of tree species with different timings and patterns of leaf‐flush, leaf‐coloring, and leaf‐fall are found (Nagai et  al., 2017; Nasahara et  al., 2008; Ohtsuka et al., 2005). The timing of leaf‐flush and leaf‐ fall showed a spatial characteristic along the altitude

232  BIOGEOCHEMICAL CYCLES (a)

(b)

Figure 11.5  Spatial distribution of the timing of (a) start (SGS) and (b) end of the growing season (EGS) in 2015 analyzed by daily Terra‐ and Aqua/MODIS‐observed GRVI in Alaska. White shows evergreen forests or points where we could not evaluate the timing of SGS and EGS. Analysis was based on three MODIS tiles (h10v02, h11v02, and h12v02).

gradient (Tadaki et al., 1994). In the area surrounding the Takayama site, altitude varies greatly over a short distance (by 600 m over 6 km). The spatial distribution characteristics of the timing of SGS and EGS caused by the altitudinal gradient might not be accurately detected by satellite‐mounted MODIS sensors, which have a coarse spatial resolution (500 m). Instead the MODIS sensors may detect the average plant phenology within a radius of 250–500 m around the Takayama site. In addition, the site has a beech overstory, in which leaf‐flush occurs about 2 weeks earlier than leaf‐flush in the other overstory species near the site (Nasahara et al., 2008). Even though beech is not a dominant tree species around the Takayama site, the MODIS sensors might have detected its leaf‐flush phenology. The satellite RS‐based SGS and EGS showed reasonable agreement with in situ observations in the deciduous coniferous forest at the Spasskaya Pad site (Figure 11.6b), possibly because the site is gently sloped, the land cover is

relatively homogeneous, and the forest structure is very simple (overstory: larch; understory: birch; Ohta et  al., 2008). The satellite RS data may reflect the phenological conditions of both the overstory larch and the understory birch because the forest canopy is not completely closed. At this site, the forest floor was covered by cowberry and there was no shrub birch about 10 years ago (Ohta et al., 2008). However, the forest floor has recently become covered by shrub birch, possibly as an effect of climate change (T. Ohta, unpublished data). This succession of vegetation may introduce complexity into the interpretation of long‐term phenology data by satellite RS. A possible reason that we could detect the timing of SGS and EGS in the evergreen coniferous forest at the PFRR site (Figure 11.6c) may be the clear seasonal variation of GRVI at that site. In a closed‐canopy evergreen coniferous forest in Japan, the GRVI observed in situ showed clear seasonal variation, but it did not show

(a)

Japan

Day of year

Takayama site

340 320 300 280 260 240 220 200 180 160 140 120 100

2004 2006 2008 2010 2012 2014 Year SGS EGS

(b)

Russia

Spasskaya Pad site

Mongolia 280 260

Day of year

240 220 200 180 160 140 120

2004 2006 2008 SGS

2010 2012

2014

Year EGS

Figure 11.6 Relationship between year‐to‐year variability of the satellite‐observed timing of SGS and EGS and canopy surface images in (a) deciduous broad‐leaved forest in Japan (Takayama site), (b) deciduous coniferous forest in Eastern Siberia (Spasskaya Pad site), and (c) evergreen coniferous forest in interior Alaska (Poker Flat Research Range: PFRR site). Phenology observations at the Takayama, Spasskaya Pad, and PFRR sites started in 2003, 2013, and 2011, respectively. In some years, we were unable to detect the satellite‐observed timing of SGS and EGS, possibly because of cloud contamination and noise in the satellite data. [(b) Adapted from Nagai et al. (2019).]

234  BIOGEOCHEMICAL CYCLES (c)

Alaska Poker flat research range: PFRR site

260 240

Day of year

220 200 180 160 140 120 2004

2006

2008

2010

2012

2014

Year SGS

EGS

Figure 11.6  (Continued)

continuous values under 0 in winter (Nagai et al., 2012). In contrast, satellite RS may mainly observe plant phenology of the forest floor vegetation in an open‐canopy evergreen coniferous forest such as that at the PFRR site (Nakai et  al., 2013). Further validation of plant phenology of the forest floor vegetation by using a time‐lapse digital camera is required. In addition, seasonal variation of GRVI from October to April was emphasized because of the forest floor was completely snow covered during that period (Sugiura et al., 2013). However, in high‐latitude regions such as eastern Siberia and Alaska, satellite RS‐observed data in winter include uncertainty caused by the solar zenith and satellite view angles (Kobayashi et al., 2016). 11.2.4. Importance of Validation at Multiple Points Long‐term continuous phenology observations at various ecosystem sites allow the validation and testing of satellite RS‐observed data. However, ground validation sites (e.g., the three discussed in section  11.2.3) are not

always completely representative of each ecosystem. The uncertainty introduced by this lack of representativeness may cause two undesirable outcomes: satellite RS‐ observed data may eventually match data observed in situ even though the data do not reflect the actual status of the ground surface, and satellite RS‐observed data may eventually be mismatched with data observed in situ even though the satellite RS data actually show the real status of the ground surface. We therefore should reduce the uncertainty introduced by a lack of representativeness of the ground validation site by using ground‐truth data from multiple ecosystem sites and as many field points as possible. In Japan, daily leaf‐color information from multiple areas is published on web sites in autumn (Nagai, Inoue, et al., 2015). There are three potential problems with this kind of information (i.e., big data): the criteria used to determine phenological stage differ among observers; the target of plant phenology ranges from individual trees to the landscape; and the locations of reported data collection points (latitude and longitude) are sometimes

IMPORTANCE OF THE COLLECTION OF ABUNDANT GROUND-TRUTH DATA  235

imprecise (Nagai, Inoue, et  al., 2015). Even with these drawbacks, the data can be useful for validating satellite RS‐observed phenology. For example, leaf‐coloring information collected at more than 750 sampling locations are published on tenki.jp (https://tenki.jp, accessed 17 December 2019, but leaf‐coloring information was not accessible), and they are updated daily from September to December. We examined the relationship between satellite RS‐based timing of EGS and “tenki. jp”‐based timing of the start of peak leaf‐coloring in 2015 in Japan. We found the satellite RS‐based timing of EGS (i.e., the first date where GRVI was below 0 in autumn) correlated with the timing of the start of peak leaf‐coloring at multiple points in 2015 (Figure  11.7; Nagai, Saitoh, et al., 2018). Phenology data collected by citizen scientists (Delbart et  al., 2015; Kosmala et  al., 2016) and phenological information published on social network services are useful sources of information to validate satellite RS‐ observed data from multiple different locations. However, if it is published in a language other than English, it is hard to find or use (Amano et  al., 2016; Nagai et  al., 2016). For this reason, phenological data that originally are published in non‐English languages need to be included in international research literature and databases (Nagai et al., 2016). Recent technological developments have allowed the installation of multiple field observation points in areas without observation towers or an electricity supply. In 360

Day of year (MODIS)

340 320 300 280 260 y = 0.48x + 162.7 R2 = 0.16 (p < 0.001) n = 293

240 220 220

240

260

280

300

320

340

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Day of year (in situ)

Figure 11.7 Relationship between satellite‐based timing of EGS (MODIS) and timing based on information from tenki.jp on the start of peak leaf‐coloring (in situ) in 2015. The bold line shows the linear model. The dashed and thin lines show the 1:1 relationship and the 95% confidence interval of the linear model, respectively.

association with the activities of PEN, inexpensive, battery‐driven time‐lapse digital cameras have been installed at multiple field sites in Alaska, eastern Siberia, and Svalbard (Anderson et al., 2016; Kobayashi et al., 2016; Sugiura et al., 2013). We have examined the relationship among the timing of leaf‐flush, latitude, and a daily warmth index at multiple sites by using daily phenology images taken at these associated and registered PEN sites (Figure  11.8). The timing of leaf‐flush showed a linear correlation with the daily warmth index. 11.3. LAND‐USE AND LAND‐COVER CHANGES 11.3.1. Development of a Reference Database Land‐use and land‐cover change maps based on satellite RS‐observed data are mainly produced by using machine learning, which requires a large amount of accurate and precise reference data (i.e., training and validation information). The Japan Aerospace Exploration Agency (JAXA) published “High‐Resolution Land Use  and Land Cover” (HRLULC) maps in Japan and Vietnam by analyzing satellite RS data, mainly data observed by the AVNIR‐2 sensor mounted on the ALOS satellite (Ishihara & Tadono, 2017; Figure  11.9; http:// www.eorc.jaxa.jp/ALOS/en/lulc/lulc_index.htm, accessed 17 December 2019). During the analysis process, JAXA constructed a database for training and validation information, the Site‐based dataset for Assessment of Changing Land cover by JAXA (SACLAJ; Kobayashi et al., 2017). In SACLAJ, users register GPS images with supplemental information from their own field surveys at multiple points in the world (Figure  11.10). Supplemental information includes latitude and longitude, category of land use and land cover, representative diameter (i.e., footprint of land use and land cover), observation date, and name of observer. The most distinct characteristic as compared with other similar databases is the method of registering latitude and longitude. In other similar databases, users register the latitude and longitude where  GPS images were taken (i.e., the location of the GPS  digital camera). However, the most important information is the latitude and longitude of the target field on the images. For example, if an image of a grassland is captured from a road, the location information for the interior of the grassland should be used for the reference data, not the location of the camera. With SACLAJ, informants are required to revise the latitude and longitude of GPS images to represent the location of the target field (Figure 11.11). The above‐mentioned reference data based on field surveys are suitable in terms of accuracy and precision, but they are insufficient in terms of spatial expanse,

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homogeneity, and randomness of the information. Field surveys tend to be limited to areas near networks of roads and railroads and wilderness areas are generally unrepresented. For this reason, we increased the coverage of reference data by using Google Earth, satellite RS‐observed data with a high spatial resolution, and aerial photographs. When we archived the reference data, we distinguished different data collection methods; SACLAJ‐G is used for field surveys and SACLAJ‐R for satellite RS‐observed data and aerial photographs. Extending SACLAJ to a larger scale, for example, global, will be labor intensive and difficult. We have tried to gather reference data for the entire world by examining previously published ground‐truth information. One of sources used is the Degree Confluence Project (DCP; http://confluence.org, accessed 17 December 2019), a

hobby network project that archives ground‐truth information on confluence points located where integer degree values of latitude and longitude intersect (e.g., N50°, E100° or S21°, W101°). The DCP participants are volunteers who upload notes and photographs obtained during their field surveys to the DCP web site. Soyama et  al. (2017) compiled a reference database with categories of land use and land cover for the whole world by examining ground‐truth information published by DCP. Iwao et al. (2011) indicated the usability of DCP for validation of several global land‐use and land‐cover maps. The field surveys have been performed repeatedly at many confluence points. Accordingly, we can compile a historical record of land‐cover change in those areas. The DCP data provide an invaluable resource as reference data in dynamic investigations of land‐cover change.

Figure 11.10  Screenshot of the data search engine window on the SACLAJ web system (Kobayashi et al., 2017); 61,512 ground‐truth points were collected as of 21 September 2018.

Figure 11.11  Screenshot of the data upload window of the SACRAJ web system (https://eorc‐jaxa.jp/lulc/SACLAJ, please contact [email protected] to access the site; accessed 17 December 2019).

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11.3.2. Requirements of Ground‐Truth Data for Land Use and Land Cover Changes In many previous studies, reference data were obtained through visual inspection of satellite‐RS observed data acquired at high spatial resolution, such as Landsat and WorldView satellite data, which are used by Google Earth (e.g., C. Li et al., 2017). It is not easy, however, to discriminate land use and land cover only by analyzing satellite RS‐observed data even when the spatial resolution is high. From the viewpoint of accuracy, direct validation by performing field surveys is the best choice. However, ground‐truth data for land use and land cover (including SACLAJ and DCP) does not usually satisfy the following four requirements. 11.3.2.1. Requirement 1: Sufficient Volume All characteristics extracted from satellite RS‐observed data should also be covered as training information. A sufficient amount of validation data is required to achieve the desired accuracy of evaluation. Let p and n be overall accuracy and number of validation data points, respectively. The error of the accuracy of evaluation ε is determined as ε = (p(1 ‐ p)/n)^(1/2). For example, to compare the accuracy between two products with overall accuracies of 95% and 96%, respectively, the desired level of error must be less than about 0.2%. In this case, if p = 0.95 and ε = 0.005, then the required n (number of validation data point) > 11,875. 11.3.2.2. Requirement 2: Responsiveness to Temporal Change Land use and land cover change over time. Accordingly, reference data are valid for a limited period that includes the date on which the reference data were obtained. It is desirable therefore to collect reference data of land use and land cover repeatedly at the same location. However, when we examined land‐cover change by using previously archived satellite RS‐observed data, often only very limited ground‐truth data were available from the relevant period of time for validation. In addition, in the case of seasonal land‐cover changes (e.g., frozen lakes in winter and snow‐covered land) or for specific land uses (e.g., cropland), we require frequent ground‐truth testing (e.g., three times per year). 11.3.2.3. Requirement 3: Spatial Representativeness To increase spatial representativeness, we need homogeneous broadening in area per pixel in satellite RS‐ observed data. However, it is not always easy to perform field surveys holistically. To confirm homogeneous broadening, we require not only ground‐truth data based on field surveys but also satellite RS‐observed data with a high spatial resolution.

11.3.2.4. Requirement 4: Spatial Homogeneity and Randomness For validation, reference points should be distributed uniformly and randomly in space (i.e., the data are independent of each other). This condition allows each reference data point to be processed with a statistically simple and appropriate method, thereby achieving an unbiased validation result. However, with a sampling method such as that used in SACLAJ, whose target point is arbitrarily selected by investigators, there is an inclination to select points at accessible locations where the reference data can be easily obtained. A system such as the DCP resolves this issue, but the robustness of noise such as aliasing is not always guaranteed because the reference points are located at a certain interval (i.e., every one degree of latitude and longitude). 11.4. CONCLUSIONS AND FUTURE DEVELOPMENTS Integrated analysis based on in situ and satellite RS‐ observed data has made it possible to map the spatial and temporal variability of plant phenology and land use and land cover. However, to advance the accuracy of satellite RS‐based spatial and temporal variability of ecosystem functions, services, and biodiversity, the following five challenging tasks should be undertaken. 11.4.1. Task 1: Collection of Ground‐Truth Data at Multiple Locations To advance the accuracy of integrated analyses by using in situ and satellite RS‐observed data, more ground‐ truth data need to be collected from multiple field points. As ground‐truth data for phenology, live camera images taken at tourist locations and highways can be useful in conjunction with information gathered by citizen scientists, phenological information published on web sites, and daily phenology images taken by automated time‐ lapse digital cameras. Morris et  al. (2013) qualitatively analyzed plant phenology by using traffic camera images taken on or near highways. In many cases, however, we cannot download historical live camera images from web sites because the cameras capture streaming images but do not record them. For this reason, we should develop a system to automatically collect and archive live camera images from these types of web sites. For ground‐truth data of land use and land cover, field survey images published on OpenStreetCam (https:// www.openstreetcam.org, accessed 17 December 2019) and Mapillary (https://www.mapillary.com, accessed 17  December 2019) are useful in conjunction with information from DCP. However, these data have two

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problems: the area and locations of the field surveys are limited, and we cannot obtain seasonal and interannual information from the same survey points. For this reason, the people responsible for creating these information sources should be encouraged to periodically collect ground‐truth data at multiple locations. 11.4.2. Task 2: Collection of Additional Ecological Information To better understand the characteristics of spectral reflectance and vegetation indexes among ecosystems, we should collect additional ecophysiological data such as leaf traits and photosynthetic rate, which help explain the characteristics of photosynthetic capacity (Wright et  al., 2004). For instance, global LAI data observed in  situ that are published on NASA ORNL DAAC (Iio  et  al., 2014; https://daac.ornl.gov/VEGETATION/ guides/LAI_Woody_Plants.html, accessed 17 December 2019) and leaf traits data published on the TRY Plant Trait Database (Kattge et al., 2011; https://www.try‐db. org/TryWeb/Home.php, accessed 17 December 2019) are useful. However, these data have two problems: the observation dates are not synchronized with satellite RS‐ observed data, and the imprecise locations of reported points cause uncertainty when validating satellite RS‐ observed data. Therefore, the accuracy and precision of the validation method should be selected to satisfy the desired goals regarding temporal variability and spatial representativeness. 11.4.3. Task 3: Spatial Scale Gap The spatial gaps between in situ and satellite RS‐ observed data need to be sufficiently validated. For example, in the case of MODIS and SPOT sensors, a perfect validation would cover all vegetation within a square that has a side 500–1000 m long. Realistically, it is probably not possible to perform a field survey for every tree or grass species within such a large area. We should construct a reasonable design to eliminate the systematic noise (bias) in the in situ observations and to reduce propagation of errors in each in situ observation to the extent possible. Akitsu et  al. (2015) tried to obtain ground‐truth data for LAI and aboveground biomass within validation sites with an area of a square that has a side 500 m long, the spatial resolution of the SGLI (Second Generation Global Imager) sensor mounted on the GCOM‐C (Global Change Observation Mission‐ Climate) satellite (launched in December 2017), in four forest ecosystems in Japan. However, we need to keep in mind that data observed in situ, which are considered as ground‐truth data, also include uncertainties and systematic and random noises.

11.4.4. Task 4: Integration of Plant Phenology and Land‐Use and Land‐Cover Changes Land‐use and land‐cover changes provide important information to interpret plant functional type, which explains the characteristics of photosynthetic function and ecosystem structures. Here, plant phenology, which correlates with photosynthetic capacity, leaf traits, and climate, may be considered to be correlated with the characteristics of plant functional type. For this reason, we require information on the spatial distribution of tree species and the phenology characteristics of various tree species. Toward this aim, we should collect ground‐truth data on the distribution of tree species by checking images published on OpenStreetCam and Mapillary and on characteristics of plant phenology for various tree species by collecting phenology images obtained from various time‐ lapse camera networks (noted in task 5) and published literature and databases. Finally, abundant ground‐truth data for plant phenology and land‐use and land‐cover changes have made it possible to create synergy between the advancement of accuracy of plant phenology and that of land‐use and land‐cover changes based on satellite RS observations. 11.4.5. Task 5: Networking of Observation Networks To better understand the ecological meaning of satellite RS‐observed data, various ecophysiological and flux‐ observed data are required. To collect data observed in situ at multiple points throughout the world requires collaboration and cooperation among observation networks and scientific communities in each country and region. Toward this aim, the networking of observation networks is an important but challenging task. For example, many phenology networks using time‐lapse digital cameras exist, including the following: Web Camera Images of National Parks and Wildlife in Japan (Ide & Oguma, 2010; http://www.sizenken.biodic.go.jp, accessed 17 December 2019); PEN in Asia (Nagai, Akitsu, et  al., 2018; http:// pen.envr.tsukuba.ac.jp, accessed 17 December 2019); PhenoCam in North and South America (Brown et al., 2016; https://phenocam.sr.unh.edu/webcam/, accessed 25 October 2019); Australian Phenocam Network (Moore et  al., 2016; https://phenocam.org.au, accessed 17 December 2019); European Phenology Camera Network (Wingate et  al., 2015; http://european‐webcam‐network. net, accessed 17  December 2019); and e‐phenology in Brazil (Alberton et al., 2017; http://www.recod.ic.unicamp. br/ephenology/client/index.html#/, accessed 17 December 2019). We can evaluate the characteristics of plant phenology among ecosystems and tree species on a global scale by examining the huge number of daily phenology images taken at multiple points in various ecosystems belonging to the above‐mentioned phenology networks.

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To perform integrated studies making use of in situ observations, satellite RS observations, and model simulations, JapanFlux (a CO2 and heat flux observation network in Japan; http://www.japanflux.org, accessed 17 December 2019), JaLTER (Japan Long‐Term Ecological Research Network; http://www.jalter.org, accessed 17 December 2019), JAXA (http://www.jaxa.jp, accessed 17 December 2019), and the Japan Agency for Marine‐ Earth Science and Technology (JAMSTEC; http://www. jamstec.go.jp, accessed 17 December 2019) started to collaborate in 2012. They are working to bridge the ­ observational gaps between various data related to biodiversity and ecosystems (Muraoka, Ishii, et  al., 2013). However, networking of observation networks has become stalled because the organizations have different aims, the spatial and temporal characteristics of observational technology and evaluation algorithms differ, and their organizational resources vary. To foster better collaboration and achieve interdisciplinary observation networking, common research topics, such as the phenology of various organisms and its consequences in ecosystem functions, could be a key motivating force (e.g., Muraoka et al., 2015; Tang et al., 2016). We might also learn from the examination of the “ecosystem integrity” of essential biodiversity variables (EBVs) by Haase et al. (2017), who analyzed various EBVs in the context of ecosystem structure and functions. At the present time, observations made by the SENTINEL constellation satellites (1‐A, 1‐B and 2‐A, 2‐B), which simultaneously observe terrestrial ecosystems with high spatial, temporal, and spectral resolutions, are greatly anticipated. However, satellite RS is not a panacea. The RS data always include systematic and random noise and uncertainties, and our ecological understanding of satellite RS‐observed data remains insufficient (Nagai et  al., 2016). However, the expectations of satellite RS observation to implement global observation networks such as Global Earth Observation System of Systems (GEOSS; https://www.earthobservations.org/geoss.php, accessed 17 December 2019) and Group on Earth Observations Biodiversity Observation Network (GEO BON; http://geobon.org, accessed 17 December 2019) increase day by day. Further development of satellite RS technology and studies using satellite RS‐observed data are strongly required. ACKNOWLEDGMENTS This work was supported by the Global Change Observation Mission (PI #102, 116, and 117) of Japan Aerospace Exploration Agency (JAXA); the JAMSTEC‐ IARC (International Arctic Research Center, University of Alaska Fairbanks) Collaboration Study (JICS); the Arctic Challenge for Sustainability (ArCS) of the Ministry

of Education, Culture, Sports, Science Technology of Japan; and the C budget of ecosystems and cities and villages on permafrost in eastern Russian Arctic (COPERA) project of the Belmont Forum. The authors thank A.V. Kononov, T.C. Maximov, and R.E. Petrov (Siberian Division of Russian Academy of Sciences), S. Tei, T. Morozumi, and R. Shakhmatov (Hokkaido University), H. Ikawa (National Agriculture and Food Research Organization), and R.C. Busey, H. Nagano, and Y. Kim (University of Alaska Fairbanks) for their assistance in the field. We thank the book’s editors and an anonymous reviewer for their kind and constructive comments. This paper is dedicated to our valued colleague, the late Dr. Rikie Suzuki of JAMSTEC. REFERENCES Akitsu, T., Nasahara, K.N., Kobayashi, H., Saigusa, N., Hayashi, M., Nakaji, T., et al. (2015). JAXA super sites 500: large‐scale ecological monitoring sites for satellite validation in Japan. Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp. 3866–3869). Alberton, B., Torres, R.S., Cancian, L.F., Borges, B.D., Almeida, J., Mariano, G.C., et al. (2017). Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspectives in Ecology and Conservation, 15, 82–90. Amano, T., González‐Varo, J.P., & Sutherland, W.J. (2016). Languages are still a major barrier to global science. PLOS Biology. https://doi.org/10.1371/journal.pbio.2000933 Anderson, H.B., Nilsen, L., Tømmervik, H., Karlsen, S.R., Nagai, S., & Cooper, E.J. (2016). Using ordinary digital cameras in place of near‐infrared sensors to derive vegetation indices for phenology studies of high Arctic vegetation. Remote Sensing, 8, 847. doi:10.3390/rs8100847 Avtar, R., Suzuki, R., & Sawada, H. (2014). Natural forest biomass estimation based on plantation information using PALSAR data. PLoS ONE, 9(1), e86121. Brown, T.B., Hultine, K.R., Steltzer, H., Denny, E.G., Denslow, M.W., Granados, J., et al. (2016). Using phenocams to monitor our changing Earth: toward a global phenocam network. Frontiers in Ecology and the Environment, 14, 84–93. Buitenwerf, R., Rose, L., & Higgins, S.I. (2015) Three decades of multi‐ dimensional change in global leaf phenology. Nature: Climate Change, 5. doi: 10.1038/NCLIMATE2533 Channan, S., Collins, K., & Emanuel, W.R. (2014). Global mosaics of the standard MODIS land cover type data. College Park, MD: University of Maryland and the Pacific Northwest National Laboratory. Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., & Eklundh, L. (2004). A simple method for reconstructing a high‐quality NDVI time‐series data set based on the Savitzky–Golay filter. Remote Sensing of Environment, 91, 332–344. DeFries, R.S., Hansen, M., Townshend, J.R.G., & Sohlberg, R. (1998). Global land cover classifications at 8‐km spatial resolution: the use of training data derived from Landsat imagery

242  BIOGEOCHEMICAL CYCLES in decision tree classifiers. International Journal of Remote Sensing, 19, 3141–3168. Delbart, N., Beaubien, E., Kergoat, L., & Le Toan, T. (2015). Comparing land surface phenology with leafing and flowering observations from the PlantWatch citizen network. Remote Sensing of Environment, 160, 273–280. Delbart, N., Kergoat, L., Le Toan, T., Lhermitte, J., & Picard, G. (2005). Determination of phenological dates in boreal regions using normalized difference water index. Remote Sensing of Environment, 97, 26–38. Delbart, N., Le Toan, T., Kergoat, L., & Fedotova, V. (2006). Remote sensing of spring phenology in boreal regions: A free of snow‐effect method using NOAA–AVHRR and SPOT– VGT data (1982–2004). Remote Sensing of Environment, 101, 52–62. Delbart, N., Picard, G., Le Toan, T., Kergoat, L., Quegan, S., Woodward, I., et  al. (2008). Spring phenology in boreal Eurasia over a nearly century time scale. Global Change Biology, 14, 603–614. Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., & Hurtt, G.C. (2009). Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research, 114, G00E02. doi:10.1029/2008JG000911 Garonna, I., De Jong, R., de Wit, A.J., Mücher, C.A., Schmid, B., &Schaepman, M.E. (2014). Strong contribution of autumn phenology to changes in satellite‐derived growing season length estimates across Europe (1982–2011). Global Change Biology, 20(11), 3457–3470. doi: 10.1111/gcb.12625 Haase, P., Tonkin, J. D., Stoll, S., Burkhard, B., Frenzel, M., Geijzendorffer, I.R., et al. (2017). The next generation of site‐ based long‐term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Science of the Total Environment, 613–614, 1376–1384. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., et al. (2013). High‐resolution global maps of 21st‐century forest cover change. Science, 342, 850–853. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. Ichii, K., Ueyama, M., Kondo, M., Saigusa, N., Kim, J., Carmelita Alberto, M., et  al. (2017). New data‐driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. Journal of Geophysical Research: Biogeosciences, 122, 767–795. Ide, R., & Oguma, H. (2010). Use of digital cameras for phenological observations. Ecological Informatics, 5, 339–347. Iio, A., Hikosaka, K., Anten, N.P.R., Nakagawa, Y., & Ito, A. (2014). Global dependence of field‐observed leaf area index in woody species on climate: a systematic review. Global Ecology and Biogeography, 23, 274−285. Inoue, T., Nagai, S., Saitoh, T.M., Muraoka, H., Nasahara, K.N., & Koizumi, H. (2014). Detection of the different characteristics of year‐to‐year variation in foliage phenology among deciduous broad‐leaved tree species by using daily

continuous canopy surface images. Ecological Informatics, 22, 58–68. Ishihara, M., & Tadano, T. (2017). Land cover changes induced by the great east Japan earthquake in 2011. Nature: Scientific Reports, 7, 45769. doi: 10.1038/srep45769 Iwao, K., Nasahara, K.N., Kinoshita, T., Yamagata, Y., Patton, D., & Tsuchida, S. (2011). Creation of new global land cover map with map integration. Journal of Geographic Information System, 3, 160−165. Jönsson, P., & Eklundh, L. (2004). TIMESAT—a program for analyzing time‐series of satellite sensor data. Computers and Geosciences, 30, 833–845. Kattge, J., Díaz, S., Lavorel, S., Prentice, C., Leadley, P., Boenisch, G., et al. (2011). TRY—a global database of plant traits. Global Change Biology, 17, 2905–2935. Kobayashi, H., Delbart, N., Suzuki, R., & Kushida, K. (2010). A satellite‐based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest. Journal of Geophysical Research: Biogeosciences, 115, G01002. doi:10.1029/2009JG000939 Kobayashi, H., Yunus, A.P., Nagai, S., Sugiura, K., Kim, Y., Van Dam, B., et  al. (2016). Latitudinal gradient of spruce forest understory and tundra phenology in Alaska as observed from satellite and ground‐based data. Remote Sensing of Environment, 177, 160–170. Kobayashi, K., Nasahara, K., Tadono, T., Ohgushi, F., Dotsu, M., & Dan, R. (2017). Development and update of “SACLAJ” a multi‐temporal ground truth dataset of land cover. International Symposium on Remote Sensing, Nagoya University, 18 May. Koh, L.P., Miettinen, J., Liew, S.C., & Ghazoul, J. (2011). Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Sciences, 108(12), 5127–5132. Kosmala, M., Crall, A., Cheng, R., Hufkens, K., Henderson, S., & Richardson, A. D. (2016). Season spotter: using citizen science to validate and scale plant phenology from near‐surface remote sensing. Remote Sensing, 8, 726; doi:10.3390/rs8090726. Kumar, K., & Mutanga, O. (2017). Remote sensing of above‐ ground biomass. Remote Sensing, 9, 935. doi:10.3390/ rs9090935 Laurin, G.V., Pirotti, F., et al. (2017). Potential of ALOS2 and NDVI to estimate forest above‐ground biomass, and comparison with lidar‐derived estimates. Remote Sensing, 9, 18. doi:10.3390/rs9010018 Langner, A., Miettinen, J., & Siegert, F. (2007). Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Global Change Biology, 13, 2329–2340. Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G.S., Yuan, C., et al. (2017). The first all‐season sample set for mapping global land cover with Landsat‐8 data. Science Bulletin, 62(7), 508–515. Li, L., Dong, J., Tenku, S. N., & Xiao, X. (2015). Mapping oil palm plantations in Cameroon using PALSAR 50‐m orthorectified mosaic images. Remote Sensing, 7, 1206–1224. Liu, L., Liang, L., Schwartz, M.D., Donnelly, A., Wang, Z., Schaaf, C.B., & Liu, L. (2015). Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest. Remote Sensing of Environment, 160, 156–165.

IMPORTANCE OF THE COLLECTION OF ABUNDANT GROUND-TRUTH DATA  243 Miettinen, J., & Liew, S.C. (2011). Separability of insular Southeast Asian woody plantation species in the 50 m resolution ALOS PALSAR mosaic product. Remote Sensing Letters, 2(4), 299–307. Miettinen, J., Shi, C., & Liew, S.C. (2011). Deforestation rates in insular Southeast Asia between 2000 and 2010. Global Change Biology, 17, 2261–2270. Moore, C.E., Brown T., et  al. (2016). Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography. Biogeosciences, 13, 5085–5102. Morris, D.E., Boyd, D.S., Crowe, J.A., Johnson, C.S., & Smith K.L. (2013). Exploring the potential for automatic extraction of vegetation phenological metrics from traffic webcams. Remote Sensing, 5, 2200−2218. Motohka, T., Nasahara, K.N., Miyata, A., Mano, M., & Tsuchida, S. (2009). Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. International Journal of Remote Sensing, 30, 4343–4357. Motohka, T., Nasahara, K.N., Oguma, H., & Tsuchida, S. (2010). Applicability of green–red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2, 2369–2387. Muraoka, H., Ishii, R., Shin Nagai, S., Suzuki, R., Motohka, T., Noda, H.M., et al. (2013). Linking remote sensing and in situ ecosystem/biodiversity observations by “satellite ecology”. In S. Nakano, T. Nakashizuka, T. Yahara, (Eds.), Biodiversity observation network in Asia‐Pacific region (pp. 277–308). Tokyo: Springer‐Verlag. Muraoka, H., Noda, H.M., Nagai, S., Motohka, T., Saitoh, T.M., Nasahara, K.N., & Saigusa, N. (2013). Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. Journal of Plant Ecology, 6, 393–407. Muraoka, H., Saigusa, N., Nasahara, K.N., Noda, H., Yoshino, J., Saitoh, T.M., et al. (2010). Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool‐temperate deciduous broadleaf forest in Takayama, Japan. Journal of Plant Research, 123, 563–576. Muraoka, H., Saitoh, T.M., & Nagai, S. (2015). Long‐term and interdisciplinary research on forest ecosystem functions: challenges at Takayama site since 1993. Ecological Research, 30, 197–200. Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., & Nemani, R.R. (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626), 698. Myneni, R., Knyazikhin, Y., & Park, T. (2015). MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8‐day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/modis/mcd15a2h.006 Nagai, S., Akitsu, T., Saitoh, T. M., Busey, R. C., Fukuzawa, K., Honda, Y., et al. (2018). 8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network. Ecological Research, 33(6), 1091–1092. doi: 10.1007/s11284‐018‐1633‐x. Nagai, S., Inoue, T., Ohtsuka, T.,Ohtsuka, T., Kobayashi, H., Kurumado, K., Muraoka, H., Nasahara, K.N. (2014).

Relationship between spatio‐temporal characteristics of leaf‐ fall phenology and seasonal variations in near surface‐ and satellite‐observed vegetation indices in a cool‐temperate deciduous broad‐leaved forest in Japan. International Journal of Remote Sensing, 35, 3520–3536. Nagai, S., Inoue, T., & Suzuki, R. (2015). Leaf‐coloring information published on web sites and its utility in the ground‐truthing of satellite remote‐sensing data for mapping autumn leaf phenology. Japanese Journal of Biometeorology, 52(2), 119–129 (in Japanese with English abstract). Nagai, S., Ishii, R., Suhaili, A.B., Kobayashi, H., Matsuoka, M., Ichie, T., et  al. (2014). Usability of noise‐free daily satellite‐observed green–red vegetation index values for monitoring ecosystem changes in Borneo. International Journal of Remote Sensing, 35, 7910–7926. Nagai, S., Kobayashi, H., & Suzuki, R. (2019) Remote sensing of vegetation. In T. Ohta, T. Hiyama, Y. Iijima, A. Kotani, T.C. Maximov (Eds.), Water–carbon dynamics in eastern Siberia (pp. 231–252). Springer. Nagai, S., Nasahara, K.N., Muraoka, H., Akiyama, T., & Tsuchida, S. (2010). Field experiments to test the use of the normalized difference vegetation index for phenology detection. Agricultural and Forest Meteorology, 150, 152–160. Nagai, S., Nasahara, K.N., Inoue, T., Saitoh, T.M., & Suzuki, R. (2016). Review: Advances in in situ and satellite phenological observations in Japan. International Journal of Biometeorology, 60, 615–627. Nagai, S., Nasahara, K.N., Yoshitake, S., & Saitoh, T.M. (2017). Seasonality of leaf litter and leaf area index data for various tree species in a cool‐temperate deciduous broad‐leaved forest, Japan, 2005–2014. Ecological Research, 32, 297–297. Nagai, S., Saitoh, T.M., Kobayashi, H., Ishihara, M., Suzuki, R., Motohka, T., Nasahara, K.N., & Muraoka, H. (2012). In situ examination for the relationship between various vegetation indices and tree phenology in an evergreen coniferous forest, Japan. International Journal of Remote Sensing, 33, 6202–6214. Nagai, S., Saitoh, T.M., Nasahara, K.N., & Suzuki, R. (2015). Spatio‐temporal distribution of the timing of start and end of growing season along vertical and horizontal gradients in Japan. International Journal of Biometeorology, 59, 47–54. Nagai, S., Saitoh, T.M., & Nasahara, K.N. (2018) Development of phenology observations and land uses and land cover classification by integrating analysis of remote sensing and open‐access data. Journal of The Remote Sensing Society of Japan, 38, 99–104. [In Japanese with English summary.] Nakai, T., Kim, Y., Busey, R.C., Suzuki, R., Nagai, S., Kobayashi, H., Park, H., Sugiura, K., & Ito, A. (2013). Characteristics of evapotranspiration from a permafrost black spruce forest in interior Alaska. Polar Science, 7, 136–148. Nasahara, K.N., Muraoka, H., Nagai, S., & Mikami, H. (2008). Vertical integration of leaf area index in a Japanese deciduous broad‐leaved forest. Agricultural and Forest Meteorology, 148, 1136–1146. Nasahara, K.N., & Nagai, S. (2015). Review: development of an in‐situ observation network for terrestrial ecological remote sensing—the Phenological Eyes Network (PEN). Ecological Research, 30, 211–223.

244  BIOGEOCHEMICAL CYCLES Ohta, T., Maximov, T.C., Dolman, A.J., Nakai, T., van der Molen, M.K., Kononov, A.V., et  al. (2008). Interannual variation of water balance and summer evapotranspiration in an eastern Siberian larch forest over a 7‐year period (1998–2006). Agricultural and Forest Meteorology, 148, 1941–1953. Ohtsuka, T., Akiyama, T., Hashimoto, Y., Inatomi, M., Sakai, T., Jia, S., et al. (2005). Biometric based estimates of net primary production (NPP) in a cool‐temperate deciduous forest stand beneath a flux tower. Agricultural and Forest Meteorology, 134, 27–38. Pfeifer, M., Kor, L., Nilus, R., Turner, E., Cusack, J., Lysenko, I., et al. (2016). Mapping the structure of Borneo’s tropical forests across a degradation gradient. Remote Sensing of Environment, 176, 84–97. Piao, S., Fang, J., Zhou, L., Ciais, P., & Zhu, B. (2006). Variations in satellite‐derived phenology in China’s temperate vegetation. Global Change Biology, 12, 672–685. Soyama, N., Akitsu, T., & Temulun, T. (2017). Reference dataset production manual for the accuracy assessment of global land cover products using information from the Degree Confluence Project. Overall Education Research Center Bulletin of Tenri University, 15, 31–39. Souza, C.M., Siqueira, J., Sales, M., Fonseca, A., Ribeiro, J., Numata, I., et  al. (2013). Ten‐year landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sensing, 5, 5493–513. Studer, S., Stöckli, R., Appenzeller, C., & Vidale, P.L. (2007). A comparative study of satellite and ground‐based phenology. International Journal of Biometeorology, 51, 405–414. Sugiura, K., Nagai, S., Nakai, T., & Suzuki, R. (2013). Application of time‐lapse digital imagery for ground‐truth verification of satellite indices in the boreal forests of Alaska. Polar Science, 7, 149–161. Suzuki, R., Kim, Y., & Ishii, R. (2013). Sensitivity of the backscatter intensity of ALOS/PALSAR to the above‐ground biomass and other biophysical parameters of boreal forest in Alaska. Polar Science, 7, 100–112. Tadaki, Y., Kitamura, H., Kanie, K., Sano, H., Shigematsu, A., & Ohtsu, S. (1994). Leaf opening and falling of Japanese

larch at different altitudes. Japanese Journal of Ecology, 44, 305–314. [In Japanese with English abstract.] Tang, J., Körner, C., Muraoka, H., Piao, S., Shen, M., Thackeray, S.J., & Yang, X. (2016). Emerging opportunities and challenges in phenology: a review. Ecosphere, 7(8), e01436. Tateishi, R., Uriyangqai, B., Al‐Bilbisi, H., Ghar, M.A., Tsend‐ Ayush, J., Kobayashi, T.,, et al. (2011). Production of global land cover data—GLCNMO. International Journal of Digital Earth, 4(1), 22–49. Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150. White, M.A., Thornton, P.E., & Running, S.W. (1997). A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycle, 11(2), 217–234. White, M.A., De Beurs, K.M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., et al. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biology, 15(10), 2335–2359. Wingate, L., Ogée, J., Cremonese, E., Filippa, G., Mizunuma, T., Migliavacca, M., et al. (2015). Interpreting canopy development and physiology using a European phenology camera network at flux sites. Biogeosciences, 12, 5995–6015. Wright, I.J., Reich, P.B., Westoby, M., Ackerly, D.D., Baruch, Z., Bongers, F., et al. (2004). The worldwide leaf economics spectrum. Nature, 428, 821–827. Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., et al. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84, 471–475. Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., et al. (2013). Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 5, 927–948.

12 Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems Elizabeth Herndon1, Lauren Kinsman‐Costello2, and Sarah Godsey3

ABSTRACT Northern high latitudes are experiencing rapid changes in climate that drive permafrost thaw and shifts in hydrology and soil saturation. These factors regulate redox conditions across permafrost‐affected landscapes, potentially altering carbon storage in soils and exacerbating climate change through accelerated decomposition of soil organic matter. Redox conditions impact soil carbon storage directly by influencing rates and pathways of organic matter decomposition, and indirectly by moderating the bioavailability of organic molecules and nutrients. Indeed, the ability of increased plant growth to offset C losses in permafrost regions will be regulated by nutrient availability (e.g., N, P) that varies across redox gradients. The purpose of this review is to examine how redox conditions shape biogeochemical cycling of ecologically important elements (P, N, S, Fe) in permafrost‐affected ecosystems. Although carbon cycling in these regions continues to be widely studied, relatively little information is available on the elements that regulate C cycling. We discuss the complex feedbacks between climate change, hydrology, and landscape change that control redox conditions, then examine how these factors regulate biogeochemical cycles. We identify key gaps in our understanding of how changing climate may alter biogeochemical cycles and carbon storage in northern high‐latitude ecosystems. 12.1. CLIMATE‐INDUCED PERTURBATIONS TO PERMAFROST ECOSYSTEMS 12.1.1. Permafrost Thaw Induces Complex Feedbacks Between Landscapes and Hydrology Climate change at northern high latitudes is more amplified than change at lower latitudes (Bekryaev et al., 2010; Hinzman et  al., 2013). Air temperature records show that median Arctic air temperatures have increased 2.5–3 times more than the global average (Stocker et al., 2013), with differences that are especially pronounced in winter (Hansen et  al., 2014). Even greater temperature  Department of Geology, Kent State University, Kent, Ohio, USA  Department of Biological Sciences, Kent State University, Kent, Ohio, USA 3   Department of Geosciences, Idaho State University, Pocatello, Idaho, USA 1 2

changes are expected by the end of the century, with projected increases of ~3–9°C in Arctic regions (Stocker et al., 2013). Permafrost (see Table 12.1 for definitions of italicized terms) temperatures have increased along with these large air temperature changes, and the largest increases are observed in shallow permafrost. In northern Alaska, borehole temperatures at 20 m depth within permafrost have risen from ~0.5°C to > 1°C between the late 1970s and early 2000s (Osterkamp, 2007), with greater increases at higher latitudes (Hinzman et  al., 2005). Accompanying warming permafrost are increases in the thickness (Romanovsky & Osterkamp, 2000) and variability (Harris et al., 2009) of the active layer, increases in thermokarst activity (Frey & McClelland, 2009), and increases in the extent and duration of thaw of long‐ frozen ground ice and permafrost soils (e.g., Sjöberg et al., 2013). Evidence also suggests that the Arctic hydrological cycle is intensifying as a result of warming (Rawlins et  al.,

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

245

246  BIOGEOCHEMICAL CYCLES Table 12.1 Definitions Term

Definition

Active layer

Shallow soil layer that overlies permafrost and thaws during the warm season Subarctic biome characterized by dominance of coniferous forests Region at high‐latitudes where permafrost is laterally continuous Region at high‐latitudes where permafrost occurs in patches that are not laterally continuous Saturated depressions that remain following sudden drainage of thaw lakes perched on permafrost Ice‐wedge polygon characterized by an elevated center mound surrounded by trough depressions Geometric ground pattern consisting of microtopographic features (troughs, rims, centers) that develop due to growth and degradation of large ice wedges in the soil Ice‐wedge polygon characterized by a low‐lying center basin that is bordered by elevated rims and depressed troughs Ground that remains frozen (< 0°C) for at least two consecutive years Elements that gain or lose electrons to occupy multiple oxidation states Arctic biome in which plant growth is hindered by cold temperatures

Boreal Continuous permafrost Discontinuous permafrost Drained thaw lake basin High‐centered polygon Ice wedge polygon

Low‐centered polygon Permafrost Redox‐sensitive Tundra

2010), leading to increases in the magnitude of all fluxes, including precipitation, runoff, and evapotranspiration, with much less certainty in subsurface flux trends (Walvoord & Kurylyk, 2016). Precipitation changes include shifts in the total magnitude of precipitation, increases in peak or average intensity, changes in the timing or rate of snowmelt, and shifts in the fraction of total annual precipitation falling as snow versus rain (Bintanja & Andry, 2017; Bring et al., 2016). Precipitation intensity changes are important because they can affect hydrologic connectivity and how much of the incoming precipitation is able to infiltrate into the subsurface and/or quickly run into nearby surface water bodies (e.g., Kumar et al., 2012; Spence, 2007; Spence & Phillips, 2015; Tromp‐Van Meerveld & McDonnell, 2006). The phase of precipitation and the seasonality of snow cover affect plant water use, ground thaw, nutrient availability, microbial activity, and food availability to foragers (Liston et al., 2002; Sturm et al., 2005). These cumulative changes in precipitation have increased surface runoff (Rawlins et  al., 2010) and groundwater baseflow contributions to stream flows (e.g.,

Bense et  al., 2009; Walvoord & Striegl, 2007; Ye et  al., 2009). Increased subsurface flows are inferred by changes in baseflow discharge (Walvoord & Striegl, 2007) or changes in stream water chemistry that reflect increased flow through deep mineral soils experiencing thaw (Keller et  al., 2010). Subsurface flows are expected to replace ­surface flows as the dominant flow path as warming proceeds (Frey & McClelland, 2009). However, the depth at which subsurface flows may dominate remains uncertain; documented increases in suprapermafrost flows are not available for subpermafrost flows due to logistic limitations (Walvoord & Kurylyk, 2016), but may be critical in certain systems. Understanding where in the subsurface is most hydrologically active is important to characterize biogeochemical reactions, rates of subsurface flow, and availability of stored water to vegetation. Evapotranspiration (ET), another key flow within the hydrologic cycle, has also increased in Arctic systems. Evapotranspiration losses have occurred through extension of the growing season and through increased vapor pressure deficit from warming. Increased ET variability— with large short‐term decreases in transpiration—can occur immediately following wildfire, an increasingly common disturbance in the region (Koch et  al., 2014; O’Donnell et al., 2009). In some unburned areas, ET fluxes have increased faster than runoff and even precipitation (Hinzman et  al., 2005). Even small shifts in the balance between precipitation, runoff and ET can affect soil redox by changing the amount of water stored in the subsurface. However, direct observations of soil storage changes thus far show varied responses, perhaps due to the complexity of watershed‐scale vegetation interactions (Jorgenson et al., 2010; Walvoord & Kurylyk, 2016). The combination of warmer temperatures and an intensified hydrological cycle has complex implications for ground thaw and associated biogeochemical cycling in permafrost‐affected systems. Ground thaw is not only a function of temperature, but also often depends on soil‐ water content because of water’s high heat capacity and latent heat (e.g., French, 2007; Ling & Zhang, 2004). In addition to soil moisture effects, thaw‐driven landscape change is also affected by the accumulation and redistribution of snow, which acts as an insulating layer for the ground (Liston et  al., 2007). Snow accumulation is in turn often affected by the presence and type of vegetation (Jorgenson et al., 2010), which is expected to change with changing climate (Sturm et al., 2001). Finally, changes in water flow across the landscape may alter advective heat transport (Sjöberg et al., 2016), which is typically assumed to be minimal compared to other fluxes (Kane et  al., 2001). However, modeling energy and water fluxes, including advective heat fluxes, through the unsaturated zone remains challenging (Walvoord & Kurylyk, 2016). Furthermore, water flows may also transport thawed

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   247

material as solute or sediment fluxes (Sjöberg et  al., 2013), indirectly affecting future thaw and potential ground collapse. The effects of thaw on permafrost‐affected landscapes depend on permafrost extent (continuous or discontinuous; Figure 12.1) and other factors such as ground‐ice content, soil physical properties, ecosystem structure, disturbance, slope, aspect, and thaw rate (Jorgenson et al., 2010, 2013; Koch et al., 2014; Kokelj et al., 2013). Broadly speaking, Arctic tundra will experience increases in precipitation and evapotranspiration, increases in active‐ layer thickness potentially leading to more water storage, and either increases in surface ponding due to depressions made by permafrost thaw and collapse or decreases due to loss to subpermafrost groundwater. Boreal forests (i.e., taiga) will experience increases in precipitation and ET and extensive loss of permafrost (Bring et al., 2016), which enhances surface–subsurface connections and can lead to drier soils in areas where infiltration is improved, or wetland formation where groundwater can now upwell to the surface (Swindles et al., 2016). Within this general framework, hydrologic response to thaw depends largely on whether permafrost will persist or disappear from a given system (Figure  12.2). For example, thermokarst lakes form when thaw of ground ice and shallow permafrost reduces soil volume and causes overlying ground to collapse into a depression that

Continuous permafrost

fills with water. Thermokarst lakes have both expanded and contracted in areas of continuous permafrost but decreased in discontinuous permafrost (Andresen & Lougheed, 2015; Jepsen et al., 2013; Smith et al., 2005). These observations are explained by permafrost degradation. Thaw leads to land subsidence and ponding where permafrost still provides barrier to drainage, although high rates of evaporation can shrink ponds in some areas (Andresen & Lougheed, 2015). Further degradation of permafrost, such as observed in discontinuous zones, results in deeper water tables and flow paths which can lead to extensive drainage as ice barriers are removed (Bring et al., 2016; Jones et al., 2011) or rapid rewetting when hydrologic connectivity to groundwater flow is established (Swindles et al., 2016). Even across zones of similar permafrost continuity, the hillslope gradient strongly affects landscape outcomes. For example, in relatively low‐gradient coastal plains, thaw can increase hydrologic connectivity and promote drainage (Liljedahl et al., 2016), whereas warming in uplands can generate a variety of slope‐dependent thermokarst features that lead to short‐term pulses of sediment and biogeochemical activity (e.g., Abbott & Jones, 2015; Dugan et  al., 2012; Osterkamp et al., 2009). Ultimately, the degree of hydrologic connectivity of terrestrial and aquatic portions of the Arctic landscape will be critical in assessing the local effects of climate change, consistent with early

Discontinuous permafrost

Lake Permafrost

Talik

Permafrost

Unfrozen soil and rock

Decreasing latitude

Figure 12.1  Latitudinal transect showing the transition from continuous permafrost at northern high latitudes to discontinuous permafrost with decreasing latitude. Gray areas represent permafrost and brown areas represent seasonally or perennially thawed ground. Lakes in the zone of continuous permafrost are underlain by taliks, which are defined as regions of unfrozen ground surrounded by permafrost. The area marked by dashed lines indicates the seasonally thawed active layer. The depth of the active layer increases with decreasing latitude as the thickness of permafrost decreases. Note that in the zone of discontinuous permafrost, seasonally frozen surface soil overlies persistently thawed ground where permafrost is not present. Northern high latitudes are dominated by tundra vegetation, which shifts to boreal forest with decreasing latitude. More complex topography, including steeper slopes, is not represented here and can affect dominant water pathways and local heterogeneity in thaw depths. Image not to scale.

248  BIOGEOCHEMICAL CYCLES (a) Continuous permafrost: drainage

Figure Legend Vegetation Organic horizon Mineral horizon Saturated soil (overlay) Permafrost Flow path

(b) Continuous permafrost: flooding

(c) Discontinuous permafrost: drainage

(d) Discontinuous permafrost: flooding

Increasing permafrost degradation Figure 12.2  Each box displays one of four potential scenarios for changing water flow through seasonally thawed active layers as depth to permafrost increases. These simplified soil profiles correspond to maximum annual thaw depth and are comprised of a vegetation layer (green), organic horizon (brown), mineral horizon (orange), and permafrost (gray). The portion of the soil that is persistently saturated is shown by the blue overlay. Arrows represent

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   249

understanding of connectivity in permafrost ecosystems (Woo & Steer, 1983). 12.1.2. Soil Redox Conditions Are Driven by Hydrology Hydrologic connectivity affects biogeochemical cycling by influencing redox conditions within soils. Redox conditions, which are controlled by the availability of electron acceptors such as oxygen gas, are broadly dependent on hydrological conditions because water limits diffusion of oxygen gas through the soil profile. Redox conditions dictate the formation and consumption of chemical species in oxidation–reduction (redox) reactions. Under oxic (oxygen‐rich) conditions, oxygen gas is abundant and can readily oxidize (accept electrons from) other chemical species. Under anoxic (oxygen‐depleted) conditions, alternative electron acceptors (e.g., Fe3+, NO3−, SO42–, organic matter) are converted into reduced species (e.g., Fe2+, NO2−, H2S, CH4, respectively) that can accumulate in the absence of oxygen. Anoxic conditions develop when oxygen gas is consumed through biochemical reactions (e.g., aerobic respiration) or geochemical reactions (e.g., sulfide oxidation) faster than it is introduced to the system by diffusion or infiltrating rainwater. Permafrost acts as an impermeable barrier that confines soil water to a shallow, seasonally thawed active layer. Consequently, many permafrost‐affected ecosystems experience periodic or persistent soil anoxia due to soil saturation (Street et al., 2016). Where vertical drainage is limited by permafrost, topography becomes an important factor that controls how quickly water drains laterally across slopes. Oxic conditions are found in well‐ drained hilltop soils and on slopes, whereas anoxic conditions prevail in deep soils and poorly drained, low‐lying areas that are persistently saturated or experience fluctuating water tables (Page et al., 2013; Street et al., 2017). Redox gradients occur over centimeter scales in flat tundra landscapes where microtopographic features generated by the formation and thaw of ground ice control water flow (Liljedahl et  al., 2011; Lipson et  al., 2010;

Zona et al., 2011). Oxic conditions persist over nearly the entire thaw depth of topographic highs, such as the mounds that form high‐centered polygons (Fiedler et al., 2004; Lipson et  al., 2010; Newman et  al., 2015; Zona et  al., 2011). In contrast, oxygen gas becomes depleted within the top 10–30 cm of saturated soils in low‐centered polygons and in the troughs that surround the polygons and constitute the drainage network. Permafrost thaw, coupled to changes in precipitation and drainage, will likely lead to greater fluctuations in water levels and redox conditions in wetland areas. For example, declines in precipitation and increases in ET in wetlands of NE Greenland (Elberling et al., 2010) have led to increased occurrence of drought conditions, and even more frequent droughts are predicted. However, summertime rain events still occur and may increase in intensity, leading to increasing fluctuations in redox conditions as soils drain and reflood (Elberling et al., 2010). Permafrost degradation can also generate patchier redox distribution in soils. In upland soil, certain thermokarst features, like thermoerosion gullies, increase the occurrence of adjacent oxic and anoxic soil patches, creating a highly microheterogeneous redox soil environment (Abbott & Jones, 2015). 12.1.3. Redox Impacts Carbon Storage in Terrestrial Ecosystems Although plant growth in permafrost regions is limited by short growing seasons and low sunlight, organic matter has accumulated because cold temperatures and widespread anoxia slow microbial decomposition. Permafrost‐affected soils store ~50% of global soil organic C (Hugelius et  al., 2014; Tarnocai et  al., 2009), but climate change threatens to destabilize these stocks and release large quantities of the greenhouse gases carbon dioxide (CO2) and methane (CH4) into the atmosphere (Ciais et  al., 2014; DeConto et  al., 2012; Schuur et  al., 2008, 2009, 2015; Walter et  al., 2006). Permafrost thaw exposes previously frozen organic matter to microbial decomposition and introduces a large

Figure 12.2 (Continued) direction of water flow. Thickness of the O horizon represents long‐term response to relatively rapid changes in saturation. (a) Shallow thaw depths in many soils in zones of continuous permafrost restrict water flow to organic horizons. Permafrost degradation enables water flow through newly thawed mineral horizons, although rapid flow through more permeable organic horizons is expected to dominate. Flow through mineral soils will increase as deeper mineral soils thaw and aeration stimulates decomposition of surface organic soils. Lowering water tables will shift surface soils toward oxic conditions and generate anoxic conditions in deeper mineral soils. (b) Permafrost degradation can also lead to ground collapse, generating depressions that collect flowing water. Flooding then creates anoxic conditions. (c) Complete permafrost loss in areas of discontinuous permafrost could lead to soil drainage and introduction of oxic conditions throughout the entire profile. (d) Conversely, gradual soil drying may be followed by rapid inundation if permafrost loss allows deeper groundwater to upwell and saturate the system. Anoxic conditions in the saturated zone may promote accumulation of organic matter.

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reservoir of carbon into the climate cycle. Soil warming accelerates decomposition of both modern and permafrost‐derived organic matter, releasing greenhouse gases that can accumulate in the atmosphere and increase warming trends (Cooper et al., 2017; Mauritz et al., 2017; Schuur et al., 2015). The potential for greenhouse gas release to drive positive climate feedbacks depends partially on redox conditions that control ecosystem C storage. While effects of increased temperature on decomposition rates are well studied (e.g., Conant et al., 2011; Davidson & Janssens, 2006; Nadelhoffer et al., 1991), it is less clear how changing redox conditions will influence greenhouse gas fluxes and the continued ability of permafrost‐affected ecosystems to act as sinks for atmospheric C. The conversion of soil organic carbon into CO2 and/or CH4 is regulated by redox conditions that vary in space and control microbial decomposition pathways (Lawrence et al., 2015). Organic matter is quickly consumed by aerobic respiration to produce CO2 but more slowly by anaerobic respiration to produce both CO2 and CH4 (Roy Chowdhury et al., 2015; Schädel et  al., 2014, 2016; Treat et  al., 2015). Methane release from permafrost ecosystems is small relative to CO2 loss but may significantly contribute to climate change due to the high warming potential of methane (Schuur et al., 2015). Increases in plant production have the potential to offset C losses derived from increased decomposition in terrestrial ecosystems. However, the ability of increased plant growth to offset C losses in Arctic and subarctic environments will be regulated by nutrient availability (e.g., N, P) that varies across redox gradients (Chapin et  al., 1995; Shaver et al., 1992; Street et al., 2017; Wieder et al., 2015). For example, shallow permafrost contains abundant organic N that can be made available to plants as inorganic N during microbial decomposition (Keuper et al., 2012). Decomposition and N release occur rapidly under oxic conditions but more slowly under anoxic conditions. Redox conditions also control the dominant species of inorganic N that accumulate following decomposition of organic N. High concentrations of nitrate (NO3−) occur in well‐drained, oxic soils while ammonium (NH4+) is more abundant in poorly drained, anoxic soils (Heikoop et al., 2015; Newman et al., 2015; Zhu et al., 2016). Despite the critical influence of redox on biogeochemical cycling in terrestrial ecosystems, there remains a paucity of information regarding redox status for permafrost‐affected ecosystems. In order to predict how terrestrial carbon storage will respond to climate change, it is necessary to evaluate how redox conditions control biogeochemical cycling of ecologically relevant elements in these regions. Here we summarize some of the complex interactions between redox and biogeochemical cycling and discuss ­ implications for the permafrost–carbon feedback.

12.2. BIOGEOCHEMICAL CYCLING OF REDOX‐SENSITIVE ELEMENTS Redox conditions influence C cycling directly by controlling how quickly soil organic matter is decomposed, and indirectly by regulating nutrient supplies that support primary production and microbial decomposition. Redox conditions influence biogeochemical cycling of ecologically important elements, and consequently C dynamics, through multiple processes that include: (a) nutrient limitation to primary production and microbial decomposition (N, P); (b) anaerobic respiration of organic matter using terminal electron acceptors (N, S, Fe); (c) increases in silicate and carbonate weathering; and (d) abiotic degradation or sequestration of organic molecules (Fe). 12.2.1. Nutrient (N and P) Cycling Nutrient limitation often severely limits plant growth in northern peatlands and ecosystems underlain by permafrost (Hobbie et al., 2002). In particular, both N and P are typically in low supply in Arctic ecosystems compared to the needs of plants and microbes that live there (Jonasson et  al., 1996, 1999; Kielland & Chapin, 1994; Shaver & Chapin, 1980, 1986, 1995). Nutrient limitation is important to the global C cycle because nutrients constrain both primary productivity and decomposition, controlling the ability of ecosystems to remove C from the atmosphere and store it in biomass and soil organic matter. Although the identity of the nutrient that most limits plant productivity and other ecosystem functions often depends on plant community composition and site conditions (Gough & Hobbie, 2003), it is certain that changes in the availability of N and/or P can drastically change Arctic and subarctic ecology and biogeochemistry (e.g., Gough et  al., 2016; Slavik et  al., 2004). For example, N addition can stimulate decomposition and C loss under low P conditions in shrub tundra, but promote primary productivity and organic matter accumulation when P is abundant (Street et al., 2017). Thus, ecosystem response to nutrient availability may vary across space and time due to interactive processes. Nutrient bioavailability is controlled by mineralization rates, i.e., the release of nutrients from organic matter as soluble inorganic species during decomposition, by competition for nutrients between plants, microorganisms, and mineral surfaces, and by hydrologic factors that control nutrient export (Giblin et al., 1991; Jones et al., 2005; Petrone et al., 2006; Schmidt et al., 2002). Based solely on warming temperatures, nutrient availability is expected to increase in these systems due to enhanced mineralization rates within the active layer (Chapin et  al., 1995; Nadelhoffer et  al., 1991; Rustad et  al., 2001). Nutrient

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   251

availability may also increase due to new inputs from thawing permafrost. Permafrost soils may contain large pools of organic nitrogen (Harden et  al., 2012; Keuper et al., 2012) and phosphorus (Chapin et al., 1978) which can be converted to inorganic forms as organic material contained in permafrost thaws and is made bioavailable to degrading microorganisms. However, predictions of nutrient bioavailability to plants and microorganisms are complicated by interactions of changing mineralization rates with alterations in redox conditions, substrate decomposability, mineral soil exposure, and hydrologic flow paths. Despite the demonstrable importance of nutrient availability to ecosystem function and carbon storage in permafrost‐affected areas (Mack et al., 2004), the potential influence of altered redox on nutrient biogeochemistry has been largely overlooked in studies of how permafrost‐affected ecosystems will respond to climate change.

Oxic

12.2.1.1. Phosphorus Phosphorus plays a critical role in regulating plant productivity and microbial processes in permafrost‐affected ecosystems. Compared to other ecosystems like temperate forests and wetlands, Arctic and subarctic ecosystems contain much less P overall (Giblin et  al., 1991), and many are either primarily P limited or N and P colimited (Chapin et  al., 1978; Giesler et  al., 2012; Shaver et  al., 1998). Although P is not itself redox‐active, P bioavail-

ability is strongly linked to redox conditions due to sorption and biological demand (Figure 12.3). Phosphorus cycling is controlled by a combination of abiotic (e.g., sorption/desorption, coprecipitation) and biotic (mineralization, biotic uptake) processes; however, studies of P cycling in permafrost‐affected ecosystems have focused on biotic processes. This may be because biological effects are considered to be dominant in these highly P‐limited systems. For example, Arctic and subarctic soils contain large amounts of P in microbial biomass (Buckeridge et  al., 2016; Chapin et  al., 1978; Edwards et al., 2006; Jonasson et al., 1996). This pool is temporally dynamic and fluctuates in magnitude throughout the year, presumably due to the boom and bust of microbial populations (Edwards et  al., 2006). Plants may acquire large amounts of P during microbial bust cycles as cells burst and bioavailable P is released into solution (Giblin et  al., 1991; Schimel et  al., 1996). Harms and Ludwig (2016) found that P is sequestered in saturated soils of permafrost‐underlain hillslopes despite the fact that low‐redox, saturated conditions tend to lead to phosphate (PO43−) removal. Phosphorus uptake by plants and microorganisms may help retain P within these soils relative to N, which is more quickly flushed downslope (Harms & Ludwig, 2016). Alternately, or perhaps in conjunction with this proposed mechanism, organic and inorganic P can be retained in tundra soils by adsorption to metal oxides (Giblin et  al., 1991; Giesler

(De)sorp

Active layer

Iron (III) oxides

Anoxic

Calcareous minerals Thaw

e tak

tion

tion/ cipita Copre lution o diss ering

Weath

Microbial biomass

Primary minerals

Up

[PO43–]T

Death

Min

eral

Soil organic matter

izati

on

Assimilation

Death

Thaw

Frozen organic matter

Figure 12.3  Summary of biogeochemical P transformations expected across redox transitions and discussed in the text. Reservoirs are indicated in bold and fluxes in italic text. The diagram represents a soil profile where brown is the active layer, gray is permafrost, and the depth of water saturation in the active layer is shown by a blue overlay. The red areas in the unsaturated organic soil represent iron oxides that precipitate above the redox interfaces. Intermediate reactions are not shown, and reactions are not balanced. [PO43−] T represents all phosphate species.

252  BIOGEOCHEMICAL CYCLES

et al., 2012; Vincent et al., 2012) such as Fe oxides that accumulate in surface soils of topographic depressions (Herndon et al., 2019). The effects of redox conditions on P cycling are well established in many terrestrial and aquatic ecosystems, but poorly explored in permafrost regions. Under oxidizing conditions, iron oxides have strong sorption capacity for phosphate, whereas under reducing conditions, microbial reduction of iron oxides can release sorbed phosphate into surrounding pore waters (Mortimer, 1942). In addition to redox state, the mineralogy of iron minerals also plays an important role in sorption capacity. Poorly crystalline and amorphous iron oxides can sorb greater amounts of phosphate than more crystalline forms (Williams et  al., 1971) due to their greater surface area and higher surface reactivity (McLaughlin et al., 1986; Roden & Zachara, 1996). Iron oxide mineralogy is shaped by hydrologic conditions, which thus control P cycling through both direct effects on speciation and indirect effects on mineralogy (Axt & Walbridge, 1999; de Vicente et al., 2010; Kerr et al., 2011). Predicted increases in P mineralization rates may create greater overall P fluxes while concurrent changes in redox conditions and hydrologic connectivity may decrease P solubility. For example, phosphate that is released during decomposition could be either sequestered in the soil via sorption to metal oxides (Giblin et al., 1991; Giesler et al., 2012; Vincent et al., 2012) or lost from the ecosystem due

to increased drainage and leaching (Frey et  al., 2007). Permafrost often constrains hydrologic flow and biological activity to surface organic‐rich layers (Woo, 1986); however, abiotic reactions between P and mineral surfaces could increase as thaw deepens into mineral horizons. More abiotic reactions within a thicker thawed mineral horizon, in combination with more oxic conditions associated with permafrost thaw and drainage, should lead to greater sorption capacity for phosphate. Consequently, low amounts of plant‐available P may constrain primary productivity and drawdown of atmospheric C. Although it is possible that P supply to the active layer will increase as P‐bearing mineral soils thaw, rates of mineral weathering may be too slow to compensate for increased P sequestration by mineral surfaces. 12.2.1.2. Nitrogen Soil warming and permafrost thaw are expected to increase N bioavailability in permafrost‐affected ecosystems. Arctic N availability is governed by inputs from atmospheric deposition (Wookey et al., 2009), mineralization (conversion of organic N to inorganic N) (Giblin et  al., 1991; Nadelhoffer et  al., 1991), and N fixation (Alexander & Billington, 1986; Troth et  al., 1976) (Figure 12.4). Indeed, plant communities in permafrost‐ affected systems are often so N limited that they will use amino acids rather than the more commonly used NO3− or NH4+ as a N source (Kielland, 1994; Persson et  al.,

N-bearing compounds

Oxic

Atmospheric deposition

nit r

ific at

ion

ion De

Ox id at

ss Gas lo

Mineralization

Death

e tak

Microbial biomass

U

NH4+

Assimilation

Up

n

N2O, N2

N2 fixation

Death

e ak pt

io

Anoxic

at fic

OMred

Uptake

NO3– tri Ni

Active layer

OMox

Soil organic matter Thaw

Frozen organic matter Figure 12.4  Summary of biogeochemical N transformations expected across redox transitions and discussed in the text. Reservoirs are indicated in bold and fluxes in italic text. The diagram represents a soil profile where brown is the active layer, gray is permafrost, and the depth of water saturation in the active layer is shown by a blue overlay. Intermediate reactions are not shown, and reactions are not balanced. OMox represents oxidized organic species, which can include CO2, and OMred represents reduced organic species, which can include CH4.

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   253

increasing nitrate export from Arctic ecosystems has already been reported (Jones et  al., 2005; McClelland et al., 2014; Petrone et al., 2006). Biological N processing is heavily controlled by redox conditions, and alterations to redox conditions due to permafrost thaw and changing hydrology will greatly influence nitrogen cycling at local scales (Thamdrup, 2012). Under anoxic conditions, microbial denitrification removes nitrate that otherwise could support plant primary production and ecosystem carbon storage. Denitrifying organisms also release CO2 during respiration of organic matter. Although in situ rates of denitrification in Arctic soils are typically low due to low nitrate availability, permafrost soil microbial communities are primed to denitrify at rapid rates when nitrate limitation is relieved (Klingensmith & Cleve, 1993). Denitrification rates tend to be highest in organic soil horizons, and decline with thaw depth into mineral horizons (Harms & Jones, 2012). While controls over increasing nitrous oxide emissions due to thawing permafrost have been explored (Abbott & Jones, 2015; Elberling et al., 2010; Voigt et al.,

Anoxic

Oxidative dissolution

n

tatio

Iron sulfides

Permafrost

ipi Prec

OMox

[S2–]T

Oxidation

Oxidation

SO42– Reduction

Active layer

Oxic

2003). Similar to expectations for P, soil warming will increase N availability by increasing organic matter decomposition and N mineralization rates (Nadelhoffer et  al., 1991; Rustad et  al., 2001; Schmidt et  al., 2002). Furthermore, permafrost thaw will release long‐frozen stores of bioavailable, inorganic N, and organic N that can be mineralized by microorganisms to release more inorganic N over time (Keuper et al., 2012). The ultimate fate of newly available, inorganic N will depend on a multitude of biotic and abiotic factors. Microorganisms can rapidly immobilize inorganic N that is made available due to soil warming (Schmidt et  al., 1999), potentially limiting its uptake by plants. However, newly thawed mineral soil contains no plant roots and relatively low numbers of microorganisms, and thaw may generate a pulse of labile N that is in excess to the ability of the biological community to assimilate or process it (Keuper et al., 2012). Nitrogen that cannot immediately be used by microorganisms may be readily exported from ecosystems that experience increased hydrologic connectivity to streams (Harms & Jones, 2012). Evidence for

OMred

Thaw

Figure 12.5  Summary of biogeochemical S transformations expected across redox transitions and discussed in the text. Reservoirs are indicated in bold and fluxes in italic text. The diagram represents a soil profile where brown is the active layer, gray is permafrost, and the depth of water saturation in the active layer is shown by a blue overlay. Intermediate species are not shown, and reactions are not balanced. OMox represents oxidized organic species, which can include CO2, and OMred represents reduced organic species, which can include CH4.

254  BIOGEOCHEMICAL CYCLES

2017), less attention has been paid to consequences of nitrous‐oxide emitting processes, including denitrification and nitrification, on ecosystem C budgets. 12.2.2. Sulfur Cycling Sulfur redox cycling is coupled to C dynamics through biotic and abiotic processes that may affect C storage in permafrost‐affected soils (Figure  12.5). For example, sulfate‐reducing bacteria respire sulfate to oxidize organic compounds, releasing CO2 and forming sulfide minerals or hydrogen sulfide gas as reaction products. In the active layer of Arctic tundra, concentrations of dissolved sulfate reflect patterns of dissolved oxygen (Lipson et al., 2010; Newman et al., 2015). Specifically, sulfate concentrations are low in saturated soils of topographic depressions relative to unsaturated soils of topographic highs in ice wedge polygons. These observations are explained by microbial sulfate reduction that depletes available sulfate in oxygen‐poor soils (Lipson, Haggerty, et al., 2013). Sulfate reduction can even occur at near freezing conditions in recently thawed permafrost (Rivkina et al., 1998). However, the importance of sulfate reduction for CO2 production has not been established for permafrost regions, and sulfate reduction may be overshadowed by microbial Fe(III) reduction in regions where inputs of sulfate are low (Lipson et  al., 2010; Lipson, Haggerty, et  al., 2013; Lipson, Raab, et al., 2013). Sulfide that is produced during sulfate reduction combines with Fe to form iron sulfides that can accumulate in anoxic soils (Herndon et al., 2017). Sulfide minerals are preserved in permafrost when anoxic soils freeze, generating a potentially large reservoir of sulfides that are susceptible to oxidative weathering upon thaw and exposure to atmospheric O2, which can occur when lowering water tables aerate previously anoxic soils. Indeed, numerous studies implicate sulfide oxidation as the source of elevated sulfate in streams draining permafrost‐affected watersheds (Calmels et  al., 2007; Lacelle et  al., 2007; Stutter & Billett, 2003; Toohey et al., 2016). An estimated 85% of sulfate in the Mackenzie River derives from biological sulfide oxidation (Calmels et  al., 2007). Gypsum (CaSO4·2H2O) dissolution may also contribute to sulfate fluxes in certain subwatersheds (Kokelj et al., 2013; Malone et al., 2013). Permafrost degradation can accelerate sulfide oxidation by increasing mineral exposure to weathering (Calmels et al., 2007). This process can happen gradually as thaw deepens and sulfide‐bearing mineral soils enter the active layer (Lacelle et al., 2007; Petrone et al., 2006; Stutter & Billett, 2003). Sulfate concentrations in receiving stream waters increase as flow paths access deeper mineral soils, both seasonally as thaw deepens and over multiyear time-

scales as permafrost degradation progresses (Keller et al., 2010; Petrone et al., 2006; Stutter & Billett, 2003; Toohey et al., 2016). However, sharp increases in stream sulfate concentrations are better explained by increased erosion than by increasing thaw depth. Permafrost degradation can result in extreme erosion events that drive rapid mineral weathering and sulfide oxidation (Calmels et  al., 2007; Kokelj et al., 2013). For example, retrogressive thaw slumps and other thermokarst features can expose large expanses of mineral soil to oxic weathering conditions at the Earth’s surface. Runoff from thaw slumps has been shown to increase sulfate concentrations in impacted rivers by orders of magnitude (Kokelj et al., 2013; Malone et al., 2013). Increased inputs of sulfide to the active layer and sulfide oxidation can in turn affect biogeochemical cycling of other elements. Sulfide that is released during thaw or generated by sulfate reduction can compete with phosphate for iron binding and impact P bioavailability. Furthermore, sulfide oxidation produces sulfuric acid that accelerates carbonate and silicate weathering, contributing to decreased C storage in carbonates and increased solute loads from watersheds (Calmels et  al., 2007; Keller et al., 2010; Lacelle et al., 2007). Finally, sulfate that is generated by sulfide oxidation may in turn fuel microbial sulfate reduction and anaerobic respiration of organic matter in the active layer. These fluxes may be intensified as more S is introduced to the active layer with increasing thaw depth. 12.2.3. Iron Cycling 12.2.3.1. Iron Redox Cycling in Permafrost‐Affected Soils Iron redox cycling may profoundly influence ecosystem processes in permafrost regions where widespread inundation generates redox gradients; however, studies describing Fe biogeochemistry in these regions remain sparse. Iron is important to ecosystem function because it serves as a micronutrient to biota, facilitates anaerobic respiration of organic matter (Lipson et al., 2010; Lovley & Phillips, 1988; Roden & Wetzel, 1996), regulates the bioavailability of phosphorus and metal micronutrients (Bjerrum & Canfield, 2002; Borggaard et al., 1990; Jensen et al., 1992), and can physically protect organic substrates from decomposition (Kleber et al., 2005; Lalonde et al., 2012; Riedel et al., 2013). Although Fe redox cycling can both increase and decrease decomposition of organic matter, as described below, the relative importance of these processes for regulating C storage in permafrost‐ affected systems is unknown. Iron accumulates at redox interfaces where anoxic water containing dissolved Fe(II) mixes with the air or oxygenated water (Figure  12.6). In poorly drained tundra, this redox interface can occur at the boundary between ­shallow

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   255

(b) Picture of Fe flocculate

Oxic

(a) Fe cycling diagram OM-Fe3+ ipita

OMbound

tion

Anoxic

Iron sulfides

O dis xida so tiv lut e ion

OMfree

tion

ita ecip

Pr

Fe3+

Oxidation

(De)stabilizing reactions

Complexation

OMox

Fe2+

Oxidation

Prec

Reduction

Active layer

Iron (III) oxides

OMred

Weathering

Primary minerals Thaw

Minerals stored in permafrost Figure 12.6  (a) Summary of biogeochemical Fe transformations expected across redox transitions and discussed in the text. Reservoirs are indicated in bold and fluxes in italic text. The diagram represents a soil profile where brown is the active layer, gray is permafrost, and the depth of water saturation in the active layer is shown by a blue overlay. Geochemical reactions shown are both abiotic and microbially mediated. Intermediate reactions are not shown, and reactions are not balanced. OMox represents oxidized organic species, which can include CO2, and OMred represents reduced organic species, which can include CH4. (b) Iron oxide flocculates observed in standing pools of water on the Arctic tundra. The pool shown here is approximately 1 ft wide. [Photograph was taken in the Barrow Environmental Observatory outside of Utqiagvik, AK on4 July 2013.]

organic soil and deeper mineral soil within the seasonally thawed active layer (Herndon et al., 2015). Organic horizons typically have high hydraulic conductivity and serve as conduits for rapid transport of oxygenated water, while water flow through deeper mineral soils is relatively slow (Hinzman et al., 1991). Especially in upland regions, the organic horizon is periodically aerated as water levels fluctuate throughout the thaw season (Hinzman et al., 1991; Rushlow & Godsey, 2017), whereas the mineral horizon typically remains saturated, oxygen‐depleted, and enriched in dissolved Fe(II) (Herndon et al., 2015; Lipson, Raab, et al., 2013; Newman et al., 2015). Dissolved Fe (II) migrates upwards to the redox interface and is oxidized to form iron oxyhydroxides (e.g., ferrihydrite and goethite) and organic‐bound Fe(III) in organic horizons (Fiedler et al., 2004; Frei et al., 2012; Herndon et al., 2015, 2017; Jessen et  al., 2014; Opfergelt et  al., 2017). Solid‐phase Fe(III) serves as a terminal electron acceptor for anaerobic respiration and is progressively reduced during periods of anoxia to generate significant releases of CO2 to the atmosphere (Lipson et al., 2010).

Redox interfaces also occur where anoxic, subsurface water discharges into oxygenated water bodies such as rivers or ponds (Pokrovsky et  al., 2016). Emerson et  al. (2015) observed that iron‐oxide‐encrusted microbial mats are prevalent in the tundra near Toolik Lake, Alaska, forming along stream banks, in sediments below shallow pools of water (0.5–2 m deep), and in wet sedge meadows. These mats contain Fe oxidizing bacteria that use dissolved Fe(II) from groundwater as an electron source and dissolved oxygen in surface water as an electron acceptor (Emerson et al., 2015). Similar observations were made in the Barrow Environmental Observatory near Utqiagvik, Alaska, where Fe oxides flocculate in pools of standing water and coat plant stems and pipes (Figure 12.4). The widespread occurrence of microbial Fe mats on the Arctic coastal plain suggests that accumulations of Fe oxides at redox interfaces are ubiquitous in permafrost regions that are connected to an Fe source such as mineral soil or deep groundwater (Emerson et  al., 2015; Herndon et al., 2015, 2017). Changes in hydrological connectivity, as outlined in Figure 12.2, could affect iron oxide mat formation by shifting the locations of these redox interfaces.

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Ferric iron that accumulates at redox interfaces potentially enhances decomposition by providing a terminal electron acceptor for anaerobic respiration (Lipson et al., 2010; Lovley & Phillips, 1988), or inhibits decomposition by generating organo‐Fe associations that are resistant to microbial attack (Baldock & Skjemstad, 2000; Herndon et al., 2017; Kleber et al., 2005). These processes depend on which Fe(III)‐bearing species are present because microorganisms preferentially reduce organic‐bound Fe(III) ions and poorly crystalline iron oxides (e.g., ferrihydrite and goethite) over crystalline iron oxide phases (Lovley & Phillips, 1986, 1987). Organic matter also preferentially associates with poorly crystalline phases through adsorption and coprecipitation reactions (Kleber et  al., 2005; Lalonde et  al., 2012). Poorly crystalline Fe oxides may dominate soils that experience fluctuating water tables because repeated cycles of dissolution and reprecipitation preclude transition to more stable crystalline phases (Axt & Walbridge, 1999; Darke & Walbridge, 2000; de Vicente et al., 2010), however, repeated redox cycling has also been shown to accelerate mineral ripening and increase crystallinity in tropical soils (Thompson et al., 2006). In permafrost regions, Fe geochemistry is strongly influenced by high concentrations of soil organic matter. Reductive and ligand‐promoted dissolution of Fe oxides dominate in anoxic peat soils with high organic content, whereas proton‐promoted dissolution dominates in well‐ drained soils (Opfergelt et  al., 2017). Dissolved organic matter stabilizes Fe(III) in solution to yield exceedingly high concentrations of dissolved Fe(III) in tundra pore waters (Herndon et al., 2015; Lipson et al., 2010; Opfergelt et al., 2017). Dissolved Fe(III) has been observed to peak at the boundary between organic and mineral horizons, likely due to stabilization of ferric iron following oxidation at the redox interface (Herndon et al., 2015). Complexation of dissolved Fe(III) by organic compounds can inhibit hydrolysis of Fe(III) to Fe(OH)3 and suppress Fe oxyhydroxide precipitation (Karlsson & Persson, 2010; Karlsson et  al., 2008; Sundman et  al., 2014); consequently, organic matter content may at least partially control the proportion of organic‐bound Fe(III) to Fe(III) oxyhydroxides (Prietzel et  al., 2007; Sundman et al., 2014). Organic compounds can also sorb to poorly crystalline ferrihydrite and inhibit transition to crystalline goethite or magnetite minerals (e.g., Amstaetter et  al., 2012; Schwertmann & Murad, 1988). These interactions are poorly understood in permafrost‐affected ecosystems but may have pronounced impact on the availability of Fe phases for carbon and nutrient stabilization and the anaerobic respiration reactions, as discussed below. Interactions between Fe and C in permafrost‐affected soils also impact delivery of Fe and C into Arctic water bodies. Rivers that drain permafrost regions, such as the Lena River in northern Siberia, contain Fe and DOC

concentrations that are much higher than global averages (Pokrovsky & Schott, 2002). Organic matter facilitates Fe transport out of soils and into lakes and rivers by stabilizing dissolved Fe(III) and colloidal Fe(III)‐oxyhydroxides in oxic waters (Ilina et al., 2013; Opfergelt et al., 2017; Pokrovsky & Schott, 2002; Sundman et al., 2014). In surface soils, litter decomposition releases large organic molecules that complex nanoparticulate Fe oxides (Pokrovsky et al., 2006). At groundwater discharge sites where anoxic water mixes with oxygenated surface water, Fe is oxidized and coprecipitates with dissolved organic matter to form organic‐rich Fe colloids (Pokrovsky et  al., 2016). These Fe‐rich organic colloids are exported from soils but destabilized over time as organic matter is decomposed, releasing nanoparticulate Fe oxides that aggregate to form larger colloids and particles (Pokrovsky et  al., 2011). Consequently, the vast majority of Fe in Arctic rivers is present in Fe oxide colloids and micron‐sized particles, albeit still associated with organic particles, while organically complexed Fe(III) ions constitute a relatively minor component (Hirst et al., 2017; Ilina et al., 2013; Pokrovsky et al., 2016). The ability of organic matter to facilitate Fe transport into rivers may decrease as thaw depth increases and flow through mineral soils begins to dominate export into water bodies (Pokrovsky et al., 2016). 12.2.3.2. Abiotic Organic Matter Degradation Coupled to Fe Oxidation Redox interfaces can serve as important zones for abiotic, Fe‐promoted organic matter degradation. Oxidation of dissolved Fe(II) by oxygen gas produces hydroxyl radicals (•OH) that indiscriminately attack and degrade organic matter to produce low molecular weight organic molecules and CO2 (Hall & Silver, 2013; Page et al., 2013; Trusiak et  al., 2018). High concentrations of •OH are generated at redox interfaces where dissolved Fe(II) mixes with the atmosphere, such as in low‐lying areas with fluctuating water tables (Page et al., 2013). Near Toolik Lake, Alaska, located in an upland permafrost region in the foothills of the Brooks Range, the highest amount of radical generation occurs in lowland wet sedge environments that contain extensive microbial Fe mats (Emerson et al., 2015; Page et  al., 2013). The full extent to which Fe‐ promoted radical chemistry degrades organic matter in the Arctic has not been established; however, this process may produce as much CO2 as microbial respiration in Arctic surface waters (Page et al., 2013). For comparison, photochemical •OH production, which is similar to Fe‐ promoted •OH production, is estimated to account for 70–95% of DOM oxidation and one‐third of CO2 release from Arctic lakes and rivers (Cory et al., 2014). Additional CO2 may be released when the organic compounds ­produced by •OH‐degradation are respired by microorganisms (Hall & Silver, 2013).

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12.2.3.3. Influence of Fe on C Stabilization Iron oxides have the potential to increase C storage in permafrost‐affected soils by increasing mineral stabilization of organic matter. Recent studies have found close associations between Fe and organic matter in permafrost (Mueller et al., 2017) and active layer soils (Herndon et al., 2017) from the Barrow Environmental Observatory in northern Alaska. Iron oxides such as ferrihydrite and goethite coat plant debris and mineral grains and form microaggregates with clays and organic matter. Iron oxide coatings on quartz grains also serve as binding sites for organic matter and clay particles (Herndon et  al., 2017; Mueller et al., 2017). In a study of permafrost‐affected soils from the East Siberian Arctic, Gentsch et al. (2015) found that organic matter was associated with minerals via sorption to clay‐ sized particles and coprecipitation with metals. They proposed that dissolved Fe coprecipitated with organic matter as poorly crystalline iron oxides and metal‐organic complexes during aeration of anoxic soil. These results are consistent with observations that shallow organic horizons in Arctic tundra accumulate organic‐bound Fe and Fe oxides due to upward translocation and oxidation of dissolved Fe from deeper anoxic soils (Herndon et al., 2015, 2017; Jessen et al., 2014). Associations between Fe and organic matter may facilitate mineral stabilization of organic C in permafrost‐ affected soils. Multiple studies have used density fractionation techniques to quantify relative proportions of “free” organic matter (low‐density soil) and organic matter bound to minerals such as iron oxides (high‐ density soil). Hobara et al. (2013, 2016) found that concentrations of mineral‐associated organic C were positively correlated with Al and Fe‐bearing minerals in acidic soils from Arctic tundra and boreal forest ecosystems across Alaska. Mineral‐associated C was estimated to comprise 30–55% of total C stored in the active layer (Hobara et  al., 2016). These values are comparable to estimates of mineral‐associated C reported for the active layer of polygonal tundra (19 ± 6 kg m−2, Herndon et al., 2017), and for the active layer and upper permafrost of drained thaw lake basins (~28% of organic C; 10 kg m−2; Mueller et al., 2015) on the Arctic coastal plain. Similarly, in the East Siberian Arctic, mineral‐associated organic matter, primarily associated with clays and Fe/Al‐oxides, comprised 54 ± 16% of total organic C in the active layer and upper permafrost (Gentsch et al., 2015). Mineral stabilization may protect organic compounds from microbial degradation and increase their residence time within soils, although the mechanisms controlling degradation potential are not fully understood. Höfle et al. (2013) examined the age, composition, and density distribution of organic matter stored in the active layer of polygonal tundra in the Lena Delta. Particulate organic

carbon occluded within soil aggregates was older and more degraded than free organic matter, and aggregate stabilization of organic matter increased with depth into the permafrost layer. Gentsch et al. (2015) reported that respiration of mineral‐bound organic matter was low in shallow soils but high in subsoils relative to particulate organic matter, indicating complex controls on decomposition. Negative correlations were observed between decomposition rates and organic‐bound Fe, suggesting that coprecipitation with Fe reduced the bioavailability of organic matter. These studies indicate that a substantial portion of soil organic matter in permafrost‐affected soils is associated with minerals. Formation of iron oxides at redox interfaces contributes to mineral stabilization of organic matter by directly binding to organic molecules or cementing organic‐bearing aggregates. Although mineral stabilization can hinder microbial decomposition and substantially increase carbon storage in soils (Lehmann & Kleber, 2015), these processes are not considered in current land–atmosphere feedback models. 12.2.4. Biogeochemical Shifts Resulting from  Changing Hydrologic Regimes Results from water‐table manipulations provide evidence for how permafrost‐affected landscapes may respond to coupled changes in hydrology and redox. One such large‐scale manipulation experiment was conducted in the Barrow Environmental Observatory (AK) on the Arctic coastal plain (Zona et al., 2009). Water tables were lowered (drained system), raised (flooded system), or unaltered (control) within a drained thaw lake basin (DLTB). Artificial flooding generated anoxic conditions similar to those naturally present in topographic depressions (Lipson et al., 2010, 2012). The short‐term effects of water table manipulation on C fluxes were complex and non‐linear. Flooding induced warming, which consequently increased thaw depth and led to increased production of CO2 (Zona et al., 2012). In comparison, CH4 efflux peaked when the water table was at the soil surface but was inhibited by the presence of standing water (Zona et al., 2009). Flooding in the DTLB also led to decreases in dissolved oxygen and increases in dissolved Fe(III) and phosphate (Lipson et  al., 2012). Increases in dissolved phosphate may be explained by reductive dissolution of iron oxides over the thaw season (Lipson et  al., 2012). Phosphate that is bound to iron oxides can be rapidly released into solution as iron oxides are reduced during periods of anoxia (Henderson et  al., 2012; Kinsman‐ Costello et al., 2014; Zak et al., 2004). These results indicate a potential for inundation to generate pulses of labile P.

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The effects of soil drainage could not be assessed because the drained and control areas exhibited similar water table heights (Lipson et  al., 2012); however, soil drainage is expected to promote Fe oxidation and the accumulation of iron oxyhydroxide minerals. Iron oxides limit P solubility by binding phosphate ions that are released during decomposition of organic matter. Giblin et al. (1991) analyzed soil P across a tundra toposequence near Toolik Lake, AK on the Arctic coastal plain. Although all soils were dominated by residual (organic) P, well‐drained hilltop soils contained relatively more P bound to Al and Fe oxides than poorly drained valley soils. These results suggest that soil drainage will increase Fe‐bound P by promoting formation of iron oxyhydroxide minerals and accelerating decomposition and release of P into solution. These factors may increase the ability of Fe oxides to bind P before biological assimilation. Indeed, dissolved P is strongly correlated with colloidal Fe in rivers draining boreal peatlands, indicating that P sorbs to or coprecipitates with Fe‐rich colloids formed in peat soils (Pokrovsky & Schott, 2002; Pokrovsky et al., 2016). 12.3. CONCLUSIONS Permafrost thaw is changing hydrologic regimes in northern high latitudes. Transition from saturated to drained soils (and vice versa) will alter redox conditions that control biogeochemical cycling of C and ecologically important elements such as P, N, S, and Fe. This review highlights the potential for redox shifts to impact biogeochemical cycles, and consequently, how much C is stored in permafrost‐affected ecosystems. However, studies on these processes are limited, and the magnitude of these effects is unknown. Although substantial progress is being made to model hydrologic response to permafrost thaw, much less is known regarding how redox regimes vary in response to hydrologic drivers. Indeed, redox potential is rarely measured in permafrost systems but sometimes inferred from the presence of dissolved oxygen, reduced chemical species (e.g., CH4, H2S, Fe2+), or certain microorganisms (methanogens). Time‐resolved data generated by redox sensors can be coupled with meteorological, soil moisture, and soil temperature observations to evaluate how coupled hydrologic and redox regimes develop over the thaw season. Establishing these sensor networks will provide baseline understanding of environmental parameters that regulate decomposition and nutrient cycling. Such information will aid modeling efforts to predict C fluxes across landscapes. Furthermore, although much research in arctic and subarctic systems has focused on C and N dynamics, it is less clear how other ecologically relevant elements may regulate C storage either directly through biological assimilation or indirectly by modulating C and N cycling. In particular,

redox‐dependent abiotic processes such as sorption, radical‐promoted degradation, and leaching represent ­ major knowledge gaps. In order to predict how C storage in permafrost regions will respond to changing climate, it is critical to elucidate complex feedbacks between redox conditions and biogeochemical cycling. REFERENCES Abbott, B.W., & Jones, J.B. (2015). Permafrost collapse alters soil carbon stocks, respiration, CH4, and N2O in upland tundra. Global Change Biology, 21(12), 4570–4587. https:// doi.org/10.1111/gcb.13069 Alexander, V., & Billington, M M. (1986). Nitrogen fixation in the Alaskan taiga. In Cleve, K. van, Chapin, F.S.I., Flanagan, P.W., Viereck, L.A., Dyrness, C.T. (Eds.), Forest Ecosysetms in the Alaskan Taiga: A Synthesis of Structure and Function (pp. 112–120). New York: Springer. Amstaetter, K., Borch, T., & Kappler, A. (2012). Influence of humic acid imposed changes of ferrihydrite aggregation on microbial Fe(III) reduction. Geochimica et Cosmochimica Acta, 85, 326–341. https://doi.org/10.1016/j.gca.2012.02.003 Andresen, C.G., & Lougheed, V.L. (2015). Disappearing Arctic tundra ponds: Fine‐scale analysis of surface hydrology in drained thaw lake basins over a 65 year period (1948–2013). Journal of Geophysical Research: Biogeosciences, 120(3), 466– 479. https://doi.org/10.1002/2014JG002778 Axt, J.R., & Walbridge, M.R. (1999). Phosphate removal capacity of palustrine forested wetlands and adjacent uplands in Virginia. Soil Science Society of America Journal, 63(4), 1019. https://doi.org/10.2136/sssaj1999.6341019x Baldock, J.A., & Skjemstad, J.O. (2000). Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Organic Geochemistry, 31, 697–710. Bekryaev, R.V., Polyakov, I.V., & Alexeev, V.A. (2010). Role of polar amplification in long‐term surface air temperature variations and modern arctic warming. Journal of Climate, 23(14), 3888–3906. https://doi.org/10.1175/2010JCLI3297.1 Bense, V.F., Ferguson, G., & Kooi, H. (2009). Evolution of shallow groundwater flow systems in areas of degrading permafrost. Geophysical Research Letters, 36(22), 2–7. Bintanja, R., & Andry, O. (2017). Towards a rain‐dominated Arctic. Nature: Climate Change, 7, 263–268. https://doi. org/10.1038/nclimate3240 Bjerrum, C., & Canfield, D. (2002). Ocean productivity before about 1.9 Gyr ago limited by phosphorus adsorption onto iron oxides. Nature, 417(6885), 159–162. Borggaard, O. K., Jorgensen, S. S., Moberg, J. P., & Raben‐ Lange, B. (1990). Influence of organic matter on phosphate adsorption by aluminium and iron oxides in sandy soils. Journal of Soil Science, 41(3), 443–449. Bring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S. H., et al. (2016). Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. Journal of Geophysical Research G: Biogeosciences, 121(3), 621–649. https://doi.org/10.1002/2015JG003131 Buckeridge, K.M., Schaeffer, S.M., & Schimel, J.P. (2016). Vegetation leachate during Arctic thaw enhances soil microbial

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   259 phosphorus. Ecosystems, 19(3), 477–489. https://doi.org/10.1007/ s10021‐015‐9947‐9 Calmels, D., Gaillardet, J., Brenot, A., & France‐Lanord, C. (2007). Sustained sulfide oxidation by physical erosion processes in the Mackenzie River basin: Climatic perspectives. Geology, 35(11), 1003–1006. https://doi.org/10.1130/ G24132A.1 Chapin III, F.S., Barsdate, R., & Barel, D. (1978). Phosphorus cycling in Alaksan coastal tundra: A hypothesis for the regulation of nutrient cycling. Oikos, 31(2), 189–199. Chapin III, F.S., Shaver, G.R., Giblin, A.E., Nadelhoffer, K.J., & Laundre, J.A. (1995). Responses of Arctic tundra to experimental and observed changes in climate. Ecology, 76(3), 694–711. Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., et al. (2014). Carbon and other biogeochemical cycles. In Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 465–570). Cambridge University Press. Conant, R.T., Ryan, M.G., Agren, G., Birge, H.E., Davidson, E.A., Eliasson, P.E., et  al. (2011). Temperature and soil organic matter decomposition rates ‐ synthesis of current knowledge and a way forward. Global Change Biology, 17(11), 3392–3404. https://doi.org/10.1111/j.1365‐2486.2011.02496.x Cooper, M.D.A., Estop‐Aragonés, C., Fisher, J.P., Thierry, A., Garnett, M.H., Charman, D.J., et al. (2017). Limited contribution of permafrost carbon to methane release from thawing peatlands. Nature Climate Change, 7(7), 507–511. https:// doi.org/10.1038/nclimate3328 Cory, R.M., Ward, C.P., Crump, B.C., & Kling, G.W. (2014). Sunlight controls water column processing of carbon in arctic fresh waters. Science, 345(6199), 925–928. Darke, A.K., & Walbridge, M.R. (2000). Al and Fe biogeochemistry in a floodplain forest: Implications for P retention. Biogeochemistry, 51(1), 1–32. https://doi.org/10.1023/ A:1006302600347 Davidson, E.A., & Janssens, I.A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440(March), 165–173. https://doi.org/ 10.1038/nature04514 DeConto, R.M., Galeotti, S., Pagani, M., Tracy, D., Schaefer, K., Zhang, T., Pollard, D. & Beerling, D.J. (2012). Past extreme warming events linked to massive carbon release from thawing permafrost. Nature, 484(7392), p.87. de Vicente, I., Andersen, F.Ø., Hansen, H C.B., Cruz‐Pizarro, L., & Jensen, H.S. (2010). Water level fluctuations may decrease phosphate adsorption capacity of the sediment in oligotrophic high mountain lakes. Hydrobiologia, 651(1), 253–264. https://doi.org/10.1007/s10750‐010‐0304‐x Dugan, H.A., Lamoureux, S.F., Lewis, T., & Lafrenière, M.J. (2012). The impact of permafrost disturbances and sediment loading on the limnological characteristics of two high Arctic lakes. Permafrost and Periglacial Processes, 23(2), 119–126. Edwards, K.A., Mcculloch, J., Kershaw, G.P., & Jefferies, R L. (2006). Soil microbial and nutrient dynamics in a wet Arctic sedge meadow in late winter and early spring. Soil Biology & Biochemistry, 38, 2843–2851. https://doi.org/10.1016/j. soilbio.2006.04.042

Elberling, B., Christiansen, H.H., & Hansen, B.U. (2010). High nitrous oxide production from thawing permafrost. Nature Geoscience, 3(7), 506–506. https://doi.org/10.1038/ngeo893 Emerson, D., Scott, J.J., Benes, J., & Bowden, W.B. (2015). Microbial iron oxidation in the Arctic tundra and the implications for biogeochemical cycling. Applied and Environmental Microbiology, 81(23), 8066–8075. https://doi.org/10.1128/ AEM.02832‐15 Fiedler, S., Wagner, D., Kutzbach, L., & Pfeiffer, E.‐M. (2004). Element redistribution along hydraulic and redox gradients of low‐centered polygons, Lena Delta, Northern Siberia. Soil Science Society of America Journal, 68(3), 1002. https://doi. org/10.2136/sssaj2004.1002 Frei, S., Knorr, K.H., Peiffer, S., & Fleckenstein, J.H. (2012). Surface micro‐topography causes hot spots of biogeochemical activity in wetland systems: A virtual modeling experiment. Journal of Geophysical Research: Biogeosciences, 117(4), 1–18. https://doi.org/10.1029/2012JG002012 French, H.M. (2007). The periglacial environment, 3rd edn. Chichester, UK: J. Wiley & Sons, Ltd. Frey, K.E., & McClelland, J.W. (2009). Impacts of permafrost degradation on arctic river biogeochemistry. Hydrological Processes, 23, 169–182. https://doi.org/10.1002/hyp Frey, K.E., McClelland, J.W., Holmes, R.M., & Smith, L.G. (2007). Impacts of climate warming and permafrost thaw on the riverine transport of nitrogen and phosphorus to the Kara Sea. Journal of Geophysical Research: Biogeosciences, 112(4), 1–10. https://doi.org/10.1029/2006JG000369 Gentsch, N., Mikutta, R., Shibistova, O., Wild, B., Schnecker, J., Richter, A., et  al. (2015). Properties and bioavailability of particulate and mineral‐associated organic matter in Arctic permafrost soils, Lower Kolyma Region, Russia. European Journal of Soil Science, 66(4), 722–734. https://doi.org/10.1111/ejss.12269 Giblin, A.E., Nadelhoffer, K.J., Shaver, G.R., Laundre, J.A., & McKerrow, A.J. (1991). Biogeochemical diversity along a riverside toposequence in Arctic Alaska. Ecological Monographs, 61(4), 415–435. Giesler, R., Esberg, C., Lagerstrom, A., & Graae, B.J. (2012). Phosphorus availability and microbial respiration across different tundra vegetation types. Biogeochemistry, 108, 429–445. https://doi.org/10.1007/S10533‐01 Gough, L., Bettez, N.D., Slavik, K.A., Bowden, W.B., Giblin, A.E., Kling, G.W., et al. (2016). Effects of long‐term nutrient additions on Arctic tundra, stream, and lake ecosystems: beyond NPP. Oecologia, 182(3), 653–665. https://doi. org/10.1007/s00442‐016‐3716‐0 Gough, L., & Hobbie, S.E. (2003). Responses of moist non‐ acidic arctic tundra to altered environment: productivity, biomass, and species richness. Oikos, 103(1), 204–216. https:// doi.org/10.1034/j.1600‐0706.2003.12363.x Hall, S.J., & Silver, W.L. (2013). Iron oxidation stimulates organic matter decomposition in humid tropical forest soils. Global Change Biology, 19(9), 2804–2813. https://doi. org/10.1111/gcb.12229 Hansen, B.B., Isaksen, K., Benestad, R.E., Kohler, J., Pedersen, Å. Ø., Loe, L.E., et  al. (2014). Warmer and wetter winters: characteristics and implications of an extreme weather event in the High Arctic. Environmental Research Letters, 9(11), 114021.

260  BIOGEOCHEMICAL CYCLES Harden, J.W., Koven, C.D., Ping, C.L., Hugelius, G., David McGuire, A., Camill, P., et al. (2012). Field information links permafrost carbon to physical vulnerabilities of thawing. Geophysical Research Letters, 39(15), 1–6. https://doi. org/10.1029/2012GL051958 Harms, T.K., & Jones, J.B. (2012). Thaw depth determines reaction and transport of inorganic nitrogen in valley bottom permafrost soils. Global Change Biology, 18, 2958–2968. https://doi.org/10.1111/j.1365‐2486.2012.02731.x Harms, T.K., & Ludwig, S.M. (2016). Retention and removal of nitrogen and phosphorus in saturated soils of arctic hillslopes. Biogeochemistry, 127(2–3), 291–304. https://doi. org/10.1007/s10533‐016‐0181‐0 Harris, C., Arenson, L. U., Christiansen, H. H., Etzelmüller, B., Frauenfelder, R., Gruber, S., et  al. (2009). Permafrost and climate in Europe: Monitoring and modelling thermal, ­ ­geomorphological and geotechnical responses. Earth‐Science Reviews, 92(3–4), 117–171. https://doi.org/10.1016/j.earscirev. 2008.12.002 Heikoop, J.M., Throckmorton, H.M., Newman, B.D., Perkins, G.B., Iversen, C.M., Roy Chowdhury, T., et  al. (2015). Isotopic identification of soil and permafrost nitrate sources in an Arctic tundra ecosystem. Journal of Geophysical Research: Biogeosciences, 120(6), 1000–1017. https://doi. org/10.1002/2014JG002883 Henderson, R., Kabengi, N., Mantripragada, N., Cabrera, M., Hassan, S., & Thompson, A. (2012). Anoxia‐induced release of colloid‐ and nanoparticle‐bound phosphorus in grassland  soils. Environmental Science and Technology, 46(21), 11727–11734. Herndon, E., AlBashaireh, A., Singer, D., Roy Chowdhury, T., Gu, B., & Graham, D. (2017). Influence of iron redox cycling on organo‐mineral associations in Arctic tundra soil. Geochimica et Cosmochimica Acta, 207, 210–231. https://doi. org/10.1016/j.gca.2017.02.034 Herndon, E.M., Yang, Z., Bargar, J., Janot, N., Regier, T.Z., Graham, D.E., et al. (2015). Geochemical drivers of organic matter decomposition in arctic tundra soils. Biogeochemistry, 126(3), 397–414. https://doi.org/10.1007/s10533‐015‐0165‐5 Herndon, E.M., Kinsman‐Costello, L., Duroe, K.A., Mills, J., Kane, E.S., Sebestyen, S.D., Thompson, A.A., & Wullschleger, S.D., (2019). Iron (oxyhydr)oxides serve as phosphate traps in tundra and boreal peat soils. Journal of Geophysical Research: Biogeosciences, 124(2), 227–246. https://doi.org/10.1029/2018JG004776 Hinzman, L.D., Bettez, N.D., Bolton, W.R., Chapin, F.S., Dyurgerov, M.B., Fastie, C. L., et  al. (2005). Evidence and implications of recent climate change in Northern Alaska and other Arctic regions. Climatic Change, 72(3), 251–298. https://doi.org/10.1007/s10584‐005‐5352‐2 Hinzman, L.D., Deal, C.J., Mcguire, A.D., Mernild, S.H., Polyakov, I.V., & Walsh, J.E. (2013). Trajectory of the Arctic as an integrated system. Ecological Applications, 23(8), 1837– 1868. https://doi.org/10.1890/11‐1498.1 Hinzman, L.D., Kane, D.L., Gieck, R.E., & Everett, K.R. (1991). Hydrologic and thermal properties of the active layer in the Alaskan Arctic. Cold Regions Science and Technology, 19(2), 95–110. https://doi.org/10.1016/0165‐232X (91)90001‐W

Hirst, C., Andersson, P.S., Shaw, S., Burke, I.T., Kutscher, L., Murphy, M.J., et  al. (2017). Characterisation of Fe‐bearing particles and colloids in the Lena River basin, NE Russia. Geochimica et Cosmochimica Acta, 213, 553–573. https://doi. org/10.1016/j.gca.2017.07.012 Hobara, S., Koba, K., Ae, N., Giblin, A.E., Kushida, K., & Shaver, G.R. (2013). Geochemical influences on solubility of soil organic carbon in Arctic tundra ecosystems. Soil Science Society of America Journal, 77(2), 473. https://doi.org/ 10.2136/sssaj2012.0199 Hobara, S., Kushida, K., Kim, Y., Koba, K., Lee, B.‐Y., & Ae, N. (2016). Relationships among pH, minerals, and carbon in soils from tundra to boreal forest across Alaska. Ecosystems, 19(6), 1092–1103. https://doi.org/10.1007/s10021‐016‐9989‐7 Hobbie, S E., Nadelhoffer, K.J., & Hogberg, P. (2002). A synthesis: the role of nutrients as constraints on carbon balances in boreal and Arctic regions. Plant and Soil, 242, 163–170. https://doi.org/10.1023/A Höfle, S., Rethemeyer, J., Mueller, C.W., & John, S. (2013). Organic matter composition and stabilization in a polygonal tundra soil of the Lena Delta. Biogeosciences, 10(5), 3145–3158. https://doi.org/10.5194/bg‐10‐3145‐2013 Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J.W., Schuur, E.A.G., Ping, C.L., et al. (2014). Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences, 11(23), 6573–6593. https://doi.org/10.5194/bg‐11‐6573‐2014 Ilina, S.M., Poitrasson, F., Lapitskiy, S.A., Alekhin, Y.V., Viers, J., & Pokrovsky, O. S. (2013). Extreme iron isotope fractionation between colloids and particles of boreal and temperate organic‐rich waters. Geochimica et Cosmochimica Acta, 101, 96–111. https://doi.org/10.1016/j.gca.2012.10.023 Jensen, H.S., Kristensen, P., Jeppesen, E., & Skytthe, A. (1992). Iron:phosphorus ratio in surface sediment as an indicator of phosphate release from aerobic sediments in shallow lakes. Hydrobiologia, 235–236(1), 731–743. https://doi.org/10.1007/ BF00026261 Jepsen, S.M., Voss, C.I., Walvoord, M.A., Minsley, B.J., & Rover, J. (2013). Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA. Geophysical Research Letters, 40, 882–887. https://doi.org/10.1002/ grl.50187 Jessen, S., Holmslykke, H.D., Rasmussen, K., Richardt, N., & Holm, P.E. (2014). Hydrology and pore water chemistry in a permafrost wetland, Ilulissat, Greenland. Water Resources Research, 50, 1–15. https://doi.org/10.1002/2013WR014376 Jonasson, S., Michelsen, A., Nielsen, E.V, & Callaghan, T.V. (1996). Microbial biomass C, N and P in two arctic soils and responses to addition of NPK fertilizer and sugar: implications for plant nutrient uptake. Oecologia, 106, 507–515. Jonasson, S., Michelsen, A., & Schmidt, I.K. (1999). Coupling of nutrient cycling and carbon dynamics in the Arctic, integration of soil microbial and plant processes. Applied Soil Ecology, 11(2–3), 135–146. https://doi.org/10.1016/S0929‐ 1393(98)00145‐0 Jones, B.M., Grosse, G., Arp, C. D., Jones, M.C., Walter Anthony, K.M., & Romanovsky, V.E. (2011). Modern thermokarst lake dynamics in the continuous permafrost ­

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   261 zone, northern Seward Peninsula, Alaska. Journal of Geophysical Research: Biogeosciences, 116(3). Jones Jr., J.B., Petrone, K.C., Finlay, J.C., Hinzman, L.D., & Bolton, W.R. (2005). Nitrogen loss from watersheds of interior Alaska underlain with discontinuous permafrost. Geophysical Research Letters, 32, 2–5. https://doi. org/10.1029/2004GL021734 Jorgenson, M.T., Romanovsky, V., Harden, J., Shur, Y., Donnell, J.O., Schuur, E.A. G., & Kanevskiy, M. (2010). Resilience and vulnerability of permafrost to climate change. Canadian Journal of Forest Research, 40, 1219–1236. https://doi. org/10.1139/X10‐060 Jorgenson, M.T., Harden, J., Kanevskiy, M., O’Donnell, J., Wickland, K., Ewing, S., et al. 2013. Reorganization of vegetation, hydrology and soil carbon after permafrost degradation across heterogeneous boreal landscapes. Environmental Research Letters, 8(3), p.035017. Kane, D.L., Hinkel, K.M., Goering, D.J., Hinzman, L.D., & Outcalt, S.I. (2001). Non‐conductive heat transfer associated with frozen soils. Global and Planetary Change, 29(3–4), 275–292. Karlsson, T., & Persson, P. (2010). Coordination chemistry and hydrolysis of Fe(III) in a peat humic acid studied by X‐ray absorption spectroscopy. Geochimica et Cosmochimica Acta, 74, 30–40. https://doi.org/10.1016/j.gca.2009.09.023 Karlsson, T., Persson, P., Skyllberg, U., Mörth, C.‐M., & Giesler, R. (2008). Characterization of Iron(III) in organic soils using extended X‐ray absorption fine structure spectroscopy. Environmental Science and Technology, 42(15), 5449– 5454. https://doi.org/10.1021/es800322j Keller, K., Blum, J.D., & Kling, G.W. (2010). Stream geochemistry as an indicator of increasing permafrost thaw depth in an arctic watershed. Chemical Geology, 273(1–2), 76–81. https://doi.org/10.1016/j.chemgeo.2010.02.013 Kerr, J.G., Burford, M., Olley, J., & Udy, J. (2011). Phosphorus sorption in soils and sediments: implications for phosphate supply to a subtropical river in southeast Queensland, Australia. Biogeochemistry, 102, 73–85. Keuper, F., van Bodegom, P.M., Dorrepaal, E., Weedon, J.T., van Hal, J., van Logtestijn, R.S.P., & Aerts, R. (2012). A frozen feast: Thawing permafrost increases plant‐available nitrogen in subarctic peatlands. Global Change Biology, 18(6), 1998–2007. https://doi.org/10.1111/j.1365‐2486.2012.02663.x Kielland, K. (1994). Amino acid absorption by arctic plants: implications for plant nutrition and nitrogen cycling. Ecology, 75(8), 2373–2383. Kielland, K., & Chapin, F.S. (1994). Phosphate uptake in Arctic plants in relation to phosphate supply: the role of spatial and temporal variability. Oikos, 70(3), 443–448. Kinsman‐Costello, L.E., O’Brien, J., & Hamilton, S.K. (2014). Re‐flooding a historically drained wetland leads to rapid sediment phosphorus release. Ecosystems, 17, 641–656. https:// doi.org/10.1007/s10021‐014‐9748‐6 Kleber, M., Mikutta, R., Torn, M.S., & Jahn, R. (2005). Poorly crystalline mineral phases protect organic matter in acid subsoil horizons. European Journal of Soil Science, 56(6), 717– 725. https://doi.org/10.1111/j.1365‐2389.2005.00706.x Klingensmith, K., & Cleve, K. van. (1993). Denitrification and nitrogen‐fixation in floodplain successional soils along the

Tanana river, interior Alaska. Canadian Journal of Forest Research, 23, 956–963. Koch, J.C., Kikuchi, C.P., Wickland, K.P., & Schuster, P. (2014). Runoff sources and flow paths in a partially burned, upland boreal catchment underlain by permafrost. Water  Resources Research, 50, 8141–8158. https://doi. org/10.1002/2013WR014222.Received Kokelj, S.V., Lacelle, D., Lantz, T.C., Tunnicliffe, J., Malone, L., Clark, I.D., & Chin, K. S. (2013). Thawing of massive ground ice in mega slumps drives increases in stream sediment and solute flux across a range of watershed scales. Journal of Geophysical Research: Earth Surface, 118(2), 681–692. https:// doi.org/10.1002/jgrf.20063 Kumar, M., Wang, R., & Link, T.E. (2012). Effects of more extreme precipitation regimes on maximum seasonal snow water equivalent. Geophysical Research Letters, 39(20), 1–6. Lacelle, D., Doucet, A., Clark, I.D., & Lauriol, B. (2007). Acid drainage generation and seasonal recycling in disturbed permafrost near Eagle Plains, northern Yukon Territory, Canada. Chemical Geology, 243(1–2), 157–177. https://doi. org/10.1016/j.chemgeo.2007.05.021 Lalonde, K., Mucci, A., Ouellet, A., & Gelinas, Y. (2012). Preservation of organic matter in sediments promoted by  iron. Nature, 483, 198–200. https://doi.org/10.1038/ nature10855 Lawrence, D.M., Koven, C.D., Swenson, S.C., Riley, W.J., & Slater, A.G. (2015). Permafrost thaw and resulting soil moisture changes regulate projected high‐latitude CO2 and CH4 emissions. Environmental Research Letters, 10(9), 094011. https://doi.org/10.1088/1748‐9326/10/9/094011 Lehmann, J., & Kleber, M. (2015). The contentious nature of soil organic matter. Nature, 1–8. https://doi.org/10.1038/ nature16069 Liljedahl, A.K., Boike, J., Daanen, R.P., Fedorov, A.N., Frost, G.V., Grosse, G., et al. (2016). Pan‐Arctic ice‐wedge degradation in warming permafrost and its influence on tundra hydrology. Nature Geoscience, 9(4), 312–318. https://doi. org/10.1038/ngeo2674 Liljedahl, A.K., Hinzman, L.D., Harazono, Y., Zona, D., Tweedie, C.E., Hollister, R. D., et al. (2011). Nonlinear controls on evapotranspiration in Arctic coastal wetlands. Biogeosciences, 8(11), 3375–3389. https://doi.org/10.5194/ bg‐8‐3375‐2011 Ling, F., & Zhang, T. (2004). A numerical model for surface energy balance and thermal regime of the active layer and permafrost containing unfrozen water. Cold Regions Science and Technology, 38(1), 1–15. Lipson, D.A., Haggerty, J.M., Srinivas, A., Raab, T.K., Sathe, S., & Dinsdale, E.A. (2013). Metagenomic insights into anaerobic metabolism along an Arctic peat soil profile. PLoS ONE, 8(5). https://doi.org/10.1371/journal.pone.0064659 Lipson, D.A., Jha, M., Raab, T.K., & Oechel, W.C. (2010). Reduction of iron(III) and humic substances plays a major role in anaerobic respiration in an Arctic peat soil. Journal of Geophysical Research: Biogeosciences, 115(4), 1–13. https:// doi.org/10.1029/2009JG001147 Lipson, D.A., Raab, T.K., Goria, D., & Zlamal, J. (2013). The contribution of Fe(III) and humic acid reduction to ecosystem respiration in drained thaw lake basins of the Arctic

262  BIOGEOCHEMICAL CYCLES Coastal Plain. Global Biogeochemical Cycles, 27(2), 399–409. https://doi.org/10.1002/gbc.20038 Lipson, D.A., Zona, D., Raab, T.K., Bozzolo, F., Mauritz, M., & Oechel, W.C. (2012). Water‐table height and microtopography control biogeochemical cycling in an Arctic coastal tundra ecosystem. Biogeosciences, 9(1), 577–591. https://doi. org/10.5194/bg‐9‐577‐2012 Liston, G.E., Haehnel, R.B., Sturm, M., Hiemstra, C.A., Berezovskaya, S., & Tabler, R.D. (2007). Simulating complex snow distributions in windy environments using SnowTran‐3D. Journal of Glaciology, 53(181), 241–256. Liston, G.E., Mcfadden, J.P., Sturm, M., & Pielke, R.A. (2002). Modelled changes in arctic tundra snow, energy and moisture fluxes due to increased shrubs. Global Change Biology, 8(1), 17–32. Lovley, D.R., & Phillips, E.J. (1986). Availability of ferric iron for microbial reduction in bottom sediments of the freshwater tidal Potomac river. Applied and Environmental Microbiology, 52(4), 751–757. Lovley, D.R., & Phillips, E.J. (1987). Rapid assay for microbially reducible ferric iron in aquatic sediments. Applied and Environmental Microbiology, 53(7), 1536–1540. https://doi. org/10.1007/BF01611203 Lovley, D.R., & Phillips, E.J. (1988). Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Applied and Environmental Microbiology, 54(6), 1472–1480. Mack, M.C., Schuur, E.A.G., Bret‐Harte, M.S., Shaver, G.R., & Chapin, F.S. (2004). Ecosystem carbon storage in arctic tundra reduced by long‐term nutrient fertilization. Nature, 431(7007), 440–443. https://doi.org/10.1038/nature02887 Malone, L., Lacelle, D., Kokelj, S., & Clark, I.D. (2013). Impacts of hillslope thaw slumps on the geochemistry of permafrost catchments (Stony Creek watershed, NWT, Canada). Chemical Geology, 356, 38–49. https://doi.org/10.1016/j. chemgeo.2013.07.010 Mauritz, M., Bracho, R., Celis, G., Hutchings, J., Natali, S. M., Pegoraro, E., et al. (2017). Nonlinear CO2 flux response to 7 years of experimentally induced permafrost thaw. Global Change Biology, 23(9), 3646–3666. https://doi.org/10.1111/ gcb.13661 McClelland, J.W., Townsend‐Small, A., Holmes, R.M., Pan, F., Stieglitz, M., Khosh, M., & Peterson, B.J. (2014). River export of nutrients and organic matter from the North Slope of Alaska to the Beaufort Sea. Water Resources Reserach, 50, 1823–1839. https://doi.org/10.1002/2013WR014722 McLaughlin, M.J., Alston, A.M., & Martin, J.K. (1986). Measurement of phosphorus in the soil microbial biomass: A modified procedure for field soils. Soil Biology and Biochemistry, 18(4), 437–443. https://doi.org/10.1016/0038‐ 0717(86)90050‐7 Mortimer, C.H. (1942). The exchange of dissolved substances between mud and water in lakes. Journal of Ecology, 30(1), 147–201. Mueller, C.W., Hoeschen, C., Steffens, M., Buddenbaum, H., Hinkel, K., Bockheim, J.G., & Kao‐Kniffin, J. (2017). Microscale soil structures foster organic matter stabilization in permafrost soils. Geoderma, 293, 44–53. https://doi. org/10.1016/j.geoderma.2017.01.028

Mueller, C.W., Rethemeyer, J., Kao‐Kniffin, J., Löppmann, S., Hinkel, K.M., & Bockheim, J.G. (2015). Large amounts of labile organic carbon in permafrost soils of northern Alaska. Global Change Biology, 21(7), 2804–2817. https://doi.org/ 10.1111/gcb.12876 Nadelhoffer, K.J., Giblin, A.E., Shaver, G.R., & Laundre, J.A. (1991). Effects of temperature and substrate quality on element mineralization in six Arctic soils. Ecology, 72(1), 242–253. Newman, B.D., Throckmorton, H.M., Graham, D.E., Gu, B., Hubbard, S.S., Liang, L., et al. (2015). Microtopographic and depth controls on active layer chemistry in Arctic polygonal ground. Geophysical Research Letters, 42(6), 1808–1817. https://doi.org/10.1002/2014GL062804 O’Donnell, J.A., Turetsky, M.R., Harden, J.W., Manies, K.L., Pruett, L.E., Shetler, G., & Neff, J C. (2009). Interactive effects of fire, soil climate, and moss on CO2 fluxes in black spruce ecosystems of interior Alaska. Ecosystems, 12, 57–72. https://doi.org/10.1007/s10021‐008‐9206‐4 Opfergelt, S., Williams, H.M., Cornelis, J.T., Guicharnaud, R.A., Georg, R.B., Siebert, C., et al. (2017). Iron and silicon isotope behaviour accompanying weathering in Icelandic soils, and the implications for iron export from peatlands. Geochimica et Cosmochimica Acta, 217, 273–291. https://doi. org/10.1016/j.gca.2017.08.033 Osterkamp, T.E. (2007). Characteristics of the recent warming of permafrost in Alaska. Journal of Geophysical Research, 112(F02S02). Osterkamp, T.E., Jorgenson, M.T., Schuur, E.A.G., Shur, Y., Kanevskiy, M.Z., Vogel, J.G., & Tumskoy, V.E. (2009). Physical and ecological changes associated with warming permafrost and thermokarst in interior Alaska. Permafrost and Periglacial Processes, 20, 235–256. Page, S.E., Kling, G.W., Sander, M., Harrold, K.H., Robert, J., Mcneill, K., & Cory, R.M. (2013). Dark production of hydroxyl radical in arctic soil and surface waters. Environmental Science and Technology, 47, 12860–12867. Persson, J., Högberg, P., Ekblad, A., Högberg, M.N., & Nordgren, A. (2003). Nitrogen acquisition from inorganic and organic sources by boreal forest plants in the field. Oecologia, 137, 252–257. https://doi.org/10.1007/ s00442‐003‐1334‐0 Petrone, K.C., Jones, J.B., Hinzman, L.D., & Boone, R.D. (2006). Seasonal export of carbon, nitrogen, and major solutes from Alaskan catchments with discontinuous permafrost. Journal of Geophysical Research: Biogeosciences, 111(2), 1–13. https://doi.org/10.1029/2005JG000055 Pokrovsky, O.S., Manasypov, R.M., Loiko, S.V., & Shirokova, L.S. (2016). Organic and organo‐mineral colloids in discontinuous permafrost zone. Geochimica et Cosmochimica Acta, 188, 1–20. https://doi.org/10.1016/j.gca.2016.05.035 Pokrovsky, O.S., & Schott, J. (2002). Iron colloids/organic matter associated transport of major and trace elements in small boreal rivers and their estuaries (NW Russia). Chemical Geology, 190(1–4), 141–179. https://doi.org/10.1016/ S0009‐2541(02)00115‐8 Pokrovsky, O.S., Schott, J., & Dupré, B. (2006). Trace element fractionation and transport in boreal rivers and soil porewaters of permafrost‐dominated basaltic terrain in Central

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   263 Siberia. Geochimica et Cosmochimica Acta, 70(13), 3239–3260. https://doi.org/10.1016/j.gca.2006.04.008 Pokrovsky, O.S., Shirokova, L.S., Kirpotin, S.N., Audry, S., Viers, J., & Dupré, B. (2011). Effect of permafrost thawing on organic carbon and trace element colloidal speciation in the thermokarst lakes of western Siberia. Biogeosciences, 8(3), 565–583. https://doi.org/10.5194/bg‐8‐565‐2011 Prietzel, J., Thieme, J., Eusterhues, K., & Eichert, D. (2007). Iron speciation in soils and soil aggregates by synchrotron‐ based X‐ray microspectroscopy (XANES, μ‐XANES). European Journal of Soil Science, 58(5), 1027–1041. https:// doi.org/10.1111/j.1365‐2389.2006.00882.x Rawlins, M.A., Steele, M., Holland, M.M., Adam, J.C., Cherry, J.E., Francis, J.A., et al. (2010). Analysis of the Arctic system for freshwater cycle intensification: Observations and expectations. Journal of Climate, 23(21), 5715–5737. https://doi. org/10.1175/2010JCLI3421.1 Riedel, T., Zak, D., Biester, H., & Dittmar, T. (2013). Iron traps terrestrially derived dissolved organic matter at redox i­ nterfaces. Proceedings of the National Academy of Sciences, 110(25), 10101–10105. https://doi.org/10.1073/pnas.1221487110 Rivkina, E., Gilichinsky, D., Wagener, S., Tiedje, J.M., & McGrath, J. (1998). Biogeochemical activity of anaerobic microorganisms from buried permafrost sediments. Geomicrobiology Journal, 15(3), 187–193. Roden, E.E., & Wetzel, R.G. (1996). Organic carbon oxidation and suppression of methane production by microbial Fe(III) oxide reduction in vegetated and unvegetated freshwater wetland sediments. Limnology and Oceanography, 41(8), 1733– 1748. https://doi.org/10.4319/lo.1996.41.8.1733 Roden, E.E., & Zachara, J.M. (1996). Microbial reduction of crystalline iron(III) oxides: Influence of oxide surface area and potential for cell growth. Environmental Science and Technology, 30(5), 1618–1628. https://doi.org/10.1021/ es9506216 Romanovsky, V.E., & Osterkamp, T.E. (2000). Effects of unfrozen water on heat and mass transport processes in the active layer and permafrost. Permafrost and Periglacial Processes, 11(June), 219–239. https://doi.org/10.1002/1099‐ 1530(200007/09)11:33.0.CO;2‐7 Roy Chowdhury, T., Herndon, E.M., Phelps, T.J., Elias, D.A., Gu, B., Liang, L., et al. (2015). Stoichiometry and temperature sensitivity of methanogenesis and CO2 production from saturated polygonal tundra in Barrow, Alaska. Global Change Biology, 21, 722–737. https://doi.org/10.1111/gcb.12762 Rushlow, C.R., & Godsey, S.E. 2017. Rainfall–runoff responses on Arctic hillslopes underlain by continuous permafrost, North Slope, Alaska, USA. Hydrological processes, 31(23), 4092–4106. Rustad, L.E., Campbell, J.L., Marion, G.M., Norby, R.J., Mitchell, M.J., Hartley, A. E., et al. (2001). A meta‐analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia, 126(4), 543–562. https://doi. org/10.1007/s004420000544 Schädel, C., Bader, M.K.‐F., Schuur, E.A.G., Biasi, C., Bracho, R., Čapek, P., et al. (2016). Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nature: Climate Change, 6(10), 950–953. https://doi. org/10.1038/nclimate3054

Schädel, C., Schuur, E.A.G., Bracho, R., Elberling, B., Knoblauch, C., Lee, H., et al. (2014). Circumpolar assessment of permafrost C quality and its vulnerability over time using long‐term incubation data. Global Change Biology, 20(2), 641–652. https://doi.org/10.1111/gcb.12417 Schimel, J.P., Kielland, K., & Chapin, F.S. (1996). Nutrient availability and uptake by tundra plants. In J. Reynolds, J. Tenhunen (Eds.), Ecological studies (pp. 203–221). Berlin: Springer. Schmidt, I.K., Jonasson, S., & Michelsen, A. (1999). Mineralization and microbial immobilization of N and P in arctic soils in relation to season, temperature and nutrient amendment. Applied Soil Ecology, 11, 147–160. Schmidt, I.K., Jonasson, S., Shaver, G.R., Michelsen, A., & Nordin, A. (2002). Mineralization and distribution of nutrients in plants and microbes in four arctic ecosystems: Responses to warming. Plant and Soil, 242(1), 93–106. https:// doi.org/10.1023/A:1019642007929 Schuur, E.A.G., Bockheim, J., Canadell, J.G., Euskirchen, E., Field, C.B., Goryachkin, S.V., et al. (2008). Vulnerability of permafrost carbon to climate change: Implications for the global carbon cycle. BioScience, 58(8), 701. https://doi. org/10.1641/B580807 Schuur, E.A.G., McGuire, A.D., Schädel, C., Grosse, G., Harden, J.W., Hayes, D.J., et al. (2015). Climate change and the permafrost carbon feedback. Nature, 520(7546), 171–179. https://doi.org/10.1038/nature14338 Schuur, E.A.G., Vogel, J.G., Crummer, K.G., Lee, H., Sickman, J.O., & Osterkamp, T.E. (2009). The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature, 459(7246), 556–559. https://doi.org/10.1038/nature08031 Schwertmann, U., & Murad, E. (1988). The nature of an iron oxide‐organic iron association in a peaty environment. Clay Minerals, 23(3), 291–299. Shaver, G.R., Billings, W.D., Chapin III, F.S., Giblin, A.E., Nadelhoffer, K.J., Oechel, W.C., & Rastetter, E.B. (1992). Global change and the carbon balance of Arctic ecosystems changes in global terrestrial carbon cycling. BioScience, 42(6), 433–441. Shaver, G.R., & Chapin III, F.S. (1980). Response to fertilization by various plant growth forms in an Alaskan tundra: Nutrient accumulation and growth. Ecology, 61(3), 662–675. Shaver, G.R., & Chapin III, F.S. (1986). Effect of fertilizer on production and biomass of tussock tundra, Alaska, U. S. A. Arctic and Alpine Research, 18(3), 261–268. Shaver, G.R., & Chapin III, F.S. (1995). Long‐term responses to factorial, NPK fertilizer treatment by Alaskan wet and moist tundra sedge species. Ecography, 18(3), 259–275. Shaver, G.R., Johnson, L.C., Cades, D.H., Murray, G., Laundre, J.A., Nadelhoffer, K.J., & Giblin, A.E. (1998). Biomass and CO2 flux in wet sedge tundras: Responses to nutrients, temperature, and light. Ecological Monographs, 68(1), 75–97. Sjöberg, Y., Coon, E., Sannel, A.B.K., Pannetier, R., Harp, D., Frampton, A., Painter, S.L., & Lyon, S.W. (2016). Thermal effects of groundwater flow through subarctic fens: A case study based on field observations and numerical modeling. Water Resources Research, 52(3), 1591–1606. Sjöberg, Y., Frampton, A., & Lyon, S.W. (2013). Using streamflow characteristics to explore permafrost thawing in northern Swedish catchments. Hydrogeology Journal, 21(1), 121–131.

264  BIOGEOCHEMICAL CYCLES Slavik, K.A., Peterson, B.J., Deegan, L.A., Bowden, W.B., Hershey, A.E., & Hobbie, J.E. (2004). Long‐term responses of the Kuparuk River ecosystem to phosphorus fertilization. Ecology, 85(4), 939–954. Smith, L.C., Sheng, Y., MacDonald, G.M., & Hinzman, L D. (2005). Disappearing Arctic lakes. Science (New York, N.Y.), 308(5727), 1429. https://doi.org/10.1126/science.1108142 Spence, C. (2007). On the relation between dynamic storage and runoff: A discussion on thresholds, efficiency, and function. Water Resources Research, 43(12), 1–11. Spence, C., & Phillips, R.W. (2015). Refining understanding of hydrological connectivity in a boreal catchment. Hydrological Processes, 29(16), 3491–3505. Stocker, T., Qin, D., Plattner, G.‐K., Tignor, M., Allen, S., Boschung, J., et al. (Eds.) (2013). Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK; New York, NY: Cambridge University Press. Street, L.E., Mielke, N., & Woodin, S.J. (2017). Phosphorus availability determines the response of tundra ecosystem carbon stocks to nitrogen enrichment. Ecosystems, 21(6), 1155–1167. https://doi.org/10.1007/s10021‐017‐0209‐x Sturm, M., Racine, C.H., & Tape, K. (2001). Increasing shrub abundance in the Arctic. Nature, 411, 546–547. Sturm, M., Schimel, J., Michaelson, G., Welker, J.M., Oberbauer, S.F., Liston, G.E., et al. (2005). Winter biological processes could help convert arctic tundra to shrubland. BioScience, 55(1), 17–26. Stutter, M.I., & Billett, M.F. (2003). Biogeochemical controls on streamwater and soil solution chemistry in a High Arctic environment. Geoderma, 113(1–2), 127–146. https://doi. org/10.1016/S0016‐7061(02)00335‐X Sundman, A., Karlsson, T., Laudon, H., & Persson, P. (2014). XAS study of iron speciation in soils and waters from a boreal catchment. Chemical Geology, 364, 93–102. https:// doi.org/10.1016/j.chemgeo.2013.11.023 Swindles, G.T., Morris, P.J., Mullan, D., Watson, E.J., Turner, T.E., Roland, T.P., et al. (2016). The long‐term fate of permafrost peatlands under rapid climate warming. Scientific Reports, 5(1), 17951. https://doi.org/10.1038/srep17951 Tarnocai, C., Canadell, J.G., Schuur, E.A.G., Kuhry, P., Mazhitova, G., & Zimov, S. (2009). Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23(2), 1–11. https://doi.org/10.1029/ 2008GB003327 Thamdrup, B. (2012). New pathways and processes in the global nitrogen cycle. Annual Review of Ecology, Evolution, and Systematics, 43(1), 407–428. https://doi.org/10.1146/annurev‐ ecolsys‐102710‐145048 Thompson, A., Chadwick, O.A., Rancourt, D.G., & Chorover, J. (2006). Iron‐oxide crystallinity increases during soil redox oscillations. Geochimica et Cosmochimica Acta, 70(7), 1710–1727. https://doi.org/10.1016/j.gca.2005.12.005 Toohey, R.C., Herman‐Mercer, N.M., Schuster, P.F., Mutter, E.A., & Koch, J.C. (2016). Multidecadal increases in the Yukon River Basin of chemical fluxes as indicators of changing flowpaths, groundwater, and permafrost. Geophysical Research Letters, 43(23), 12,120–12,130.

Treat, C.C., Natali, S.M., Ernakovich, J., Iversen, C.M., Lupascu, M., Mcguire, A.D., et al. (2015). A pan‐Arctic synthesis of CH4 and CO2 production from anoxic soil incubations. Global Change Biology, 21(7), 2787–2803. https://doi. org/10.1111/gcb.12875 Tromp‐Van Meerveld, H.J., & McDonnell, J.J. (2006). Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resources Research, 42(2), 1–11. Troth, J.L., Deneke, F.J., & Brown, L.M. (1976). Upland aspen/ birch and black spruce stands and their litter and soil properties in interior Alaska. Forest Science, 22(1), 33–44. Trusiak, A., Treibergs, L.A., Kling, G.W., & Cory, R.M. (2018). The role of iron and reactive oxygen species in the production of CO2 in arctic soil waters. Geochimica et Cosmochimica Acta, 224, 80–95. https://doi.org/10.1016/j.gca.2017.12.022 Vincent, A.G., Schleucher, J., Grobner, G., Vestergren, J., Persson, P., Jansson, M., & Giesler, R. (2012). Changes in organic phosphorus composition in boreal forest humus soils: the role of iron and aluminum. Biogeochemistry, 108, 485–499. https://doi.org/10.1007/S10533‐01 Voigt, C., Marushchak, M.E., Lamprecht, R.E., Jackowicz‐ Korczyński, M., Lindgren, A., Mastepanov, M., et  al. (2017). Increased nitrous oxide emissions from Arctic peatlands after permafrost thaw. Proceedings of the National Academy of Sciences, 114(24), 6238–6243. https://doi.org/10.1073/pnas.1702902114 Walter, K.M., Zimov, S.A., Chanton, J.P., Verbyla, D., & Chapin, F.S. (2006). Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming. Nature, 443(7107), 71. Walvoord, M.A., & Kurylyk, B.L. (2016). Hydrologic impacts of thawing permafrost—a review. Vadose Zone Journal, 15(6). https://doi.org/10.2136/vzj2016.01.0010 Walvoord, M. A., & Striegl, R.G. (2007). Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: Potential impacts on lateral export of carbon and nitrogen. Geophysical Research Letters, 34(12). https://doi.org/10.1029/2007GL030216 Wieder, W.R., Cleveland, C.C., Smith, W.K., & Todd‐Brown, K. (2015). Future productivity and carbon storage limited by terrestrial nutrient availability. Nature Geoscience, 8(6), 441–444. https://doi.org/10.1038/ngeo2413 Williams, J.D.H., Syers, J.K., Harris, R.F., & Armstrong, D.E. (1971). Fractionation of inorganic phosphate in calcareous lake sediments. Soil Science Society of America Journal, 35(2), 250–255. Woo, M. (1986). Permafrost hydrology in north america. Atmosphere–Ocean, 24(3), 201–234. https://doi.org/10.1080/ 07055900.1986.9649248 Woo, M., & Steer, P. (1983). Slope hydrology as influenced by thawing of the active layer, Resolute, N.W.T. Canadian Journal of Earth Sciences, 20(6), 978–986. Wookey, P. A., Aerts, R., Bardgett, R. D., Baptist, F., Bråthen, K., Cornelissen, J. H. C., et al. (2009). Ecosystem feedbacks and cascade processes: Understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Global Change Biology, 15, 1153–1172. https://doi. org/10.1111/j.1365‐2486.2008.01801.x Ye, B., Yang, D., Zhang, Z., & Kane, D.L. (2009). Variation of hydrological regime with permafrost coverage over Lena

Biogeochemical Cycling of Redox‐Sensitive Elements in Permafrost‐Affected Ecosystems   265 Basin in Siberia. Journal of Geophysical Research Atmospheres, 114(7). https://doi.org/10.1029/2008JD010537 Zak, D., Gelbrecht, J., & Steinberg, C.E.W. (2004). Phosphorus retention at the redox interface of peatlands adjacent to surface waters in Northeast Germany. Biogeochemistry, 70(3), 357–368. https://doi.org/10.1007/s10533‐003‐0895‐7 Zhu, Q., Riley, W.J., Tang, J., & Koven, C.D. (2016). Multiple soil nutrient competition between plants, microbes, and mineral surfaces: Model development, parameterization, ­ and example applications in several tropical forests. Biogeosciences, 13(1), 341–363. https://doi.org/10.5194/ bg‐13‐341‐2016 Zona, D., Lipson, D.A., Paw U.K.T., Oberbauer, S.F., Olivas, P., Gioli, B., & Oechel, W.C. (2012). Increased CO2 loss from

vegetated drained lake tundra ecosystems due to flooding. Global Biogeochemical Cycles, 26(2), 1–16. https://doi. org/10.1029/2011GB004037 Zona, D., Lipson, D.A., Zulueta, R.C., Oberbauer, S.F., & Oechel, W.C. (2011). Microtopographic controls on ecosystem functioning in the Arctic Coastal Plain. Journal of Geophysical Research: Biogeosciences, 116(3), 1–12. https:// doi.org/10.1029/2009JG001241 Zona, D., Oechel, W.C., Kochendorfer, J., Paw U, K.T., Salyuk, A.N., Olivas, P.C., et  al. (2009). Methane fluxes during the initiation of a large‐scale water table manipulation experiment in the Alaskan Arctic tundra. Global Biogeochemical Cycles, 23(2), 1–11. https://doi. org/10.1029/2009GB003487

13 Anthropogenic Interactions with Rock Varnish Ronald I. Dorn

ABSTRACT This chapter focuses on the ubiquitous biogeochemical coating known as rock varnish (sometimes called desert ­varnish in an arid climate) that forms naturally as a result of interaction between budding bacteria and clay ­minerals. This chapter interfaces with this book through exploring natural processes that generate this coating and the sensitivity of rock varnish to both natural climatic changes and anthropogenic forcings. High‐resolution electron microscopy reveals that budding bacteria concentrate both manganese (Mn) and iron (Fe) on cell ­surfaces. Postdeposition processes mobilize nanoscale Mn and Fe that cements clay minerals to the underlying rock or preexisting varnish. Although other hypotheses exist to explain varnish formation, this model is the only proposed formation process that has a rate‐limiting step and does not fall pray to the “varnish rate paradox.” Despite rates of varnish accretion of microns per millennia in warm deserts and microns per century in more mesic settings, anthropogenic processes have altered its biogeochemistry in a variety of ways. Globally, lead fallout has contaminated the surface‐most micrometer. Regionally, acid fog and anthropogenic dust generation alters varnish textures. Locally, ash from wildfire combines with graffiti such as chalk to coat varnishes. Humans have also applied “artificial varnish” to minimize the aesthetic impact of road construction in wealthier communities. 13.1. INTRODUCTION Prior to the mid‐19th century, western thought focused on positive aspects, both religiously and culturally, of the impacts of human activity on Earth’s surfaces (Glacken, 1967). Thought began to change with the writings of geographer George Perkins Marsh and naturalist Count Buffon (comte de Buffon, 1749–1804; Marsh, 1864). These approaches to the study of the destructive effects of human activities initiated the modern sustainability movement (Lowenthal, 2000). In envisioning this book, Dontsova et al. (2020) present a level of detail and evidence for the pervasiveness of human impact unthinkable in the time of George Perkins Marsh—teasing out anthropogenic from natural drivers of biogeochemical change. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA

The chapters of this book analyze biogeochemical changes from three broad perspectives: (a) natural forcings such as mineral and rock decay; (b) climate change that potentially mixes anthropogenic and natural impacts on; and (c) anthropogenic forcings. This chapter interfaces with all three perspectives by reviewing natural ­forcings that lead to the accretion of the ubiquitous biogeographical rock coating known as rock varnish (and sometimes also termed desert varnish), and then how rock varnish responds to both natural climatic changes and anthropogenic processes. The dark ferromanganese‐rich and clay accretion of rock varnish is often termed desert varnish because it is most noticeable in warm arid regions. However, Krumbein and Jens (1981) and Dorn and Oberlander (1982) emphasized that rock varnish is a better term because of the presence of this same coating in all terrestrial environments. Recent examples of varnish studies in nondeserts include settings in SE Asia (Casanova‐Municchia et al.,

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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2016), southern Belgium (Goossens et al., 2015), caves in Spain (Lozano & Rossi, 2012), a German Gothic cathedral (Macholdt et  al., 2017), and different settings in China (Xu et al., 2018). Rock varnish greatly alters the appearance of rock surfaces at all different scales, even though it is typically less than 100 μm thick (Figure 13.1). Its constituents do not derive from the underlying rock (Dorn, 1998; Krumbein & Jens, 1981; Potter & Rossman, 1977), but instead derive from external sources such as dust deposited on rock surfaces. Rock varnish typically grows at rates of microns per thousand years in arid environments (Liu & Broecker, 2000), although faster growth rates do occur in more mesic settings (Dorn & Meek, 1995; Krinsley et al., 2017). These very slow rates of accretion deposit millennial‐ scale microlaminations that are recognized as a paleoclimatic indicator and dating technique (Liu, 2017; Liu & Broecker, 2013; Liu et al., 2013). With such slow rates of accretion, however, rock varnish is not generally recognized as an indicator of anthropogenic activity. This chapter presents evidence that rock varnish that forms in both warm desert and wetter settings should receive more recognition as a biogeochemical indicator of anthropogenic interactions. This chapter starts with a summary of current knowledge on the processes by which rock varnish accretes on rock surfaces. The next section then explores how, prehistorically, humans used varnished surfaces as natural blackboards on which to engrave rock art—sometimes leaving behind iron‐rich paint and quartz mechanically driven into engraved surfaces. Historically, humanity’s imprint on rock varnish processes rest at global scales where the accretion of lead and other heavy metals contaminate the surface‐most layer of rock varnish, even in such distant locations as Greenland. Regional impacts include anthropogenic acid fog dissolving varnish and increases in atmospheric dust loading inundating varnishes with particle fragments. More local impacts include ash from wildfires accreting onto nearby rock surfaces and the creation of “artificial varnish” to try to disguise the impact of development in deserts. 13.2. LANDSCAPE GEOCHEMISTRY OF ROCK VARNISH Clay minerals dominate the composition of rock varnish (Dorn & Oberlander, 1982; Krinsley et  al., 1995; Potter & Rossman, 1977), comprising up to two‐thirds of a typical rock varnish found in warm deserts. Although over 40 other minor and trace elements also occur in rock varnish (Bard et al., 1978; Dorn, 1998; Dorn et al., 1990; Engel & Sharp, 1958; Fleisher et  al., 1999; Macholdt et  al., 2015; Nowinski et  al., 2010), the key to varnish formation rests in the oxyhydroxides of manganese and

iron (Hooke et al., 1969; McKeown & Post, 2001; Potter & Rossman, 1979) that typically range from 15 to 40% by weight and are the agents that cement clay minerals together and to the underlying rock surface (Dorn & Oberlander, 1982; Krinsley, 1998; Krinsley et  al., 2013; Potter, 1979). Table  13.1 presents variations in the ­elemental chemistry of bulk samples of scraped rock ­varnish from different global settings. Landscape geochemistry (Fortescue, 1980; Perel’man, 1966) is an environmental geochemistry paradigm focused on explaining spatial geochemical patterns found in low‐temperature weathering environments. From a landscape geochemistry perspective, the different constituents of rock varnish accrete because specific physical, biological, and geochemical barriers exist on rock surfaces (Perel’man, 1986). The clay minerals and other small bits of eolian particles initially attach to varnish (Aulinas et al., 2015; Dorn et al., 2013) through van der Waals forces (Figure 13.2); these particles are loosely cemented by nanoscale deposits of silica (Langworthy et  al., 2010), carbonate, Mn, and Fe (Dorn et al., 2013; Krinsley et al., 2009). Clay minerals are then fixed to the underlying rock (Dorn, 1998; Dorn, Krinsley, et al., 2012) and to other rock varnish (Krinsley et al., 2013) by Mn–Fe oxyhydroxides. Budding bacteria are the key agents that concentrate both Mn and Fe (Dorn & Oberlander, 1982; Krinsley et  al., 2017). Budding bacteria, for example the genera Pedomicrobium (Dorn & Oberlander, 1982), encrust Mn and Fe oxides around their cells and hyphae (Figures 13.2 and 13.3). The budding process enables these bacteria to be buried underneath the accumulation of varnish and still reproduce by hyphae extension. After cells are encrusted with Mn and Fe, ongoing diagenesis breaks apart these cell encustrations into nanoscale granules (Dorn, 1998; Krinsley, 1998) that are then remobilized and reprecipitated as nanoscale Mn–Fe (Krinsley et al., 2017) amongst the mixed‐layer clays (Figure  13.2) in a process first explained by Potter (1979, 174–175): Deposition of the manganese and iron oxides within the clay matrix might then cement the clay layer…the hexagonal arrangement of the oxygen in either the tetrahedral or octahedral layers of the clay minerals could form a suitable template for crystallization of the layered structures of birnessite.

However, varnish is not stable at the nanoscale; McKeown and Post (2001, 712) explained that ongoing disequilibria exists even after the varnish formed: [e]ven if analysis methods are improved, the situation will remain complicated by the flexibility and great variety of Mn oxide structures. The common elements of these structures enable them to easily intergrow with and transform with one another.

(a)

(b)

(c)

(d) Si AI K Ca

Ti

Mn FeFe Mn Fe

Ti Fe

Si AI K Ca Mn Fe Fe

Si AI

Mn Ca K

Figure 13.1  Rock varnish viewed at different scales. (a) Alluvial fans debouching from the Panamint Range into Death Valley on the east and the Panamint Valley on the west are visibly darkened with varying amounts of rock varnish. (b) Petroglyph carved into a joint face partly coated with rock varnish from Petrified Forest National Park. (c) Secondary electron image of rock varnish coating rocks of the Orinoco River, the first site for the scholarly study of rock varnish. (d) Bacteria sheaths concentrating Mn–Fe rich material in rock varnish from Tibet, as indicated by energy dispersive spectra of bacteria hyphae as compared with spectra of the adjacent varnish. [(a) www.earthobservatory.nasa.gov/images/6470/death‐valley‐national‐park (Public Domain); (b) and (c) image by Ronald Dorn; (d) Krinsley et al. (2009). Reproduced with permission of Mary Ann Liebert.]

Ti

Fe

270  BIOGEOCHEMICAL CYCLES Table 13.1  Examples of elemental variation exhibited in bulk chemical analyses of rock varnishes found in desert regions. Samples were analyzed by particle induced X‐ray excitation Site and sample details Manix Lake, Trail Fan, Death Mojave Desert/ Makanaka Till, Sinai Peninsula, Hawaii/ With Egypt/ > 1 m Valley/ Former > 1 m above soil silica skin above soil Element rock fracture

Petroglyph South Australia/ > 1 m above soil

Ingenio, Peru Ayers Rock, Desert/ At Australia/ From soil surface rock fracture

Na Mg Al Si P S K Ca Ti Mn Fe Ni Cu Zn Rb Sr Zr Ba Pb

0.17 1.21 22.81 33.34 0.53 BLD 2.79 2.18 0.65 21.7 13.26 BLD 0.44 0.44 BLD BLD BLD 0.14 0.34

NA 2.11 20.45 45.88 0.53 1.13 2.91 6.22 0.85 4.94 12.03 BLD 0.04 0.16 BLD 0.11 BLD 2.42 0.22

BLD 0.14 23.74 39.09 0.49 0.7 3.45 4.87 1.52 10.87 13.47 0.13 0.12 0.27 BLD BLD 0.29 0.85 BLD

1.1 3.44 25.77 32.35 1.15 0.3 2.11 1.35 0.84 12.47 18.09 BLD 0.22 0.3 0.25 0.21 0.22 0.19 0.74

0.62 1.98 21.13 29.77 0.69 0.2 3.3 4.89 0.73 13.6 21.13 BLD 0.33 0.49 BLD BLD BLD 0.16 0.98

0.28 1.5 22.94 32.81 BLD BLD 2.42 2.91 0.68 11.97 22.94 BLD 0.25 0.42 BLD 0.42 BLD 0.18 0.27

NA 1.58 28.77 35.69 BLD BLD 2.11 1.45 1.19 11.91 16.57 BLD BLD BLD BLD BLD BLD 0.73 BLD

Note: From Dorn et al. (1990). BLD, below limit of detection; NA, not analyzed.

Thus, while budding bacteria originally concentrate the Mn–Fe, it is geochemical dissolution at the nanoscale and subsequent reprecipitation of oxyhydroxides in clays that results in varnish formation. It is important to stress, however, that shifts at the nanoscale do not create instability in the laminations seen at the micron scale that are stable for tens of millennia (Liu, 2017; Liu & Broecker, 2013; Liu et  al., 2013)—much like cars moving around inside a parking lot do not change the lot itself. Budding bacteria comprise only a small component of the microbial community that has been found associated with rock varnish through culturing techniques (Dorn & Oberlander, 1982; Krumbein & Jens, 1981; Northup et al., 2010; Palmer et al., 1985; Perry et al., 2004; Taylor‐ George et al., 1983) and phylogenetic insight (Benzerara et al., 2006; Eppard et al., 1996; Esposito et al., 2015; Irit et al., 2019; Kuhlman et al., 2005, 2008; Kuhlman, Fusco, et al., 2006; Kuhlman, McKay, et al., 2006). In general, these studies find a broad diversity of eukaryotic and bacterial taxa, most of which do not actively participate in manganese or iron concentration through oxidation. However, the authors of these studies admit that sampling thus far has been analyzing varnish materials from just a few sampling sites. In addition, other sorts of organic analyses reveal that the nature of the organic

matter in analyzed varnish samples is consistent with a bacterial origin (Malherbe et  al., 2017) and that the microbial community of varnish is similar to adjacent soils in that prokaryotic and fungal communities exist, with gram‐positive bacteria found more often (Schelble et al., 2005). In brief, varnish is home to a diverse microbial community, most of which is not directly involved in Mn–Fe concentration or varnish formation. The budding bacteria (Figure 13.3) hypothesis (Dorn & Oberlander, 1982; Krinsley et  al., 2017) to explain the great enrichment of Mn and also Fe in rock varnish has a number of competing explanations that can be grouped into several broad categories. Some favor enrichment through purely abiotic processes that involve alternating reducing and oxidizing environments (Engel & Sharp, 1958; Goldsmith et  al., 2014; Soleilhavoup, 2011), or a role of fluids moving upwards from the underlying soil (Lebedeva et  al., 2019). Another group of hypotheses favors organisms different than budding bacteria that can concentrate Mn and Fe (Krumbein & Jens, 1981; Northup et  al., 2010; Palmer et  al., 1985; Taylor‐George et  al., 1983). Recent research points to a role for photooxidation perhaps related to electroactive bacterial communities that produce the Mn‐mineral birnessite (Lu et al., 2019; Ren et al., 2019; Xu et al., 2019).

Anthropogenic Interactions with Rock Varnish  271

Physical barrier: van der Walls force promotes dust accumulation, providing raw ingredients of clays

Biological barrier: Mn and Fe fixation in bacteria sheaths Si

Varnish

Al K Ca

Mn Fe Mn

Si Al

K Ca Ti

Fe

Spot on bacteria

Dissolution from cell wall (granular fragments)

20 nm

20 nm

8 nm

& Physiochemical barrier: fixation in clay matrix cementing mixed-layered clays

Figure 13.2  Rock varnish accretion requires a series of different types of barriers to transport of the various constituents. [Dorn (1998). Reproduced with permission of Elsevier.]

Still others potentially draw connections to growing phylogenetic insight about the organisms growing on and in varnish (Benzerara et  al., 2006; Eppard et  al., 1996; Esposito et  al., 2015; Kuhlman et  al., 2005, 2008; Kuhlman, Fusco, et al., 2006; Kuhlman, McKay, et al., 2006). The problem with these competing explanations is that they all lack a rate‐limiting step and fall pray to the “varnish rate paradox.” The varnish rate paradox, presented previously (Dorn, 2007; Dorn & Krinsley, 2011; Krinsley et  al., 2017), is that there are so many different ways proposed to explain Mn (and Fe) enrichment in varnish—yet warm desert locations studied by the various researchers display rates of varnishing of microns per millennia (Dorn, 1998; Liu & Broecker, 2000). Nondesert sites display much faster rates of varnishing and often contain in situ evidence of budding bacteria (e.g. Dorn & Meek, 2005; Krinsley et  al., 2017). Although the process of bacterial adsorption and oxidation can be quite fast (Namgung et  al.,

2018; Vázquez‐Ortega & Fein, 2017), budding bacteria are only rarely observed in situ in warm desert varnishes. Still, Dorn & Krinsley (2011) emphasized that only budding bacteria have been observed in situ concentrating Mn and Fe, while other organisms have never been seen actively enhancing Mn or Fe in varnish samples. In contrast to the occasional growth of budding bacteria, abiotic processes would generate varnishes 100– 10,000 times faster than observed (Dorn & Krinsley, 2011; Krinsley et  al., 2017). The reason for faster formation through abiotic enrichment is that there is no rate‐limiting step. Theoretically, Mn and Fe concentration relies on leaching of the divalent cations from dust sources during acidic wetting events, followed by increases in pH to oxidize the Mn and Fe. Given that dust deposition and wetting events occur tens of thousands of times over a millennia, if small pH–Eh shifts actually generated varnish as proposed (Engel & Sharp, 1958; Goldsmith et  al., 2014; Soleilhavoup, 2011), varnishes should be meters thick and not microns thick as found naturally. Similarly, if all the various organisms growing in and on varnish (Benzezara et  al., 2006; Brewer & Fierer, 2018; Dragovich, 1993; Eppard et  al., 1996; Esposito, 2015; Gleeson et  al., 2018; Kuhlman, Fusco, et  al., 2006; Kuhlman, McKay, et  al., 2006; Kuhlman et  al., 2008; Kutovaya et al., 2015; Lang‐Yona et al., 2018; Lozano & Rossi, 2012; Malherbe et  al., 2017; Northup et  al., 2010; Palmer et al., 1985; Paulino‐Lima et al., 2016) contributed to varnish formation, rates of accretion would be orders of magnitude higher than observed for the warm desert sites studied (Dorn, 2007; Krinsley et al., 2017). The only hypothesis that explains Mn and Fe concentration, as well as the slow rate of varnish growth in warm deserts, and also some faster growth rates in nondesert environments (e.g. Dorn & Meek, 1995; Spilde et al., 2013), involves the rare event of the growth of budding bacteria (Figure  13.3) and associated concentration of Mn and Fe (Krinsley et al., 2017). The landscape geochemistry of rock varnish is not only one of accretionary processes that fix constituents on rock surfaces. Rock varnish is also dissolved naturally by acid‐secreting organisms such as lichens (Dragovich, 1987) and microcolonial fungi (Dragovich, 1993). The organic acids dissolve the Mn–Fe, thus destroying the cement that binds the varnish together (Figure 13.4). In summary, the landscape geochemistry of rock varnish is a complex dance among processes that fix constituents to rock surfaces and those that release coating components. 13.3. PREHISTORIC ANTHROPOGENIC INTERACTIONS Rock engravings (or petroglyphs) represent the rare circumstance where this biogeochemical deposit has actually received widespread attention for its anthropogenic

272  BIOGEOCHEMICAL CYCLES (a)

(b) 100

15.33.13 Acquire EDX Acquire HAADF Area 1

C Ga

80 Counts

Ga

60

O

Ga Cu

Pt Ga

Si Si

40 Cu

20

Pt Pt

Si

Cu

Pt

Mn

Cu Pt

Ca

Mn

Ca Ca

5

Pt

Fe Fe Mn

Fe

Pt

Energy (keV)

Pt Pt Pt

Cu Pt

Ga

Pt Pt

10

15

Figure 13.3  High‐resolution perspective on fixation of Mn and Fe. (a) Electron microscope image of budding bacteria concentrating Mn and Fe where a budding hyphae emerges from a cocci bacterial form. (b) Location in image (a) matching the energy dispersive spectra showing the concentration of Mn and Fe with Si and small amounts of Ca. The other peaks are artifacts associated with sample preparation. [Krinsley et al. (2017).] (a)

(b)

Figure 13.4  Biofilms grow on rock surfaces where sufficient moisture exists (Viles, 2011). Such biofilms in the Sonoran Desert of central Arizona typically consist of fungi and lichens. (a) Microcolonial fungi secrete organic acids that then dissolve the Mn and Fe in varnish. In the case of this cross‐section view using backscattered electrons some of the Mn and Fe have been reprecipitated in rock fractures. (b) Field site near Black Canyon City, where flower widths of 2 cm provide scale. [images by Ronald Dorn]

interactions. The act of engraving art into a rock face coated by dark Mn–Fe‐rich varnish is commonly discussed in scholarship (Black et al., 2017; Whitley, 2001), in teaching materials about archaeology (Whitley & Loendorf, 1994), and in popular culture describing various rock art sites (https://www.nps.gov/pefo/learn/ historyculture/newspaper‐rock.htm) where ancient peoples have carved motifs (Figure 13.5). The prehistoric artists did not only engrave motifs into rock varnish, but also applied painting materials. Figure  13.6 presents an example from Buffalo Eddy, Washington, where ocher—identified as a strong iron

energy dispersive X‐ray signal—was applied to a pattern of dots. The varnish microlamination pattern that formed on top of the paint material has a pattern consistent with varnish layering unit Wet Holocene Unit 4 that has a calibrated calendar age of about 2800 year BP (Liu & Broecker, 2007). Luminescence occurs when quartz minerals are mechanically fractured. Quartz also exhibits luminescence when it is rubbed. Dr. David Whitley and colleagues have compiled evidence that quartz was used in the making of many petroglyphs. In particular, basalt  flows that generally lack free quartz provide an

Anthropogenic Interactions with Rock Varnish  273

Figure 13.5  Newspaper Rock at Petrified Forest National Park exemplifies how rock varnish provides a ‘blackboard’ for prehistoric rock engravings that range in age from terminal Pleistocene to the 20th century (Dorn, 2006).

(a)

(b)

Figure 13.6  An engraving consisting of a pattern of dots (image a), at Buffalo Eddy, Washington, USA, was subjected to painting (Merrell & Dorn, 2009). Iron‐rich material, perhaps goethite, was painted into the dots. (b) Then, rock varnish formed on top of the paint material, as seen in a light microscope ultrathin cross‐section of varnish. Arrows in (b) identify the iron‐rich materials as confirmed by energy dispersive X‐ray analyses.

appropriate way to study whether quartz was used in engraving rock art. The basalt flows of the Coso Range and basalt flows in the Mojave Desert, both in eastern California, reveal shards of quartz embedded into engravings (Whitley, 2000, 2001; Whitley et  al., 1999). The flows hosting the Conejo Mine petroglyphs (e.g. Figure 13.7) do not contain free quartz. The archaeological

interpretation is that the shamans making the art likely knew of the luminescence and perhaps engraved the art at night (Whitley et al., 1999). Prehistoric humans undertook considerable effort to modify stones on Earth’s surface. Such modifications are sometimes called earthen art (Frink & Dorn, 2001; von Werlhof, 1989). Earthen art such as the Nasca geoglyphs of

274  BIOGEOCHEMICAL CYCLES cm 3

cm 8

20

5

cm 14

3

cm 7

15

cm 13

cm 6

cm 2

4

25

3

Figure 13.7  The Conejo Mine petroglyph site in the Coso Range, eastern California, consists of a basalt flow that lacks free quartz. Backscattered electron micrographs of cross‐sections of rock varnish formed on top of engravings regularly reveal the presence of quartz—identified by the letter q. The numbers CM3, CM8, CM7, CM14, CM13, CM 6 and CM2 refer to specific engravings sampled. [Whitley et al. (1999).]

Peru and SW North America are some of the most well‐ studied (Clarkson, 1994; Dorn et  al., 2001). However, earthen art can be found all over the deserts of the world in the form of rock cairns (Figure 13.8a). The rocks that were assembled to make a cairn in the Panamint Valley of e­ astern California had an original arrangement of rock coatings (Figure 13.8b), most typically dark rock varnish on top of a boulder, a thin shiny black ground‐line band, iron film underneath the boulder, and perhaps laminar calcrete if the boulder was originally embedded into the Bk (carbonate) soil horizon. When the boulder was moved to build a cairn, sometimes, the orientation of the rock coatings changed (Figure 13.8c). This change provides the opportunity to utilize dating techniques, for example, radiocarbon dating carbonate formed over rock varnish. This carbonate only started to form after the rock was flipped and embedded into the ground (Cerveny et al., 2006). In summary, prehistoric humans interacted with and altered rock coatings such as rock varnish in a variety of different ways. Although the most common example involves carving motifs into heavily varnished rock surfaces, a careful inspection of both rock art and earthen

art reveals that people painted art, mechanically abraded art with quartz that exhibits luminescence, and moved boulders and rocks to create earthen art. 13.4. HISTORIC BIOGEOCHEMICAL INTERACTIONS WITH ROCK VARNISH 13.4.1. Artificial Varnish Imitation is the sincerest form of flattery that ­mediocrity can pay to greatness. Oscar Wilde Urban development in deserts leaves behind visual scars when bedrock is disturbed to create features such as road cuts. The aesthetic problem rests in the contrast between naturally dark varnish and the much brighter hues of freshly exposed rock. Figure  13.9a illustrates colluvium coated with rock varnish and where freshly broken rock surfaces stand out prominently. Wealthy subdivisions in the Phoenix metropolitan area have experimented with the application of “artificial

Anthropogenic Interactions with Rock Varnish  275 (a)

(b)

Rock coatings around undisturbed boulder Rock varnish (black) Ground surface

Ground-line band (black)

(c)

Iron film (orange) laminar carbonate (white)

20 cm

Rock coatings on geoglyph boulder flipped over

New ground surface Original ground surface

20 cm

Black varnish on iron film iron film on black varnish laminar carbonate formed over former black surface varnish

Exposed carbonate dissolved

Figure 13.8  Rock cairn from the Panamint Valley, eastern California. The boulders used to build this cairn came from a desert pavement with considerable antiquity. Some of the boulders were flipped on their side and embedded deeply in the ground. These boulders formed laminar carbonate on top of rock varnish. Thus, the original sequence of rock coatings in (b) was altered to (c). The laminar carbonate only started to form after the cairn was constructed, thus a radiocarbon age for the laminar carbonate provides a minimum age for the cairn. [(a) Seong et al. (2016). Reproduced with permission of Elsevier. (b,c) Cerveny et al. (2006). Reproduced with permission of John Wiley & Sons.]

varnish”—a process whereby sodium hydroxide is first sprayed onto places like exposed road cuts, followed by the application of a mixture of divalent Mn and Fe in solution. Upon contact with the alkaline sodium hyroxide, the Mn and Fe oxidizes and the rock surfaces are thus coated with an artificial varnish (Elvidge & Moore, 1980). Figure  13.9c displays a road cut covered with artificial varnish, where only the uppermost bit of soil is light in color. A key difference between true rock varnish (e.g., Figure 13.9b) and artificial varnish (e.g. Fig. 13.9d) is the lack of clays. The result is that the artificial varnish applied over two decades ago is undergoing disaggregation into granules that detach and degrade the artificial varnish (Figure 13.9d).

varnish that had accreted on the stones that were exposed to make the Bouse Fisherman. This varnish was greatly enriched in lead, analyzed with a 300 s counting time with a wavelength dispersive electron microprobe yielding limits of detection at about 0.03% PbO (Dorn, 1998). In contrast, the natural varnish had a very different situation explained as follows:

13.4.2. Lead Contamination of Varnish

Figure 13.9b exemplifies what is normally encountered when rock varnishes are analyzed. In this figure, lead measurements are superimposed on a color thin‐section showing varnish microlaminations; note that the lead contamination occurs only after the Wet Holocene Unit 1, which is a microlamination pattern that formed during the Little Ice Age. Thus, Dorn (1998) found that the Bouse Fisherman is not prehistoric. It was made in the 20th century, perhaps for fun or perhaps to become a tourist attraction, consistent

The first study of lead in relation to rock varnish occurred with respect to the earthen figure (or geoglyph) called the Bouse Fisherman, a human holding a spear with a quartz tip. The spear is aimed at a wavy line with fish symbols below the wavy line. A field trip lead by the Arizona Geological Survey visited this motif (Spencer & Pearthree, 2015). Working with the Bureau of Land Management, lead  concentrations were measured in the micron‐thick

Lead accumulates in rock varnishes and dust films on desert surfaces. Electron microprobe profiles reveal that lead is a contaminant in the uppermost surfaces of rock varnishes, but these concentrations drop to background levels below the very surface of natural rock coatings that have formed since lead additives were introduced into gasoline in 1922. (Dorn, 1998, 139)

276  BIOGEOCHEMICAL CYCLES (a)

(c)

(b)

(d)

Epoxy

Quartz Quartz Figure 13.9  Rock varnish as the dominant natural rock coating in metropolitan Phoenix. (a) Colluvial boulder field at Shaw Butte darkened by rock varnish. The occasional orange iron film indicates rocks spalled by the dirt cracking physical weathering process. (b) Microlaminations form discrete black, orange, and yellow layers in rock varnish thin sections. Electron microprobe analyses reveal a spike in lead in the uppermost micron of this rock varnish from the Phoenix area. Background levels occur once the layer of the varnish is beneath the 20th century. The layer Wet Holocne Unit 1 (WH1) ceased forming about 1850, and its lead concentrations are below the limit of detection at < 0.03% PbO. (c) Urbanization tends to create scars across rock faces, but developers in an affluent Phoenix neighborhood applied “artificial varnish” to minimize the aethestic impact of this road cut. (d) Back‐scattered electron microscope image of artificial varnish from image (c) that is experiencing ongoing dissolution, generating a granule‐like appearance. [all images by Ronald Dorn]

with the use of 20th century symbols for water and fish. Since then, lead profiles have been used to authenticate prehistoric petroglyphs (Dorn, 2006; Merrell & Dorn, 2009) as well as to indicate that a regionally famous Marcos de Niza engraving is not real, but 20th century in origin (Dorn, Gordon, et al., 2012). The basic observation that lead and other anthropogenic pollutants are enriched in the surface‐most layer of varnish now has extensive replication (Fleisher et  al., 1999; Goldsmith et  al., 2014; Hoar et  al., 2011; Hodge et al., 2005; Nowinski, 2009; Nowinski et al., 2013; Sims et al., 2017; Spilde et al., 2013). The iron or manganese in

varnish scavenges lead from the surrounding environment (Adams et al., 2009; Dong et al., 2002; Grangeon et  al., 2017; Hassellöv & von der Kammer, 2006; van Genuchten & Peña, 2016). Twentieth‐century industrial activities spread lead and other elemental pollutant around the globe, even in areas distant from major lead‐ pollution sources (Andersen, 1994; Getty et  al., 1999). Figure  13.10a is an epiglacial deposit of boulders near the margin of the Greenland Ice Cap. Figure  13.10b superimposes electron microprobe measurements of lead on a back‐scattered electron microscope image of rock varnish on the identified boulder in Figure 13.10a. Even

Anthropogenic Interactions with Rock Varnish  277 (a)

(b)

Figure 13.10  Epiglacial till of the Greenland glacier contaminated by lead. (a) Greenland outlet glacier study site on a medial moraine, identifying the boulder where rock varnish accreted and has been contaminated by lead. (b) Back‐scattered electron images of a cross‐section of rock varnishes where the surface‐most layer is contaminated with lead. Each electron microprobe measurement point is about 0.5 μm apart, and this means that there exists spatial overlap in the focused beam analyses. Less than 0.03% lead is background, below the limit of detection. [(a) and (b) from Ronald Dorn]

in such remote locations as Greenland, rock varnish can record an anthropogenic lead signal. 13.4.3. Effects of Acidification on Varnish Industrial activity near rock art in the Burrup Peninsula of Western Australia has increased the acidity of atmospheric fallout, exposing rock surfaces to pH values just above 4. Prior to industrialization, the pH value of rock surfaces was near neutral (Black et  al., 2017). The substantial decrease in pH leads to reduction of Fe(III) and Mn(IV) to mobile divalent forms. This changes the color of rock and petroglyph surfaces and hence endangers the priceless rock engravings. Acidity from urban activities in the Los Angeles area led to the development of acid fog in the region (Brewer et  al., 1983; Waldman et  al., 1982). Figure  13.11 compares a sample of varnish‐coated sandstone in the Santa Monica Mountains in 1941 in what is now Tuna Canyon Park less than 1 km from the Pacific Ocean (Fig. 13.11a) and sample collected from the same site in 1983 (Figure  13.11b). The 1941 sample shows the typical

laminar appearance of rock varnish. In 1983, however, no laminar varnish was found. Instead, the texture of varnish shows evidence of considerable leaching (Dorn & Krinsley, 1991) in the form of increased porous zones and redistribution of Mn–Fe in the form of stringers deposited along the walls of fractures. One explanation for the substantial change in texture in just four decades could be acid fog increasing the mobility of Mn–Fe. 13.4.4. Dust Loading onto Varnish Rock varnish naturally shows considerable variation in its texture as a result of the abundance of eolian dust. Some varnishes show a finely layered texture without the incorporation of angular pieces of dust (e.g. Figure 13.11a), but rock varnishes collected from particularly dusty locations do show considerable evidence of angular particles of dust being incorporated into varnish (Aulinas et al., 2015; Dorn et al., 2013). Anthropogenic activities are known to increase the abundance of dust in many settings (Baddock et al., 2013; Brazel, 1989; Goudie, 2014), with Owens (Dry) Lake as

278  BIOGEOCHEMICAL CYCLES (a)

(b)

Figure 13.11  Comparison of rock varnish collected near the Pacific Ocean in Los Angeles that was impacted by acid fog. (a) A representative back‐scattered electron image of the laminar texture observed from a sample collected by Joseph Spencer in 1941. (b) The author collected samples from the same site in 1982 and did not find any laminar textures. Instead, the irregular surface, evidence of reprecipitation of Mn–Fe on fracture walls, and zones of leaching could reflect the impact of acid fog.

an example. Diversion of water to Los Angeles lead to Owens (Dry) Lake becoming the largest single source of particulate matter under 10 μm in the United States (Gillette, 2013). Severe drought in California exacerbates the dust problem in the region (Borlina & Rennó, 2017). An example of the impact of anthropogenic dust loading in the region can be found even in the high alpine setting of the nearby Palisades Glacier of the Sierra Nevada, California. Boulders immediately adjacent to the margin of the Palisades Glacier accumulated dust material whose source could be from Owens (Dry) Lake. A reason why anthropogenic dust from Owens (Dry) Lake is the suspected cause is the presence of chlorine (sum spectrum in Figure 13.12b). Chlorine would not be expected if the source of dust was local, but ongoing additions of chlorine, and then removal by leaching from snowmelt, would be consistent if at least some of the dust had a source related to Owens (Dry) Lake. The dust is cemented by silica glaze rather than manganiferous rock varnish (Figure  13.12). Silica glaze is a coating composed primarily of amorphous silica, but sometimes mixed with aluminum and iron (Dorn, 1998). Its formation is abiotic and results from nanoscale deposition of silica spheroids (Langworthy et al., 2010). Silica glaze often interdigitates with other rock coatings such as varnish (Dorn, 1998). 13.4.5. Wildfire Interactions with Varnish Wildfires, both natural and human caused, result in millimeter and centimeter‐scale spalling of rock surfaces, thus removing rock varnish (Dorn, 2003). However,

human activities have accelerated the incidence of wildfires in dryland settings such as the western United States (Abatzoglou & Williams, 2016). A minimally explored arena of research involves the interactions of rock coatings and wildfire ash (Tratebas et al., 2004). Figure 13.13 illustrates how chalking of petroglyphs has combined with ash from wildfire to make a paste that adheres to and coats the underlying natural rock varnish. This one pilot study indicates interactions between human‐induced wildfires and rock varnish, however, and there are undoubtedly far more interactions than those observed by Tratebas et al. (2004). 13.5. SUMMARY This chapter concerns the natural, slow growing rock coating known as rock varnish. Rock varnish (sometimes called desert varnish in warm arid settings) is typically < 50 μm thick, but it can completely darken rock surfaces changing even the lightest colored rock black. The key to understanding its formation rests in the process by which iron and especially manganese is greatly concentrated in this rock coating—well over 50 times concentrations in the underlying rock, dust, or nearby soil. There are a number of competing explanations for how the manganese (and iron) concentration occurs. Abiotic explanations involving small ph–Eh fluctuations and a plethora of different organisms such as bacteria and fungi can explain Mn concentration found in varnish. However, all extant explanations except one do not involve a rate‐limiting step. If any of the proposed abiotic explanations actually made varnish, it would form in

Anthropogenic Interactions with Rock Varnish  279 (a)

(b)

Figure 13.13  Back‐scattered electron image of petroglyph that was chalked by amateur archaeologists in hopes of improving a photograph of the motif at Whoop‐up Canyon, Wyoming. Then, a wildfire influenced the area, and the chalk mixed with soot from a wildfire. The combination of chalk and ash adhered to the underlying rock varnish for a period of at least 1.5 years when the sample was collected. [Tretabas et al. (2004). Reproduced with permission of Taylor & Francis.] Figure 13.12  Silica glaze is the cement for dust fall on a glacial boulder next to the Palisades Glacier, Sierra Nevada, California. (a) Back‐scattered electron image of dust inorganic mineral and organic (C, carbon) particles. (b) Several energy‐dispersive X‐ray spectroscopy (EDS) analyses of this and similar cross sections were combined and the spectrum represents the sum of all of the EDS data gathered for the coating. The Cl in the sum spectrum is a signal consistent with the source of the dust being from Owens Dry Lake, even though snowmelt would gradually leach Cl over time in this environmental setting. The strong C signal reflects the carbon coating and organic materials; the other elements derive from the dust particles and inorganic minerals that compose much of the dust.

­eserts at rates orders of magnitude faster than the d observed microns per millennia rates. If all of the various biotic concentration mechanisms were in play, formation rates would be even faster. The only process that explains the Mn (and Fe) concentration and also slow rate of formation involves the occasional growth of budding bacteria. Also, budding bacteria are the only proposed agent of varnish formation with direct observational support of in situ concentration of Mn (and Fe). The focus of this chapter rests in exploring interactions between natural rock varnish and human modification of the environment. Prehistorically, humans used rock surfaces darkened by varnish as ‘blackboards’ on which to

engrave motifs called petroglyphs. Humans also altered stones and boulders to create earthen art, and in the process modified rock varnish in such a way as to change is relative position, and as a consequence allow its dating by radiometric and varnish microlamination methods. The biogeochemistry and structure of rock varnish have been modified by human activities in a number of different ways. At the global scale, atmospheric lead pollution (as well as other heavy metals) has contaminated the surface‐most micrometer of varnish, even in such remote areas as Greenland. At the regional scale, modification of the earth to create dry lakebeds has created massive dust loadings that have altered the structure of rock‐coating formation in general in the surrounding region. Also at the regional scale, acidification of the environment in the Los Angeles area of California and the Burrup Peninsula in Australia has dissolved Mn and Fe from the varnish and altered its structure; a ripe area for future biogeochemical research involves the alteration of rock varnish in other settings acidified by anthropogenic activities. More locally, ash from wildfire has combined with anthropogenic graffiti such as chalk on rock surfaces to alter rock coatings. A final example of human activities involves the attempt by wealthier suburban housing developments to mimic natural rock varnish by coating road scars with artificial varnish, still

280  BIOGEOCHEMICAL CYCLES

composed of Mn and Fe, but lacking clay minerals and hence lacking long‐term stability. ACKNOWLEDGMENTS I thank two anonymous reviewers and the handling co‐ editor for their suggestions that improved the chapter. Carolynne Merrell and Jason Lyon provided National Park Service permission and opportunity to analyze priceless samples from Buffalo Eddy. Barbara Murphy supplied the Greenland Ice Cap sample. Joseph Spencer collected the sample from the Santa Monica Mountains in 1941. REFERENCES Abatzoglou, J.T., & Williams, A.P. (2016). Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences, 113, 11770–11775. Adams, J.P., Kirst, R., Kearns, L.E., & Krekeler, M.P.S. (2009). Mn‐oxides and sequestration of heavy metals in a suburban catchment basin of the Chesapeake Bay watershed. Environmental Geology, 58, 1269–1280. Andersen, S.T. (1994). History of the terrestrial environment in the Quaternary of Denmark. Bulletin of the Geological Society of Denmark, 41, 219–228. Aulinas, M., Garcia‐Valles, M., Fernandez‐Turiel, J.L., Gimeno, D., Saavedra, J., & Gisbert, G. (2015). Insights into the formation of rock varnish in prevailing dusty regions. Earth Surface Processes and Landforms, 40, 447–458. Baddock, M.C., Strong, C.L., Murray, P.S., & McTainsh, G.H. (2013). Aeolian dust as a transport hazard. Atmospheric Environment, 71, 7–14. Bard, J.C., Asaro, F., & Heizer, R.F. (1978). Perspectives on the dating of prehistoric Great Basin petroglyphs by neutron activation analysis. Archaeometry, 20, 85–88. Benzerara, K., Chapon, V., Moreira, D., Lopez‐Garcia, P., Guyot, F., & Heulin, T. (2006). Microbial diversity on the Tatahouine meteorite. Meteoritics and Planetary Science, 41, 1259–1265. Black, J.L., MacLeod, I.D., & Smith, B.W. (2017). Theoretical effects of industrial emissions on colour change at rock art sites on Burrup Peninsula, Western Australia. Journal of Archaeological Science Reports, 12, 457–462. Borlina, C.S., & Rennó, N.O. (2017). The impact of a severe drought on dust lifting in California’s Owens Lake area. Nature: Scientific Reports, 7, 10.1038/s41598‐41017‐ 01829‐41597. Brazel, A.J. (1989). Dust and climate in the American southwest. In M. Leinen, M. Sarnthein (Eds.), Paleoclimatology and paleometeorology: Modern and past patterns of global atmo­ spheric transport. Dordrecht, Netherlands: Kluwer Academic Publishers. Brewer, R.L., Gordon, R.J., Shepard, L.S., & Ellis, E.C. (1983). Chemistry of mist and fog from the Los Angeles urban area. Atmospheric Environment, 17, 2267–2270.

Brewer, T.E., & Fierer, N. (2018). Tales from the tomb: the microbial ecology of exposed rock surfaces. Environmental Microbiology, 20, 958–970. Casanova‐Municchia, A., Bartoli, F., Bernardini, S., Caneva, G., DellaVentura, G., Ricci, M.A., BounSuy, T., & Sodo, A. (2016). Characterization of an unusual black patina on the Neang Khmau temple (archaeological Khmer area, Cambodia): a multidisciplinary approach. Journal of Raman Spectroscopy, 47, 1467–1472. Cerveny, N.V., Kaldenberg, R., Reed, J., Whitley, D.S., Simon, J., & Dorn, R.I. (2006). A new strategy for analyzing the chronometry of constructed rock features in deserts. Geoarchaeology, 21, 181–203. Clarkson, P.B. (1994). The cultural insistence of geoglyphs: the Andean and Southwestern phenomena. In Ezzo, J.A. (Ed.), Research along the Colorado River (pp. 149–177). Proceedings from a Symposium Presented at the 59th Annual Meeting of the Society for American Archaeology, Anaheim, California, April. Tucson, AZ: Statistical Research. comte de Buffon, G.L.L. (1749–1804). Histoire Naturelle, générale et particulière, avec la description du Cabinet du Roi. Paris: Imprimerie Yorale. Dong, D., Hua, X., & Zhonghua, L. (2002). Lead adsorption to metal oxides and organic material of freshwater surface coatings determined using a novel selective extraction method. Environmental Pollution, 119, 317–321. Dontsova, K., Balogh‐Brunstad, Z., & Le Roux, G. (Eds.) (2020). Biogeochemical cycles: Anthropogenic and ecological drivers. American Geophysical Union. Dorn, R.I. (1998). Rock coatings. Amsterdam: Elsevier. Dorn, R.I. (2003). Boulder weathering and erosion associated with a wildfire, Sierra Ancha Mountains, Arizona. Geomorphology, 55, 155–171. Dorn, R.I. (2006). Petroglyphs in Petrified Forest National Park: Role of rock coatings as agents of sustainability and as indicators of antiquity. Bulletin of Museum of Northern Arizona, 63, 53–64. Dorn, R.I. (2007). Rock varnish. In D.J. Nash, S.J. McLaren (Eds.), Geochemical sediments and landscapes (pp. 246–297). Oxford, UK: Blackwell Science. Dorn, R.I., Cahill, T.A., Eldred, R.A., Gill, T.E., Kusko, B., Bach, A., & Elliott‐Fisk, D.L. (1990). Dating rock varnishes by the cation ratio method with PIXE, ICP, and the electron microprobe. International Journal of PIXE, 1, 157–195. Dorn, R.I., Gordon, M., Pagán, E.O., Bostwick, T.W., King, M., & Ostapuk, P. (2012). Assessing early Spanish explorer routes through authentication of rock inscriptions. Professional Geographer, 64, 415–429. Dorn, R.I., & Krinsley, D.H. (1991). Cation‐leaching sites in rock varnish. Geology, 19, 1077–1080. Dorn, R.I., & Krinsley, D.H. (2011). Spatial, temporal and geographic considerations of the problem of rock varnish diagenesis. Geomorphology, 130, 91–99. Dorn, R.I., Krinsley, D.H., & Ditto, J. (2012). Alexander von Humboldt’s initiation of rock coating research. Journal of Geology, 120, 1–12. Dorn, R.I., Krinsley, D.H., Langworthy, K.A., Ditto, J., & Thompson, T.J. (2013). The influence of mineral detritus on rock varnish formation. Aeolian Research, 10, 61–76.

Anthropogenic Interactions with Rock Varnish  281 Dorn, R.I., & Meek, N. (1995). Rapid formation of rock varnish and other rock coatings on slag deposits near Fontana. Earth Surface Processes and Landforms, 20, 547–560. Dorn, R.I., & Oberlander, T.M. (1982). Rock varnish. Progress in Physical Geography, 6, 317–367. Dorn, R.I., Stasack, E., Stasack, D., & Clarkson, P. (2001). Through the looking glass: Analyzing petroglyphs and geoglyphs with different perspectives. American Indian Rock Art, 27, 77–96. Dragovich, D. (1987). Weathering of desert varnish by lichens. In: A. Conacher (Ed.), Readings in Australian Geography (pp. 407–412). Proceedings of the 21st IAG Conference, Perth. Dragovich, D. (1993). Distribution and chemical composition of microcolonial fungi and rock coatings from arid Australia. Physical Geography, 14, 323–341. Elvidge, C.D., & Moore, C.B. (1980). Restoration of petroglyphs with artificial desert varnish. Studies in Conservation, 25, 108–117. Engel, C.G., & Sharp, R.S. (1958). Chemical data on desert varnish. Geological Society of America Bulletin, 69, 487–518. Eppard, M., Krumbein, W.E., Koch, C., Rhiel, E., Staley, J.T., & Stackebrandt, E. (1996). Morphological, physiological, and molecular characterization of actinomycetes isolated from dry soil, rocks, and monument surfaces. Archives of Microbiology, 166, 12–22. Esposito, A., Ahmed, E., Cizzazzo, S., Skorski, J., Overmann, J., Holmstrom, S.J.M., & Brusetti, L. (2015). Comparison of rock varnish bacterial communities with surrounding non‐ varnished rock surfaces: Taxon‐specific analysis and morphological description. Microbial Ecology, 70, 741–750. Fleisher, M., Liu, T., Broecker, W., & Moore, W. (1999). A clue regarding the origin of rock varnish. Geophysical Research Letters, 26(1), 103–106. Fortescue, J.A.C. (1980). Environmental geochemistry. A holistic approach. New York. Springer‐Verlag. Frink, D.S., & Dorn, R.I. (2001). Beyond taphonomy: Pedogenic transformations of the archaeological record in monumental earthworks. Arizona–Nevada Academy of Sciences Journal, 34, 24–44. Getty, S.R., Gutzler, D.S., Asmerom, Y., Shearer, C.K., & Free, S.J. (1999). Chemical signals of epiphytic lichens in southwestern North America; natural versus man‐made sources for airborne particulates. Atmospheric Environment, 33, 5095–5104. Gillette, D., Ono, D. and Richmond, K. (2004). A combined modeling and measurement technique for estimating windblown dust emissions at Owens (dry) Lake, California. Journal of Geophysical Research: Earth Surface, 109(F1). doi:10.1029/2003JF000025 Glacken, C.J. (1967). Traces on the Rhodian shore. Nature and culture in western thought from ancient times to the end of the Eighteenth Century. Berkeley. CA: University of California Press. Gleeson, D.B., Leopold, M., Smith, B., Black, J.L. (2018). Rock‐art microbiome: influences on long term preservation of historic and culturally important engravings. Microbiology Australia, 39, 33–36. Goldsmith, Y., Stein, M., & Enzel, Y. (2014). From dust to varnish: Geochemical constraints on rock varnish formation in

the Negev Desert, Israel. Geochimica Cosmochimica Acta, 126, 97–111. Goossens, D., Mees, F., Ranst, E.V., Tack, P., Vincze, L., & Poesen, J. (2015). Rock fragments with dark coatings in slope deposits of the Famenne region, southern Belgium. Belgeo: Revue belge de géographie, 4, 1–14. Goudie, A.S. (2014). Desert dust and human health disorders. Environment International, 63, 101–113. Grangeon, S., Fernandez‐Martinez, A., Claret, F., Marty, N., Tournassat, C., Warmont, F., & Gloter, A. (2017). In‐situ determination of the kinetics and mechanisms of nickel adsorption by nanocrystalline vernadite. Chemical Geology, 459, 24–31. Hassellöv, M., & von der Kammer, F. (2006). Iron oxides as geochemical nanovectors for metal transport in soil–river systems. Elements, 4, 401–406. Hoar, K., Nowinski, P., Hodge, V.F., & Cizdziel, J.V. (2011). Rock varnish: A passive forensic tool for monitoring recent air pollution and source identification. Nuclear Technology, 175, 351–359. Hodge, V.F., Farmer, D.E., Diaz, T.A., Orndorff, R.L. (2005). Prompt detection of alpha particles from Po‐210: another clue to the origin of rock varnish? Journal of Environmental Radioactivity, 78, 331–342. Hooke, R.L., Yang, H., & Weiblen, P.W. (1969). Desert varnish: an electron probe study. Journal of Geology, 77, 275–288. Irit, N., Hana, B., Yifat, B., Esti, K.W., & Ariel, K. (2019). Insights into bacterial communities associated with petroglyph sites from the Negev Desert, Israel. Journal of Arid Environments, 166, 79–82. Krinsley, D. (1998). Models of rock varnish formation constrained by high resolution transmission electron microscopy. Sedimentology, 45, 711–725. Krinsley, D., Ditto, J., Langworthy, K., Dorn, R.I., & Thompson, T. (2013). Varnish microlaminations: new insights from focused ion beam preparation. Physical Geography, 34, 159–173. Krinsley, D., Dorn, R.I., & DiGregorio, B.E. (2009). Astrobiological implications of rock varnish in Tibet. Astrobiology, 9, 551–562. Krinsley, D.H., Dorn, R.I., DiGregorio, B.E., Razink, J., & Fisher, R. (2017). Mn–Fe enhancing budding bacteria in century‐old rock varnish, Erie Canal, New York. Journal of Geology, 125, 317–336. Krinsley, D.H., Dorn, R.I., & Tovey, N.K. (1995). Nanometer‐ scale layering in rock varnish: implications for genesis and paleoenvironmental interpretation. Journal of Geology, 103, 106–113. Krumbein, W.E., & Jens, K. (1981). Biogenic rock varnishes of the Negev Desert (Israel): An ecological study of iron and manganese transformation by cyanobacteria and fungi. Oecologia, 50, 25–38. Kuhlman, K.R., Allenbach, L.B., Ball, C.L., Fusco, W.G., La_ Duc, M.T., Kuhlman, G.M., et al. (2005). Enumeration, isolation, and characterization of ultraviolet (UV‐C) resistant bacteria from rock varnish in the Whipple Mountains, California. Icarus, 174, 585–595. Kuhlman, K.R., Fusco, W.G., La Duc, M.T., Allenbach, L.B., Ball, C.L., & Kuhlman, G.M., et  al. (2006). Diversity of  microorganisms within rock varnish in the Whipple

282  BIOGEOCHEMICAL CYCLES Mountains, California. Applied and Environmental Micro­ biology, 72, 1708–1715. Kuhlman, K.R., McKay, C.P., Venkat, P., La Duc, M.T., Kulman, G.M., & Principe, E. (2006). Microbial fauna associated with rock varnish at Yungay, Atacama Desert, Chile. Astrobiology, 6, 153–154. Kuhlman, K.R., Venkat, P., La Duc, M.T., Kulman, G.M., & McKay, C.P. (2008). Evidence of a microbial community associated with rock varnish at Yungay, Atacama Desert, Chile. Journal of Geophysical Research, 113, G04022. doi:04010.01029/02007JG000677, 002008 Kutovaya, O.V., Lebedeva, M.P., Tkhakakhova, A.K., Ivanova, E.A., & Andronov, E.E. (2015). Metagenomic characterization of biodiversity in the extremely arid desert soils of Kazakhstan. Eurasian soil science, 48, 493–500. Lang‐Yona, N., Maier, S., Macholdt, D.S., Müller‐Germann, I., Yordanova, P., Rodriguez‐Caballero, E., et al. (2018). Insights into microbial involvement in desert varnish formation retrieved from metagenomic analysis. Environmental microbi­ ology reports, 10, 264–271. Langworthy, K., Krinsley, D., & Dorn, R.I. (2010). High resolution transmission electron microscopy evaluation of silica glaze reveals new textures. Earth Surface Processes and Landforms, 35, 1615–1620. Lebedeva, M.P., Golovanov, D.L., Shishkov, V.A., Ivanov, A.L., & Abrosimov, K.M. (2019). Microscopic and tomographic studies for interpreting the genesis of desert varnish and the vesicular horizon of desert soils in Mongolia and the USA. Boletín de la Sociedad Geológica Mexicana, 71, 21–42. Liu, T. (2017). VML Dating Lab. http://www.vmldating.com/ [accessed 1 October 2019]. Liu, T., & Broecker, W.S. (2000). How fast does rock varnish grow? Geology, 28, 183–186. Liu, T., & Broecker, W.S. (2007). Holocene rock varnish microstratigraphy and its chronometric application in drylands of western USA. Geomorphology, 84, 1–21. Liu, T., & Broecker, W.S. (2013). Millennial‐scale varnish microlamination dating of late Pleistocene geomorphic features in the drylands of western USA. Geomorphology, 187, 38–60. Liu, T., Broecker, W., & Stein, M. (2013). Rock varnish evidence for a Younger Dryas wet event in the Dead Sea basin. Geophysical Research Letters, 40, 2229–2235. Lowenthal, D. (2000). George Perkins Marsh: Prophet of conservation. Seattle: University of Washington Press. Lozano, R.P., and Rossi, C. (2012). Exceptional preservation of Mn‐oxidizing microbes in cave stromatolites (El Soplao, Spain). Sedimentary Geology, 255, 42–55. Lu, A., Li, Y., Ding, H., Xu, X., Li, Y., Ren, G., et al. (2019). Photoelectric conversion on Earth’s surface via widespread Fe‐ and Mn‐mineral coatings. Proceedings of the National Academy of Sciences, 116, 9741–9746. Macholdt, D.S., Herrmann, S., Jochum, K.P., Kilcoyne, A.D., Laubscher, T., Pfisterer, J.H., et al. (2017). Black manganese‐ rich crusts on a Gothic cathedral. Atmospheric Environment, 171, 205–220. Macholdt, D.S., Jochum, K.P., Pöhlker, C., Stoll, B., Weis, U., Weber, B., et al. (2015). Microanalytical methods for in‐situ high‐resolution analysis of rock varnish at the micrometer to nanometer scale. Chemical Geology, 411, 57–68.

Malherbe, C., Hutchinson, I.B., Ingley, R., Boom, A., Carr, A.S., Edwards, et al. (2017). On the habitability of desert varnish: A combined study by micro‐Raman spectroscopy, X‐ray diffraction, and methylated pyrolysis–gas chromatography– mass spectrometry. Astrobiology, 17, 1123–1137. Marsh, G.P. (1864). Man and Nature; or physical geography as modified by human action. New York: Charles Scribner. McKeown, D.A., & Post, J.E. (2001). Characterization of manganese oxide mineralogy in rock varnish and dendrites using X‐ray absorption spectroscopy. American Mineralogist, 86, 701–713. Merrell, C.L., & Dorn, R.I. (2009). Indian Writing Waterhole and Tom’s Spring: Two central Idaho petroglyph sites in the Great Basin tradition. American Indian Rock Art, 35, 203–217. Namgung, S., Chon, C.M., & Lee, G. (2018). Formation of diverse Mn oxides: a review of bio/geochemical processes of Mn oxidation. Geosciences Journal, 22(2), 373–381. http:// dx.doi.org/10.1007/s12303‐018‐0002‐7, 1‐9. Northup, D.E., Snider, J.R., Spilde, M.N., Porter, M.L., van de Kamp, J.L., Boston, P.J., Nyberg, A.M., & Bargar, J.R. (2010). Diversity of rock varnish bacterial communities from Black Canyon, New Mexico. Journal of Geophysical Research, 115, G02007. doi:10.1029/2009JG001107 Nowinski, P. (2009). Desert varnish as an indicator of modern‐ day air pollution in southern Nevada, PhD dissertation. Las Vegas: University of Nevada, Environmental Science. Nowinski, P., Hodge, F.W., Gerstenberger, S., & Cizdziel, J.V. (2013). Analysis of mercury in rock varnish samples in areas impacted by coal‐fired power plants. Environmental Pollution, 179, 132–137. Nowinski, P., Hodge, V.F., Lindley, K., & Cizdziel, J.V. (2010). Elemental analysis of desert varnish samples in the vicinity of coal‐fired power plants and the Nevada Test Site using laser ablation ICP‐MS. The Open Chemical and Biomedical Methods Journal, 3, 153–168. Palmer, F.E., Staley, J.T., Murray, R.G.E., Counsell, T., & Adams, J.B. (1985). Identification of manganese‐oxidizing bacteria from desert varnish. Geomicrobiology Journal, 4, 343–360. Paulino‐Lima, I.G., Fujishima, K., Navarrete, J.U., Galante, D., Rodrigues, F., Azua‐Bustos, A., & Rothschild, L.J. (2016). Extremely high UV‐C radiation resistant microorganisms from desert environments with different manganese concentrations. Journal of Photochemistry and Photobiology B: Biology, 163, 327–336. Perel’man, A.I. (1966). Landscape geochemistry (Translation No. 676, Geological Survey of Canada, 1972). Moscow: Vysshaya Shkola. Perel’man, A.I. (1986). Geochemical barriers: theory and practical applications. Applied Geochemistry, 1, 669–680. Perry, R.S., Dodsworth, J., Staley, J.T., & Engel, M.H. (2004). Bacterial diversity in desert varnish (pp. 259–260). Third European Workshop on Exo/Astrobiology, Mars: The search for life. European Space Agency Publications. Potter, R.M. (1979). The tetravalent manganese oxides: clarifica­ tion of their structural variations and relationships and charac­ terization of their occurrence in the terrestrial weathering environment as desert varnish and other manganese oxides,

Anthropogenic Interactions with Rock Varnish  283 PhD dissertation (245 pp.). Pasadena, CA: California Institute of Technology. Potter, R.M., & Rossman, G.R. (1977). Desert varnish: The importance of clay minerals. Science, 196, 1446–1448. Potter, R.M., & Rossman, G.R. (1979). The manganese‐ and iron‐oxide mineralogy of desert varnish. Chemical Geology, 25, 79–94. Ren, G., Yan, Y., Nie, Y., Lu, A., Wu, X., Li, Y., Wang, C., & Ding, H. 2019). Natural extracellular electron transfer between semiconducting minerals and electroactive bacterial communities occurred on the rock varnish. Frontiers in Microbiology, 10. doi: 10.3389/fmicb.2019.00293 Schelble, R.T., McDonald, G.D., Hall, J.A., & Nealson, K.H. (2005). Community structure comparison using FAME analysis of desert varnish and soil, Mojave Desert, California. Geomicrobiology Journal, 22, 353–360. Seong, Y.B., Dorn, R.I., & Yu, B.Y. (2016). Evaluating the life expectancy of a desert pavement. Earth‐Science Reviews, 162, 129–154. Sims, D.B., Hudson, A.C., Keller, J.E., Konstantinos, V.I., & Konstantinos, M.P. (2017). Trace element scavenging in dry wash surficial sediments in an arid region of southern Nevada, USA. Mine Water and the Environment, 36, 124–132. Soleilhavoup, F. (2011). Microformes d’accumulation et d’ablation sur les surfaces desertiques du Sahara. Geomorphologie: relief, processus, environement, 2, 173–186. Spencer, J.E., & Pearthree, P.A. (2015). Fall 2015 Arizona Geological Society field‐trip guide: Northern Plomosa Mountains and Bouse Formation in Blythe Basin. Arizona Geological Survey Open File Report, 15‐10, 1–24. Spilde, M.N., Melim, L.A., Northrup, D.E., & Boston, P.J. (2013). Anthropogenic lead as a tracer for rock varnish growth: implications for rates of formation. Geology, 41, 263–266. Taylor‐George, S., Palmer, F.E., Staley, J.T., Borns, D.J., Curtiss, B., & Adams, J.B. (1983). Fungi and bacteria involved in

desert varnish formation. Microbial Ecology, 9, 227–245. Tratebas, A.M., Cerveny, N., & Dorn, R.I. (2004). The effects of fire on rock art: Microscopic evidence reveals the importance of weathering rinds. Physical Geography, 25, 313–333. van Genuchten, C.M., & Peña, J. (2016). Sorption selectivity of birnessite particle edges: a d‐PDF analysis of Cd(II) and Pb(II) sorption by δ‐MnO2 and ferrihydrite. Environmental Science: Processes and Impacts, 18, 1030–1041. Vázquez‐Ortega, A., & Fein, J.B. (2017). Thermodynamic modeling of Mn(II) adsorption onto manganese oxidizing bacteria. Chemical Geology, 464, 147–154. Viles, H.A. (2011). Microbial geomorphology: a neglected link between life and landscape. Geomorphology, 157, 6–16. von Humboldt, A. (1812). Personal narrative of travels to the equinoctial regions of America during the years 1799–1804 by Alexander von Humboldt and Aime Bonpland. Written in French by Alexander von Humboldt V. II (Translated and Edited by T. Ross in 1907). London: George Bell & Sons: von Werlhof, J. (1989). Spirits of the Earth. A study of earthen art in the North American deserts, Volume 1, The north des­ erts. El Centro, CA: Imperial Valley College Museum. Waldman, J.M., Munger, J.W., Jacob, D.J., Flagan, R.C., Morgan, J.J., & Hoffmann, M.R. (1982). Chemical composition of acid fog. Science, 218, 677–680. Whitley, D.S. (2000). The art of the Shaman: Rock art of California. Salt Lake City: University of Utah Press. Whitley, D.S. (Ed.), 2001). Handbook of rock art research. Altamira Press. Whitley, D.S., & Loendorf, L.L. (1994). Off the cover and into the book. In D.S. Whitley, L.L. Loendorf (Eds.), New light on old art: Recent advances in hunter–gatherer rock art research (Monograph 36, pp. xi–xx). Los Angeles, CA: UCLA Institute of Archaeology. Xu, X., Li, Y., Li, Y., Lu, A., Qiao, R., Liu, K., Ding, H., & Wang, C. (2019). Characteristics of desert varnish from nanometer to micrometer scale: A photo-oxidation model on its formation. Chemical Geology, 522, 55–70.

14 Cycling of Natural Sources of Phosphorus and Potassium for Environmental Sustainability Biraj B. Basak1, Ashis Maity2, and Dipak R. Biswas3

ABSTRACT Assurance of adequate supply of nutrient to sustain yield with minimum environmental consequence is a basic requirement of any sustainable production system. There is a growing international demand for organically produced food, which can be addressed only thorough a sustainable organic production system. Both conventional and organic cultivation systems need an adequate supply of phosphorus (P) and potassium (K). Managing P and K requirement in the organic cultivation system is a challenge since there are limited sources approved under existing norms and guidelines. Therefore, there is, a need to examine the potential of organic P and K sources to meet the crop demand in an organic production system. Recycling of on‐farm resources and utilization of low‐cost naturally available materials through ecofriendly approaches can address this issue. In this chapter, we summarize the existing knowledge and recent advances of P and K sources approved in organic farming guidelines and their possible best management practices. The scope of organic sources (e.g., crop residue, plant and animal byproducts, manures and composts, rocks and mineral powders, and microbes) alone or in combination to supply P and K to meet crop demand is also discussed. 14.1. INTRODUCTION Sustainability of a production system implies optimum yields that can be maintained with minimum or acceptable environmental consequence through sustainable management tools (Tilman et al., 2002). It is well known that any farming always leads to dimininution of natural soil fertility because a certain portion of total plant nutrient supply is removed. At the same time, there is no denying that application of fertilizers is indispensable in order to sustain agricultural production and meet global food requirements. Synthetic chemical fertilizers such as urea, di‐ammonium phosphate (DAP), superphosphates, 1  ICAR‐Directorate of Medicinal and Aromatic Plants Research (DMAPR), Anand, India 2  ICAR‐National Research Center for Pomegranate, Solapur, India 3  Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute (IARI), New Delhi, India

and muriate of potash are not allowed in organic production system (Codex Alimentarius Commission, 2001). The soluble chemical fertilizers can supply nutrients quickly, which feed the plant directly through bypassing the natural processes (weathering, mineralization, and solubilization) in soil. In an organic production system, nutrient supply to crop plants is sustained through r­ecycling and management of natural resources. It is very important to mention here that an organic production is the only sustainable farming practice legally approved in many countries (Diacono & Montemurro, 2010). So, it remains a great challenge to recycle and manage the nutrient supply to meet the continuous demand of crop plants. Application of plant nutrient under an organic cultivation system is viewed differently to that in a conventional cultivation system. Shifting from “conventional” to “organic” agriculture is not just a replacement of synthetic chemicals with organics rather it requires a greater level of management effort and skill (Hue & Silva, 2000). Organic wastes generated from agricultural activities,

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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geological mineral deposits, and other natural resources like seaweeds and microbes have the potential to supply plant nutrients and maintain soil fertility status (Nick & Bradley, 1994). However, it is an important concern that in the organic cultivation system, phosphorus (P) and potassium (K) may be mined from soil reserves because of the paucity of acceptable sources of these nutrients. Different organic farming bodies recognize this issue and a list of allowed nutrient sources and soil amendments has also been published (UKROFS, 1999). The important organic materials listed for organic farming are farm wastes, crop residues, animal manures, seaweeds, and some relatively insoluble mined products like RP and silicate minerals. These nutrient sources with low solubility are supposed to supply balanced plant nutrition through the action of roots, soil microbes, and natural processes of weathering minerals (Goulding, 2000). However, in certain circumstances relatively more soluble sources such as potassium sulfate (K2SO4) from natural deposits are allowed (Basak & Sarkar, 2017). The only concern regarding the use of these sources is that they need to be approved by the organic farm certifying bodies. Rising costs of soluble P and K fertilizers is a major concern, particularly in developing countries of South East Asia, Africa, and South America, where no significant ore deposits are available for chemical P and K fertilizer production (FAO, 2015). Under such circumstances, managing P and K requirement by recycling all possible natural sources will provide not only an alternative approach of sustainable farming but also reduce the cost of cultivation by avoiding chemical fertilizers. Furthermore, this approach also adds environmental benefits including an increase in biodiversity, which in turn improves soil quality and reduces pollution (Cobb et al., 1999). Thus, there is a need for a sustainable supply of P and K by recycling natural sources and to minimize adverse environmental impacts. Several studies have shown an increase in the concentration of total P in organic production systems (Lotter, 2003); however, other studies have shown a decrease in the mineral or available P pools (Gosling & Shepherd, 2005; Mäder et al., 2002). Although some organic farms are found to have a P deficit, others are likely to have a P surplus due to the continuous application of manures and compost in terms of N requirement of the crop (Mikkelsen, 2008). So, P and K management in organic agriculture should be focused on crop requirement. namely P and K availability in N amendments, soil test recommendation, and best management practices. In this chapter, we enlist the alternative sources approved under the regulation and guidelines of organic agriculture and also study their P and K supplying potential. We also assess the sustainability of the alternative source in the context of their availability and P and K supplying capacity. The prospects

and problems related to reliance on organic and natural minerals and possible environmental consequences have also been considered. 14.2. ORGANIC PRODUCTION SYSTEM The basic philosophy of nutrient management in organic agriculture emphasizes sustaining agricultural productivity with minimum inputs (Elmaz et al., 2004). Organic agriculture is considered the best known alternative to the conventional method, which has ­witnessed the ill effects produced by chemical agriculture. However, the basic understanding of nutrient management practices in both the organic and conventional production systems has many common objectives. Organic agriculture has the potential to provide benefits in terms of environmental protection, conservation of non‐renewable resources, and improved food quality. They mainly differ in respect to the source used for plant nutrition. However, the fundamental principle of supporting soil fertility and plant nutrition remains the same (Stockdale et al., 2002). The main objectives of organic nutrient management are to: (a) work within natural systems and cycles; (b) maintain or improve long‐term soil fertility status; (c) obtain optimum use of renewable resources; and (d) produce food that is safe with optimum quality. The idea of organic nutrient management is to feed plants organically, with natural minerals, and with the help of biological agents instead of using synthetic fertilizers. Thus organic farming is considered a comprehensive management approach that avoids or largely excludes the use of synthetic chemical fertilizers (Lampkin et  al., 1999). In the wider sense, organic nutrient management is: recycling of natural mined sources, maintenance of soil fertility, optimum use of inputs, and environmental sustainability (Diacono & Montemurro, 2010; Liebig & Doran, 1999). It relies on carbon‐based nutrient sources (e.g., crop residues, manures, and composts), unprocessed minerals (RP and silicate minerals), seaweeds, and bio‐agents (Kirchmann et  al., 2009). The complete exclusion of synthetic fertilizers is the pillar of organic nutrient management practices, which makes it fundamentally different to conventional practices. 14.3. CERTIFICATION AND REGULATIONS The most important aspect in the modern era of organic farming is following a certification program consistsing of standards (rules), inspection (checking whether the rules are implemented), and certification (judgment). Only by following this certification program will organic farming be distinguished from other methods of sustainable agriculture. Certification programs vary among countries or regions because of differences in environmental, climatic,

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  287

Phosphate rock and guano

Plant uptake

advised to consult with the representative or a subject matter specialist of the certifying body when planning to use particular nutrient sources. 14.4. PHOSPHORUS AND POTASSIUM CYCLE IN ORGANIC SYSTEMS The ultimate objective of nutrient management in an organic production system is to produce food in a more environmentally sustainable system that takes advantage of internal nutrient cycling and reduces losses (Stockdale et  al., 2002; Watson et  al., 2002). Nutrient inputs to organic production systems mainly focus on carbon‐based sources (e.g., crop residues, manures, and composts) and unprocessed mineral sources (e.g., RP, guano, and silicate minerals). Soil processes involved in P cycling and making P bioavailable are similar in both conventional and organic managed system (Figure 14.1), but the relative significance of P cycling may differ between the two systems (Stockdale et al., 2002). Soil properties like soil organic matter content, microbial activity, microbial community structure, soil aggregation, water‐holding content, and soil chemistry, which could affect P cycling and which is potentially influenced by the organic production practices. In agricultural systems, P is removed through crop harvest and creates a P deficiency in soil without P addition. So, for any sustainable system it is necessary to add P to avoid such deficiency. As organic agriculture is based on minimum use of input, it is advised that producers replace the same P as removed in the harvested crop (Gosling & Shepherd, 2005). In organic agriculture the P cycle includes addition, export, cycling, and transformation within the soil. The P cycle in a conventional system involves the addition of chemical fertilizer, which is not in the scope of organic agriculture. However, Animals

Residues Primary P minerals

Mineralization

Solubilization

Wastes

Organic P

Soil microbes Desorption

Adsorbed P Adsorption

Plant available P H2PO4–/HPO4–

Precipitation Dissolution

Leaching

Figure 14.1  Proposed phosphorus cycling in organically managed system.

Secondary P minerals

Root zone

social, and cultural factors. Globally, there are more than 60 standards that are governed by a variety of international organizations (Heckman, 2006). The International Federation of Organic Agriculture Movements (IFOAM) is the worldwide umbrella organization for organic agriculture. The IFOAM maintains an organic farming standard and also provides an accreditation and certification service. The Food and Agriculture Organization (FAO) and World Health Organization (WHO) of the United Nations have a joint venture named Codex Alimentarius guidelines for the maintenance of organic standards. In the United States, the US Department of Agriculture (USDA) National Organic Program (NOP) is responsible for the maintenance and regulation of organic standards. In Europe, the setting and maintenance of organic standards are governed by EU organic regulation EC20192/91. Some nations have developed their own organic standards, for example, Argentina Standards for Organic Production, Australia National Standards, Canadian General Standard Boards (CGSB), and Japan Agriculture Standards (JAS) for organics. Unlike the conventional system, synthetic chemical fertilizers are not allowed in organic production systems. The basic principle of the existing regulations allows only unprocessed products for supplying nutrients to crops. Different international certifying bodies for organic products (CGSB, 2006; NOP‐USDA, 2007) detail the sources allowed in the organic production system. However, there are differences in guidelines among organizations regarding materials allowed in organic production systems. Each individual organization has its own interpretation of acceptance and rejection criteria for nutrient sources in an organic production system (Heckman, 2006). So, the materials or products used as nutrient sources should be approved under organic regulations (Caplan, 1992). Growers and producers are

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addition of an unprocessed mineral source (e.g., RP, guano) can serve as an external input in organic agriculture. Recycling crop residue, introduction of cover crop, and organic matter management in soil are some important tools that play a significant role in P management in organic agriculture. Management of P in organic agriculture should be emphasized in the cropping system, crop rotation, and cover crop, which may alter P cycling. Organic matter content, decomposition of cover‐crop residue and increase in P‐solubilizing microbes effectively improve P availability in an organic production system. The chemical environment of the soil controls the equilibrium level of dissolved P in the soil solution (Nelson & Logan, 1983). If soil is low in P, both phosphate solubilizing microbes (PSM) and arbuscular mycorrhizal fungi (AMF) can help increase P uptake from the native P present in soil. Like P, the organic agriculture K cycle also includes addition, recycling, and transformation within the soil (Figure 14.2). Management of K in organic agriculture is not much different from the conventional practice, except some of the approved nutrient sources. In organic agriculture, producers need to rely on only untreated products for K supply, whereas in conventional practices there is a  wide range of fertilizers available to meet the crop demand. However, there are several options existing for successful K management in organic production systems (Askegaard et al., 2004). There are many silicate minerals (e.g., feldspar, mica, granite, glauconite) and untreated K salts (e.g., potassium sulfate) that can be used as an external K source in organic agriculture (Mikkelsen, 2008). However, K release from the silicate minerals is slow as compared to chemical fertilizer. In this context,

different biointervention strategies (K solubilizing microbes and composting) can be utilized to increase K release from silicate minerals (Basak et  al., 2017). The farms that produce both livestock and crops differ greatly from the farms that strictly produce crops for off‐farm sale. In the mixed livestock/crop systems, animal nutrition is generally the first priority and the residual manure is returned to surrounding cropland. Plant roots have been shown to enhance soil mineral weathering by depleting rhizosphere K, causing a shift in the K equilibrium. This shift can hasten the natural processes and increase the rate of clay transformations (Hinsinger et  al., 1993). Subsoil K reserves may be important for some crop rotation systems where deep‐rooted plants can extract K, which may be subsequently used by shallow‐ rooted crops (Witter & Johansson, 2001). Some K is lost in the process of soil erosion, runoff, and leaching to groundwater. When plants are harvested and taken away entirely from the soil, the nutrients including K go to wherever the plant matter goes. Thus, recycling crop residue in soils is needed for K balance. 14.5. ORGANIC AND NATURAL SOURCES 14.5.1. Crop Residues Crop residues are important sources of organic matter that can be returned to the soil for nutrient recycling. Globally, the total crop residue production is estimated at 3.8 billion tons per year, of which 74% are from cereals, 8% from legumes, 3% from oil crops, 10% from sugar crops, and 5% from tubers (Lal, 2005). Besides C, crop residues contain significant amounts of P and K, the

Animals

Plant uptake

Untreated soluble salts

Wastes

Residues Mineralization

Soil microbes

Readily available K

Root zone

Solution K Rapid Dissolution

Minimal loss

Exchangeable K

Mineral K (feldspar, mica etc.)

Leaching

Figure 14.2  Proposed potassium cycling in organically managed system.

Nonexchangeable K Weathering

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  289

content of which varies among crop species depending on the fertility of the soil (Table  14.1). It is, however, difficult to predict how much of the P and K in the ­residues will become available to crops during a given time because of the complex processes governing residue decomposition and nutrient release. In addition, the nature of crop residues and their management can ­significantly affect the amount of nutrients (P and K) available for subsequent crops (Kumar & Goh, 1999). Organic matter in residues contains significant quantities of organic P and orthophosphate is released into the soil solution during mineralization. In addition, humic substances and organic acids are generated during the decomposition of crop residue, improving the native soil P availability (Iyamuremye et al., 1996). Crop residues may contain some plant nutrients in a soluble inorganic form (e.g. K+, SO42−), or associated with readily mineralizable organic constituents. Straw and crop residues, particularly the cereal crop residues, generally contain significant amounts of K that can be effectively recycled as a source of K (Basak & Sarkar, 2017).

Table 14.1  Phosphorus and potassium content in different farm waste Nutrient content (% on oven dry basis) Crop residues Rice straw Wheat straw Maize stalk Sorghum Par millet Alfalfa hay Legume straw Groundnut Other oil seeds Sugarcane bagasse Tobacco stem

Phosphorus (P2O5)

Potassium (K2O)

0.18 0.16 0.18 0.23 0.75 0.60 0.36 0.23 0.21 0.18 0.50

1.75 1.18 1.35 2.17 2.50 2.10 1.64 1.37 0.93 1.20 5.00

Note: From Basak & Sarkar (2017).

14.5.2. Plant and Animal Based Products The byproducts of plant, food, and fiber industries and agricultural wastes contain a significant amount of plant nutrients that can be used as a source of P and K (Table 14.2). This also returns the nutrients to lands that might otherwise be wasted. Biochar is produced by the thermal decomposition of biomass in an oxygen‐depleted atmosphere called pyrolysis. The fresh biochar has the potential for sufficient nutrient supply, particularly significant amounts of P and K (Hue & Silva, 2000). Hardwood ash has served as one of the earliest sources of K for building soil fertility. Wood ash contains significant amounts of P (1.7%) and K (5–7%), which are quickly available to plants. Seaweed‐derived liquid extracts have also been commercially available worldwide (Craigie, 2001) and extensively used as fertilizer by horticulturists, gardeners, farmers, and orchardists to enhance plant growth and fruit yields. Seaweeds contain 1.5–2.0% P and 3–10% K on a dry weight basis. Most of the seaweed fertilizers come from kelp that has been harvested, dried, and ground. Kelp meal is suitable for application directly to fields for cultivation of various crops including grains, fruits, and vegetables, since these seaweed extracts are acceptable among organic farmers (Sullivan, 2000). Oilcakes produced as a byproduct from oil extraction mills contain not only N but also substantial amounts of P and K (Table 14.2). Press mud is an important source of P and K derived from the sugarcane industry and can be used both directly as well as after value addition through composting (Dotaniya et al., 2016). Animal‐based products like bone, blood, and fish meal are also promising sources of P and K for the organic production system. Recycling these materials as a plant nutrient source is traditionally practiced in many countries. Bone meal, which is produced by grinding raw animal bones, is one of the oldest P sources used in agriculture. Bone meal is often acceptable as an organically approved P source, however, it is relatively costly (Parnes, 1986) and also there are limited supplies (Bekele & Hofner, 1993). It contains significant amounts of P (12%

Table 14.2  Average P and K contents of plant and animal based products Nutrient content (% on oven dry basis) Products

Phosphorus (P2O5)

Potassium (K2O)

Nutrient availability

Straw biochar Wood ash Seaweed meal Bone meal Blood meal Fish meal Press mud Oil cakes

0.34–1.13 1.7 0.2–1.3 12.0 2.0 3.0 7.00 0.80–1.80

0.18–0.48 5.6 3.0–10.0 0.50 – 2.0 5.00 1.20–1.80

Slow Quick Slow Slow Moderately Slow Moderately Moderately

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P2O5) that are more soluble than RP, but is much less soluble than conventional P fertilizers (Dorozhkin, 2007). These plant‐ and animal‐based products are well recognized in organic farming and their utilization is authorized for organic production with some restrictions (Codex Alimentarius Commission, 2001). 14.5.3. Manures and Compost Manures and composts are the most common products of aerobic and anaerobic digestion processes of various organic wastes. These organic materials are extremely variable in terms of P and K content due to their raw material source and handling (Table 14.3). The manures and composts are usually good sources of P and K with high plant availability. Despite having bulky organic matter in the manure and compost, the majority of P is Table 14.3  Summary of phosphorus and potassium content in animal manures and compost Nutrient content

Manures and compost Animal manures dairy beef cattle swine poultry sheep goat horse farmyard Green manuresb Compostc Vermicompostd

Pt (%)

Pi (% of Pt) Po (% of Pt) K (%)

0.28–1.83 0.25–1.25 0.39–1.87 0.86–3.04 0.72–1.07 1.03–1.31 0.54–1.24 0.5 0.5–0.6 0.6–0.9 0.8–1.5

28–96 48–91 51–92 32–48 48–81 36–44 45–49 – – – –

a

 8–61  8–44  8–37 14–68 11–23 18–33 11–25 – – – –

2.1 2.5 1.2 2.9 1.0 0.45 1.0 1.0 1.8–2.5 0.5–1.0 0.7–1.9

Pt, total P; Pi, inorganic P; Po, organic P.  Zhongqi et al., 2016; b  Caplan (1992) and Hue & Silva (2000); c  Caplan (1992); d  Bhat et al. (2018). a

present in inorganic form and readily available to plants. Inorganic P accounts for 75–90% of the total P present in manure and compost (Eghball et  al., 2002). It is also found that P uptake from manure and compost was equal to or greater than P uptake from commercial P fertilizers. Manure‐based P has less impact on soil P availability and plant uptake within a short period of time as compared to commercial P fertilizer, and it is generally considered that 70% of total P from manures and composts may be available to plants. Therefore it is suggested that this information is taken account of during P recommendation in organic farming (Eghball et  al., 2002). Manures and compost used intensively in fruit and vegetable cultivation significantly influence P balance in the soil. Animal manures and compost contain N, P, and K, however, the N/P/K ratio in most manures and compost is less than plant uptake. This phenomenon indicates that application of manures and compost to meet the N requirement leads to excess P supply (Mikkelsen, 2008). Excess application of P does not affect crop growth and yield, but it is reflected in soil test P values. Over application of P, as indicated by high soil test P values, can increase the risk of environmental pollution (Correll, 1998), as discussed in a later section. Repeated applications of large amounts of manure can result in K accumulation in soils, which may result in luxury consumption of K in the plant. Prior to blanket application of manures and composts, analysis of soil chemical composition and soil test recommendation are therefore necessary in order to obtain maximum benefit. 14.5.4. Natural Mineral Deposits Rocks, minerals, and natural geological deposits are the most potential sources of P and K that can be exploited in an organic production system (Table 14.4). Rock phosphate (RP), the most commonly used P source that has been practiced for over 100 years, continues to be an important resource in developing countries due to it being a cheap source of P (Zapata & Roy, 2004).

Table 14.4  Average P and K content in natural mineral deposits Nutrient content (%) Products Major P source Major K source

Rock phosphate Guano, Peru Guano, Bat Potassium sulfate Sul–Po–Mag Green sand Granite dust Feldspar Mica

Phosphorus (P2O5)

Potassium (K2O)

Nutrient availability

Reference

17–16 8 4 – – – – – –

– 2 2 40 18 6.0 1.9 2–12 8–10

Very Slow Moderately Moderately Quickly Moderately Very slow Very slow Very slow Very slow

Hue & Silva (2000) Zapata & Arrillaga (2002) Zapata & Arrillaga (2002) Mikkelsen (2008) Mikkelsen (2008) Heckman & Tedrow (2004) Coreonos et al (1996) Sauchell (1963) Basak & Biswas (2012)

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  291

Rock phosphate contains relatively high P (> 15% P2O5), however, it is a slowly soluble P source. Therefore, some basic aspects must be considered when RP is directly applied as P source in agriculture, including RP properties, soil properties, plant species, and management practices (Khasawneh & Doll, 1978; Rajan et al., 1996). Decreasing particle size of RP to 150 μm increases the chemical reactivity. but further decreasing particle size does not result in additional agronomic benefit (Basak, 2018b). Origin of RP is also important in determining P supplying capacity. Sedimentary RPs have more surface area and higher carbonate substitution than igneous origin. These properties increase P solubility and make the ­sedimentary RPs more suitable for direct application (Van Kauwenbergh & McClellan, 2004). Concentration of H+ (pH) and Ca2+ ions plays an important role in RP dissolution in the soil. In neutral to alkaline soils, the P use efficiency of RP is very low, however, it is effective in acidic and highly weathered tropical soils. Direct application of finely ground RP and hydroxyapatite was found to be a cheaper alternative to soluble P fertilizer in a tropical environment (Basak, 2018b; Montalvo et  al., 2015). Favorable environmental condition exists in the surface layer of highly weathered soil of humid tropics for satisfactory rates of RP dissolution (Sale & Mokwunye, 1993). Application of RP as controlled release of P fertilizer may provide a match between P solubility and crop demand, reducing the P loss (leaching and fixation) in highly weathered tropical soils (Chien & Menon, 1995). Soil with low pH (high H+concentration) and Ca2+ ion concentration tends to maintain high apatite dissolution, thereby, increasing P release (Rajan et  al., 1996). Rock phosphate was found as an effective P source in acidic soils (pH < 6) particularly in plantation and pasture crops (Sale & Mokwunye, 1993). Under certain situations RP therefore can be an effective P source, and its relative agronomic efficiency is comparable to application of chemical P fertilizers (Chien & Menon, 1995). Guano, a  less commonly known P source, has a significant P content (4–9%). Guano originates from continuous deposits of bird or bat excreta in a low rainfall environment. Struvite (magnesium ammonium phosphate) has been found as a primary P mineral in guano (Cullen, 1988; Grover et  al., 1997) that dissolves slowly in soil. Guano as a P fertilizer produces variable agronomic responses. In some studies guano is at par with soluble P fertilizer (Aliyu & Kuchinda, 2002), whereas it has also been found to be less effective (Zapata & Arrillaga, 2002). There are several silicate mineral deposits that can be used effectively as K fertilizers in organic production systems (Basak & Sarkar, 2017). Like P, there is a wide variation regarding nutrient contents and solubility of K among different K sources (Table 14.4). Natural sources of potassium sulfate and potassium magnesium sulfate

(langbeinite) contain a significant amount of K with higher water solubility and they can be useful in organic agriculture, whereas silicate minerals like greensand, feldspar, granite, and micas contain less K with lower solubility (Harley & Gilkes, 2000; Manning, 2010). Silicate minerals have been evaluated as K sources in several studies and greenhouse experiments (Basak et al., 2017). These minerals are considered as a slow released K source, as plants are able to take up the limited amount of K from biotite, phlogopite, muscovite, and nepheline (Sparks, 1987). In organic farming, the source must release K at a sufficient rate to meet the demand of the crop. However, there is no in situ technique to predict the release rate. The rate of K release from silicate minerals is quite variable and is often inadequate to supply the demand of the crop (Basak et al., 2017; Manning, 2010). Despite a wide difference, these sources can be used in organic agriculture considering the prevalent soil conditions and crop requirements. The silicate minerals like mica (Basak & Biswas, 2012; Hinsinger et al., 1993), feldspar (SanzScovino & Rowell, 1988), granite (Bolland & Baker, 2000), nepheline synite (Mohammed et al., 2014), and greensand (Heckman & Tedrow, 2004) have been used as sources of K in several studies, and it is found even more effective than chemical fertilizers in highly weathered soils (Leonardos et al., 1987). Rock dusts and mineral powders have occasionally been used as a source of K in isolated local agronomic trials (Basak et al., 2018; van Straaten & Chesworth, 1985); due to limited natural availability, the use of materials has been restricted to the local people only. 14.6. RECYCLING THROUGH BIOLOGICAL INTERVENTIONS Application of rocks and mineral powder as such is not as effective as water‐soluble fertilizer and agronomic effectiveness is also very low (Basak, 2018a; Basak et al., 2018). Interventions are therefore needed to speed up the P and K release rates. Biological intervention means involvement of bioagents for specific purposes in any process, i.e., specific to the involvement of plants, microbes, and composting process in improving nutrient availability of the natural sources. In order to improve immediate P and K availability from minerals, different bioagents (plants and microbes) and biological processes (composting) may be used in an organic production system. Thus, biological intervention can be an effective means to improve P and K availability from natural sources for plant nutrition. Moreover, biointerventions are green practices that reduce chances of pollution and energy consumption while improving nutrient availability at the same time (Basak et al., 2017).

292  BIOGEOCHEMICAL CYCLES

14.6.1. Microbial Interventions Microbial inoculants, also known as biofertilizers, are widely used in agriculture, particularly in an organic production system for improving nutrient availability and mobilization in soil. There are well known microbial species, including bacteria and fungi, capable of solubilization and mobilization of P and K from insoluble minerals (Table 14.5). The P biofertilizer includes phosphate solubilizing bacteria (PSB) and fungi associated with P solubilization through releasing low molecular weight organic acids (Kpomblekou & Tabatabai, 1994). Phosphate solubilizing microbes (PSM), including bacteria and fungi, are well known for dissolving P from mineral and relatively insoluble phosphate compounds. Endomycorrhiza, including well known arbuscular mycorrhizal fungi (AMF), aid P nutrition through an improvement of P availability as

well as an increase in its mobility (Bolan, 1991). Through AMF the plant can access P from the soil zone that is not exploited by the root system. There are a number of studies of P being released by microbial interventions from insoluble P minerals, particularly from RP. Table 14.6 summarizes the P solubilization ability of several microbial species from various insoluble P substrates (tricalcium phosphate, hydroxyapatite, and RP). Although there is no quantitative estimation of P release from different sources, the microbes are found to be very promising in increasing P bioavailability. Rock phosphate was found as an efficient source of P fertilizer in neutral to alkaline soils if PSMs are used as inoculants (Arora & Gaur, 1979; Gaur, 1988). Application of RP in combination with PSB was found superior to RP alone and comparable to soluble P fertilizers (Kumari & Phogat, 2008). Arbuscular mycorrhizal fungi are well known for P mobilization from insoluble P minerals under P deficient condition.

Table 14.5  Nature and function of microbes plays important role in P and K mobilization Group

Microorganisms

Functions

Phosphate solubilizing microbes (PSM)

Bacteria: Bacillus megaterium var. phosphaticum, Bacillus circulans, and Pseudomonas striata Fungi: Penicillium sp, Aspergillus awamori AMF: Glomus sp., Gigaspora sp., Scutellospora sp. and Sclerocystis sp.

Increase solubilization of insoluble P from native and applied sources; subsequently improve P avilability

Phosphate mobilizing microbes

Improve P bioavailability through increasing P mobilization

Ectomycorrhizae: Potassium mobilizing microbes (KSM)

Laccaria sp., Pisolithus sp., Boletus sp. and Amanita sp. Bacteria: Bacillus edaphicus, Bacillus mucilaginosus, and Bacillus circulans Fungi: Aspergillus niger, Aspergillus fumigates, and Penicillium purpurogenum

Increase K solubilization from native and applied silicate minerals; subsequently improve K availability

Table 14.6  Phosphorus mobilization by microbial intervention from insoluble mineral phosphates Group

Microorganisms

Mineral

Reference

Bacteria

Bacillus megaterium Bacillus polymyxa Bacillus subtilis Pseudomonas striata Aspergillus awamori Aspergillus niger A. japonicus Penicillium pinophilum Glomus intraradices Glomus constrictum

TCP, HAP, and RP TCP, HAP, and RP TCP TCP, HAP, and RP DCP and TCP RP Indian RP RP RP RP

Arora & Gaur (1979) Arora & Gaur (1979) Swain et al. (2012) Arora & Gaur (1979) Jain et al. (2012) Vassileva et al. (1997) Singal et al. (1994) Maity et al. (2014) Duponnois et al. (2005) Omar (1997)

Fungi

VAM

DCP, dicalcium phosphate; TCP, tricalcium phosphate; HAP, hydroxyapatite; RP, rock phosphate.

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  293 Table 14.7  Potassium solubilization from silicate minerals through microbial intervention Mineral

Microorganisms

Outcome

Reference

Feldspar Mica and feldspar Muscovite, microcline, and orthoclase Feldspar and illite

Bacillus cereus Bacillus mucilaginsus Bacillus mucilaginsus

Badr et al. (2006) Liu et al. (2006) Sugumaran & Janarthanam (2007) Lian et al. (2008)

Alkaline ultramafic rock powder Waste mica

Yeast (Torulaspora globosa) Bacillus mucilaginsus

Increased K release from feldspar K release increased by 66% from mica Significant amount of potassium released from different minerals Drastically increased K release from the K minerals 38% of total K released from rock powder

Biswas & Basak (2014)

Muscovite

Penicillium purpurogenum

About 34% increase in available K content after 28 days 30% K dissolved from muscovite

Aspergillus fumigatus

Similarly, K can be released from the mineral structure by the action of a group of microorganisms (Table 14.7). These microbes are quite efficient in releasing K from minerals by producing low molecular weight organic acid (Biswas & Basak, 2014). These microbes, popularly known as K‐solubilizing microorganisms (KSM), include mainly bacteria and a few fungi. Potassium solubilizing bacteria (KSB) are also known as biological potassium fertilizer (BPF). The BPF is quite popular in China and South Korea (Basak et al., 2015) where KSB plays a vital role in the conversion of native soil K into readily available form for plant uptake (Sheng & He, 2006; Sheng et al., 2002). K‐solubilizing microbes are able to solubilize “unavailable” forms of K‐bearing minerals, such as micas, illite, and orthoclase, by excreting organic acids, which either directly dissolves K from rock or chelates silicon ions to bring the K into solution (Basak & Biswas, 2009; Sheng et al., 2002). A wide range of KSM, including bacteria (Bacillus mucilaginosus, Bacillus edaphicus, Bacillus circulans, Bacillus cereus) and fungus (Aspergillus fumigatus, Aspergillus terreus, Penicillium purpurogenum), was reported to release K from silicate minerals (Basak et al., 2017). The dissolution of a variety of silicate minerals by application of KSB, namely Bacillus mucilaginosus and Bacillus edaphicus, has been found most effective in releasing K (Lin et al., 2002; Sheng & He, 2006). It has been found previously that application of mica alone did not respond well, but bioactivation with KSB was found to be promising as a source of K for the plant (Biswas & Basak, 2014). The AMF is also able to release K from mineral structures by releasing H+, CO2, and organic acids in the surrounding environment (Basak et al., 2017). The AMF mostly improved P bioavailability (Bolan, 1991), but was also able to solubilize K from phlogopite, biotite, feldspar, and other silicate minerals (Jongmans et al., 1997; Paris et al., 1995). However, the K release rate is usually very slow and occurs only in a K‐limiting environment.

Rosa‐Magri et al. (2012)

Song et al. (2015)

14.6.2. Composting Composting of organic matter with rocks and mineral powder can release nutrients and improve the P and K availability of the system (Table 14.8). Mineral structure can be disintegrated during the composting process because of the production of organic acids, rendering more available P and K for plant nutrition. This is because of loss of mass of organic materials as CO2 evolution during the composting process, which produces a low pH environment and accelerates the P and K released from the mineral structure. Composting of organic matter (crop residue and animal manures) with RP is well known to increase P dissolution (Adhami et  al., 2014; Biswas et al., 2009; Mahimairaja et al., 1994; Reza et al., 2017). In the process of composting, many organic and mineral acids are produced as a result of organic matter decomposition. Further, much CO2 is produced during the decomposition of organic matter, resulting in the formation of carbonic acids which dissolve P from the RP (Hellal et al., 2012).These organic acids are actually low molecular weight, having one or more carboxylic group carrying varying negative charges. This negative charge allows complexation of metal cations (Ca and Mg) and release of anions (P) into the solution (Kpomblekou & Tabatabai 2003). Further, organic acid anions can increase P availability from RP by ligand exchange reaction through competition with phosphate anions that are adsorbed to the surface of iron and aluminium oxides (Biswas et al., 2009). In the presence of organic acids (oxalic and acetic), P dissolution from the insoluble P mineral can increase several times, however, P release varies widely with the type and concentration of organic acids (Basak, 2018b). During composting, P is solubilized from RP and transformed into available forms (water and citrate soluble P), resulting in a P‐enriched product (Biswas et al., 2009; Basak, 2017). Significant build up of microbial P and phosphatase enzymes activity improves P mobilization from RP

294  BIOGEOCHEMICAL CYCLES Table 14.8  Mobilization of P and K from insoluble minerals through composting process Mineral

Organic material

Outcome

Reference

Francolite and rock phosphate Rock phosphate

Poultry manure

20% francolite and 27% rock phosphate dissolved Significant P mobilization from RP

Mahimairaja et al. (1994)

Rock phosphate Rock phosphate Rock phosphate Rock phosphate Feldspar Waste mica

Quartz powder

Rice straw and fresh cow dung Rice straw and farmyard manure Sheep dung and leaf compost Press mud Isabgol straw and fresh cow dung slurry Rice straw Rice straw and fresh cow dung Isabgol straw and fresh cow dung slurry Cow dung

Increased available P content in final product Available P content increased in final product Available P content significantly improved in the mature compost Significant improvement in available P in final product Significant K mobilization from feldspar Sharp increase in water soluble K after 120 days Significant K released from waste mica Significantly increased available K content

(Basak, 2017), and the improvement in available P (water soluble, citrate soluble, and microbial P) content and phosphatase enzyme activity in the mature phosphocompost (Biswas & Narayanasamy, 2006; Basak, 2017) contributes to P bioavailability and plant uptake (Iqbal et al., 2016; Basak & Gajbhiye, 2018). In comparison to P, there is little information available in the literature that demonstrates enhanced K release from silicate minerals through the composting process (Badr, 2006; Basak, 2018a; Biswas et al., 2009). Release of K from feldspar significantly increases through the composting process, resulting in an increase in available K content in feldspar‐charged compost (Badr, 2006). In another study, a significant amount of K was released from waste mica when composted with waste biomass and fresh cow dung slurry (Basak, 2018a; Biswas et  al., 2009). Similarly, vermicomposting (introduction of earthworm) enhances mineral dissolution in the earthworm intestine due to presence of an acidic environment with a high microbial population (Liu et al., 2011). In the case of vermicomposting, more nutrient release is expected due to the enhancement of mineral dissolution by both the physical and chemical actions of earthworms (Zhu et  al., 2013). Hence, the vermicomposting process can improve nutrient mobilization from rocks and mineral powder and may be a promising option for enriched organic fertilizer production. 14.7. BEST MANAGEMENT PRACTICES The main objectives of best management practices (BMPs) is to increase the use efficiency of nutrients from natural sources and reducing the loss of nutrients from

Biswas & Narayanasamy (2006); Biswas et al. (2009) Hellal et al. (2012) Adhami et al. (2014) Reza et al. (2017) Basak (2017) Badr (2006) Biswas et al. (2009) Basak (2018a) Zhu et al. (2013)

agricultural ecosystem. Improving soil properties through organic nutrient management may be a suitable option for increasing P and K use efficiency and reducing nutrient loss. Both the soil and plant perspective must be considered, however, in order to bring out the best results from the organic production system. There are marked differences in the ability of plant species to utilize P and K from rocks and minerals (Harley & Gilkes, 2000). Plant species influence on rhizosphere pH may have important implications for mineral dissolution. Increasing soil acidity (low pH) in the rhizosphere enhances the dissolution of insoluble phosphate and silicate minerals (Harley & Gilkes, 2000). Some the plant species (finger‐millet, pearl‐millet, buckwheat, and rapeseed) are very efficient in P utilization from RPs (Kumari & Phogat, 2008). Substantial dissolution of apatite and release of P is observed due to lowering of 2 pH in lupin rhizosphere (Hinsinger & Gilkes, 1995). Cover crops add significant amounts of P after decomposition, which become available to the next crop. In addition, some cover crops release root exudates, improving P availability in soil, which would not happen under other crops (Nuruzzaman et  al., 2005). Significant release of K from the mineral structure (phlogopite) was found due to lowering of pH in rape rhizosphere (Hinsinger et  al., 1993). Therefore, choice of plant or crop species is very important for maximizing the P and K use efficiency from insoluble minerals. Increase in soil organic matter can increase plant P availability by decreasing bulk density and improving soil porosity. Similarly, addition of organic matter improves cation exchange capacity (CEC) of soil, thus the capacity of soil to retain cations such as K will also be increased (Johnston, 1986).

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  295

Application of RP during green manuring improves P availability by the decomposition process of manure residue, followed by release of organic and carbonic acid (Kumari & Phogat, 2008). Application of low‐grade RP in acid soil acts as an amendment through correcting soil pH, subsequently increasing P availability (Basak & Biswas, 2016). A number of strategies, such as reducing particle size, blending with organics, composting, and microbial intervention, were found promising for improvement of P and K use efficiency of insoluble minerals. Furthermore, application of insoluble minerals, organics, and microbial inoculants can be more effective for long‐duration crops (plantation and fruit), where the slow but continuous supply of the nutrient is required. The above‐mentioned strategies therefore can be adopted, alone on in combination, to improve P and K use efficiency of natural sources in order to meet crop demand in an organic production system. Another way to improve efficiency of the natural sources is to reduce the loss of nutrients from the soil. Efficiency of a highly soluble source of P and K is very low in deeply weathered soil (Alfisols, Oxisols, and Ultisols) of the tropical environment (Leonardos et al., 1987). In this context, organic nutrient sources, particularly relatively less soluble and insoluble sources, have the advantage over soluble sources. There are varieties of BMPs that can be used to reduce the P and K loss from agricultural ecosystems. Loss of P and K from agricultural systems generally occurs through surface runoff, erosion, and leaching. However, BMPs like reduced tillage, rotation with cover crops, and incorporation of crop residues and organic matter can effectively reduce nutrient loss and are well suited to organic farming (Sharpley et  al., 2004). The BMPs like reduced tillage, contour farming, and terracing that stop erosion and trap eroded sediment in situ are also effective in reducing P and K losses (Heathwaite et  al., 1998). Balancing P inputs by maintaining moderate soil test P concentration can also effectively reduce P losses. Thus BMPs should focus on managing both particulate and dissolved P losses. Application of manures and compost need extra attention to reduce the risk of P losses to surface water. The organic farming systems that do not depend on manures and compost for the N requirement of crops may have P deficiency. However, farms that use manures and compost to compensate N requirement usually have P surplus (Mikkelsen, 2008). Integration of animals, N fixing cover crops, and slow release mineral sources is a promising approach that has been practiced successfully to avoid excess P and K build‐up (Stockdale et  al., 2002; Sharpley et al., 2004). Consideration of crop removal data and soil test recommendations can effectively balance P requirements in organic farms. Common methods of soil testing are not entirely sufficient for holistic evaluation of organic farming. The main objective of organic farming is to promote soil life, subsequently maintaining soil fertility

(Mader et al., 2002). For example, microbial biomass P and phosphatase enzymes play a significant role in P turnover in soil. So, soil biological parameters are more important in organic farming as compared to conventional agriculture. Accordingly soil biological parameters (microbial biomass, release of CO2, and metabolic enzyme activities) need to be given more emphasis in soil test recommendations, along with physical and chemical parameters (Haneklaus et al., 2005). 14.8. ENVIRONMENTAL SUSTAINABILITY The organic farming system is based on environmental sustainability. Worldwide organic agriculture movements place environmental protection as a primary objective of organic production systems (IFOAM, 1998). Organic farming reduces the chances of environmental pollution (e.g. pesticides, heavy metals), but the environmental impact of approved nutrient sources needs to be considered. Potassium has little consequence in terms of environmental issues, but phosphorus management is an important part of environmental protection. Soluble P inputs to freshwater ecosystems are a main cause of eutrophication and water quality degradation (Correll, 1998). Excessive P inputs can also disturb the aquatic ecosystem and stimulate growth of toxic blue‐green algae. The soluble chemical P and K fertilizers are more susceptible to loss and contribute inputs to freshwater bodies. In these circumstances, relatively less soluble organic inputs have advantages over chemical fertilizer. However, overuse of manures and compost in any intensive organic system islikely to lead to high P concentration and subsequently increase the chances of P losses to surface water. Thus, high plant available P content in manures and composts needs extra consideration in order to reduce risk of P loss to freshwater bodies. Application of organic and microbial inoculation leads to the release of K from silicate minerals by direct dissolution or by chelation of Si and Al ions. There is a significant possibility of structural alteration and breakdown. Structural alteration of silicate minerals, e.g., feldspar (Liu et al., 2006), mica (Basak & Biswas, 2009), and montmorillonite (Yang et al., 2016) has been observed due to biointerventions. This structural degradation led to reduction in water holding capacity of the mineral, which has raised a concern about the long‐term sustainability of the practice (Yang et  al., 2016). However, more study is needed in order to determine structural changes of silicate mineral due K release in the soil in detail and its possible environmental consequences (Basak et al., 2017). Therefore, organic production systems should be designed with minimum environmental consequences, which limits P and K loss through proper management organic inputs, cropping system, and soil resources.

296  BIOGEOCHEMICAL CYCLES

14.9. CONCLUSION

REFERENCES

In the organic production system, improvement of soil organic matter and biological diversity might have an impact on nutrient cycling (P and K) and subsequently their uptake by crop plants. A grower in an organic production system needs an adequate supply of nutrient (P and K) in order to achieve a sustainable target yield. Using only organic sources (crop residue, animal manures, and green manure and compost) is not always sufficient to meet the crop demand. Managing organic farms without application of external inputs will lead to negative P and K balances in the long run. In contrast, intensive organic farming based on manures and composts could lead to P surplus. Organic production systems are not sustainable without external inputs, and use of natural sources of P and K is essential to counteract their depletion and maintain adequate levels. Crop removal and soil test values of P and K, however, need to be considered for effective P and K management in organic agriculture. There are many excellent natural geological sources of P and K approved in the organic production system that can effectively supply the nutrient at a sufficient rate to meet the crop demand. Even though rocks and mineral powder can supply P and K, their release rate is quite variable and sometimes inadequate to meet crop demand. So, there is a need for biointervention technologies (composting and microbial inoculation) to speed up the nutrient release from the rocks and mineral powder. Several strategies like blending with organics, composting, and microbial intervention are found to be  promising in improving P and K availability from natural sources. Therefore, the organic producer may select locally available natural sources of P and K along with biointervention strategies to harness the full potential. However, economic consideration, local availability, and amount of nutrient required to meet the crop demand play a vital role in producer’s decision of which source or combination of sources is to be used in an organic production system.

Adhami, E., Hosseini, S., & Owliaie, H. (2014). Forms of phosphorus of vermicompost produced from leaf compost and sheep dung enriched with rock phosphate. International Journal of Recycling of Organic Waste in Agriculture, 3(3), 5. Aliyu, L., & Kuchinda, N.C. (2002). Analysis of the chemical composition of some organic manures and their effect on the yield and composition of pepper. Crop Research Hisar, 23, 362–368. Arora, D., & Gaur, C. (1979). Microbial solubilization of different inorganic phosphates. Indian Journal of Experimental Biology, 17, 1258–1261. Askegaard, M., Eriksen, J., & Johnston, A.E. (2004). Sustainable management of potassium. In P. Schjonning, B. Christensen, S. Elmholt (Eds.), Managing soil quality— challenges in modern agriculture (pp. 85–102). Wallingford, UK: CABI Punlishing. Badr, M.A. (2006). Efficiency of K‐feldspar combined with organic materials and silicate dissolving bacteria on tomato yield. Journal of Applied Sciences Research, 2, 1191–1198. Badr, M.A., Shafei, A.M., & Sharaf El‐Deen, S.H. (2006). The dissolution of K and P‐bearing minerals by silicate dissolving bacteria and their effect on sorghum growth. Research Journal of Agriculture and Biological Sciences, 2(1), 5–11. Basak, B.B. (2017). Phosphorus supplying capacity of value added compost prepared from low‐grade Indian rock phosphates and crop residue. Waste and Biomass Valorization, 8(8), 2653–2662. Basak, B.B. (2018a). Recycling of waste biomass and mineral powder for preparation of potassium‐enriched compost. Journal of Material Cycles and Waste Management, 20(3), 1409–1415. Basak, B.B. (2018b). Phosphorus release by low molecular weight organic acids from low‐grade Indian rock phosphate. Waste and Biomass Valorization, 10(11), 3225–3233. doi 10.1007/s12649‐018‐0361‐3 Basak, B.B., & Biswas, D.R. (2009). Influence of potassium solubilising microorganism (Bacillus mucilaginosus) and waste mica on potassium uptake dynamics by sudan grass (Sorghum vulgare Pers.) grown under two Alfisols. Plant and Soil, 317, 235–255. Basak, B.B., & Biswas, D.R. (2012). Modification of waste mica for alternative source of potassium: Evaluation of potassium release in soil from waste mica treated with potassium solubilizing bacteria (KSB). Saarbrucken, Germany: Lambert Academic Publishing. Basak, B.B., & Biswas, D.R. (2016). Potentiality of Indian rock phosphate as liming material in acid soil. Geoderma, 263, 104–109. Basak, B.B. & Gajbhiye, N.A. (2018). Phosphorus enriched organic fertilizer, an effective P source for improving yield and bioactive principle of Senna (Cassia angustifoliaVhal.). Industrial Crops and Products, 115, 208–213. Basak, B.B., Meena, H.M., Meena, M.S., & Dotaniya, M.L. 2015. Potassium bio‐fertilizer: Viable technology for sustainable agriculture. Indian Framing, 64(11), 2–4.

ACKNOWLEDGMENTS We thank the editors for their invitation to contribute this chapter to the book. We also thank anonymous reviewers for their constructive comments that helped to improve the presentation of the chapter. Dr. Ashis Maity and Dr. D. R. Biswas acknowledge the support from the Indian Council of Agricultural Research, New Delhi. The senior author is thankful to the Director, ICAR‐ Directorate of Medicinal and Aromatic Plants Research for support.

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  297 Basak, B.B., & Sarkar, B. (2017). Scope of natural sources of  potassium in sustainable agriculture. In Adaptive soil management: From theory to practices (pp. 247–259). Singapore: Springer‐Verlag. Basak, B.B., Sarkar, B., Biswas, D.R., Sarkar, S., Sanderson, P., & Naidu, R. (2017). Bio‐intervention of naturally occurring silicate minerals for alternative source of potassium: challenges and opportunities. Advances in Agronomy, 141, 115–145. Basak, B.B., Sarkar, B., Sanderson, P., & Naidu, R. (2018). Waste mineral powder supplies plant available potassium: Evaluation of chemical and biological interventions. Journal of Geochemical Exploration, 186, 114–120. Bekele, T., & Höfner, W. (1993). Effects of different phosphate fertilizers on yield of barley and rape seed on reddish brown soils of the Ethiopian highlands. Fertilizer Research, 34(3), 243–250. Bhat, S.A., Singh, J., & Vig, A.P. (2017). Earthworms as organic waste managers and biofertilizer producers. Waste and Biomass Valorization, 9(7), 1073–1086. Biswas, D.R., & Basak, B.B. (2014). Mobilization of potassium from waste mica by potassium solubilizing bacteria (Bacillus mucilaginosus) as influenced by temperature and incubation period under in‐vitro laboratory condition. Agrochimica, 38, 309–320. Biswas, D.R., & Narayanasamy, G. (2006). Rock phosphate enriched compost: an approach to improve low‐grade Indian rock phosphate. Bioresource Technology, 97(18), 2243–2251. Biswas, D.R., Narayanasamy, G., Datta, S.C., Singh, G., Begum, M., Maiti, D., Mishra, A. & Basak, B.B. (2009). Changes in nutrient status during preparation of enriched organomineral fertilizers using rice straw, low‐grade rock phosphate, waste mica, and phosphate solubilizing microorganism. Communications in Soil Science and Plant Analysis, 40(13–14), 2285–2307. Bolan, N.S. (1991). A critical review on the role of mycorrhizal fungi in the uptake of phosphorus by plants. Plant and Soil, 134(2), 189–207. Bolland, M.D.A., & Baker, M.J. (2000). Powdered granite is not an effective fertilizer for clover and wheat in sandy soils from Western Australia. Nutrient Cycling in Agroecosystems, 56, 59–68. Caplan, B. (1992). Organic gardening. London: Headline Book Publishing. CGSB. (2006). Organic production systems: Permitted substances lists. Gatineau, Quebec: Canadian General Standards Board. Chien, S.H. & Menon, R.G. (1995). Factors affecting the agronomic effectiveness of phosphate rock for direct application. Fertilizer Research, 41(3), 227–234. Cobb, D., Feber, R., Hopkins, A., Stockdale, L., O’Riordan, T., Clements, B., et al. (1999). Integrating the environmental and economic consequences of converting to organic agriculture: Evidence from a case study. Land Use Policy, 16(4), 207–221. Codex Alimentarius Commission. (2001). General requirements (food hygiene), Vol. 1. Rome: Joint FAO/WHO Food Standards Programme and World Health Organization. Coreonos, C., Hinsinger, P., & Gilkes R.J. (1996). Granite powder as a source of potassium for plants: a glasshouse bioassay comparing two pasture species. Fertilizer Research, 45, 143–152.

Correll, D.L. (1998). The role of phosphorus in the eutrophication of receiving waters: A review. Journal of Environmental Quality, 27, 261–266. Craigie, J.S. (2011). Seaweed extract stimuli in plant science and agriculture. Journal of Applied Phycology, 23(3), 371–393. Cullen, D.J. (1988). Mineralogy of nitrogenous guano on the bounty islands SW Pacific ocean. Sedimentology, 35, 421– 428. Diacono, M., & Montemurro, F. (2010). Long‐term effects of organic amendments on soil fertility. A review. Agronomy for Sustainable Development, 30(2), 401–422. Dorozhkin, S.V. (2007). Calcium orthophosphates. Journal of Material Science, 42, 1061–1095. Dotaniya, M.L., Datta, S.C., Biswas, D.R., Dotaniya, C.K., Meena, B.L., Rajendiran, S., Regar, K.L., & Lata, M. (2016). Use of sugarcane industrial by‐products for improving ­sugarcane productivity and soil health. International Journal of Recycling of Organic Waste in Agriculture, 5(3), 185–194. Duponnois, R., Colombet, A., Hien, V., & Thioulouse, J. (2005). The mycorrhizal fungus Glomus intraradices and rock phosphate amendment influence plant growth and microbial activity in the rhizosphere of Acacia holosericea. Soil Biology and Biochemistry, 37(8), 1460–1468. Eghball, B., Wienhold, B.J., Woodbury, B.L., & Eigenberg, R.A. (2005). Plant availability of phosphorus in swine slurry and cattle feedlot manure. Agronomy Journal, 97(2), 542–548. Elmaz, O., Cerit, H., Ozcelik, M., & Ulas. S. (2004). Impact of  organic agriculture on the environment. Fresenius Environmental Bulletin, 13, 1072–1078. FAO. (2015). Current world fertilizer trends and outlook to 2014–18. Rome: Food and Agriculture Organization of the United Nations. Gaur, A.C. (1988). Phosphate solubilizing biofertilizers in crop productivity and their interaction with VA mycorrhizae. Proceedings of Mycorrhizae Round Table Workshop (pp.505–529). Delhi, India: Jawaharlal Nehru University, 13–15 March. Gosling, P. & Shepherd, M. (2005). Long‐term changes in soil fertility in organic arable farming systems in England, with particular reference to phosphorus and potassium. Agriculture, Ecosystems and Environment, 105(1–2), 425–432. Goulding, K., Stockdale, E., Fortune, S., & Watson, C. (2000). Nutrient cycling on organic farms. Journal of the Royal Agricultural Society of England, 161, 65–75. Grover, J.E., Rope, A.F., & Kaneshiro, E.S. (1997). The occurrence of biogenic calcianstruvite, (Mg,Ca)NH4PO4.6H2O, as intracellular crystals in Paramecium. Journal of Eukaryotic Microbiology, 44, 366–373. Haneklaus, S., Schnug, E., Paulsen, H.M. & Hagel, I. (2005). Soil analysis for organic farming. Communications in Soil Science and Plant Analysis, 36(1–3), 65–79. Harley, A.D., & Gilkes, R.J. (2000). Factors influencing the release of plant nutrient elements from silicate rock powders: a geochemical overview. Nutrient Cycling in Agroecosystems, 56, 11–36. Heathwaite, A.L., Griffiths, P. & Parkinson, R.J. (1998). Nitrogen and phosphorus in runoff from grassland with buffer strips following application of fertilizers and manures. Soil Use and Management, 14(3), 142–148.

298  BIOGEOCHEMICAL CYCLES Heckman, J. (2006). A history of organic farming: Transitions from Sir Albert Howard’s war in the soil to USDA National Organic Program. Renewable Agriculture and Food Systems, 21,143–150. Heckman, J.R., & Tedrow, J.C.F. (2004). Greensand as a soil amendment. Significance, 500(105), 1–063. Hellal, F.A., Nagumo, F., & Zewainy, R.M. (2012). Influence of phospho‐composting on enhancing phosphorus solubility from inactive rock phosphate. Australian Journal of Basic and Applied Sciences, 6(5), 268–276. Hinsinger, P., & Gilkes, R.J. (1995). Root induced dissolution of phosphate rock in the rhizosphere of lupins grown in alkaline soil. Australian Journal of Soil Research, 33, 477–489. Hinsinger, P., Elsass, F., Jaillard, B., & Robert, M. (1993). Root induced irreversible transformation of a trioctahedral mica in the rhizosphere of rape. Journal Soil Science, 44, 535–545. Hue, N.V., & Silva, J.A. (2000). Organic soil amendments for sustainable agriculture: organic sources of nitrogen, phosphorus, and potassium. In J.A. Silva, R. Uchida (Eds.), Plant nutrient management in Hawaii’s soils: approaches for tropical and subtropical agriculture. Manoa: College of Tropical Agriculture and Human Resources, University of Hawaii. IFOAM (1998). IFOAM basic standards for organic production and processing. Bonn, Germany: International Federation of Organic Agriculture Movements, IFOAM Publications. Iqbal, T., Jilani, G., Siddique, M.T. & Rasheed, M. (2016). Impact of rock phosphate enriched compost and phosphorus solubilizing bacteria on maize growth and nutrient uptake. Journal of Agricultural Research, 54(2), 207–219. Iyamuremye, F., Dick, R.P., & Baham, J. (1996). Organic amendments and phosphorus dynamics: II. Distribution of soil phosphorus fractions. Soil Science, 161(7), 436–443. Jain, R., Saxena, J., & Sharma, V. (2012). Solubilization of inorganic phosphates by Aspergillusawamori S19 isolated from rhizosphere soil of a semi‐arid region. Annals of Microbiology, 62(2), 725–735. Johnston, A.E. (1986). Potassium fertilization to maintain a K‐balance under various farming systems. In Nutrient balances and the need for potassium (pp. 177–204). Reims, France: 13th Congress of the International Potash Institute. Jongmans, A.G., Van Breemen, N., Lundström, U., Van Hees, P.A.W., Finlay, R.D., Srinivasan, M., et al. (1997). Rock‐eating fungi. Nature, 389(6652), 682. Khasawneh, F.E., & Doll, E.C. (1978). The use of phosphate rock for direct application. Advance in Agronomy, 30, 159–206. Kirchmann, H., Kätterer, T., & Bergström, L. (2009). Nutrient supply in organic agriculture–plant availability, sources and recycling. In H. Kirchmann, L. Bergström (Eds.), Organic crop production—ambitions and limitations (pp. 89–116). Dordrecht, Netherlands: Springer. Kpomblekou, A.K., & Tabatabai, M.A. (1994). Effect of organic acids on release of phosphorus from phosphate rocks. Soil Science, 158(6), 442–453. Kpomblekoua, A.K., & Tabatabai, M.A. (2003). Effect of low‐ molecular weight organic acids on phosphorus release and phytoavailabilty of phosphorus in phosphate rocks added to soils. Agriculture, Ecosystems and Environment, 100(2–3), 275–284.

Kumar, K., & Goh, K.M. (1999). Crop residues and management practices: effects on soil quality, soil nitrogen dynamics, crop yield, and nitrogen recovery. Advances in Agronomy, 68, 197–319. Kumari, K. & Phogat, V.K. (2008). Rock phosphate: its availability and solubilization in the soil—a review. Agricultural Review, 29(2):108–116. Lal, R. (2005). World crop residues production and implications of its use as a biofuel. Environment International, 31(4), 575–584. Lampkin, N., Foster, C., Padel, S., & Midmore, P. (1999). The policy and regulatory environment for organic farming in Europe. In Organic farming in Europe: Economics and policy (VoL. 2). Stuttgart: University of Hohenheim. Leonardos, O.H., Fyfe, W.S., & Kronberg, B.I. (1987). The use of ground rocks in laterite systems: an improvement to the use of conventional soluble fertilizers? Chemical Geology, 60, 361–370. Lian, B., Wang, B., Pan, M., Liu, C., & Teng, H.H. (2008). Microbial release of potassium from K‐bearing minerals by thermophilic fungus Aspergillus fumigatus. Geochimicaet Cosmochimica Acta, 72(1), 87–98. Liebig, M.A., & Doran, J.W. (1999). Impact of organic production practices on soil quality indicators. Journal of Environmental Quality, 28, 1601–1609. Lin, Q.M., Rao, Z.H., Sun, Y.X., Yao, J., & Xing, L.J., (2002). Identification and practical application of silicate‐dissolving bacteria. Agricultural Sciences in China, 1(1), 81–85. Liu, D., Lian, B., Wang, B., & Jiang, G. (2011). Degradation of potassium rock by earthworms and responses of bacterial communities in its gut and surrounding substrates after being fed with mineral. PLoS One, 6(12), e28803. Liu, W., Xu, X., Wu, X., Yang, Q., Luo, Y., & Christie, P. (2006). Decomposition of silicate minerals by Bacillus mucilaginosus in liquid culture. Environmental Geochemistry and Health, 28(1–2), 133–140. Lotter, D.W. 2003. Organic agriculture. Journal of Sustainable Agriculture, 21, 59–128. Mäder, P., Fliessbach, A., Dubois, D., Gunst, L., Fried, P. & Niggli, U. (2002). Soil fertility and biodiversity in organic farming. Science, 296(5573), 1694–1697. Mahimairaja, S., Bolan, N.S., & Hedley, M.J. (1994). Dissolution of phosphate rock during the composting of poultry manure: an incubation experiment. Fertilizer Research, 40(2), 93–104. Maity, A., Pal, R.K, Chandra, R., & Singh N.V. (2014). Penicillium pinophilum—a novel microorganism for nutrient management in pomegranate (Punica granatum L.). Scientia Horticulturae, 169, 111–117. Manning, D.A.C. (2010). Mineral sources of potassium for plant nutrition. A review. Agronomy for Sustainable Development, 30, 281–294. Mikkelsen, R.L. (2008). Managing potassium for organic crop production. Better Crops, 92(2), 26–29. Mohammed, S.M.O., Brandt, K., Gray, N.D., White, M.L. & Manning, D.A.C. (2014). Comparison of silicate minerals as sources of potassium for plant nutrition in sandy soil. European Journal of Soil Science, 65(5), 653–662. Montalvo, D., McLaughlin, M.J. & Degryse, F. (2015). Efficacy of hydroxyapatite nanoparticles as phosphorus fertilizer in andisols and oxisols. Soil Science Society of America Journal, 79(2), 551–558.

CYCLING OF NATURAL SOURCES OF P AND K FOR ENVIRONMENTAL SUSTAINABILITY  299 Nelson, D.W., & Logan, T.J. (1983). Chemical processes and transport of phosphorus. In F.W. Schaller, G.W. Bailey (Eds.), Agricultural Management and Water Quality. Ames, Iowa: Iowa State University Press. Nick, J., & Bradley, F. (1994). Growing fruits and vegetables organically. Emmaus, PA: Rodale Press. Nuruzzaman, M., Lambers, H., Bolland, M.D. & Veneklaas, E.J. (2005). Phosphorus benefits of different legume crops to subsequent wheat grown in different soils of Western Australia. Plant and Soil, 271(1–2), 175–187. Omar, S.A. (1997). The role of rock‐phosphate‐solubilizing fungi and vesicular–arbusular‐mycorrhiza (VAM) in growth of wheat plants fertilized with rock phosphate. World Journal of Microbiology and Biotechnology, 14(2), 211–218. Paris, F., Bonnaud, P., Ranger, J., & Lapeyrie, F. (1995). In vitro weathering of phlogopite by ectomycorrhizal fungi. Plant and Soil, 177(2), 191–201. Parnes, R. (1986). Organic and inorganic fertilizers. Mt. Vernon, ME: Woods End Agricultural Institute. Rajan, S.S.S., Watkinson, J.H., & Sinclair, AG. (1996). Phosphate rocks for direct application to soils. Advances in Agronomy, 57, 78–160. Reza, S.K., Singh, S., Datta, S.C., Purakayastha, T.J., & Singh, S.K. (2017). Phosphorus solubilization through organic acids production in pressmud composted with rock phosphate. National Academy Science Letters, 40(1), 13–16. Rosa‐Magri, M.M., Avansini, S.H., Lopes‐Assad, M.L., Tauk‐ Tornisielo, S.M., & Ceccato‐Antonini, S.R. (2012). Release of potassium from rock powder by the yeast Torulaspora globosa. Brazilian Archives of Biology and Technology, 55(4), 577–582. Sale, P.W. & Mokwunye, A.U. (1993). Use of phosphate rocks in the tropics. Fertilizer Research, 35(1–2), 33–45. SanzScovino, J.I., & Rowell, D.L. (1988). The use of feldspars as potassium fertilizers in the savannah of Columbia. Fertilizer Research, 17, 71–83. Sauchell, V. (1963). Chemistry and technology of fertilizers, 2nd Printing. American Chemical Society Monograph Series. New York: Reinhold Publishing Corporation. Sharpley, A., Kleinman, P., & Weld, J. (2004). Assessment of best management practices to minimize the runoff of manure‐born phosphorus in the United States. New Zealand Journal of Agricultural Research, 47, 461–477. Sheng, X. F., & He, L. Y. (2006). Solubilization of potassium‐ bearing minerals by a wild‐type strain of Bacillus edaphicus and its mutants and increased potassium uptake by wheat. Canadian Journal of Microbiology, 52(1), 66–72. Sheng, X.F., He, L.Y., & Huang, W.Y. (2002). The conditions of releasing potassium by a silicate‐dissolving bacterial strain NBT. Agricultural Sciences in China, 1(6), 662–666. Singal, R., Gupta, R., & Saxena, R.K. (1994). Rock phosphate solubilization under alkaline conditions by Aspergillus japonicas and A. foetidus. Folia Microbiologica, 39(1), 33–36. Song, M., Pedruzzi, I., Peng, Y., Li, P., Liu, J., & Yu, J. (2015). K‐extraction from muscovite by the isolated fungi. Geomicrobiology Journal, 32(9), 771–779. Sparks, D.L. (1987). Potassium dynamics in soils. In B.A. Stewart (Ed.), Advances in soil science (pp. 1–63). New York: Springer.

Stockdale, E.A., Shepherd, M.A., Fortune, S., & Cuttle, S.P. (2002). Soil fertility in organic farming systems—fundamentally different? Soil Use Management, 18, 301–308. Sugumaran, P., & Janarthanam, B. (2007). Solubilization of potassium containing minerals by bacteria and their effect on plant growth. World Journal of Agricultural Sciences, 3(3), 350–355. Sullivan, P. (2000). Alternative soil amendments. Horticulture Technical Note. www.attra.ncat.org.Mattilsynet Swain, M.R., Laxminarayana, K., & Ray, R.C. (2012). Phosphorus solubilization by thermotolerant Bacillus subtilis isolated from cow dung microflora. Agricultural Research, 1(3), 273–279. Tilman D., Cassman K.G., Matson, P.A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418, 671–677. UKROFS. (1999). Standards for organic food production. London: United Kingdom Register of Organic Food Standard. USDA. (2007). The National Organic Program. U.S. Department of Agriculture. 1 June. http://www.ams.usda.gov/NOP Van Kauwenbergh, S.J., & McClellan, G.H. (2004). Characterization of phosphate rocks, p. 17–26. In F. Zapata, R.N. Roy (Eds.), Use of phosphate rocks for sustainable agriculture. FAO Fertilizer and Plant Nutrition Bulletin 13. Rome: Food and Agriculture Organization of the United Nations. van Straaten, P., & Chesworth, W. (1985). Low cost fertilizers: Local geological sources for subsistence farmers in Eastern Africa. Second Regional Conference on the Development and Utilization of Mineral Resources in Africa (pp. 4–9). Lusaka: United Nation Economics Commission for Africa. Vassileva, M., Vassilev, N., & Azcon, R. (1997). Rock phosphate solubilization by Aspergillus niger on olive cake‐based medium and its further application in a soil–plant system. World Journal of Microbiology and Biotechnology, 14(2), 281–284. Watson, C.A., Atkinson, D., Gosling, P., Jackson, L.R., & Rayns, F.W. (2002). Managing soil fertility in organic farming systems. Soil Use and Management, 18, 239–247. Witter, E. & Johansson, G. (2001). Potassium uptake from the subsoil by green manure crops. Biological Agriculture & Horticulture, 19(2), 127–141. Yang, X., Li, Y., Lu, A., Wang, H., Zhu, Y., Ding, H., & Wang, X. (2016). Effect of Bacillus mucilaginosus D4B1 on the structure and soil‐conservation‐related properties of montmorillonite. Applied Clay Science, 119, 141–145. Zapata, F., & Arrillaga, J. L. (2002). Agronomic evaluation of guano sources by means of isotope techniques, In Assessment of soil phosphorus status and management of phosphatic fertilizers to optimise crop production (IAEATECDOC 1272, pp. 83–89). Vienna: International Atomic Energy Agency. Zapata, F., & Roy, R.N. (Eds.) (2004). Use of phosphate rocks for sustainable agriculture. FAO Fertilizer and Plant Nutrition Bulletin 13. Rome: Food and Agriculture Organization of the United Nations. Zhongqi, H.E., Pagliari, P.H., & Waldrip, H.M. (2016). Applied and environmental chemistry of animal manure: A review. Pedosphere, 26(6), 779–816. Zhu, X., Lian, B., Yang, X., Liu, C., & Zhu, L. (2013). Biotransformation of earthworm activity on potassium‐ bearing mineral powder. Journal of Earth Science, 24, 65–74.

15 Ecological Drivers and Environmental Impacts of Biogeochemical Cycles: Challenges and Opportunities Katerina Dontsova1,2, Zsuzsanna Balogh‐Brunstad3, and Gaël Le Roux4

ABSTRACT This chapter summarizes the cycling of the elements in the environment and outlines open questions that need to be answered and opportunities for research. We briefly explain the need to reexamine biogeochemical cycles in the context of new knowledge, analytical and research techniques, and environmental challenges. Biological drivers of weathering, changes in elemental cycles as influenced by anthropogenic drivers, including climate change, and examples from frontier and managed ecosystems, which are more sensitive to human impacts, are discussed. Key themes from each chapter are outlined. The chapter is concluded with the list of topics and critical questions that book contributors believe need further study in order to improve our understanding of biogeochemical cycles as both environment and science evolve.

15.1. INTRODUCTION The flow of the elements through the environment was originally examined by chemists and geologists in the abiotic context until Ukrainian mineralogist Volodymyr Vernadsky introduced the concept of biogeochemistry in his lectures published in 1924 (Vernadsky, 1924) and  in  1929 his book Biosphere (Vernadsky, 1929). Biogeochemistry explicitly puts focus on the fact that fluxes of the elements on Earth are strongly influenced by the biota and the effects of biological processes on dissolution, uptake, recycling, and precipitation of the elements have to be considered. Vernadsky, together with Édouard LeRoy and Pierre Teilhard de Chardin, also highlighted human influence on biogeochemical   Department of Environmental Science, University of Arizona, Tucson, Arizona, USA 2  Biosphere 2, University of Arizona, Tucson, Arizona, USA 3   Department of Geology and Environmental Sciences, Hartwick College, Oneonta, New York, USA 4   Laboratory of Functional Ecology and Environment, University of Toulouse, Toulouse, France 1

processes, “noosphere,”, because “mankind taken as a whole is becoming a mighty geological force” (Steffen et al., 2011; Teilhard De Chardin, 1956; Vernadsky, 1945). Since then, biogeochemical cycles of various elements have been studied from nano‐ to global scales. With the establishment of the Critical Zone Observatories (CZO) and an Exploration Network (CZEN), and utilization of the latest technologies, biogeochemical processes are better and better understood. Critical Zone science has been developed as a new framework for examining all the interactions between biological, chemical, and physical processes in the portion of Earth’s crust and its immediate vicinity affected by life (Brantley et al., 2006). Interdisciplinarity is the foundation of the Critical Zone science. CZOs rely on naturally varying bioclimatic and human pressure gradients to examine and explain variation in system properties (Brantley et  al., 2007), as a result, biogeochemical processes and properties are being examined in concert with their drivers. Advances in microscopy, new (geo)chemical and microbiological analytical techniques, computational modeling, and their combination, as well as interdisciplinary collaboration, have allowed a significant knowledge gain

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

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in this field. However, interactions and cascading effects between biogeochemical compartments and ecological actors are still insufficiently studied and understanding is minimal. For example, weathering processes are strongly affected by biota (microorganisms and plants), but it is difficult to quantify and predict the effect and the rates of biological weathering, because of interdependence between plant growth and abiotic processes. Quantification of the biological and ecological processes and rates is even more difficult if the ecosystems are subjected to changes induced by climate variation and management shifts. To evaluate interdependencies between processes in the Critical Zone, frontier ecosystems can be of particular interest due to their sensitivity to changes and their relative simplicity compared to older and more developed ecosystems. Moreover, a large part of the land is under agricultural management (including forestry) and the studies that incorporate these ecosystems are focused on yield improvements, water pollution, and pest management rather than underlying biogeochemical processes. With the challenges of climate change and water security, these can no longer be ignored. Climate change also necessitates an evaluation of elemental cycles on a global scale, with remote sensing serving as an important tool helping monitor and model Earth’s ­watersheds at large scales. The book examines the current state of knowledge and remaining challenges as related to biological weathering, elemental cycles in general, and in frontier and managed ecosystems. 15.2. BIOLOGICAL WEATHERING The contribution of biological weathering to fluxes of most elements is usually acknowledged but is often ignored in large‐scale quantification due to the complexity of biological processes. However, life exerts significant influence on the fluxes of the elements in the environment, transforming the Earth over time. Zacharescu and co‐authors (Chapter  1) walked us through the influences of biota and the changes in these influences in the light of Earth’s evolution over time. For example, over geologic timescales oxygen and the carbon dioxide gasses affected the transformation of the Earth crust, while biological processes regulated their concentrations in the atmosphere. In modern times, biology (microorganisms, plants, associated mycorrhizal fungi, animals, and humans) continues to exert influence on Earth through weathering. The greatest biological driver of weathering is carbon fixation by plants via photosynthesis (Dontsova and coauthors, Chapter  2). This provides the chemical energy necessary for weathering directly through plants or indirectly through heterotrophic bacteria and fungi using plant‐produced organic compounds. Chapter  2 described the main mechanisms

of plant weathering (see Figure 2.2), and summarized the evidence of plant weathering across scales and levels of control, from aseptic microcosm experiments to watershed observations, and global estimates, including how climate change and other anthropogenic impacts on vegetation affect weathering. Samuels and co‐authors (Chapter  3) discussed microbial weathering of minerals and rocks in natural environments (see Figure 3.1). They focused on the mechanisms of biological weathering by microorganisms, the role weathering reactions play in the life of the microorganism, microbial ecology of weathering environments (types of microorganisms and microbial communities), biosignatures, approaches and techniques to study microbially mediated weathering, and manifestation of microbial weathering on larger scales (studied by the emerging field of microbial biogeomorphology). While heterotrophic microorganisms are most common, the authors pointed out that weathering microbial communities often act as ecological producers within ecosystems, supporting the growth of other microorganisms and higher organisms such as plants. Balogh‐Brunstad and co‐authors (Chapter  4) examined traditional and new nano‐ and microscale tools available for examining bacteria and fungi interactions with silicate minerals (see Table 4.1). They describe how scanning electron microscopy (SEM) (conventional, environmental, low temperature, dual‐beam SEM, and focused ion beam sample preparation and analysis), transmission electron microscopy, atomic force microscopy, Helium ion microscopy, vertical scanning interferometry, X‐ray absorption spectroscopy, secondary ion mass spectroscopy, X‐ray diffraction, X‐ray fluorescence, Fourier‐transform infrared, and nuclear magnetic resonance have been used in studies of biological weathering. The authors identified multiple new opportunities and challenges of applications of these tools. Abs and Ferriere in Chapter 5 address the challenges of modeling microbial dynamics to predict heterotrophic soil respiration, especially in the context of climate change. Conventional models of soil carbon cycling predict soil heterotrophic respiration from standing carbon stocks and abiotic factors and overlook the microbial mechanisms, primarily microbial growth and production of extracellular enzymes. The chapter reviews a new generation of models that integrate the general climate controls of soil carbon cycling (temperature, CO2 concentration, precipitation) with microbial ecophysiology (e.g., respiration, exoenzyme production) and microbial functional diversity. The authors discuss fine‐scale “microscopic” models of microbial decomposition of soil organic matter; show how the effect of climate change can be incorporated in these models; and address the challenge of scaling “microscopic” models up to global Earth scale. They also review key insights regarding the effect of

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climate change on soil carbon cycling derived from the models. They highlight the process of soil carbon stabilization as a critical nexus of biological and geophysico‐ chemical processes (discussed in other chapters) that future modeling efforts should focus on. 15.3. ELEMENTAL CYCLES Deep comprehension of the past and present biogeochemical functioning of the Critical Zone is necessary to understand the future geochemical cycles on Earth. By linking the structure and functions of the Critical Zone via a common theoretical framework based on the existing literature, Moravec and Chorover (Chapter  6) reminded us of the fundamental role of CZOs and the CZEN. They emphasized the need to examine more complex terrains and incorporate the perspective of landscape evolution. New knowledge obtained via observatories and modeling allows extrapolating results to a larger scale. Bailey (Chapter  7) demonstrated the relevance of long‐term watershed studies using a stoichiometric mass balance. As an example he used plagioclase weathering in the Hubbard Brook Experimental Forest in New Hampshire, United States, which suffers from acidic deposition. He also showed the importance of vegetation change such as logging or herbicide application on major element cycles in a forested watershed. In a French CZO similarly impacted by acidic deposition, Nägler and co‐ authors (Chapter 8) used traditional (Sr) and nontraditional (Mo) stable isotopes to investigate elemental cycles at a small watershed scale. This study demonstrated that a high‐resolution spatial sampling (landscape and vertical distribution) of a small catchment in a context of CZO could improve our understanding of the global cycle of biogeochemically active elements, such as molybdenum. Le Roux and co‐authors (Chapter  9) combined trace metal concentration and Pb isotope measurements to investigate the long‐term fate of legacy metal pollutants (such as Pb) in mountain catchments. Using environmental archives (peat and lakes), they showed the impact of humans on the Pb cycle in a remote, frontier environment over more than 2000 years. The legacy of Pb contamination of soil, moss, and fish in these environments is demonstrated by using a large‐scale spatial sampling approach. In addition to directly modifying the geochemical cycles of specific elements, such as Pb or Hg, through industrial and mining activities, humans also abruptly and deeply modify the landscape by enhancing erosion. Based on geochemical studies on environmental archives, the authors conceptualized the future of the mountain Critical Zones impacted by both global changes and local human (socio‐ecological) pressures. Sullivan and co‐authors (Chapter 10) further discussed a roadmap for “earthcasting”—modeling and forecasting

the future functioning of the Critical Zone in the Anthropocene. They emphasize, similarly to Chapter  9, the importance of physical erosion caused by humans, which is impacting rates of weathering and solute fluxes to streams and rivers. Several opportunities and ­challenges are outlined to understand the past and future functioning of the Critical Zone. 15.4. FRONTIER AND MANAGED ECOSYSTEMS Frontier and managed ecosystems are an integral part of the biosphere and their contribution to ecosystem services and elemental cycles requires further investigations. Humans have a significant capacity to modify their environment. They influence elemental cycles in multiple ways, directly, by adding and removing elements, and indirectly, by modifying or even completely replacing ecosystems via land‐use change, mining, rain acidification, and climate change (Chapter 1). Studying the biogeochemical behavior of the Critical Zone in the agricultural and urban areas that have been managed by humans for a long time, called managed environments, is fundamental to promote sustainable management of these “shared” environments between different human communities and biologicals species. On other hand, frontier environments, such as high latitudes and altitudes, deserts, tropical forests, in general, experienced less direct human influence, but still, have been impacted by humans since at least the Neolithic (e.g. Chapter 9). Remote sensing can be an effective tool to study temporal and spatial vegetation dynamics, particularly in remote (frontier) environments. The method combines the use of satellite remote sensing coupled with ground‐ truth data collection. In Chapter  11, Nagai and coauthors showed results of two projects to illustrate how integrated ecosystem studies could use in situ and satellite remote sensing observed data to map temporal and spatial variability of plant phenology, and land‐use and land‐cover information. One of the last frontiers, the permafrost‐affected ecosystems, is especially vulnerable to climate warming. Herndon and co‐authors, in Chapter  12, provided insights into why it is important to turn some attention to redox‐sensitive elements and their indirect impact on the carbon budget. Thawing permafrost directly influences the hydrology and the water saturation of these organic‐matter‐rich soils, thus, changing the redox conditions, affecting the carbon storage, and providing positive feedback to climate warming. These feedbacks between climate warming, hydrology, and landscape position (topography) control the redox responses and regulate the biogeochemical cycles. Studies of these processes are limited and the magnitude of their effects is still unknown.

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Rock varnish, a thin coating firmly attached to rock surfaces where budding bacteria concentrate Mn and Fe combined with clay minerals, represents another vulnerable environment that has directly and indirectly been impacted by human activities. Dorn, in Chapter  13, discussed the interactions between humans and rock varnish emphasizing the importance of it as a biogeochemical indicator of anthropogenic interactions. Humans used rock varnish as “a messaging board” by engraving motifs, known as petroglyphs, during prehistoric times. The activities of industrialized humans have altered the chemistry and structure of rock varnish by increased dust loading from local sources and wildfires, and increased pollution deposition, such as lead, and acidic precipitation globally. The Green Revolution in the 1960s expanded intensive management of agricultural lands and the usage of high amounts of synthetic chemicals to wider regions of the world, which contributed to the degradation of soil health worldwide. Organic agriculture eliminates the use of synthetic mineral fertilizers, which require high‐energy inputs to produce and can lead to water contamination, relying on organic fertilizers instead. However, organic sources, such as crop residue and animal and green manure, do not supply mineral nutrients in sufficient amounts. In Chapter 14, Basak and co‐authors discussed the use of naturally sourced of P and K in combination with biointervention technologies, such as microbial inoculation and composting, to improve environmental sustainability in organic agriculture. Addition of slow‐release natural sources to soils, such as ground rock containing phosphorus and potassium, can help satisfy the nutrient demand of the crop. While rock weathering is a slow ­process, combining it with biointervention technologies can speed up the weathering (see Chapter 3) and increase the nutrient uptake by plants (see Chapter 2). 15.5. CHALLENGES AND OPPORTUNITIES Despite the large strides that biogeochemistry has made since its inception, a number of challenges remain. In Chapter 10, Sullivan and co‐authors summarized several of these challenges for representing biogeochemical fluxes and alterations to soil structure in hind‐ and earthcasts. Here we list the challenges they identified as well as other challenges and goals raised by the book contributors. 1. Capturing non‐linear feedbacks of landscape and CZ evolution. 2. Developing techniques and tools to span spatial (decimeters to continental) and temporal (seconds to millennia) scales. 3. Coupling physical, chemical and biological processes at appropriate time scales to represent mechanistic relationships and potential feedback processes. 4. Better process representation of soil organic carbon dynamics.

5. Understanding of deep biosphere properties and processes. 6. Quantifying the effects of biological weathering across scales, and particularly predicting how it will change in the future in response to anthropogenic factors, climate change, and extreme events. 7. A better understanding of human and ecological interactions, for example, climate change effects on biogeochemical processes and feedbacks between them. 8. Understanding of the impact of extreme events on biogeochemical cycles. 9. Application of biogeochemical knowledge to solve social problems such as sustainable food production and carbon sequestration. Contributing authors collectively identified a number of possible solutions and future directions for the development of biogeochemistry. They are listed below. 15.5.1. Interdisciplinary Science: Integrating Earth Science Disciplines and Laboratories in the Study of the Critical Zone One of the challenges of biogeochemistry is interconnectedness of biological, chemical, and physical processes in the Critical Zone that makes it difficult to examine them separately, but also presents challenges in studying them together due to the presence of complex nonlinear feedbacks. Some of the possible solutions include collecting data across disciplines on appropriate time scales that represent mechanistic relationships between the processes and combing databases to allow processing them together as suggested by Fortescue (1980). Some of the existing efforts of developing common databases include Earth Cube and Ultimate Earth. Integration also needs to happen between observation networks. According to Nagai and co‐authors (Chapter 11) identifying common goals is required to achieve a global understanding of the phenology response to climate change. They also suggest well‐defined and challenging tasks to improve remote sensing, for example, collection of abundant ground‐truth data from multiple field sites to be able to validate simultaneously observed satellite remote sensing data and then conduct integrated analyses using both types of data. This is true not only for remote sensing of phenology data but also for all other aspects of Critical Zone processes, which can be better understood with closer collaboration across CZOs and CZEN nationally and internationally. 15.5.2. Identification of Proxies and Biosignatures for Biological Weathering Reactions The complexity of biogeochemical processes including biological weathering and the importance of identifying drivers that influence the process calls for development

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of proxies and markers for distinguishing past and present biogeochemical events. Stable isotopes can serve as tracers that distinguish between different sources and pathways in the environment for many elements (Chapters 8 and 9). Sometimes the selected isotopes are proxies for the element of interest, for example, using Sr isotopes to describe Ca behavior in the environment. Isotopes and other proxies provide a powerful tool of tracing biogeochemical processes. As availability of mass spectrometers that allow measurements of isotopic composition increases and their sensitivity improves, the use of proxies in biogeochemistry continues to expand. Identification and quantification of biomarkers and biosignatures for past and present biological activity would improve our understanding of Earth’s history and past environments. These improvements also allow advancing the quantification of biologically mediated processes in the present Earth. 15.5.3. Integrating New Techniques, New Disciplines, and New Frontiers into the Critical Zone Studies Another difficulty is understanding how effects of the biogeochemical processes change across spatial and temporal scales and finding appropriate methodologies to capture biogeochemical fluxes on all scales. This book has described multiple developing techniques, such as “Omics” (Chapters 3 and 5), micro‐ and nanoscale techniques (Chapter  4), and remote sensing (Chapter 11) that improve our understanding of the biogeochemical processes. Recent advances in proteomics and metabolomics allow an unprecedented advancement in understanding mechanisms of how microbial communities interact with their geological environment (Chapter 3). The increasing availability of “omics” data can also improve understanding of the underlying processes of soil carbon dynamics and improve predictions (Chapter 5). Remote sensing (Chapter 11), with recent (i.e., sentinels constellation) and future (for example SWOT satellite) satellite capabilities, will provide opportunities to upscale results and observations from CZOs to a global perspective. It also gives scientists better ability to monitor frontier environments that are often difficult to reach. Since the majority of biogeochemical research is concentrated in a few areas around the world within easy access of the universities, this would allow improving uniformity of research coverage. Remote sensing also allows frequent assessment of intensively managed environments that may need close monitoring for sustainable management. In addition to lateral expansion of study areas, for example to tundra where melting of the permafrost releases the stored carbon, biogeochemistry is expanding vertically, deeper into Earth’s crust, where deep biosphere is an area of active research.

15.5.4. Interdisciplinary Science: Considering Humans as a Main Eco‐Geochemical Driver of the Critical Zone Humans exert significant influence on the environment including the biogeochemical cycles. Direct effects through addition, removal, or translocation of the elements within the cycle are more apparent and relatively easy to quantify, but a significant and indirect effect is coming from climate change. Effects of climate change on biogeochemical cycles are not well understood because of multiple impacts, responses, and complex interactions within each ecosystem, such as, the effects of elevated temperature and CO2 on biota, which in turn influences weathering (Chapter 2) and carbon fluxes (Chapter 12). Organic carbon dynamics is important in itself for its influence on global warming (Chapter  5), but organic carbon is also an energy source that drives cycling of other elements (Chapters 1–3, 10, and 12). Therefore, resolving the dynamics of total organic C and individual organic compounds (Chapters 3 and 5) would help in understanding biochemical cycles of all elements. Humans, whose activity precipitated climate change, can also use natural biogeochemical processes, such as weathering, to mitigate climate change. Some recent research examines whether there are ways weathering reactions that promote the formation of bicarbonates and carbonates can be used to actively remove CO2 from the atmosphere. In the managed agricultural and forestry ecosystems that cover large areas of Earth’s surface, human activity is the main driver of biogeochemical fluxes and therefore there is great potential for implementing changes that would affect the sustainability of food (Chapter  14) and lumber production and improve carbon sequestration and mitigate climate change. In addition, urban systems present a largely unexamined part of the global biogeochemical cycles. These are some of the proposed solutions identified by contributing authors that will bring biogeosciences forward in the coming decades. We hope that this book will start discussions that will generate more questions and more ideas for solutions that will keep further expanding research and knowledge of biogeochemistry. We invite all the readers to join authors on this journey! REFERENCES Brantley, S.L., Goldhaber, M.B., & Ragnarsdottir, K.V. (2007). Crossing disciplines and scales to understand the Critical Zone. Elements, 3, 307–314. Brantley, S.L., White, T.S., White, A.F., Sparks, D., Richter, D., Pregitzer, K., et  al. 2006. Frontiers in exploration of the Critical Zone (30 pp.). Newark, DE: Report of a workshop sponsored by the National Science Foundation, 24–26 October 2005. Fortescue, J.A. (1980). Environmental geochemistry: A holistic approach. Springer Science & Business Media.

306  BIOGEOCHEMICAL CYCLES Steffen, W., Grinevald, J., Crutzen, P., & McNeill, J. (2011). The Anthropocene: conceptual and historical perspectives. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369, 842–867. Teilhard De Chardin, P. (1956). The antiquity and world expansion of human culture. In W.L. Thomas, Jr. (Ed.) with the

collaboration of C.O. Sauer, M.Bates, L. Mumford, Man’s role in changing the face of the Earth (pp. 103–112). Chicago: University of Chicago Press. Vernadsky, V. (1924). La géochimie. Paris: Librairie Félix Alcan. Vernadsky, V. (1929). La biosphere. Paris: Librairie Félix Alcan. Vernadsky, W.I. (1945). The biosphere and the noösphere. American Scientist, 33, 1–12.

INDEX AAS. See Atomic absorption spectroscopy Abiotic weathering, 9–10 in CZ, 136 Acer saccharum, 153 Acidification rain, 17 rock varnish and, 277 Acidithiobacillus ferrooxidans, 61, 68, 74 Acinetobacter, 68–69 Active layer, defining, 246t Adenosine triphosphate (ATP), 65 AFM. See Atomic force microscopy Agar‐plate‐based phenotypic assays, 67f Agriculture. See also Organic agriculture conventional, 285–86 sustainability of, 285 Alaska, 234 GRVI in, 232f Aluminosilicate, 62 Aluminum (Al), 152 mineralogic contributions to, 154t plagioclase weathering and, 160–61 sodium and flux of, 157 soil development and, 160–61 AM. See Arbuscular mycorrhizae Ammonia, 6 Anabaena cylindrica, 64, 68 Angiosperms, 47 Animal‐based products in organic agriculture, 289–90 phosphorus in, 289t potassium in, 289t Animal world, weathering and, 13–15 Anoxia, 255 Anthropocene, 208 Anthropogenic effects dust loading and, 277 weathering and, 49–50 Arbuscular mycorrhizae (AM), 13, 37, 46f, 47, 292 Archean Eon carbon in, 5f energy flows during, 5f Arctic hydrological cycle, 245–46 Aseptic microcosm experiments, 39–40 Aspergillus niger, 69

Atomic absorption spectroscopy (AAS), 69, 87–88 Atomic force microscopy (AFM), 40, 94t of biotite, 90f of fungal hypha, 89f SEM and, 89 ATP. See Adenosine triphosphate Bacillus chitinolyticus, 69 Bacillus subtilis, 65 Bassiès Valley, lead in, 194–96 Beauveria caledonica, 64 Bedrock, molybdenum and, 178–79 Beggiatoa alba, 59 Best management practices (BMPs), 294–95 Betula papryifera, 153 Bioavailability, 250–51 of nitrogen, 252 Biodiversity, 226 Biofilms, growth of, 272f Biogeochemistry of CZ, 133–35 history of, 301 hydrology and, 257 of redox‐sensitive elements, 250–58 of rock varnish, 274–80 subsurface, 213–14 Biological interventions, recycling through, 291–95 Biological weathering, 3–79, 136, 302–3 Biomolecules, 88 Biosignatures, 72f identification of, 304–5 of microbial rock weathering, 71–73 Biosphere (Vernadsky), 301 Biota mineral interactions, 5 as soil‐forming factor, 33 Biotite, 44f, 82 AFM of, 90f TEM of, 86f Bioturbation, 14–15 Birch effect, 113 BMPs. See Best management practices Boreal forests defining, 246t ground thaw and, 247

Bouse Fisherman, 275 Bronze Age, 194–95 Brooks Range, Alaska, 256 Budding bacteria, 268 growth of, 271 imaging of, 272f manganese and, 270 in rock varnish, 270 Buffalo Eddy, Washington, 273f Burkholderia, 41, 71 Burrup Peninsula, Australia, 279 Calcium (Ca) catchments and, 152 mineralogic contributions to, 154t plagioclase weathering and, 155–56, 159–60 sodium and flux of, 155–56 soil depletion of, 159 CarboEurope, 123 Carbon. See also Dissolved inorganic carbon; Dissolved organic carbon in Archean Eon, 5f flows on Earth surface, 9f fluxes, climate change and, 49 fungi symbiosis and, 37–38 inputs, 112 isotope analysis, 68–69 Carbonate weathering, 45 Carbon cycle in CZ, 135–38, 137f weathering and, 5–6 Carbon dioxide (CO2), 49–50, 210 carbonic acid from, 34, 63 elevated levels of, 17, 49, 114–15 permafrost ecosystems and, 249–50 plant intake of, 33 pulse‐labeling experiment, 37f SOM decomposition and, 114–15 weathering and, 17 Carbonic acid from CO2, 34, 63 formation of, 63 weathering and, 34–36 Carbon storage permafrost ecosystems and, 249 soil redox and, 249–50

Biogeochemical Cycles: Ecological Drivers and Environmental Impact, Geophysical Monograph 251, First Edition. Edited by Katerina Dontsova, Zsuzsanna Balogh-Brunstad and Gaël Le Roux. © 2020 American Geophysical Union. Published 2020 by John Wiley & Sons, Inc.

307

308 Index Catchments annual fluxes, 160t calcium and, 152 depths of, 159 DOC in, 160 mass balance analysis of, 151, 154 molybdenum at scale of, 174–85, 186 reference, 155f sodium and, 152 Strengbach, 164–66 study area of, 152–53 treatment of, 160 waters and, 174–78 Cation exchange capacity (CEC), 168, 294 CCI. See Charge contrast imaging CDMZ models climate change and, 123 consistency of, 121–22 four pool, 115–18 learning from, 115–19 microscale decomposition processes and, 121–22 model parameterization in, 112 soil C stabilization in, 122–23 soil moisture and, 114f soil respiration and, 115–18 of SOM decomposition, 104–6 CEC. See Cation exchange capacity Channel formation, 82 Channel incision, climate change and, 214 Charge contrast imaging (CCI), 84 Cheater strains, 121 Chemical fertilizers, 285 Chemical weathering, 215 Chemolithotrophy, 59–60 Chronosequences, hindcasting of, 209–10 Cladosporium, 67 Clean Air Act (1970), 17, 192 Climate change Allison on, 117f carbon fluxes and, 49 CDMZ models and, 123 channel incision and, 214 C stocks and, 118–19 CZ and, 135f feedbacks in, 250 global estimates for, 48 greenhouse gases and, 249–50 humans and, 17–18 permafrost ecosystems and, 245–50 soil respiration and, 115–18, 117f SOM decomposition and, 106–15 subsurface oxidation and, 214 weathering and, 17–18, 18f, 49–50 Climosequences, hindcasting of, 209–10 Clostridium, 69 CMIP5. See Coupled Model Intercomparison Project Phase 5 CO2. See Carbon dioxide

Comamonas, 69 Compost in organic agriculture, 290 phosphorus in, 290t, 293–94, 294t potassium in, 290t, 293–94, 294t Conejo Mine, 274 Continuous permafrost defining, 246t drainage of, 248 flooding of, 248 ground thaw and, 247 latitude and, 247f Continuum scale, upscaling to, 143 Conventional agriculture, 285–86 CORPSE, 217 Coso Range, California, 273 Coupled Model Intercomparison Project Phase 5 (CMIP5), 119 Critical Zone (CZ), 8 abiotic weathering in, 136 biogeochemistry of, 133–35 biotic weathering in, 136 bottom‐up controls on, 139–40 carbon cycle in, 135–38, 137f climate change and, 135f defining, 133 drilling in, 140 ecology of, 138–39 evolution, 139, 213 features of, 134 fractured bedrock in, 141 future directions in, 198–203 geochemistry of, 138–39 geomorphology of, 138–39 geophysics of, 140 hillslope evolution and, 213–14 humans and, 305 hydrology of, 138–39 interdisciplinary study of, 304 lithogenic element cycles in, 135–38 modeling approaches, 143 mountain, 192, 198–203 new techniques in study of, 305 PHTE in, 198–203 pore‐scale processes in, 140–42 saprolite in, 141–42 soil aggregates in, 142 in soil structural changes, 213–14 structure of, 134–35, 140 subsurface architecture of, 140 top‐down controls on, 139–40 vertical architecture of, 140 water cycle in, 135–38 Critical Zone Exploration Network (CZEN), 301 Critical Zone observatory (CZO), 134, 209, 301 Crop residues, in organic agriculture, 288–89

Crustal materials, thermodynamic disequilibrium of, 10 C stabilization iron and, 257 soil, 122–23 C stocks, 120f climate change and, 118–19 Culturing techniques, for microbial rock weathering, 66 Cyanobacteria, nitrogen fixation and, 6 CZ. See Critical Zone CZEN. See Critical Zone Exploration Network CZO. See Critical Zone observatory Daily warmth index, leaf‐flush and, 236f Darcy flow, 142 DBF. See Deciduous broad‐leaved forest DCP. See Degree Confluence Project Dechloromonas, 69 Deciduous broad‐leaved forest (DBF), 229f Degree Confluence Project (DCP), participants in, 237 DEMENT model, 106, 110 Denaturing gradient gel electrophoresis (DGGE), 68, 69 Department of Agriculture (USDA), US, 287 Desert regions, rock varnish in, 270t Desferrioxamine mesylate (DFAM), 65, 68 DGGE. See Denaturing gradient gel electrophoresis DHBA. See 3,4‐Dihydroxybenzoic acid Di‐ammonium phosphate (DPA), 285 DIC. See Dissolved inorganic carbon 3,4‐Dihydroxybenzoic acid (DHBA), 64 Discontinuous permafrost defining, 246t drainage of, 248 ground thaw and, 247 Dissolution, 38 Dissolved inorganic carbon (DIC), 48 Dissolved organic carbon (DOC), 15, 159 in catchments, 160 Dissolved organic matter (DOM), 136 DLTB. See Drained thaw lake basin DOC. See Dissolved organic carbon DOM. See Dissolved organic matter DPA. See Di‐ammonium phosphate Drainage of continuous permafrost, 248 of discontinuous permafrost, 248 Drained thaw lake basin (DLTB) defining, 246t flooding in, 257 Dual‐beam SEM, 84, 94t advantages of, 85 slice and view, 85

Index  309 Dust loading anthropogenic effects of, 277 onto rock varnish, 277–78 Earth carbon flows on surface of, 9f early anoxic, 4–6 energy flows on surface of, 9f oxygen on surface of, 4–5 system evolution, 7f Earthcasting advances in, 210 aspect informing, 211 continental‐scale, 211–13 defining, 207 future directions, 211, 216–17 goals of, 207–8 nonlinear feedbacks captured by, 213 processes, 209 scaling, 214–15 spatial scaling in, 215–16 SSHO and, 211 temporal scaling in, 216 Earth system models (ESMs), 119, 209 global‐scale, 217 Earthworm effects, 14–15 EBVs. See Essential biodiversity variables ECM. See Ectomycorrhizae Ecology, of CZ, 138–39 Ecosystem Demography, 123 Ectomycorrhizae (ECM), 13, 37, 47 EDS. See Energy dispersive X‐ray spectroscopy EDX. See X‐ray spectroscopy EELS. See Electron energy‐loss spectroscopy EEMT. See Effective energy and mass transfer EF. See Enrichment factors Effective energy and mass transfer (EEMT), 139 EGS. See End of growing season Electron energy‐loss spectroscopy (EELS), 86 Electron microscopy, 83–86 Elemental cycles, 303 Element mass balance, 151 End‐members marine aerosol, 183f sea spray/marine, 182 weathering, 182 End of growing season (EGS) in PFRR, 231 satellite RS‐based, 232, 233f spatial distribution of, 231f, 232f timing of, 232, 235f Energy dispersive X‐ray spectroscopy (EDS), 69, 90–91, 94t

Energy flows, on earth surface, 9f Enrichment factors (EF), 198f Entropy, 4f Environmental scanning electron microscopy (ESEM), 83–84, 94t Enzyme acclimation, 116 Enzyme production, 112 EPS. See Exopolymeric substances ESEM. See Environmental scanning electron microscopy ESLP. See European Standard Lead Pollution ESMs. See Earth system models Essential biodiversity variables (EBVs), 241 ET. See Evapotranspiration Etch pit, 82 European Phenology Camera Network, 240 European Standard Lead Pollution (ESLP), 198 Evapotranspiration (ET), 208 EXAFS. See Extended X‐ray absorption fine‐structure spectroscopy Exometabolites, 12 Exopolymeric substances (EPS), 63 Extended X‐ray absorption fine‐structure spectroscopy (EXAFS), 93 FACE. See Free Air Carbon‐dioxide Enrichment Fagus grandifolia, 153 Fagus sylvatica, 45 FAME. See Fatty acid methyl esters FAO. See Food and Agriculture Organization Farm waste phosphorus in, 289t potassium in, 289t Fatty acid methyl esters (FAME), 68–69 Feedback loops, 17 Feldspar tunneling, 48 Ferric iron, 63 Ferrous iron, 63 Fertilizers chemical, 285 molybdenum and, 184–85 phosphorus and, 286 potassium and, 286, 291 production of, 286 FIB. See Focused ion beam First‐order kinetics, in SOM decomposition, 104–6 Flooding of continuous permafrost, 248 of discontinuous permafrost, 248 in DLTB, 257 Focused ion beam (FIB), 39, 84 advantages of, 85 slice and view, 85 Foliage, molybdenum and, 174

Food and Agriculture Organization (FAO), UN, 287 Force curves, 88 Force spectroscopy (FS), 94t Forest decline, 165–66 Fourier transform infrared (FTIR) spectroscopy, 68 Fractured bedrock, in CZ, 141 Free Air Carbon‐dioxide Enrichment (FACE), 17 Freeze‐thaw effects, 39 Frontier ecosystems, 303–4 FS. See Force spectroscopy FTIR. See Fourier transform infrared spectroscopy Fungal hypha, AFM of, 89f Gallionella ferruginea, 71 Garnet, 44f Gaseous secondary electron detector (GSED), 83 GCMs. See Global Circulation Models GCOM‐C. See Global Change Observation Mission‐Climate Geochemistry. See also Biogeochemistry of CZ, 138–39 landscape, 268–71 of rock varnish, 268–71 Geomicrobiology, 10 Geomorphology, of CZ, 138–39 Geophysics, of CZ, 140 GEOSS. See Global Earth Operation Systems Geothrix fermentans, 71 Glacial drift, 153 Global Change Observation Mission‐ Climate (GCOM‐C), 226, 240 Global Circulation Models (GCMs), 123 Global Earth Operation Systems (GEOSS), 241 GOE. See Great Oxidation Event Grand Canyon National Park, Arizona, 34f Great Oxidation Event (GOE), 6–8 Greenhouse gases climate change and, 249–50 permafrost ecosystems and, 249–50 Green‐red vegetation index (GRVI), 229 in Alaska, 232f in Japan, 231f, 232–33 MODIS observation of, 231f, 232f in Russia, 231f Green Revolution, 304 Ground thaw Boreal forests and, 247 continuous permafrost and, 247 discontinuous permafrost and, 247 effects of, 247 factors in, 246 in permafrost ecosystems, 245–49

310 Index Ground‐truth data collection of, 239–40 land cover changes and, 239 land‐use and, 239 plant phenology and, 239–40 GRVI. See Green‐red vegetation index GSED. See Gaseous secondary electron detector Gymnosperms, 47 Haut‐Vicdessos watershed, 194 knowledge gaps impacting, 202f Helium ion microscopy (HeIM), 87f, 94t disadvantages of, 87 Herringbone texture, 41f Heterotrophic microbial consortia, 136 High‐centered polygon, defining, 246t High‐Resolution Land Use and Land Cover (HRLULC), 235 of Japan, 237f High‐resolution transmission electron microscopy (HRTEM), 39, 94t Hillslope evolution, 213–14 Hindcasting advances in, 209–10 of chronosequences, 209–10 of climosequences, 209–10 defining, 207 future directions, 216–17 processes, 209 scaling, 214–15 spatial scaling in, 215–16 temporal scaling in, 216 HRLULC. See High‐Resolution Land Use and Land Cover HRTEM. See High‐resolution transmission electron microscopy Hubbard Brook Experimental Forest, New Hampshire, 44, 151 locus map of, 153f study of, 152–54 Humans climate change and, 17–18 CZ and, 305 land use of, 16 mining and, 17 rain acidification and, 17 weathering and, 15–18 Humus, molybdenum and, 179–80 Hydraulic redistribution, plants and, 39 Hydrocarbon complexes, 5 Hydrology Arctic, 245–46 biogeochemistry and, 257 of CZ, 138–39 iron and, 255–56 permafrost ecosystems and, 245–49 soil redox and, 249

Ice nucleation, 10 Ice wedge polygon, 254 defining, 246t ICP‐MS. See Inductively coupled plasma mass spectrometry ID‐ICZ. See Integrated critical zone model IFOAM. See International Federation of Organic Agriculture Movements Incipient weathering, 8 drivers of, 11f Inductively coupled plasma mass spectrometry (ICP‐MS), 68 Infrared absorption (IR), 88 Integrated critical zone model (ID‐ICZ), 209 Interdisciplinary science, 304–5 Intergovernmental Panel on Climate Change (IPCC), 103 International Federation of Organic Agriculture Movements (IFOAM), 287 IPCC. See Intergovernmental Panel on Climate Change IR. See Infrared absorption Iron (Fe) C stabilization and, 257 fixation of, 272f hydrological connectivity and, 255–56 nutrient cycling, 254–57 organic matter degradation and, 256 oxidation, 256 in permafrost ecosystem, 254–57 redox reactions, 254–56 soil profiles, 174f transformations, 255f Isotope tracers, for metal legacy, 201 JaLTER, 241 JAMSTEC. See Japan Agency for Marine‐Earth Science and Technology Japan GRVI in, 231f, 232–33 HRLULC, 237f plant phenology in, 234–35 seasonal variation in, 232–33 Japan Aerospace Exploration Agency (JAXA), 235, 237f Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), 241 Japan Agriculture Standards (JAS), 287 JapanFlux, 241 JAS. See Japan Agriculture Standards JAXA. See Japan Aerospace Exploration Agency K‐feldspar, 44f, 165 Lacustrine sediments, 6 Land colonization, by vascular plants, 8

Land cover changes ground‐truth data and, 239 plant phenology and, 240 reference database, 235–39 spatial homogeneity and, 239 spatial representativeness and, 239 sufficient volume and, 239 Landscapes, 73–74 geochemistry, 268–71 oxidation of, 6 Land use ground‐truth data and, 239 of humans, 16 plant phenology and, 240 reference database, 235–39 for satellite RS, 227t spatial homogeneity and, 239 spatial representativeness and, 239 sufficient volume and, 239 temporal change and, 239 weathering and, 16 Laser ablation ICP‐MS analysis, 168 LBB. See Leucoberberlin blue Lead (Pb), 192 accumulation chronology, 196f in Bassiès Valley, 194–96 contamination of rock varnish, 275–77 inventories, 194f isotope ratios, 199f metal legacy of, 196–98 moss bio‐indicators of, 196–98 remobilization in Pyrenees, 193–96 Leaf‐fall, 231 Leaf‐flush, 231 daily warmth index and, 236f timing of, 235 Leptochloa dubia, 49 Leptothrix discophora, 65 Leptothrix ocracea, 71 Leucoberberlin blue (LBB), 67 Life interactions, across scales, 18–20 Ligand promoted dissolution, weathering and, 64–65 Lithogenic element cycles, in CZ, 135–38 Litter from experimental plots, 175t molybdenum and, 174, 179–80 LMWOAs. See Low‐molecular‐weight organic acids Long range transboundary air pollution (LRTAP), 192 Long Term Ecological Research Program (LTER), 209 LORICA, 15 Low‐centered polygon, defining, 246t Low‐molecular‐weight organic acids (LMWOAs), 36 Low temperature scanning electron microscopy, 84, 94t

Index  311 Low‐voltage SEM, 94t LRTAP. See Long range transboundary air pollution LTER. See Long Term Ecological Research Program Luminescence, 272 Managed ecosystems, 303–4 Manganese (Mn) budding bacteria and, 270 fixation of, 272f redox reactions, 65 soil profiles and, 174f Manure in organic agriculture, 290 phosphorus in, 290t potassium in, 290t Marcellus Shale, 210 Marcos de Niza engraving, 276 Marine aerosol end‐members, 183f Marine sediments, 6 Marsh, George Perkins, 267 Mass balance analysis, 8 of catchments, 151, 154 of molybdenum, 181t plagioclase weathering and, 154 of plants, 42f Mass loss, from weathering, 46f Matter interactions, across scales, 18–20 Medicago sativa, 49 Mesocosm experiments, 50f design of, 42f plant‐only, 40–41 Metal chelation, weathering and, 64–65 Metal legacy. See also Trace Metal Legacy on Mountain Aquatic Ecogeochemistry environmental archives, 200–201 isotope tracers for, 201 of lead, 196–98 in mountains, 192 proxy combinations, 201–2 Metal pollution, in Pyrenees, 193–98 Methane, 5–6 permafrost ecosystems and, 249–50 MGE. See Microbial growth efficiency Michaelis‐Menten relation, 104 reverse, 105 Microbeam X‐ray diffraction, 92 Microbes mineral particles and, 10 phosphate solubilizing, 292 rock interactions of, 12f weathering and, 10–12, 59–79 Microbial biogeomorphology, weathering and, 73–74 Microbial ecology, weathering and, 70–71 Microbial growth efficiency (MGE), 106 temperature, 112

Microbial interventions in organic agriculture, 292–93 phosphorus and, 292t potassium and, 292t Microbial rock weathering as adaptation, 61 approaches to studying, 60–61 biosignatures of, 71–73 chemical analysis of products of, 68–69 culturing techniques for, 66 future research on, 74 impact of, 62f mechanisms of, 61–70 methodology in study of, 66–71 ’omics technologies in, 69–70 sequencing in, 69–70 Microscale decomposition processes, CDMZ models and, 121–22 Micro‐X‐ray fluorescence spectroscopy (μ‐XRF), 92, 94t MILESD. See Model for Integrated Landscape Evolution and Soil Development Mineral dissolution enhancements, 16f products, plant uptake of, 38 Mineral particles, microbes and, 10 Mineral stabilization, 257 Mineral weathering, mechanisms of, 61–70 Mining, 17 Mixing models, molybdenum, 182t Model for Integrated Landscape Evolution and Soil Development (MILESD), 15 Model parameterization, in CDMZ models, 112 Modern‐day oxidative weathering, 8–18 MODIS, 226, 240 GRVI, 231f, 232f satellite RS and, 235f sensors, 232 Mojave Desert, California, 273 Molybdenum (Mo) abundance of, 163 aerosols as source of, 164 bedrock and, 178–79 calculation of flux of, 180 at catchment scale, 174–86 chemical purification of, 167–68 concentrations of, 179t disequilibrium of, 180 experimental data on, 170t, 171t fertilizer and, 184–85 first‐order models of, 180–82 in foliage, 179–80 foliage and, 174 humus and, 179–80 inputs, 163 isotope cycle of, 175f

isotope data, 169t isotope fractionation, 168–69 isotope models, 182–84 litter and, 174, 179–80 mass balance analysis of, 181t mixing models, 182t at plot scale, 168–74, 185–86 roots and, 174 sampling methods, 167 soil profiles and, 172f, 176f in Strengbach catchment, 166f, 175f, 179f waters and, 174–78 Morphological scaling factors (MSF), 216 Moss bio‐indicators of lead, 196–98 PHTE, 197f Moulton, K. L., 48 Mountains CZ in, 192, 198–203 metal legacy in, 192 MSF. See Morphological scaling factors MultiSpectral Instrument, 226 M‐XRF. See Micro‐X‐ray fluorescence spectroscopy Mycorrhiza. See also specific types effect, weathering and, 12–13 growth of, 37 plants with, 43–44 symbiosis, 14f weathering and, 43–44 Nasca geoglyphs, Peru, 273–74 National Organic Program, 287 Natural mineral deposits in organic agriculture, 290–91 phosphorus in, 290t potassium in, 290t NDVI. See Normalized difference vegetation index Net ecosystem exchange (NEE), 48 Newspaper Rock, 273f New Zealand AM in, 47 EM in, 47 Next generation technologies (NGT), 69 Nitrogen bioavailability of, 252 biological processing of, 253 fixation, Cyanobacteria and, 6 inorganic, 253 nutrient cycling, 252–54 in permafrost ecosystem, 250–54 redox environment and, 252f transformations, 251f Nonlinear feedbacks, earthcasting capturing, 213 Normalized difference vegetation index (NDVI), 229

312 Index North American Carbon Program, 123 Norway spruce, 45, 47 Nutrient cycling iron, 254–57 nitrogen, 252–54 in permafrost ecosystem, 250–51 of phosphorus, 251–52, 287–88 of potassium, 287–88 sulfur, 254 Observatoire Hommes et Environnement, 194, 203 Observatoire Hydrogeochimique de l’Environnement, 165, 186 Ocean‐floor volcanism, 5 OES. See Optical emission spectrometer OM. See Organic matter ’Omics technologies, in microbial rock weathering, 69–70 Open field precipitation, 178t Optical emission spectrometer (OES), 154 Optically stimulated luminescence (OSL), 14 ORCHIDEE. See Organizing Carbon and Hydrology in Dynamic Ecosystems Organic acids, 62 Organic agriculture animal‐based products in, 289–90 best management practices and, 294–95 certification of, 286–87 compost in, 290 crop residues in, 288–89 defining, 285 manures in, 290 microbial interventions in, 292–93 natural mineral deposits in, 290–91 phosphorus in, 287f plant‐based products in, 289–90 production system, 286 regulation of, 286–87 sustainability and, 295 Organic matter (OM). See also Dissolved organic matter; Soil organic matter decomposition breakdown of, 65–66 degradation, 256 formation of, 66 iron oxidation and, 256 at plot scale, 168–74 Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE), 216 OSL. See Optically stimulated luminescence Owens Dry Lake, California, 278 Oxalates, 63, 88 Oxidation, of terrestrial landscapes, 6

Oxidative weathering, modern‐day, 8–18 Oxygen, on Earth’s surface, 4–5 Pacific Ocean, rock varnish from, 278f Panamint Valley, California, 275f Paxillus involutus, 40, 40f, 63 Pedomicrobium, 268 PEN. See Phenological Eyes Network Penicillium simplicissimum, 73 Perfect‐Plasticity Approximation, 123 Permafrost ecosystems carbon dioxide and, 249–50 carbon storage and, 249 climate change and, 245–50 continuous, 246t, 247, 247f, 248 defining, 245 discontinuous, 246t, 247, 248 greenhouse gases and, 249–50 ground thaw in, 245–49 hydrology and, 245–49 iron in, 254–57 methane and, 249–50 nitrogen in, 250–54 nutrient cycling in, 250–51 phosphorus in, 250–52 redox‐sensitive elements in, 250–58 soil redox and, 249 Petrified Forest National Park, Arizona, 273f Petroglyphs, 279, 279f PFRR. See Poker Flat Research Range PGE. See Platinum group elements pH. See also Acidification proton promoted, 62 weathering and, 62–64 Phanerozoic, 8 Phenological Eyes Network (PEN), 226–27, 228 maps of, 228f observation data from, 230 Phenotype tests agar‐plate‐based, 67f microbial rock weathering, 66–67 Phosphate solubilizing bacteria (PSB), 292 Phosphate solubilizing microbes (PSM), 292 Phosphorus (P) in animal‐based products, 289t in compost, 290t, 293–94, 294t cycling, 251 in farm waste, 289t fertilizer and, 286 in manure, 290t microbial interventions and, 292t mineralization of, 251 mobilization of, 294t in natural mineral deposits, 290t nutrient cycling of, 251–52, 287–88 in organic agriculture, 287f

in permafrost ecosystem, 250–52 in plant‐based products, 289t transformations, 251f Photoautotrophic microbial consortia, 136 PHTE. See Potentially harmful trace elements Picea abies, 42 Picea rubens, 153 Pinus resinosa, 43, 44 Pinus rigida, 44 Pinus sylvestris, 40, 40f, 41f Plagioclase weathering aluminum and, 160–61 calcium flux and, 155–56 long‐term variation in, 158–59 mass balance and, 154 silicon and, 159–60 sodium flux and, 154–55 soil calcium and, 159–60 study area of, 152–54 Plant‐based products in organic agriculture, 289–90 phosphorus in, 289t potassium in, 289t Plant‐only mesocosm experiments, 40–41 Plant phenology ecological information for, 240 ground‐truth data and, 239–40 in Japan, 234–35 land cover changes and, 240 land‐use and, 240 multiple validation points in, 234–35 in PFRR, 233f satellite RS of, 229–30 in situ observation of, 228–29 in Spasskaya Pad, 233f spatial scaling and, 240 in Takaya, 233f Plant root exudation, weathering and, 36–37 Plants hydraulic redistribution and, 39 mass balance analysis of, 42f mechanical effects of, 39 with mycorrhiza, 43–44 senescence of, 39 water uptake of, 39 weathering by, 34–48 Plant uptake, of mineral dissolution products, 38 Platinum group elements (PGE), 192 Plot scale molybdenum at, 168–74, 185–86 organic matter at, 168–74 soil profile at, 168–74 Poker Flat Research Range (PFRR), Alaska, 230 EGS in, 231

Index  313 plant phenology in, 233f SGS in, 231 Pore‐scale processes in CZ, 140–42 upscaling in, 143 Potassium in animal‐based products, 289t in compost, 290t, 293–94, 294t in farm waste, 289t fertilizer and, 286, 291 in manure, 290t microbial interventions and, 292t mobilization of, 294t in natural mineral deposits, 290t nutrient cycling of, 287–88 in plant‐based products, 289t removal of, 286 Potentially harmful trace elements (PHTE), 192, 193f concentrations of, 197f in CZ, 198–203 history of, 202–3 moss bio‐indicators, 197f Precipitation regime, SOM decomposition and, 113 Prosopis velutina, 49 Proxies, 304–5 PSB. See Phosphate solubilizing bacteria Pseudomonas putida, 65, 68–69 PSM. See Phosphate solubilizing microbes Pyrenees lead remobilization in, 193–96 metal pollution in, 193–98 Pyrenochaeta, 67 Pyrite, 72f, 73 dissolution, 65 Rain acidification, humans and, 17 Rainfall pulses, soil respiration and, 118f Raman spectroscopy, 68 Rare earth elements (REE), 192 RCP 8.5. See Representative Concentration Pathway 8.5 Reactive transport modeling, 143, 214 Recycling, through biological interventions, 291–95 Redox reactions iron, 254–56 manganese, 65 modulation of, 38 nitrogen and, 252f soil, 249–50 sulfur and, 253f weathering and, 65 Redox‐sensitivity defining, 246t elements, 250–58 REE. See Rare earth elements Reference database

land‐cover changes, 235–39 land‐use, 235–39 Regulation EC20192/91, 287 Representative Concentration Pathway 8.5 (RCP 8.5), 119 Rhizodeposition, 13 Rhizosphere, 294 Rhodoferax ferrireducens, 71 Rock cairn, 275f Rock‐eating fungi, 47–48 Rock phosphate (RP), 290, 294 Rock varnish accretion, 271f acidification and, 277 artificial, 274–75 biogeochemistry of, 274–80 budding bacteria in, 270 bulk chemical analyses of, 270t in desert regions, 270t at different scales, 269f dust loading onto, 277–78 effects of, 268 geochemistry of, 268–71 growth of, 268 lead contamination of, 275–77 at nanoscale, 268–70 Pacific Ocean, 278f prehistoric anthropogenic interactions, 271–74 rate paradox, 271 at Shaw Butte, 276f stability of, 268–70 wildfire and, 278 Roots, molybdenum and, 174 RP. See Rock phosphate RS. See Satellite remote sensing Russia, GRVI in, 231f SACLAJ. See Site‐based dataset for Assessment of Changing Land Cover by JAXA SAED. See Selected area electron diffraction San Dimas Experimental Forest, 45 Santa Monica Mountains, California, 278 Saprolite, 74 in CZ, 141–42 sandy, 165 SAR. See Synthetic aperture radar Satellite remote sensing (RS), 225 EGS, 232, 233f for land use, 227t MODIS and, 235f of plant phenology, 229–30 SGS, 232, 233f spatial resolution in, 226 temporal resolution in, 226 timing of, 232 validation of, 230–34

Scanning electron microscopy (SEM), 39, 44f, 69, 94t AFM and, 89 conventional, 83 dual‐beam, 84–85, 94t environmental, 83–84, 94t examples of, 83f low temperature, 84, 94t low‐voltage, 94t Scanning probe microscopy (SPM), 87–88 Schimel‐Weintraub model, 105 Sea spray/marine end‐member, 182 Second Law of Thermodynamics, 4 Selected area electron diffraction (SAED), 86, 94t SEM. See Scanning electron microscopy SENTINEL‐2, 226, 241 Serpula himantioides, 73 SGS. See Start of growing season Shale formations, 74 Shaw Butte, rock varnish at, 276f Siberia, 234 Siderophores, 64, 88 Silicate, 278, 279f weathering, 5–6, 11 Silicon (Si), 152 dynamics, 159–60 mineralogic contributions to, 154t plagioclase weathering and, 159–60 sodium and flux of, 156–57 Siloxane, 62 Silurian Rangeley Formation, New Hampshire, 153 Silver (Ag), 192 Site‐based dataset for Assessment of Changing Land Cover by JAXA (SACLAJ), 235 data search window, 238 data upload window, 238 extension of, 237 Sodium (Na) aluminum and flux of, 157 calcium and flux of, 155–56 calculation of flux of, 155f catchments and, 152 mineralogic contributions to, 154t net catchment export of, 152 plagioclase weathering and flux of, 154–55 silicon and flux of, 156–57 Sodium hydroxide, 275 Soil aggregates, in CZ, 142 calcium, plagioclase weathering and, 159–60 crusts, 6 C stabilization, in CDMZ models, 122–23 development, aluminum and, 160–61 fertility, 286 formation, biota and, 33

314 Index Soil moisture CDMZ models and, 114f SOM decomposition and, 113 Soil organic matter (SOM) decomposition CDMZ models, 104–6 challenges in study of, 120–21 climate change and, 106–15 CO2 and, 114–15 first‐order kinetics in, 104–6 precipitation regime and, 113 soil moisture and, 113 spatio‐temporal dynamics of, 121f structure of models of, 104f temperature and, 106–13 Soil profiles iron, 174f manganese and, 174f molybdenum and, 172f, 176f at plot scale, 168–74 of Strengbach catchment, 166–67 WITCH for, 212f Soil redox carbon storage and, 249–50 hydrology and, 249 permafrost ecosystems and, 249 Soil respiration CDMZ models and, 115–18 climate change and, 115–18, 117f rainfall pulses and, 118f Soil structural changes, CZ in, 213–14 SOM. See Soil organic matter decomposition Sonoran Desert, Arizona, 272f Spasskaya Pad site, Siberia, 232 plant phenology in, 233f Spatial representativeness, land‐use and, 239 Spatial scaling in earthcasting, 215–16 in hindcasting, 215–16 plant phenology and, 240 Sphagnum, 196 Sphingomonas, 71 SPM. See Scanning probe microscopy Spodosols, 152 SSHO. See Susquehanna Shale Hills Critical Zone Observatory Start of growing season (SGS), 232f in PFRR, 231 satellite RS‐based, 232, 233f spatial distribution of, 231f, 232f timing of, 232 Strengbach catchment, 164 analytical techniques of, 166–67 geological overview of, 165f molybdenum in, 166f, 175f, 179f sample description of, 166–67

sampled waters in, 166f sampling methods, 167 soil profile of, 166–67 topsoil samples in, 172 waters from, 176f, 177f, 184f Streptomyces, 64 Subsurface architecture, of CZ, 140 Subsurface oxidation, climate change and, 214 Sulfur (S) nutrient cycling, 254 redox reactions and, 253f transformations, 253f Sulfurovum, 71 Susquehanna Shale Hills Critical Zone Observatory (SSHO), Pennsylvania, 68 earthcasting and, 211 Sustainability of agriculture, 285 defining, 285–86 organic agriculture and, 295 Svalbard, Arctic, 236f Symbiotic fungi, weathering by, 39–48 Synthetic aperture radar (SAR), 225 Taiga. See Boreal forests Takayama (TKY), 229f plant phenology in, 233f TEM. See Transmission electron microscopy Temperature MGE, 112 SOM decomposition and, 106–13 Temporal change land cover changes and, 239 land‐use and, 239 Temporal resolution in satellite remote sensing, 226 spatial resolution and, 226 Temporal scaling in earthcasting, 216 in hindcasting, 216 Thermodynamics defining, 4 disequilibrium of crustal materials, 10 Second Law of, 4 Thermo Fisher TRITON, 168 Thiobacillus, 73 Thiothrix, 71 TKY. See Takayama Topsoil samples, in Strengbach catchment, 172 Trace Metal Legacy on Mountain Aquatic Ecogeochemistry (TRAM), 199 circular flow chart, 200f Trait‐based models, 110, 121

TRAM. See Trace Metal Legacy on Mountain Aquatic Ecogeochemistry Transmission electron microscopy (TEM), 69 biotite, 86f high‐resolution, 39, 85, 94t spatial resolution of, 85–86 X‐ray spectroscopy and, 91 Tuna Canyon Park, California, 278 Tundra, defining, 246t UN. See United Nations Uncertainty, 103 United Nations (UN), 287 United States (US), 287 Urbanization, 276f US. See United States USDA. See Department of Agriculture Varnish rate paradox, 271 Vascular plants land colonization by, 8 weathering and, 12–13 VEGETATION sensor, 226 Vertical architecture, of CZ, 140 Vicdessos, 194 Volcanic degassing, 5f Water adsorption, 10 catchments and, 174–78 cycle, in CZ, 135–38 flow regulation, 38–39 molybdenum and, 174–78 from Strengbach catchment, 176f, 177f, 184f uptake, of plants, 39 Watershed scales, weathering at, 48 Weathering abiotic, 9–10, 136 animal world and, 13–15 anthropogenic effects and, 49–50 biotic, 136 carbonate, 45 carbon cycle and, 5–6 carbonic acid production and, 34–36 chemical, 215 climate change and, 17–18, 18f, 49–50 CO2 and, 17 defining, 3–4, 60 end‐members, 182 entropy in, 4f field evidence of, 39–48 field observation in situ, 47–48 future research on, 20, 50–51 in heterogeneous media, 215 humans and, 15–18

Index  315 imaging methods for visualizing, 69 incipient, 8, 11f investigation of, 60–61 land use and, 16 ligand promoted dissolution and, 64–65 mass loss from, 46f mechanisms of, 34–39, 61–70 metal chelation and, 64–65 microbes and, 10–12 microbial, 66–71 microbial biogeomorphology and, 73–74 microbial ecology of environments, 70–71 microbial rock, 61–70, 62f, 69–70, 71–72 mineral, 61–70 mining and, 17 modern‐day oxidative, 8–18 mycorrhiza and, 43–44 mycorrhizae effect and, 12–13

pH and, 62–64 plagioclase, 152–61 plant root exudation and, 36–37 by plants, 34–48 redox reactions and, 65 across scales, 19f silicate, 5–6, 11 by symbiotic fungi, 39–48 temperature and, 9–10 vascular plants and, 12–13 at watershed scales, 48 Weathering‐induced fracturing (WIF), 139 Web Camera Images of National Parks and Wildlife, 240 Western thought, 267 Wet Holocene Unit 1 (WH1), 276f White Mountain National Forest, New Hampshire, 152 WHO. See World Health Organization WIF. See Weathering‐induced fracturing

Wildfire, rock varnish and, 278 WITCH, for soil profiles, 212f Wood Wide Web, 8 World Health Organization (WHO), UN, 287 XANES. See X‐ray absorption near‐edge spectroscopy XAS. See X‐ray absorption spectroscopy X‐ray absorption near‐edge spectroscopy (XANES), 68, 93 X‐ray absorption spectroscopy (XAS), 93, 94t X‐ray diffraction (XRD), 68, 91, 94t microbeam, 92 X‐ray spectroscopy (EDX), 39. See also Energy dispersive X‐ray spectroscopy advances in, 91 energy dispersive, 90–91 TEM and, 91 XRD. See X‐ray diffraction