Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation [1 ed.] 9781619427983, 9781612093925

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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science Publishers,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

ENVIRONMENTAL SCIENCE, ENGINEERING AND TECHNOLOGY

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

ALPINE ENVIRONMENT: GEOLOGY, ECOLOGY AND CONSERVATION

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Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

ENVIRONMENTAL SCIENCE, ENGINEERING AND TECHNOLOGY

ALPINE ENVIRONMENT: GEOLOGY, ECOLOGY AND CONSERVATION

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JOHN G. SCHMIDT EDITOR

Nova Science Publishers, Inc. New York Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

Copyright ©2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‟ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data The alpine environment : geology, ecology, and conservation / editors, John G. Schmidt. p. cm. Includes bibliographical references and index. ISBN:  (eBook)

1. Mountain ecology. 2. Mountain biodiversity conservation. 3. Mountains. 4. Geology. I. Schmidt, John G. (John George), 1953QH541.5.M65A47 2012 577.5'3--dc22 2011003685

 New York Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

CONTENTS Preface Chapter 1

Chapter 2

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Chapter 3

vii Aquatic Insects of Remote Alpine Lakes: Taxonomic Composition, Ecological Patterns and Seasonal Dynamics in Altitudinal Environmental Gradient (Tatra Mts, Slovakia) Zuzana Čiamporová-Zaťovičová Herbaceous Species to Control the Alpine Soil Erosion: Field and Laboratory Experimental Tests Elena Comino, Paolo Marengo and Valentina Rolli

47

Estimation of the Growing Season Length in Alpine Areas: Effects of Snow and Temperatures Arvid Odland

85

Chapter 4

Socioeconomics of Conservation in the Alps Birgit Bednar-Friedl, Doris A. Behrens and Michael Getzner

Chapter 5

The Alpine Environment and Anthropisation of the Landscape in the Western Pyrenees (Spain) During the Holocene María-José Iriarte-Chiapusso and Alvaro Arrizabalaga

Chapter 6

Chapter 7

1

Tectonic Control on the Evolution of the Middle Triassic Platforms in the Alpine-Carpathian-Dinaric Region (Differences in the Evolution of Two Opposite Shelves of the Neotethys Ocean) Felicitász Velledits Genetic Diversity and Population Structure of Alpine Plants Endemic to Qinghai-Tibetan Plateau, with Implications for Conservation Under Global Warming Yupeng Geng, John Cram and Yang Zhong

Index

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155

173

191 205

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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PREFACE This new book presents topical research in the study of the Alpine environment with a focus on geology, ecology and conservation. Topics discussed include aquatic insects of remote Alpine lakes; the socio-economics of conservation in the Alps; the genetic diversity and population structure of Alpine plants endemic to the Tibetan plateau and climate change impacts on Alpine basins. (Imprint: Nova Press) Chapter 1 - Remote alpine lakes are unique aquatic ecosystems with high ecological and environmental value, generally perceived to be in pristine condition, supporting unique plant and animal communities. They are regarded as excellent indicators of environmental change (mostly atmospheric pollution and climate change) and "early warning" systems for entire mountain environment. Despite obvious similarities between these lakes in Pan-European scale, most of biological groups underlie the strong zoogeographical aspect, emphasizing the need of detail study of individual lake districts´ fauna. Systems of alpine lakes are excellent examples of ecosystems covering several well-defined continuous environmental gradients. Within this study, the effect of altitudinal environmental gradient (amalgam of variables correlated with altitude – primarily temperature) of four (sub)alpine lakes in the High Tatra Mts (Slovakia, Central Europe) on the macroinvertebrate fauna was studied, with emphasis on aquatic insects. Littoral, inlet and outlet assemblages were assessed in terms of taxonomic composition, distribution, ecological and seasonal patterns. Results showed clear trends in several biotic metrics with altitudinal/temperature gradient, and also great capability of aquatic insects (temporal fauna) to reflect this gradient, thus they can be used as good indicators of temperature changes. While permanent fauna remains more or less stable in alpine lakes, diversity of aquatic insects is strongly affected through an increase in the number of more thermophilic species typical for lower altitudes, as well as impoverishment of the native fauna due to extinction of cold stenothermal ones. Regarding relatively frequent quantitative sampling of macroinvertebrates during two years, assessing the seasonal dynamics of aquatic assemblages and dominant insects, also subjected to temperature changes mostly through life cycles of particular species, was possible. Chapter 2 - Landslide, soil slip and superficial erosion are typical natural phenomena which involve the Alpine environment. Long since, the scientific community has the objective to measure and provide the best methodology to reduce the effects of critical events such as soil erosion, improving also slope stability. The alpine environment within the NorthWest Italian territory (Piedmont region) has been studied for geological and ecological aspects in order to improve the environment conservation. Regarding this aim, bioengineering technique has been investigated to control and reduce soil erosion. These studies are focused

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viii

John G. Schmidt

on the presentation of an appropriate methodology to quantify the contribution of the herbaceous vegetation to prevent the soil erosion on mountain and hill slopes. The herbaceous species tested belong both to the Poaceae family, such as Lolium perenne and Festuca pratense, and Fabaceae family such as Trifolium pratense, Lotus corniculatus and Medicago sativa. These species have been chosen because autochthonous and widespread in the Alpine environment in relation to the climate conditions of the investigated area. For this reason the tested species could be useful for the mechanical effects and for the landscape management. To quantify the soil reinforcement effect given by roots, situ shear tests on rooted and norooted soil clods were realized in three different sites (located in Val Pellice, Piedmont region - Italy). Moreover laboratory tests were conducted to study the physical behavior of the roots in the soil during shallow landslides phenomena. The experimental data were used to implement two different models widely used in scientific literature, verifying their applicability to describe the complex system of alpine rooted soil. The research, developed in the last years, has confirmed that the presence of grass roots increases the shear strength of the first soil layers, reducing the soil susceptibility to erosion phenomena and shallow landslides. The obtained results can be useful for the improvement of soil bioengineering techniques in the Alpine environment, with widespread potential applications, for example in the ski runs, river banks or slopes running along roads. Chapter 3 - Alpine areas have frequently been characterized by having a very short growing season length (GSL). A major problem with such statements is that GSL has no generally accepted definition. This also makes comparisons between results from regional studies difficult. The objective of this study has been to quantify differences in GSL from different study plots based on three different definitions of the start of the growing season, and to discuss their ecological significances. The start of the growing season is here intended reflect the timing of initiation of plant growth and the end when plants have ended their annual growth period. During the spring and summer of 2004, patterns of snow-melt and increases in soil temperature were investigated at 187 study plots from three mountain areas. The study plots were selected to cover major environmental and vegetation gradients from the forest limit ecotone up to mountain summits (1400 m). Air temperature for each plot was interpolated from the nearest meteorological station using a lapse rate of 0.6 oC per 100 m. Start of the growing season was defined in three different ways: (1) Julian day when air temperature the 5 first consecutive days in spring was higher than 5 oC, (2) Julian day of snow-melt, and (3) Julian day when the soil threshold temperature exceeds 6 oC. The end of the growing season was defined as the date when average air temperature (2 m level) for the last 10 consecutive days was higher than 5 oC. Based on these definitions, GSL varied more than three months between different plots. Air temperature data and snow-melt data were generally considered to be poor predictors for the start of the growing season. On exposed sites with a sparse snow cover the soil was frozen, and a period of more than 2 months was needed to reach the soil threshold temperature while in lee sides and snow beds, less than 6 days were required. GSL decreased in average by approximately 6 days per 100 m increase in altitude, and it decreased from oceanic to continental areas. The most continental area generally had an earlier date of snow-melt and a considerable longer period (25.3 days ±19.2) between snow-melt date and the soil threshold date compared to the two more oceanic sites (12.0 days ±7.0 and 13.0 days ±16.0 respectively). The study was performed in 2004 during which both spring and autumn temperatures were higher than normal and the estimated GSL are probably longer than during a “normal” year. The variation in GSL as defined by either

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Preface

ix

date of snow-melt or day when the soil temperature threshold was reached varied more than three months in the forest limit ecotone (Northern Boreal zone) and the low alpine zone while in the middle alpine zone the variation was generally less than one month. Chapter 4 - The current chapter presents a review of socioeconomic research on nature conservation in the Alps, especially in Alpine national parks. We present a basic conceptual model of the interdependencies between ecosystems, nature-based tourism and national park management, as well as the public perception of conservation and regional economic development. This is complemented by a review of studies on the valuation of nature conservation, on the potential contribution of national parks to regional economic development and by a discussion on costs (including opportunity costs) and funding of national parks. As the title “socioeconomics of conservation” indicates, an effective and efficient conservation authority needs to jointly manage both ecosystem and economic activities within a park‟s boundaries and beyond. This includes the need for conservation plans to be credibly implemented together with stakeholders in the park region. Only under such circumstances will visitors, as well as the general public, be able to support (conceptually and/or monetarily) nature conservation in the long run. Chapter 5 - The Pyrenees is a mountain chain running east to west, separating France and Spain. Altitudes range practically from sea level (at the eastern and western ends of the chain) to over 3400 metres (Mt Aneto, 3404m), with the corresponding vegetation zones. Although during much of the Pleistocene, these mountains were not suitable for human settlement or tree growth at altitude, in the Holocene this situation began to change, with the climatic improvement it brought. First, forests rapidly developed in areas at higher altitudes in the mountains and second, from the Neolithic on, humans began to colonise land for agriculture and, above all in alpine environments, for pastoralism. The signs of anthropisation at different altitudes in montane areas are seen clearly from quite old chronologies onwards. Deforestation, the appearance of invasive tree species, the massive presence of microcharcoal, attributed to clearing land by fire, and the entry of nitrophile species and weeds, mark some of the dominant trends in this process of altering the natural environment. Chapter 6 - Based on the Middle Triassic evolution of the carbonate platforms of the Alpine-Carpathian-Dinaric region two different types can be distinguished. The first is characterized by Middle-Upper Triassic terrestrial sediments together with volcanites. They suffered repeated uplift in the Anisian-Carnian: 1. Piz da Peres Conglomerate, (Bithynian); 2. Voltago Conglomerate, (Early Pelsonian); 3. Richthofen Conglomerate (Illyrian: Trinodosus zone/Trinodosus subzone); 4. Ugovizza Breccia 2 (upper Illyrian: Avisianum subzone); 5. Conglomerate and sandstone of Fassanian age; 6. Bauxite: Upper Ladinian-Carnian. The uplift was accompanied by volcanic activity, and followed by rapid subsidence. Carbonate platforms belonging to the first type can be found in the Dolomites, Carnic Alps, Julian Alps, South Karavank Mts., Bükk Mts., and External Dinarides. The age of the uplift is younger and younger as we proceed from the Dolomites towards the Carnic and Julian Alps, Karavanks, External Dinarids. On the southern shelf the platforms are small, and the shape is more or less round and the basins cover a much bigger area than platforms. The second carbonate platform type is characterized by the lack of terrestrial sediments and volcanics. Such platforms can be found in the Northern Calcareous Alps (NCA), the Western Carpathians (WNC) and the Drina Ivanjica Element of the Internal Dinarids. The ages of the drownings: 1. Balatonicus zone: Balatonicus subzone, 2. boundary between the Balatonicus and Trinodosus zones: Binodosus-Trinodosus subzones, 3. Reitzi zone: Avisianum subzone.

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John G. Schmidt

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The age of many terrestrial sediments, i.e., uplifts, coincide with that of the drowning events. On the northern shelf the platforms are big and have long, elongated shapes; the platforms cover a much bigger area than the basins. The carbonate platforms of the first type were deposited in the Triassic on the southern shelf, and the second type on the northern shelf of the ocean, the remnants of which build up the Dinaride Ophiolite Belt. The asymmetric evolution of the two shelves, and the younger and younger age of the uplift, and accompanying volcanic activity can be explained by mantle plume activity. Chapter 7 - The Qinghai-Tibetan Plateau is one of the most important centers of biodiversity for alpine species in the world and is among the areas that are most sensitive to global warming. Knowledge about population genetics is essential for understanding the dispersal ability and evolutionary potential of alpine species in a warming world. In this chapter, we review the genetic diversity and population structure of 19 alpine plant species endemic to the Qinghai-Tibetan Plateau. Generally, the population genetic variation can varygreatly among different species and the endangered species have much lower levels of genetic diversity than the co-occurring common species. Although a few species showed increased levels of genetic diversity along altitude, we dectected no significiant correlation between diversity and altitude in most species. In addition, the isolation-by-distance model cannot explain the spatial genetic structure in most alpine species that have been investigated, which may partially due to the discontinous distribution of alpine species shaped by complex geomorphology in Qinghai-Tibetan Plateau. The implications of these results for the conservation of alpine plants during global warming are discussed.

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In: Alpine Environment: Geology, Ecology and Conservation ISBN: 978-1-61209-392-5 Editor: John G. Schmidt, pp. 1-46 ©2011 Nova Science Publishers, Inc.

Chapter 1

AQUATIC INSECTS OF REMOTE ALPINE LAKES: TAXONOMIC COMPOSITION, ECOLOGICAL PATTERNS AND SEASONAL DYNAMICS IN ALTITUDINAL ENVIRONMENTAL GRADIENT (TATRA MTS, SLOVAKIA) Zuzana Čiamporová-Zaťovičová* Institute of Zoology, Slovak Academy of Sciences, Bratislava, Slovakia

ABSTRACT Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

Remote alpine lakes are unique aquatic ecosystems with high ecological and environmental value, generally perceived to be in pristine condition, supporting unique plant and animal communities. They are regarded as excellent indicators of environmental change (mostly atmospheric pollution and climate change) and "early warning" systems for entire mountain environment. Despite obvious similarities between these lakes in Pan-European scale, most of biological groups underlie the strong zoogeographical aspect, emphasizing the need of detail study of individual lake districts´ fauna. Systems of alpine lakes are excellent examples of ecosystems covering several welldefined continuous environmental gradients. Within this study, the effect of altitudinal environmental gradient (amalgam of variables correlated with altitude – primarily temperature) of four (sub)alpine lakes in the High Tatra Mts (Slovakia, Central Europe) on the macroinvertebrate fauna was studied, with emphasis on aquatic insects. Littoral, inlet and outlet assemblages were assessed in terms of taxonomic composition, distribution, ecological and seasonal patterns. Results showed clear trends in several biotic metrics with altitudinal/temperature gradient, and also great capability of aquatic insects (temporal fauna) to reflect this gradient, thus they can be used as good indicators of temperature changes. While permanent fauna remains more or less stable in alpine lakes, diversity of aquatic insects is strongly affected through an increase in the number of more thermophilic species typical for lower altitudes, as well as impoverishment of the native fauna due to extinction of cold stenothermal ones. Regarding relatively frequent *

Institute of Zoology, Slovak Academy of Sciences, Dúbravská cesta 9, 845 06 Bratislava, Slovakia, e-mail: [email protected]

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Zuzana Čiamporová-Zaťovičová

2

quantitative sampling of macroinvertebrates during two years, assessing the seasonal dynamics of aquatic assemblages and dominant insects, also subjected to temperature changes mostly through life cycles of particular species, was possible.

Keywords: alpine lakes, littoral, inlet, outlet, temperature/altitudinal gradient, climate change, macroinvertebrates, aquatic insects, seasonal dynamics

INTRODUCTION

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Alpine Lakes According to the general climatic criteria and altitudinal distribution of vegetation, the alpine zone is defined as being situated between the tree line and the permanent snowline. Ecosystems at high altitudes face extremely harsh conditions such as low air temperature causing extended periods of winter snow- and ice- cover, daily and annual extremes in wind, low humidity and precipitation, or intense solar radiation during the relatively short snow-free season. Above the tree line, the environment is generally characterized by snow beds, bare rock surfaces, skeletal base poor soils and sparse vegetation cover, giving way to permanent snowfields and glaciers at the highest altitudes. The lakes situated within this altitudinal layer are known as "alpine lakes" (sometimes called "high-mountain lakes"). Alpine lakes tend to be abundant in high mountain ranges and have high ecological and environmental value – they are often the focal points of mountain landscapes, support unique plant and animal communities, or are the headwater catchments for water supplies [65]. Most of them are of glacial origin and are clustered into "lake districts". Despite obvious environmental similarities among lake districts, there are also conspicuous differences related mainly to latitudinal changes in photoperiod and relief, which combine to produce different land covers between mid-latitude alpine areas and high latitude lands [26]. Alpine lakes have a number of features in common due to their environmental and ecological settings in comparison with lakes in lower areas: majority of them are relatively small, with low water temperature (usually below 12 °C; max. 15 °C in warmer years), very low light regime for most months of the year, high UV-radiation, and high transparency. In terms of available nutrients most of them are clear, oligotrophic, dilute and unproductive, with simple food-webs due to their thin, poorly developed soils and small sparsely vegetated catchments, relative to lake volume [26]. Spring snowmelt is the dominant hydrologic event there. Due to natural migration barriers, most of alpine lakes are fishless. Despite these similarities, alpine lakes exhibit large chemical and biological variability that is closely related to the geomorphological, climatic, hydrological factors and biotic history typical of the altitude at which their basins are located, and degree to which their basins have been disturbed by human activity [53,122].

Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

Aquatic Insects of Remote Alpine Lakes

3

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Alpine Lakes as Sentinel Systems Alpine regions are supposed to represent the least disturbed environments in Europe due to their remote location with relatively low or absent direct human impact. Despite remote mountain lakes, as well as their inseparable parts - inlets and outlets, are generally perceived to be in pristine condition and are much less influenced by pollution from agriculture and wastewater [120], almost all of them have been impacted to a greater or lesser degree by airborne contaminants such as acid deposition (S and N) [30,38,70], deposition of persistent organic pollutants (POPs) [50], trace metals (mainly Pb and Hg) [23,131] and nutrient deposition. Extreme sensitivity of alpine lake ecosystems to disturbances is given mainly by their catchment characteristics enabling easy and quick transport of deposited pollutants to lakes by runoff and snowmelt, along with characteristics of lakes themselves [1,107]. The alpine lakes are also very sensitive to climate change [73,123]. The climate is changing rapidly, possibly at a faster rate than at any time in the last ten thousand years [3,127]. Climate warming registered in last 30 years is the most evident exactly in alpine and arctic regions, where increase in temperature registered during the past few decades was 1.5– 2 °C in average compared to 0.5 °C globally [4]. Anthropogenic climate change poses serious consequences for the biodiversity and ecosystem functioning of high-elevation mountain lakes, through a series of both direct and indirect effects. In such sensitive areas even slight change in temperature can influence hydrological cycle, extent and duration of ice-cover, thermal and light regime, and consequently duration of growing season, water level fluctuations, or structure and productivity of lacustrine ecosystems [73,116]. Global warming is beginning to reduce ice-cover, increase the length of the growing season and alter biogeochemical and ecological processes. The warming threatens mountain lake biodiversity directly (e.g. by reduction suitable habitats for oligostenothermic taxa), and also indirectly e.g. by behavior of pollutants [1]. General Circulation climate Models (GCMs) simulating presumable future climatic scenarios [87] predict continuing climate warming mainly in arctic and alpine areas [4,60]. As a consequence, forested areas will expand to the north and tree line to higher altitudes then are at present. Subsequent enrichment of oligotrophic lakes by organic matter and increase of their trophy will lead to biological changes in assemblages of lacustrine organisms [74,124]. Mountain lakes, while subject to the same chemical and biological processes controlling lowland lakes, are more sensitive to any subtle changes in their surroundings. Their vulnerability makes them suitable "early response" indicators of changes in atmospheric deposition of long range transported air pollutants and in the regional climate. Thus high altitude lakes and their biocenoses are regarded as excellent sentinel systems of global environmental change for entire mountain environments [94,120,126] and are considered ideal sites for long-term monitoring of environmental change all around the world [2,24].

Biota of Alpine Lakes and Environmental Gradients The tree line and the permanent snowline constitute two important boundaries for life in alpine environments. Both terrestrial and aquatic ecosystems of alpine zone are influenced by extremely harsh conditions, like circadian and annual wind fluctuations, low air and water temperature, extended period of snow- and ice-cover, intensive solar radiation during the

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Zuzana Čiamporová-Zaťovičová

short growing period, or restricted energy and nutrient sources. Combination of these factors affects surviving, development and reproduction of organisms and allows existence just of relatively poor communities with short food chains and a few dominant species. Year-round low temperature is the most important factor limiting biological assemblages of this zone [21,40,100,110]. A crucial part in the assessment of ecological status of freshwaters is the benthic (bottom) macroinvertebrate fauna, which represents a useful proxy integrating e.g. climatic, hydrological and geological variables. Benthos also helps to elucidate changes in environmental variables within a lake and long-term changes in nature [102]. Bottom invertebrates of alpine aquatic ecosystems are not assessed as good as in lower elevations. Their assessment used to be restricted mainly to the short growing season as winter samplings are negatively affected by ice-cover [14]. Benthic macroinvertebrates are frequently used as bioindicators of water quality and global climate changes like outcome of human activities (acidification, climate warming...) and serve as “early warning” organisms detecting possible disturbances and changes in these ecosystems earlier than e.g. fish populations [54,108]. Moreover, it is predicted that these changes and their impact on macroinvertebrates will be strongest right in alpine environment [36]. Biota of these areas is namely adapted to limiting water quality and cold climate and thus is very sensitive to even negligible changes in environmental conditions [120], and so understanding of environmental parameters determining distribution of biota is really crucial [14]. Higher heterogeneity and seasonal variation of littoral zone of lakes enables development of more diverse macroinvertebrate assemblages bound to certain microhabitats, if compared with relatively stable and predictable profundal zone [134]. While in profundal the main factors determining benthic assemblages are oxygen content and trophy, structure and organic content of sediments, catchment vegetation, wind activity and water level fluctuation, interspecies interactions, perhaps even fish populations and submerged vegetation in lower elevations are more important in littoral zone [18]. Even the importance of littoral zone, due to its relatively high proportion of lake area [115] and higher productivity than pelagial [105], is clear, increased interest in its study appeared only recently, when negative human impact on aquatic environments increased. The alpine areas are characterized by steep, well defined continuous gradients in environmental parameters. At least three generally independent gradients with relatively broad range and defined ecological thresholds exist related to altitude, bedrock mineral composition and lake size. For example, the most significant ecological threshold for climate change is 190 days of ice-cover duration corresponding with about 2100–2200 m a.s.l. in the Alps [26]. The most distinct appears to be altitudinal environmental gradient, representing an amalgam of environmental features that change with altitude within a mountain range, in addition to its own direct effects on the lake dynamics [26]. Several studies show that the assemblage structure and composition of aquatic organisms exhibit conspicuous changes along climatic gradient [55] and alpine lakes situated along altitudinal/temperature gradient (gradient lakes) have begun to be studied as potential indicators of climate change (e.g. the EMERGE project) [104]. Elevation differences accompanied by temperature changes are reflected in changing riparian vegetation, production of organic matter in particular catchments, as well as in conditions of aquatic environment subjected mainly to water temperature and duration of ice-free season. These effects can be seen even along short altitudinal gradients [135]. All these factors, including catchment geology, strongly influence

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Aquatic Insects of Remote Alpine Lakes

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the structure of benthic assemblages [14,36]. Temperature is the crucial factor determining distribution, diversity and abundance of benthos in remote mountain areas, influencing their growth, metabolism, reproduction and emergence [22,40]. Surviving and distribution of aquatic macroinvertebrates are affected by temperature mainly indirectly through water chemistry, intensity of erosion, microbial processes, trophic interactions, or nutrient input [123]. Influence of altitudinal/temperature gradient on macroinvertebrates is well documented mainly in alpine glacial streams including inlets and outlets of lakes [13,88,113]. Depending on decreasing elevation and increasing temperature, number of orders and families [63] as well as species richness, abundance and diversity [42,90] increase. Organisms inhabiting fresh waters of the alpine zone, especially diverse developmental stages of aquatic insects, had to adapt to harsh environmental conditions (primarily low water temperature and low input of allochthonous organic matter into the water), which distinctly influence their life cycles [40,111,137] and consequently seasonal dynamics of both single species and whole assemblages. Long-lasting snow- and ice-cover caused by low temperature enable surviving and reproduction of insects, however their production and growth intensity are relatively low. During the ice-free season, environmental conditions are milder hence also larval development accelerates [117].

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The European and the Tatra Lakes European mountain lakes should not be considered as a homogenous ecoregion as there are distinct (bio) geographical differences among them. Summarizing complex analysis showed that for most biological groups (except of diatoms) the geographical location of particular lake tends to explain the greatest variation in its taxonomic composition. For instance, species assemblages of chironomids show a clear regional structure with the taxa composition being most distinct in the Nordic and the Alpine regions. It has been found, that lakes in Scotland, Central Norway and Northern Finland have very different species assemblages from those in central and southern Europe. Sites in the Tatra Mts are also distinct from those in other central European Lake Districts [65]. Hence, species distribution is strongly related to a zoogeographical aspect of mountain lakes [36], while altitude is important mainly when analyzing lakes within specific mountain regions [41,82]. These studies strongly emphasize the importance of local surveys of aquatic organisms and detailed knowledge of regional fauna, their faunistic and ecological patterns within particular mountain regions for an understanding of climate-driven processes on a broader geographical scale. Alpine and subalpine lakes of the Tatra Mts has been in the center of scientific interest since the 19th century. First publications of lakes biota were focused on faunistics [66,97,136]. The first classification of the Tatra lakes, based on macroinvertebrates, was published by Hrabě [61] and particular groups of aquatic insects of this area were firstly studied by Hrabě [62], Mayer [95], Zavřel [138], or Zelinka [139]. Intensive hydrobiological research in Tatra lakes started in 1960ties with few lower elevation lakes and continues till present over the whole Tatra area covering all limnological aspects of lakes. Thorough compilation of the main results obtained also during multilateral EU projects (MOLAR, EMERGE), in which selected Tatra lakes were included, was published in 2006 [5]. One of

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the crucial objectives of EMERGE project, within the frame of which also survey presented in this chapter was performed, was the regional survey of environmental (including human impact) and biotic features of a large number of lakes (cca 300) distributed along 14 European lake districts of 15 European countries. At present, research is focused mainly on chemical and biological recovery of lakes from acidification as majority of lakes were in the last century affected or threatened by acid deposition [69], on possible impacts of climate warming [32], and palaeolimnological analyses of lacustrine sediments [7].

Objectives of this Chapter This chapter summarizes the results of a two years intensive investigation of macroinvertebrate fauna, which occur and is typical in the littoral of (sub)alpine lakes of the High Tatra Mts (Slovakia, Central Europe) representing natural climatic gradient. Accordingly, main objectives of this survey were to (i) assess the selection of the gradient lakes by studying surface water temperature and particulate organic matter, (ii) give updated information on taxonomic composition and basic ecological patterns of macroinvertebrate assemblages of gradient Tatra lakes, their inlets and outlets, with emphasis on aquatic insects, (iii) examine the ability of differences in the faunal composition of these lakes to indicate possible climatic events, and (iv) study seasonal dynamics of macroinvertebrate (especially insect) assemblages in a context of the gradient lakes concept.

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STUDY AREA Study area is located in the High Tatra Mountains in northern Slovakia (20° 10' E, 49°10' N; Central Europe), belonging to the Carpathian chain (Fig. 1). The massif of the High Tatra Mts, situated at the Slovak-Polish border, is formed mostly of granodiorite (biotite granodiorites to tonalites) [47,101]. This mountain range is characterized by steep changes in temperature and precipitation with altitude. The average annual air temperature decreases with elevation by 0.6 °C [83] to 1.4 °C [125] per 100 m, being 1.6 and -3.8 °C at elevations of 1778 and 2635 m respectively. The amount of precipitation varies from ~ 1.0 to ~ 1.6 m yr-1 between 1330 and 2635 m a.s.l., but reaches > 2.0 m yr-1 in some valleys [67]. At elevations > 2000 m, snow cover usually lasts from October to June. There are about 120 permanent lakes of glacial origin and dozens of small ponds and temporary shallow pools filled with water from melting snow during the spring. Most of them (~ 70 %) are situated in the alpine zone above 1800 m a.s.l.. The main criterion for selection of studied lakes was their location along an altitudinal gradient above the natural tree line, with approximately equal elevation differences among lakes, ensuring gradient in temperature. If possible, the effect of other variables (geology, lake morphometry, chemistry) was minimized in order to emphasize differences in thermal regimes of lakes due to different elevations. The lakes selected are located in different valleys relatively close to each other, representing separate branches within the same river system (the Váh river basin). They are situated above the tree line at 2157 (Vyšné Wahlenbergovo pleso – VW), 1940 (Nižné Terianske pleso – NTR) and 1725 m a.s.l. (Vyšné Temnosmrečinské pleso – VTS)

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respectively, so that elevation differences between adjacent lakes are ~ 200 m. The only aberrance in the altitudinal gradient of lakes is the lowest situated Nižné Temnosmrečinské pleso lake – NTS, connected with higher elevation VTS through its inlet. However, thermal regime of NTS falls into the gradient in water temperature, which enabled its inclusion into this study (Fig. 2).

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Figure 1. Map of the studied area: location of the High Tatra Mts and the surveyed gradient lakes Vyšné Wahlenbergovo pleso (VW), Nižné Terianske pleso (NTR), Vyšné Temnosmrečinské pleso (VTS), and Nižné Temnosmrečinské pleso (NTS).

Figure 2. Simplified diagram of the studied gradient lake system. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

Zuzana Čiamporová-Zaťovičová

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NTR and VW are located in the alpine zone, VTS and NTS in the sub-alpine zone. They are all relatively deep with lake surface areas ~ 5 ha (except of NTS with area ~ 11 ha). All lakes are perennial, oligotrophic, fishless, containing soft water. Substrates of the littoral zones are characterized mainly by a large amount of rocks with a small proportion of sand and gravel. Dominant vegetation of the lake catchments changes with increasing altitude from sub-alpine bushes with dwarf pine (Pinus mugo) to alpine meadows (dry tundra) with an increase in the percent of bare rocks and screes. There have been no significant direct human activities occurring in the lake catchments since the 1950s when the Tatra Mountains became a national park. Even though acid deposition in the second half of the 20th century had a significant impact on many Tatra lakes including VW [69], at a time of our survey this lake was no longer considered acidified [70]. For more details on hydromorphology and water chemistry of the lakes see Table 1; additionally [5,49].

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Table 1. Basic environmental characteristics of the studied High Tatra lakes: Vyšné Wahlenbergovo pleso (VW), Nižné Terianske pleso (NTR), Vyšné Temnosmrečinské pleso (VTS), and Nižné Temnosmrečinské pleso (NTS); R – rocks, S – sand, O - organic matter, M – moraines, A – alpine meadows [49,68, EMERGE project DB] Lake Zone Latitude N Longitude E Altitude (m a.s.l.) Lake area (m2) Maximal depth (m) Lake volume (m3) Residence time (yrs) Ice-cover duration (days) Precipitation (mm.yr-2) Littoral substrate – R:S:O (%) Secchi disc depth (m) Presence of inlet / outlet Part within lake chain Lake catchment description R:M:A (%) pH Ca (mg L-1) Alkalinity (µeq L-1) Conductivity (µS cm L-1 20ºC)

WV Alpine 49°09´51,12 20°01´37,56 2157.0 51,655 20.6 392,078 1.01 217 1538 95:04:01 6.2 0/0 1

NTR Alpine 49°10´11,28 20°00´51,48 1940.4 55,580 47.3 871,668 0.65 203 1446 90:10:00 12.0 1/1 2

VTS Sub-alpine 49°11´20,76 20°02´22,2 1724.8 55,625 20.0 414,712 0.36 189 1326 88:08:04 14.0 1/1 1

NTS Sub-alpine 49°11´34,44 20°01´50,16 1677.0 117,045 38.1 1501,500 0.9 185 1311 80:15:05 14.2 1/1 2

37:51:12 6.26 1.26 29.25 10.52

40:32:28 6.73 2.60 89.02 17.36

40:34:26 7.19 4.82 261.92 33.20

35:15:50 7.21 4.56 235.71 30.37

MATERIAL AND METHODS Macroinvertebrates Sampling macroinvertebrates in lakes, inlet and outlets was performed during the ice-free seasons of 2000 and 2001 (May – October) at monthly intervals. Relatively high frequency of

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sampling was necessary to get information also on the seasonal variation. Littoral zone of VW (two sampling sites) was sampled 9 times, NTR (three sampling sites) 8 times, VTS (one sampling site) and NTS (three sampling sites) 13 times. Quantitative benthic samples from the littoral zones were taken with a modified Hess sampler [56] with sampling area 0.1 m2 and mesh-size 500 μm (application in standing water followed [80]). Sampling locations reached from the shore to a water depth of ~ 0.4 m. Each sample consisted of three partial sampling units with a total area of 0.3 m2. Even though pebbles were the dominant substrate type, the substrate size varied from fine gravel to small cobbles at all sites; the whole substrate spectrum was considered. The material collected was placed into plastic bottles and fixed in situ with formalin to a final concentration of 4 %. Adjacent inlet and outlets of lakes (inlet of NTR; outlets of NTR, VTS, and NTS) were sampled at the same dates as littorals. Within each sampling site, semi-quantitative samples of zoobenthos were collected (in maximal distance 20 m from lake shore), using the kick sampling method [39] sieving through 300 μm mesh-size hand net (25 x 25 cm), and disturbing the substrate for 5 min. All present substrates, composed mainly of pebbles, gravel and moss/algae mats, were taken into account. On behalf of proper identification of juveniles and some problematic species of aquatic insects, mature larvae and adults (if possible) were sampled also individually. In the laboratory, all animals were picked up from sediment, sorted to the higher taxonomic groups using stereomicroscope (under 10 x magnification), and counted. Invertebrates were identified to the lowest possible taxonomic level, given their size and the availability of identification literature (usually species) using stereomicroscope (max. 80 x magnification) or microscope. Species identification is a prerequisite for detailed ecological research, because most genera include species with different ecological demands that cannot be pooled into larger taxonomic units without losing substantial information [114]. Nematoda, Acarina and Chironomidae (Diptera) were not further identified for purposes of this chapter. Biomass of invertebrates was defined as wet formalin weight after 3 months of conservation (mg).

Temperature and Organic Matter Miniature thermistors with integrated data loggers (8-TR Minilogs, Vemco Ltd., Shad Bay, Nova Scotia, Canada) were used to measure lake surface water temperatures (LSWTs) of the studied lakes in one-hour intervals from June 2000 to October 2001 (in NTS from October 2000 to September 2001). The thermistors were inserted into the underside of rectangular styrofoam blocks (13 cm x 13 cm x 5 cm). The thermistor sensors reached approximately 5 cm under the lake surface and the styrofoam blocks shaded the sensors from direct solar radiation. The thermistors were anchored either near the lake outflow to ensure a continual flow of epilimnetic water around them (NTR, VTS and NTS) or in deep central region, far enough from shore (VW) to avoid any local littoral effects and to minimize disturbance. Temperature measurement in 5 cm depth is representative for the lakes´ epilimnion [87]. Unfortunately, the thermistor in VTS was lost after three months of measurement due to harsh climatic conditions, and was replaced in July 2001; however, the available data still represent the temperature regime of this lake for an entire ice-free period. The temperature data from data loggers were downloaded in the field and daily averages were

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Zuzana Čiamporová-Zaťovičová

used for subsequent analyses and charts. Automatic temperature measurements were supplemented with point measurements (mercury thermometer) realized in littorals, inlet and outlets during all benthos sampling dates. The amount of particulate organic matter (POM) from the lake littorals was estimated from samples of macrozoobenthos taken in 2001. The minimum size of the particles gathered was determined by the mesh-size of the sampler. Particles smaller than 0.5 mm passed through the sampler, therefore only two fraction sizes were considered: coarse – CPOM (> 1 mm) and partly fine – FPOM (1–0.5 mm). In the laboratory, fractions of POM were separated, dried at 105 °C for 3.5 hours and weighed. The weight loss upon combustion at 550 °C (3.5 h) was taken as the amount of organic matter in the sample. Values of POM are given as g m-2 of ash-free dry mass (AFDM).

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Data Analysis Dominance of species/taxa was evaluated as classes of dominance 1–5 (1: 0–2 % subrecedent, 2: 2–10 % recedent, 3: 10–20 % subdominant, 4: 20–50 % dominant, 5: 50–100 % eudominant). Constancy was evaluated according to Tischler [128] (AD: 0–25 % accidental, AS: 25–50 % accessory, C: 50–75 % constant, EC: 75–100 % euconstant). Abundance and biomass data and their standard deviations were log transformed in graphs. The Shannon-Wiener diversity index (H´) and evenness (Simpson´s E) were calculated for each locality and sampling date using Species Diversity & Richness 4.0 software [118]. Subsequently, one-way analysis of variance (ANOVA) was used to compare selected biotic metrics of the littoral zones (numbers of species/taxa, genera and higher taxonomic groups, diversity, evenness, density, biomass); values of p < 0.05 were considered significant. For ANOVA and creation of all graphs excluding dendrograms, the SigmaPlot for Windows 11.0 software was used. Cluster analysis of presence/absence of macroinvertebrate data of inlet/outlets and lake littorals (not shown) was performed using CAP software [119] with Sörensen Index of similarity and the complete linkage method.

RESULTS AND DISCUSSION Altitudinal Environmental Gradient The crucial environmental factor considered in relation to changes in benthic macroinvertebrate assemblages in this survey was altitude, closely correlated with many other environmental factors, primarily temperature [87]. Selected lakes located at different elevations represent a natural climatic gradient and could serve as models for predicting the possible impacts of temperature change on their biota. Whereby altitudinal gradient is closely correlated with the main climatic gradient reflecting mainly air temperature, which increases with decreasing elevation [89], similar gradient in water temperature was supposed. Results of the LSWT measurements in surveyed lakes show similar trends in all lakes (Fig. 3), with maxima in summer months July - August (Fig. 3 - black arrows). Differences between lakes clearly reflected their elevation: highest

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elevated VW reached the lowest maximum temperature, and the lowest elevation NTS the highest. During the simultaneous measurement of temperature in all lakes, the average temperatures in lake littorals were 6.3, 6.9, 7.5 and 10.3 °C for VW, NTR, VTS and NTS, respectively. In the same period, the highest daily average temperatures were 11.6, 11.8, 13.5 and 14.9 °C emphasizing the difference between alpine (VW, NTR) and sub-alpine (VTS, NTS) lakes. Strong negative correlation between LSWT and altitude was expected on the basis of previous studies [87,125] confirming also positive correlation between air and water temperature. In high elevations (above 2000 m a.s.l.) linear relationship of correlated environmental parameters (air, water temperature, altitude) could be influenced by another factors, such as long-lasting ice-cover, lake depth, local shading, etc. [48,87,125]. Seasonal fluctuations in LSWT corresponded well with those reported in comparable studies of different European lake districts [100,109]. During the winter ice-cover, temperatures of all lakes were around zero. Timing of ice-on and ice-off (Fig. 3 - grey arrows), and consequently ice-cover duration, were also correlated with altitude (positively for ice-on and negatively for ice-off).

Figure 3. Lake surface water temperature (LSWT) of the studied High Tatra lakes in 2000 and 2001. Data results from continual (lines) measurements in lake littorals and point (symbols) measurements in adjacent inlet and outlets (VW, NTR, VTS, NTS - see Table 1; lit - littoral, in - inlet, out - outlet).

Temperature measured in lake outlets corresponded very closely with LSWT of respective littorals – temperature of littoral and adjacent outlet was in all measurements almost equal. This complies with further ascertaining that high mountain lakes influence to high extent environmental conditions, especially temperature of their outlets [58]. Maximum temperatures in outlets were then recorded also in August. The only inlet studied (NTR) showed quite different thermal regime with very low measured temperatures in all dates: average 2.1 °C and maximal 5.0 °C (August 2001) (Fig. 3

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– down black triangles). Maximum water temperature up to 4 °C is in alpine zone typical for kryal type of stream [133] – glacier fed streams with exactly defined macroinvertebrate assemblage given by low temperature. Development of characteristic kryal assemblage in the Tatra Mts is limited by water temperature 8–10 °C; optimal temperature is between 1–6 °C [76]. Although inlet of NTR should be ranged to kryal streams pursuant to its thermal regime, origin of inlet water and benthic assemblage structure indicate crenal type of stream [133]. Trends consistent with those of LSWT are apparent also if particulate organic matter of lake littorals is considered (Fig. 4). The amount of coarse (CPOM), partly fine (1–0.5 mm; FPOM) and total obtained POM showed increase as elevation decreases. The average amount of total POM in the highest elevation VW was 0.9 g m-2, about 1/4 of that in NTR (3.5 g m-2); in the sub-alpine VTS and NTS it reached 6.6 and 9.7 g m-2 of AFDM, respectively. Clear trend in the increase of both, the CPOM and FPOM fractions with decreasing altitude was also recognized. The difference in AFDM was ~ 2.2–3 g m-2 between each two lakes adjacent in altitudinal environmental gradient. Distinct difference in POM amount between oligotrophic lakes of higher and lower elevation was previously recorded in the Tatra Mts also on bigger data set [79].

Figure 4. Average values and standard deviations of AFDM (g m-2) of > 1 mm = coarse (CPOM), 0.5–1 mm = part of fine (FPOM) and total particulate organic matter (POM) in the littorals of the studied lakes in 2001 (VW, NTR, VTS, NTS - see Table 1).

The most evident increase of POM was recognized in littoral of NTS. Shoreline of this sub-alpine lake is more densely overgrown by dwarf pine, needles and sprigs of which form bulk of organic material in lake littoral. Productivity and trophy of lakes is, namely in alpine environment, determined primarily by soils and vegetation present in their catchments, decreasing with loss of soil and vegetation cover [71].

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If compared with lower elevation lakes or streams, most of POM is allochtonous [37,44,106], and especially in the highest altitudes its input into water is very low [42]. In such low productive environment of alpine waters, allochtonous POM is very important part of trophic chains and primary energy source for many macroinvertebrate taxa [45,106]. FPOM, important food source for benthos in alpine lakes [37], correlated stronger with altitude than CPOM as a consequence of faster CPOM decomposition in higher temperatures of subalpine lakes and increased input of FPOM from catchment soils. Both, temperature and POM data from four considered lakes fulfilled and supported our assumptions about "altitudinal environmental gradient" and surveyed lakes could successfully represent a gradient model, thus macroinvertebrate assemblages could have been subsequently analyzed in terms of "gradient lake concept" and changing temperature.

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Littoral Macroinvertebrates Littoral zone of lakes is heterogeneous habitat controlled by complex of dynamic factors in contrast with more stable and predictable profundal conditions [134]. Thanks to variable substrate [17,98,121], more suitable light [115] and oxygen conditions [9,18], littoral macroinvertebrate fauna is more species rich and diverse, while profundal fauna reflects primarily trophic and oxygen conditions [61,64,75]. Nevertheless, organisms inhabiting alpine lake littorals have to overcome harsh environmental conditions: low nutrient concentrations and food availability, a short growing season, extreme temperature variations, light conditions, high solar radiation, fluctuations of water level, etc. [12], resulting in a typical fauna considered to be highly adapted to severe conditions of (ultra)oligotrophic ecosystems. Hence, we can expect relatively simple biotic communities in alpine lakes with a few dominant but to extreme environment well adapted species [41]. About 70 species/taxa of aquatic macroinvertebrates were identified in four lake littorals during ice-free seasons 2000 and 2001, belonging to 13 higher taxonomic groups. List of all macroinvertebrate species/taxa with their dominance and constancy data are presented in Table 2. Insect groups were much more diverse than non-insect (almost three times more species/taxa). The most diverse group was Plecoptera (14 taxa) followed by Oligochaeta, Trichoptera and Diptera. Taxa richness of Diptera would be much higher if chironomids, considered to be the richest group in aquatic biotopes, were identified to species. Seeing that a lot of previously published works from alpine lakes and streams were focused exactly on the family Chironomidae [8,51,35,85,88,102], our attention was paid to the remaining benthos groups with emphasis on aquatic insects. Chironomid data from our survey were processed elsewhere [32,51].

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Table 2. List of the macroinvertebrate taxa from studied lake littorals with their dominance and constancy classes. (Key: dominance: 1 – 0–2% subrecedent, 2 – 2–10% recedent, 3 – 10–20% subdominant, 4 – 20–50% dominant, 5 – 50–100% eudominant; constancy: AD – 0–25% accidental, AS – 25–50% accessory, C – 50–75% constant, EC – 75–100% euconstant; * - taxa found in littoral of NTS only on site under strong influence of inlet; VW, NTR, VTS, NTS - see Table 1) Taxa HYDROZOA TURBELLARIA NEMATODA MOLLUSCA OLIGOCHAETA Naididae Tubificidae Lumbriculidae

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Haplotaxidae Enchytraeidae

Hydra sp. Crenobia alpina (Dana, 1766) indet. Pisidium casertanum (Poli, 1791) Chaetogaster diaphanus (Gruithuisen, 1828) Nais variabilis Piguet, 1906 Spirosperma ferox Eisen, 1879 Tubifex tubifex (O. F. Müller, 1774) Lumbriculus variegatus (O. F. Müller, 1774) Stylodrilus heringianus Claparède, 1862 Haplotaxis gordioides (Hartmann, 1821) Cernosvitoviella tatrensis (Kowalewski, 1916) Cernosvitoviella carpatica Nielsen & Christensen, 1959 Cognettia glandulosa (Michaelsen, 1888) Cognettia sphagnetorum (Vejdovský, 1877) Cognettia sp. juv. Mesenchytraeus armatus (Levinsen, 1884) Enchytraeidae juv. indet. Niphargus sp.

HYDRACARINA AMPHIPODA EPHEMEROPTERA Ameletidae Ameletus inopinatus Eaton, 1887 Baetidae Baetis alpinus Pictet, 1843-1845 Baetis vernus Curtis, 1834 Heptageniidae Electrogena lateralis (Curtis, 1834) Rhithrogena loyolaea Navás, 1922 PLECOPTERA Nemouridae Amphinemura standfussi (Ris, 1902) Nemoura cinerea (Retzius,1783) Nemurella pictetii Klapálek, 1900 Protonemura brevistyla (Ris, 1902) Protonemura nimborum (Ris, 1902) Capniidae Capnia vidua Klapálek, 1904 Leuctridae Leuctra armata Kempny, 1899 Leuctra nigra (Olivier, 1811) Leuctra pseudosignifera Aubert, 1954 Leuctra pusilla Krno, 1985 Perlodidae Diura bicaudata (Linnaeus, 1758) Isoperla sudetica (Kolenati, 1859) Perlidae Dinocras cephalotes (Curtis, 1827) Chloroperlidae Siphonoperla neglecta (Rostock, 1881) MEGALOPTERA Sialis lutaria (Linnaeus, 1758)

Lake littorals VW NTR 3/C 2/C 1/C 1 / AS -

VTS 1 / AS 2 / EC 1 / EC

NTS 3 / EC 1 / EC 1 / EC 1/C

1 / AS 1 / AD 2 / AS 1 / AD 4 / EC

4 / EC 1 / AD 3 / EC 3 / EC 1 / AS

3 / EC 1 / AD 1 / AS 3 / EC 1/C 1 / EC

2/C 2 / EC 2 / EC 1 / EC 2 / EC 2 / EC 1 / AS 1 / AD

1 / AD -

1 / AD 2 / AS 1 / AD 2 / AD 1 / AD -

2 / AS 1 / AD 1 / AS 1 / AS -

1 / AD 1 / AS 1 / AD 1 / AD 1 / AD 1 / EC 1 / AD

-

1 / AD -

1 / AS 1 / AD -

1/C *2/C * 1 / AS 1 / AS * 1 / AS

2 / AS -

1 / AD 1 / AS -

2 / EC 1 / AD 1 / AD 1/C -

1 / AD 2 / EC 1/C * 1 / AD * 1 / AD * 1 / AD 1/C 1/C 1 / AS 1/C * 1 / AS 1 / AD 1 / AD 1 / EC

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Table 2. (Continued) Lake littorals VW NTR

VTS

NTS

1 / AD

-

1 / EC 2/C 1 / AD -

1 / AS 1 / AD *1/C -

Rhyacophila fasciata Hagen, 1859 Rhyacophila philopotamoides McLachlan, 1880 Rhyacophila tristis Pictet, 1835 Acrophylax zerberus Brauer/sowai Szczęsny 2 / C Allogamus starmachi Szczęsny, 1967 Apatania fimbriata (Pictet, 1834) Drusus annulatus (Stephens, 1837) Drusus discolor (Rambur, 1842) Drusus monticola McLachlan, 1876 1 / AD Drusus trifidus McLachlan, 1868 Chaetopteryx sp. Lithax niger (Hagen, 1859) -

-

-

* 1 / AD

2/C 1 / AD 1 / AS -

1 / AD 1 / AS 1 / AD 1 / AS 1 / AS -

* 1 / AD * 1 / AD 1 / AS 1 / AD 1 / AD * 1 / AD 1/C 1 / EC 1 / AD

indet. Prosimulium rufipes (Meigen, 1830) Simulium brevidens (Rubtsov, 1956) Chionea sp. Orimarga sp. Dicranota sp. Pedicia rivosa rivosa (Linnaeus, 1758) Pedicia sp. juv. Tipula alpina Loew, 1873 Tipula benesignata Mannheims, 1954 Bazarella subneglecta (Tonnoir, 1922) Berdeniella illiesi (Wagner, 1973) Hemerodromia sp. Wiedemannia sp.

4 / EC -

4 / EC -

4 / EC 1 / AD 1 / AD 1 / AD 1 / AD 1 / AS * 1 / AD 1 / AD 1 / AD * 1 / AD * 1 / AD * 1 / AD * 1 / AD

Taxa COLEOPTERA Dytiscidae

Elmidae Helophoridae TRICHOPTERA Rhyacophilidae

Limnephilidae

DIPTERA Chironomidae Simuliidae Limoniidae

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Pediciidae

Tipulidae Psychodidae Empididae

Agabus bipustulatus (Linnaeus, 1767) Hydroporus memnonius Nicolai, 1822 Rhantus suturellus (Harris, 1828) Elmis latreillei Bedel, 1878 Helophorus flavipes Fabricius, 1792

4 / EC 1 / AD -

Density data were consistent with previous records from European alpine lakes [9,62,78,79,82,93,103] - the most ubiquitous and the most abundant groups in all lakes were oligochaetes (25–64 % proportion of littoral fauna) and chironomids (22–39 %) (Figure 5). While in higher alpine lakes (VW, NTR) the dominance of groups of permanent fauna (especially Oligochaeta and Turbellaria) was distinct, in lower elevated lakes (VTS, NTS) the proportion of larvae of aquatic insects (Diptera followed by Plecoptera) rose. Higher proportion of permanent fauna in high elevated alpine lakes, in comparison with sub-alpine ones, is caused probably by generally more unfavorable climatic conditions negatively influencing survival and life cycles of temporal fauna both in aquatic and terrestrial developmental stage [22,40]. Biomass was in all lakes much more equally divided among Oligochaeta, Turbellaria and several insect groups (Fig. 5) as a consequence of presence of larger mature insect larvae of several genera (e.g. Ameletus, Diura, Acrophylax, Agabus).

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Figure 5. Bubble plots of mean abundance and biomass proportions of higher taxonomic groups in lake littorals in 2000-2001. (Bubbles: light grey – >50% of mean abundance/biomass at particular site, dark grey – 20–50%, black – 10–20%, white – 1 ind m-2), with their standard deviations are provided in Fig. 10.

Figure 10. Mean abundance (ind m-2) and biomass (mg m-2) of insect species/taxa with their standard deviations (log transformed data). Species are ordered by decreasing mean abundance (species with mean abundance > 1 ind m-2 were considered).

The gradient lake concept [104] has made the scenario of potential changes in (sub)alpine lake fauna more predictable. A decrease in density or extinction of the most oligostenothermic species of late-glacial fauna characteristic for the highest elevated lakes, as well as regular occurrence of species characteristic for lower elevated lakes in higher altitudes, could be considered as reliable signal of warming of the alpine lakes in the Tatra Mts. Hence, in consistence with published data [32,51], an upward shift of some insect species could be expected under warmer conditions, leading to increase of the site diversity of alpine lakes due to successful colonization of more thermophilic species typical for lower altitudes, and concurrently extinction of cold stenothermal species will lead to impoverish-ment of the native fauna of alpine lakes. Moreover, species richness of subalpine lakes could increase due to the presence of species from lowland habitats. However, it is clear that this simple model

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will not be applicable for entire Tatra area and that thermal conditions of lakes depend markedly also on local factors, e.g. lake orientation, mean lake depth, lake surface to volume ratio, landscape morphometry [16,125].

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Macroinvertebrates of Inlet and Outlets Macroinvertebrate assemblages of studied flowing aquatic habitats (inlet of NTR and outlets of NTR, VTS, and NTS) showed similar basic patterns as adjacent littorals. Almost 65 species/taxa from 11 higher taxonomic groups (Mollusca and Megaloptera absented) were found (Table 3). Despite littorals and inlets/outlets represent quite different habitat types, bulk of taxa recorded (almost 80 %) were common for streams and littorals, suggesting very close relation of both biotopes. Presence of rheophilic taxa in littorals of the Tatra lakes was previously explained as input of allochtonous elements into lakes by inlets [35]. Boggero and Lencioni [9] outlined similarly presence of lotic species B. alpinus and C. alpina in the southern Alps, or Cameron et al. [25] and Bitušík et al. [9] presence of non-lacustrine chironomid species in sediments, respectively in littorals of alpine lakes. However, in studied littorals rheobiontic species formed stable and numerous component of benthic assemblages, also at sampling sites remote from lotic habitats. Thus successful surviving of such lotic taxa in littorals could be the consequence of suitable physico-chemical conditions, mainly oxygen sufficiency [6], rocky substrate typical for alpine littorals simulating lotic habitats [115], or wind activity stimulating oxygen enrichment of water [86]. According to the results, biotopes of (sub)alpine lakes show common features with adjacent lotic habitats, they could simulate conditions of low order streams [33], and they are suitable habitats for many lotic macroinvertebrate taxa. Insect taxa richness of lotic habitats was much higher than of non-insect fauna; the most taxa richest groups were Plecoptera (15 taxa), Trichoptera (12 taxa) and Oligochaeta (11 taxa) (chironomids were not further identified). Similarly, in terms of abundance the same groups dominated there as in littorals: in both, inlet and outlets insects prevailed, mainly Diptera (Chironomidae) and Oligochaeta, followed by C. alpina (Turbellaria) (Fig. 11). Similar results are reported for instance by Marchetto et al. [93], or Maiolini et al. [91]. Lake outlets represent a unique stream type, which benefit from more stable physicochemical and hydrological features (discharge regime, turbidity, temperature range, substrate stability) buffered by the source lake [96]. Presence of lakes ameliorates habitat conditions of outlets in comparison to the generally harsher environment of the inlets [91]. In contrast to lowland lake outlets, oligotrophic alpine lakes poor in organic material may act as sinks rather than sources for organic matter - they transport only small amount of organic seston into their outlets. Hence, an increase of proportion of filter-feeders (especially Simuliidae) in alpine lake outlets is not expected, and non-insect taxa may prevail [9,20,22,57,59,77,92]. That is the case of NTR outlet, where benthic assemblage was, due to dominance of scraper N. variabilis, almost exclusively composed by Oligochaeta, while all other sites were dominated by Diptera (Chironomidae, or Simuliidae in some dates). Nearly during the whole growing season, Simuliidae reached (with two exceptions in June samplings) negligible proportions of benthos. This is in contrast to lower elevated outlets characterized by "outlet specialists" with high proportion of simuliids [58,59,99].

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Table 3. List of the macroinvertebrate taxa from studied inlet and outlets with their dominance and constancy classes. (For key see Table 2)

Hydra sp. Crenobia alpina (Dana, 1766) indet.

Inlet NTR 3 / EC 1/C

Outlets NTR 1 / AD 2 / EC

VTS 3 / EC 1/C

NTS 2 / EC 3 / EC 1/C

Chaetogaster diaphanus (Gruithuisen, 1828) Nais variabilis Piguet, 1906 Spirosperma ferox Eisen, 1879 Tubifex tubifex (O. F. Müller, 1774) Stylodrilus heringianus Claparède, 1862 Haplotaxis gordioides (Hartmann, 1821) Cernosvitoviella tatrensis (Kowalewski, 1916) Cognettia anomala (Černosvitov, 1928) Cognettia glandulosa (Michaelsen, 1888) Cognettia sphagnetorum (Vejdovský, 1877) Cognettia sp. juv. Mesenchytraeus armatus (Levinsen, 1884) Enchytraeidae g. sp. juv. indet. Niphargus sp.

1 / AD 1 / AD 1 / AD 4 / EC 2 / AS 2/C 1 / AD 1 / AS 1 / AD 1 / AS -

5 / EC 1 / AS 1 / AD 1 / AD 1 / AD 3 / EC 2 / AS 1 / AD -

3/C 1 / AD 1 / AD 1 / AS 1 / AS 1 / AD 1 / EC 1 / AD

1 / AD 1 / EC 1 / AD 1/C 1 / AD 1 / AD 1 / AD 1 / AS 2 / AD 2 / AD

1 / AD -

-

1 / AD 2 / AD 1 / AD 1 / AD

1 / AS 1 / AD 1 / AD 1 / AD -

2 / AS 1 / AD 3 / EC -

2 / AD 1 / AD 1 / AS -

1 / AD 1 / AD 1 / AD 1 / AD 1 / AD 1 / AS 2 / EC 1 / AD -

2 / EC 1/C 1 / AS 1 / AD 1 / AD 1 / AS 1 / AD 1 / EC

1 / AD

-

1 / AS 1 / AD 1 / AD 1 / AD 1 / AD

1 / AD -

Taxa HYDROZOA TURBELLARIA NEMATODA OLIGOCHAETA Naididae Tubificidae

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Lumbriculidae Haplotaxidae Enchytraeidae

HYDRACARINA AMPHIPODA EPHEMEROPTERA Leptophlebiidae Habroleptoides confusa Sartori & Jacob, 1986 Ameletidae Ameletus inopinatus Eaton, 1887 Baetidae Baetis alpinus Pictet, 1843-1845 Baetis vernus Curtis, 1834 Heptageniidae Electrogena lateralis (Curtis, 1834) Rhithrogena loyolaea Navás, 1922 PLECOPTERA Nemouridae Amphinemura standfussi (Ris, 1902) Nemoura cinerea (Retzius,1783) Nemurella pictetii Klapálek, 1900 Protonemura auberti Illies, 1954 Protonemura brevistyla (Ris, 1902) Protonemura montana Kimmins, 1941 Capniidae Capnia vidua Klapálek, 1904 Leuctridae Leuctra armata Kempny, 1899 Leuctra nigra (Olivier, 1811) Leuctra pseudosignifera Aubert, 1954 Leuctra pusilla Krno, 1985 Leuctra rosinae Kempny, 1900 Perlodidae Diura bicaudata (Linnaeus, 1758) Isoperla sudetica (Kolenati, 1859) Perlidae Dinocras cephalotes (Curtis, 1827) COLEOPTERA Dytiscidae Agabus bipustulatus (Linnaeus, 1767) Agabus guttatus (Paykull, 1798) Hydroporus longicornis Sharp, 1871 Hydroporus sp. – larvae Elmidae Elmis latreillei Bedel, 1878

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Table 3. (Continued) Outlets NTR VTS -

NTS -

Rhyacophila fasciata Hagen, 1859 Rhyacophila tristis Pictet, 1835 Acrophylax zerberus Brauer/sowai Szczęsny 1 / AD Allogamus starmachi Szczęsny, 1967 1 / AD Drusus annulatus (Stephens, 1837) Drusus discolor (Rambur, 1842) Drusus monticola McLachlan, 1876 1/C Drusus trifidus McLachlan, 1868 Halesus rubricollis (Pictet, 1834) Chaetopteryx cf. polonica Dziędzielewicz, 1889 Chaetopteryx sp. Potamophylax nigricornis (Pictet, 1834) -

1 / AS 1 / AD 1 / AD 1 / AD

1 / AD 2 / AS 1 / AS 1 / AD 1 / AD 1 / AD 1 / AD 1 / AS -

1 / AS 1 / AD 1 / AD 1 / EC -

indet. Prosimulium latimucro (Enderlein, 1925) Simulium brevidens (Rubtsov, 1956) Simulium cryophilum (Rubtsov, 1959) Dicranota sp. Pedicia rivosa rivosa (Linnaeus, 1758) Bazarella subneglecta (Tonnoir, 1922) Berdeniella illiesi (Wagner, 1973) Wiedemannia sp.

2 / EC 2 / AS 1 / AD 1 / AD 1 / AD -

5 / EC 1 / AD 1 / AD 1 / AD 1 / AD -

5 / EC 2 / AS 1 / AD 1 / AD 1 / AD 1 / AS

Taxa Hydraenidae TRICHOPTERA Rhyacophilidae Limnephilidae

DIPTERA Chironomidae Simuliidae

Pediciidae Psychodidae

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Empididae

Hydraena gracilis Germar, 1824

Inlet NTR 1 / AD

4 / EC 1 / AS 1 / AD 1/C

Figure 11. Bubble plots of mean abundance and biomass proportion of higher taxonomic groups in studied inlet and outlets in 2000-2001. (Bubbles: light grey – >50% of mean abundance/biomass at particular site, dark grey – 20-50%, black – 10-20%, white – 1 ind 5min-1 were considered).

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Seasonal Dynamics of Macroinvertebrates in Altitudinal Environmental Gradient The physical conditions of alpine lakes undergo major seasonal changes, which affect the chemical and biological dynamics of the lake. The ecosystem response is based on the different growth of distinct components in the food web, which finally modify assemblage composition. Differences can arise from changes in the length of the growing season, intensity of organisms growth within a given period, or both. Changes in intensity might be related to variations in resource availability, thermal cycle (during ice-free season driven mainly by seasonal variation in radiation), and environmental conditions, and may favor the growth of certain species. The lake biota includes species with contrasting generation times (from a few hours to several years), therefore the sensitivity and time responses of community components may be quite different. In the case of insect larvae, the number of degree days for development may play a relevant role in the relationship between climate and fluctuations in their populations [27]. Seasonal dynamics of aquatic macroinvertebrates, focused on insect groups, was studied on the basis of quantitative (resp. semiquantitative) data, obtained in 2000 and 2001. Such detail series of data from alpine lake districts is not very common, as the climatic conditions make often difficult to take samples with such high periodicity. This study was virtually the first of such extent performed in the Tatra Mts., as (sub)alpine lakes are mostly sampled 1 or 2 times per season. Situation is very similar in other alpine lake districts - this is partly due to

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harshness of alpine environment that hinders ecological surveys. Moreover, bulk of existing seasonal data from other alpine regions comes mainly from alpine streams, not lakes [21,45,57,112].

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Littoral Higher elevated alpine lakes (VW, NTR) were characterized by the dominance of noninsect fauna during the whole growing seasons (Fig. 17, dotted area), while in lower elevated subalpine lakes proportion of non-insects and insects was almost equal, or insects prevailed in some dates. Generally, seasonal dynamics of whole macroinvertebrate assemblages corresponded to large extent with seasonality of insects. Non-insect fauna settles lakes bottom permanently, with only small changes in abundance during the year, while insect abundance is highly influenced by life cycles of single species [115]. Amongst insects, Chironomidae (grey area) dominated markedly at most of localities and dates; their dominance was most evident in higher elevated alpine lakes. Thus, seasonality of insects was (mainly in the higher elevated lakes) determined by seasonality of chironomids. Other insect groups (black area) reached higher percentage mainly in littorals of the lower elevated subalpine lakes (VTS, NTS). Generally, the proportion of insect abundance reaches two peaks during the growing season: in spring/early summer (NTR, VTS, NTS) and/or autumn (VW, NTS), suggesting these periods are more favorable for insect larvae development than summer months, when their abundance decreases mostly due to emergence of adults (best visible in littoral of NTS). Hence, seasonal oscillations of insect abundance are given mainly by life cycles of present species (emergence of adults, hatching of juveniles), and mass occurrence of some non-insect species in some dates (e.g. C. tatrensis and S. heringianus in VW in autumn, or Ch. diaphanus, Hydra sp. in NTS in August 2001). Seasonal dynamics of benthos can be influenced also by quality and quantity of food [21], or variation in abiotic conditions among seasons [117], more obvious in littorals than in profundal zone [19]. Trends in several biotic metrics of insects during two growing seasons are showed in Fig. 18. In many cases, abundance and biomass (Fig. 18A, B) are closely related. In VW both metrics increased from the time of ice-off towards autumn months, September and October maxima are given by numerous chironomids. They caused also higher values of abundance in littoral of NTR in early spring of 2001, otherwise abundance and biomass were constantly low. Insect abundance and biomass of subalpine lakes (VTS, NTS) reached their minima generally in summer months (July, August) and maxima in spring and autumn. It is mainly a consequence of insect life cycles, with emergence period of most species restricted to summer, and occurrence of numerous juveniles in spring and autumn. This is also the reason of higher taxa richness and diversity in spring and autumn, mainly in subalpine lakes (Fig. 18C, D). Such macroinvertebrate abundance maxima in summer months were observed for example in a Finnish oligotrophic lake [115], or in the Tatra Mts by Juriš et al. [64]; autumn maxima in Italian mountain lakes [100]. Despite missing data from winter, high autumn abundances indicate high abundance also in winter, mainly in lower elevated littorals. Correspondingly, the increase in biomass was already recorded in autumnal and winter periods [11].

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Figure 17. Proportion of insect and non-insect fauna in littorals of studied lakes during growing seasons 2000 and 2001 (NTR, VTS, NTS - see Table 1).

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Figure 18. Seasonal dynamics of the selected biotic metrics of insects in lake littorals: abundance (A), biomass (B), species richness (C), and Shannon-Wiener diversity index (H´) (D).

Deviations in seasonal trends of biotic metrics in altitudinal environmental gradient are primarily caused by different taxonomic composition of insect assemblages. Each altitudinal zone was characterized by different dominant species with different life histories. These trends are best explained by seasonal abundance diagrams of the single dominant insect taxa/species (Fig. 19). Only few species, e.g. N. cinerea, S. lutaria, D. trifidus, D. bicaudata (almost all with asynchronous, semivoltine or merovoltine life cycle, [31]) occurred in particular littorals in similar numbers during the whole growing season. Most of dominant taxa reached their abundance maxima either in spring/early summer (e.g. A. inopinatus, E. lateralis, Acrophylax sp., A. starmachi) or autumn (e.g. C. vidua, N. pictetii, Hydroporus sp.), as a consequence of their mostly synchronous life cycles. The summer period is the usual time of their emergence, and thus the period of their lowest abundance or absence in samples. Moreover, explanation of seasonal dynamics of insect assemblages would be clearer if life cycles of dominant chironomid species were considered.

Figure 19. Seasonal dynamics of abundance of the dominant insect species/taxa in lake littorals (circles = log transformed abundances). Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Zuzana Čiamporová-Zaťovičová

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Studying seasonal dynamics of single species in altitudinal gradient is almost impossible, as most of species occurred in sufficient numbers only in one of the littorals, representing certain altitudinal zone. On the example of caddisfly Acrophylax sp., gentle shift of seasonal curve of abundance towards summer months was recognized in VW, due to later ice-off and warming up delay, and consequently delay of larvae development in higher altitude (VW) in comparison with the lower elevated lake (NTR).

Figure 20. Proportion of insect and non-insect fauna in inlet and outlets of studied lakes during growing seasons 2000 and 2001 (NTR, VTS, NTS - see Table 1). Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Inlet, Outlets

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Seasonal dynamics of macroinvertebrate assemblages of studied lotic habitats showed some differences in comparison with the littorals, due to much higher proportion of aquatic insects in all localities (Fig. 20), determining the seasonality to a notable extent. The only exception was outlet of NTR, where Oligochaeta (represented mainly by M. armatus and N. variabilis) represented about 80 % of macroinvertebrates during almost the whole growing season, and seasonal variations of less abundant insects were negligible. Insects with evident prevalence of chironomids formed higher percentage of abundance during the whole studied period in subalpine lake outlets (VTS, NTS). No clear seasonal trends of the selected insect biotic metrics were detected in lotic habitats. Abundance and biomass curves of the NTR inlet (Fig. 21A,B) were only slightly undulated due to dominance of taxa with relatively equal abundance during entire growing season (e.g. L. rosinae, P. brevistyla, D. monticola, Wiedemannia sp.; Fig. 22), caused probably by year round stable low water temperature (up to 5 °C). Outlet of NTR showed similar pattern except for spring 2000 when P. latimucro dominated markedly. Drastic decrease of this species abundance (and subsequent dominance of N. variabilis) in the next period could be explained by drifting of its larvae downstream [77]. Similar situation repeated in outlet of NTS in case of S. brevidens. Increase of drifting Simuliidae in alpine lake outlets in summer and autumn months was confirmed also from the Swiss Alps [112].

Figure 21. Seasonal dynamics of the selected biotic metrics of insects in lake inlet and outlets: abundance (A), biomass (B), species richness (C), and Shannon-Wiener diversity index (H´) (D).

Outlets of subalpine lakes are inhabited by many taxa with different life cycles (semi/merovoltine or asynchronous D. bicaudata, D. cephalotes, Chaetopteryx sp.; univoltine synchronous A. inopinatus, A. standfussi, Acrophylax sp.; Fig. 22) and plenty of undetermined Chironomidae species with unknown life cycles. Thus their abundance and biomass (as well as species richness and diversity; Fig. 21C,D) curves did not show any distinct seasonal patterns, except for slight increase of metrics of NTS outlet in spring (June), caused mainly by extremely numerous S. brevidens (Fig. 22). Abundance and biomass of this outlet reached, in all sampling dates, values order of magnitude higher than all remaining

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Zuzana Čiamporová-Zaťovičová

sites. Summer abundance decreases and spring, autumn and winter abundance peaks in insects (e.g. mayflies B. alpinus, R. loyolaea) were reported also from alpine streams. Winter assemblages are generally richer (both in species numbers and abundances) than that of summer, because of reduced environmental stress in winter (lower discharge, higher channel stability and transparency, abundant Diatoms, growing of H. foetidus mats, etc.). For a better understanding of the relationship between fauna and environment, the winter season should be taken into account in the future ecological research [20,21,42,113,117,129].

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Figure 22. Seasonal dynamics of abundance of dominant insect species/taxa in lake inlet and outlets (circles = log transformed abundances).

The insect abundance in outlets of subalpine lakes was very high due to rich mats of mosses (Drepanocladus sp.) and alga Hydrurus foetidus, providing food and habitat to insect larvae [76]. H. foetidus is, like in the Alps [130], considerable component of the periphyton in the Tatra alpine streams. Development of insect assemblages of alpine lake outlets could be influenced also by seasonality of stream (drying/freezing out, [77]), as well as discharge and current speed [15].

CONCLUSION Atmospheric pollution and mostly global climate warming are of the major issues currently confronting mankind, with vital ecological and economic consequences. While water chemical composition in alpine lake districts responds positively to the improving quality of atmospheric deposition despite the hysteresis in the chemical reversal of acidification [72], and first signs of biological recovery were also identified [93], effects of climate change are actual like never before. Although monitoring of climate change proceeds, relations between alpine aquatic biota and temperature changes are still not sufficiently known. Many biological changes are expected to appear in the future in coherence with global warming, mainly upward shift of geographical ranges of species, colonization of alpine lakes by species typical for lower

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altitudes, increase of regional and local species richness, or extinction of cold stenothermal species, leading to successive resembling of alpine system to subalpine [32,51,103]. In some cases, external (landscape) lake filters could reduce the effect of climatic change, preserving these lakes as refugia of relict fauna [51]. The „gradient lake concept‟, this chapter deals with, could contribute to an understanding of the ecological impacts of climate change on lake biota, and to predicting the possible development of lake fauna under scenarios of climate warming. However, many other factors (watershed characteristics, lake morphometry, orientation, local meteorology, hydrological regime, ice-cover duration, etc. [16,103]), besides altitude itself with closely related variables, modify and complicate these predictions. In relation to climate change, particular attention should be paid to flagship groups and species, mainly cold stenothermal species of Chironomidae and Trichoptera for alpine lakes [32,51]; Coleoptera could constitute an alternative flagship group for alpine ponds [103]. The population dynamics of such species can indicate fingerprints of global change [103]. Proper understanding of climate driven processes at pan-European, or even at larger geographical scale is impossible without detailed knowledge on the local fauna. It may be expected, that the biota in general would be determined by the particular multi-variable environment of each region, and therefore, also for conservation and management purposes it is recommended to classify and assess reference conditions of lakes at regional scales [26]. Quantitative data obtained within this study with relatively high frequency of sampling are of high importance, and could serve as reference data for the future research. Although mountain lakes remain difficult to sample and monitor, it is important to maintain and extend their study. Mainly studying some ecological aspects (species distribution, biogeography, diversity, food webs, etc.) is crucial for better understanding of their relationships with the environmental variables, hence it should be in focus of the future research.

ACKNOWLEDGMENTS This research was funded by the European Commission Environment Programme through the EMERGE project (EVK-1-CT-1999-00032), manuscript preparation was partially supported by the Slovak Scientific Grant Agency (VEGA project No. 2/0028/09). Thanks are due to F. Šporka, P. Bitušík and I. Krno for valuable comments during the study, L. Hamerlík for help during the fieldwork and reviewing the chapter, specialists from the Institute of Zoology SAS and Comenius University Bratislava for help with determination of questionable taxa and F. Čiampor Jr. for editing figures and comments on the earlier version of the manuscript. Reviewed by Ladislav Hamerlík, PhD., Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Denmark.

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[129] Uehlinger, U., Malard, F., & Ward, J. V. (2003). Thermal patterns in the surface waters of a glacial river corridor (Val Roseg, Switzerland). Freshwater Biology, 48, 284-300. [130] Uehlinger, U., Zah, R., & Bürgi, H. (1998). The Val Roseg project: temporal and spatial patterns of benthic algae in an Alpine stream ecosystem influenced by glacier runoff. In K. Kovar, U. Tappeiner, N. E. Peters, & R. G. Craig (Eds.), Hydrology, water resources and ecology in headwaters (pp. 419-424). Wallingford, UK: IAHS Press. [131] Vilanova, R., Fernandez, P., & Grimalt, J. O. (1998). Atmospheric persistent organic pollutants in high altitude mountain lakes. A preliminary study. In J. M. Pacyna, D. Broman, & E. Lipiatou (Eds.), Sea-air exchange: processes and modeling (pp. 209215). Luxembourg, Luxemburg: Office for official publications of the European Communities. [132] Vranovský, M., Krno, I., Šporka, F., & Tomajka, J. (1994). The effect of antropogenic acidification on the hydrofauna of the lakes of the West Tatra Mountains (Slovakia). Hydrobiologia, 274, 163-170. [133] Ward, J. V. (1994). Ecology of alpine streams. Freshwater Biology, 32, 277-294. [134] Wetzel, R. G. (2001). Limnology: Lake and River Ecosystems. Academic Press. [135] Whiteman, D. (2000). Mountain Meteorology. Oxford, UK: Oxford University Press. [136] Wierzejski, A. (1882). Materyały do fauny jezior Tatrzańskich. Sprawozdań Komisyi fizyograficznej Akademii Umiejętności w Krakowie, 16, 215-241. [137] Zah, R., & Uehlinger, U. (2001). Particulate organic matter inputs to a glacial stream ecosystem in the Swiss Alps. Freshwater Biology, 46, 1597-1608. [138] Zavřel, J. (1935). Chironomidaenfauna der Hohen Tatra. Verhandlungen der Internationalen Vereinigung für Theoretische und Angewandte Limnologie, 7, 439-448. [139] Zelinka, M. (1953). K poznání jepic (Ephemeroptera) Vysokých Tater. Spisy Přírodovědecké Fakulty Masarykovy University, 348, 157-167.

Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

In: Alpine Environment: Geology, Ecology and Conservation ISBN: 978-1-61209-392-5 Editor: John G. Schmidt, pp. 47-83 ©2011 Nova Science Publishers, Inc.

Chapter 2

HERBACEOUS SPECIES TO CONTROL THE ALPINE SOIL EROSION: FIELD AND LABORATORY EXPERIMENTAL TESTS Elena Comino*, Paolo Marengo and Valentina Rolli Department of Land, Environment and Geoengineering – Applied Ecology; Politecnico di Torino, Italy

ABSTRACT

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Landslide, soil slip and superficial erosion are typical natural phenomena which involve the Alpine environment. Long since, the scientific community has the objective to measure and provide the best methodology to reduce the effects of critical events such as soil erosion, improving also slope stability. The alpine environment within the NorthWest Italian territory (Piedmont region) has been studied for geological and ecological aspects in order to improve the environment conservation. Regarding this aim, bioengineering technique has been investigated to control and reduce soil erosion. These studies are focused on the presentation of an appropriate methodology to quantify the contribution of the herbaceous vegetation to prevent the soil erosion on mountain and hill slopes. The herbaceous species tested belong both to the Poaceae family, such as Lolium perenne and Festuca pratense, and Fabaceae family such as Trifolium pratense, Lotus corniculatus and Medicago sativa. These species have been chosen because autochthonous and widespread in the Alpine environment in relation to the climate conditions of the investigated area. For this reason the tested species could be useful for the mechanical effects and for the landscape management. To quantify the soil reinforcement effect given by roots, situ shear tests on rooted and no-rooted soil clods were realized in three different sites (located in Val Pellice, Piedmont region - Italy). Moreover laboratory tests were conducted to study the physical behavior of the roots in the soil during shallow landslides phenomena. The experimental data were used to implement two different models widely used in scientific literature (Wu et al., 1979 and Pollen and Simon, 2005), verifying their applicability to describe the complex system of alpine rooted soil. The research, developed in the last years, has confirmed that the presence of grass roots increases the shear strength of the first soil *

[email protected]

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Elena Comino, Paolo Marengo and Valentina Rolli layers, reducing the soil susceptibility to erosion phenomena and shallow landslides. The obtained results can be useful for the improvement of soil bioengineering techniques in the Alpine environment, with widespread potential applications, for example in the ski runs, river banks or slopes running along roads.

INTRODUCTION

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Mountain areas are a sensitive environment, because they are subject to human and natural actions. Hillslopes are strongly crossed by the normal construction equipment used in the traditional engineering works. Actions to be privileged should be driven to reduce maintenance concerns and be easily realized on. They have to be environmentally compatible, trying to integrate the work with the adjacent natural territory.

Figure 1. Work methodology of the research. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Slope instability causes economic and ecological problems (land failure, breaking of structures…) while the delivery of sediment to channels can cause downstream aggravation and worsening of water quality (Casagli et al., 1999). It is therefore crucial to understand and quantify the key controlling processes and more accurately predict the role vegetation can play in stabilizing slope. Scientific community accepts that plant roots provide soil reinforcement due to different effects, both mechanical and hydrological. The stabilizing effect of vegetation is essential in preventing shallow landslides (Abe, 1997; Gray, 1995; Genet et al., 2008), in the control of water erosion (De Baets et al., 2007) and in remediation works based on soil bioengineering techniques, to such an extent that vegetation is considered as a building material (Schiechtl, 1980). The present research deals with the study of the effect of root reinforcement of grass species in terms of the additional shear strength provided by roots in rooted soils (mechanical root effect). The study have been carried out in the last three years (2006-2009) and still going on. It concerned with field and laboratory tests (for shear and tensile strength measurements), chemical and physical soil analysis and moisture monitoring. In Fig. 1 the framework of the study have been presented. The tested species belong both to the Poaceae family, such as Lolium perenne, Festuca pretense and Poa pratense and Fabaceae family such as Trifolium pratense, Lotus corniculatus and Medicago sativa. The herbaceous species have been chosen because widespread in Alpine environment that is the area in which we focused our research.

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SOIL EROSION ON MOUNTAIN HILLSLOPES The presence of vegetation increases the soil burden stability along slopes and reduces soil erosion. Soil erosion by water is a worldwide environmental problem which degrades soil productivity and water quality, causes sedimentation and increases the probability of flood (Zhou et al., 2008). Soil erosion depends on different factors like soil texture, permeability and antecedent moisture, rainfall intensity, land use and the type and density of the land vegetation cover and land slope. The primary energy causing erosion by water is gravity, acting through falling precipitation and water flow down a terrain slope (Vahabi and Nikkami, 2008). Plants reduce soil erosion rates by intercepting raindrops, enhancing infiltration, transpiring soil water and by providing additional surface roughness by adding organic substances to the soil. Many studies focus on the effects of vegetation cover on water erosion rates, whereas little attention has been paid to the effects of the below ground biomass. In reality the measured soil loss reduction results from the combined effect of both roots and above ground biomass (Gyssels and Poesen, 2003). Although well reported in literature the role of roots in reducing soil erosion is often neglected. Plant roots have a mechanical effect on soil strength. By penetrating the soil mass, roots reinforce the soil and increase the soil shear strength. Recent research indicates that roots can reduce concentrated flow erosion rates significantly (De Baets et al., 2007). This study focuses on the soil reinforcement given by root systems of herbaceous species, in order to estimate the susceptibility of the soil to the erosion

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phenomena. It is especially concerned with the way the functioning of ecosystems can be influenced by several properties like vegetable species (grass, shrub, tree), root network and distributions, physical and chemical characteristics of soil, moisture, site weather conditions. One of the most challenging aspect of root-slope systems investigations is the acquisition of data concerning these properties. The ecosystem is a complex and integrated system composed by the relations between soil and roots, and it has evolving properties due to seasonal and external phenomena. The complexity of natural system where several mechanisms occurs is due to the mechanical and hydrologic root effects and the particular environmental conditions can be positive or negative to slope superficial stability and to different soil types.

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SITES DESCRIPTION All the experimental tests realized in situ, have been carried out in three different fields located in the Val Pellice valley in Piedmont, Italy. The experimental sites are located in two town, Bibiana (400 m a.s.l.) and Bricherasio (380 m a.s.l.). Val Pellice (Fig. 2) is located near to the Cozie Alps and near to the Monviso mountain (3,841 m). The mountain community of the Val Pellice is surrounded by Val Chisone in the North-East side, by Val Germanasca in the North-West, by Pinerolo plain in the East side and finally by ridge that divides the Pellice valley from the Po valley. Pellice valley has been originated in the glacial period and its actual morphology is consequence of the Quaternary period. Nowadays Pellice valley occupies a surface of 29,302 ha and is crossed by the Pellice torrent that flows in West-East direction for 29 km. Pellice torrent is a Po tributary that flows for a long part in the mountain area and for a part in a plain area in correspondence of Bibiana and Bricherasio towns, in this valley the main towns have been developed along the torrent.

Figure 2. Val Pellice and geographic position of the experimental sites. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Main characteristics of the landscape could be grouper in five categories:     

alluvial terrace: in the plain valley mountains and alpine valleys: until height of 1300 m a.s.l. mountains and alpine valleys: characterized by conifer and vegetation that reaches higher altitude alpine grassland: beyond the higher alpine valleys higher alpine mountains.

A GIS elaboration realized on the Pellice valley, underlines the most important land uses of the soil (Table 1), that shows the predominance of woods. Table 1. main land uses of Pellice valley Categories Woods Lakes Rocks Bushes Grassland Orchard, arboriculture Agricultural land Towns and quarries Roads, water bodies

Surface (Ha) 13793.3 4.06 3553.12 1083.79 7802.69 1415.51 434.45 715.55 605.78

% 46.91 0.01 12.02 3.69 26.54 4.81 1.48 2.43 2.10

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Experimental Sites The experimental sites in Val pellicle are located in two different towns: Bricherasio and Bibiana (Fig.1). In Bricherasio there are Ghiaie (Site A) and Belvedere sites (Site B). In Belvedere (Site B) the surface available is 140 m2 and three tests have been carried out in different periods such as:   

May- September 2006 June 2007 April- May 2008

In Ghiaie (Site A) the surface available is 110 m2, here the experimental tests have been realized in five periods:     

May- September 2006 June 2007 April- May 2008 October 2008 May 2009

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The second experimental site is located in Bibiana tows (Site C), it has a surface of 140 m , and here the tests have been carried out in: 2

   

May- September 2006 June 2007 April- May 2008 October 2008

During the realization of the tests, the first steps concerned some chemical and physical analysis to evaluate main soil parameters such as: grain size, pH, organic matter, cationic exchange, saltiness. Four samples (o for each site), have been taken in two different depths: 015 cm and 15-30 cm. All the chemical analysis results have been summarized below, in Table 2, the tested soils contain a high percentage of thin materials such as silt and clay. Table 2. Results of chemical and physical analysis

Characteristics Sand Silt Clay pH Texture USDA

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Plasticity index Organic matter Phosphorous Potassium Magnesium Calcium Nitrogen Nitrate Ammonium

Studied sites BELVEDERE 0 - 15 15 - 30 62% 59% 30% 31% 8% 10% < 5.5 5.2 Sandy loam Sandy loam 80.05 88.15 Very high Very high plasticity plasticity Middle high Middle Very high Very high High Middle Low Low Mediocre Scarce Very high Very high Poor Scarce Scarce Scarce

GHIAIE 0 - 15 43% 45% 12% 7.6 Loam 91.79 Very high plasticity Middle Very high High Low Mediocre High Scarce/poor Poor

15 - 30 56% 34% 10% 7.6 Sandy loam 90.25 Very high plasticity Low Middle Low Very low Moderate Very high Poor Scarce

BIBIANA 0 - 15 46% 54% 0% 5.4 Silt loam

15 - 30 49% 43% 8% 5.2 Loam 84.87 37.71 Very high High plasticity plasticity Middle/high Low Very high Very high Low Very low Middle/low Low Scarce Scarce Very high High Mediocre Scarce Absent Very low

VEGETATION VS SOIL EROSION The scientific community widely recognizes the importance of vegetation growing on slopes. Vegetation has an important function concerning two aspects: the hydrological and the mechanical processes. The hydrological processes are:   

Interception: due to leaf that absorbs a percentage of meteoric rain. Seepage: due to the fractures caused by roots that guarantee the water movement within the soil. Evapotranspiration: to reduce the water content of the plants.

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Herbaceous Species to Control the Alpine Soil Erosion      

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The mechanical processes are: Reinforcement: through the friction between soil and roots. Buttress effect: the arborous vegetation is linked to the substratum through the root system and the tresses offer their buttress effect to the soil included between them. Soil breaking due to the roots growing. Lever action: due to aerial part of the trees in case of strong winds Soil restrain: due to the root system.

In this research much more attention has been given to the herbaceous characteristics of the root systems. Their main functions could be represented by the water absorption through the capillary exploration of the soil and the reinforcement between soil and roots. Root systems could be divided in to four typologies (Fig.2):   

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

Fasciculate: characterized by uniform distribution along the maximal depth reached. The lateral roots have similar length and diameter of the principal ones (Fig.3 a). Taproot: the root dimension show a linear decrease of length and diameter when the depth increases. The principal root has a higher diameter and length (Fig.3 b). Simpodium: decreasing distribution described by logarithmic, exponential or power functions (Fig.3 c). Superficial root system: the distribution reaches the maximum near the soil surface and decreases in depth (Fig.3 d).

Figure 3. Typologies of root systems (a=fasciculate, b=taproot, c=simpodium, d=superficial root system).

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An important parameter depending on the root system typology is the root area ratio (RAR), that represents the ratio between the area occupied by roots and transversal section of the soil crossed by the root system. It assumes very different values in consequence of the characteristics of the root system (i.e. fasciculate or superficial) and in consequence of the soil depth at which RAR is measured. The testes species chosen for the experimental tests belong to the Fabaceae and Poaceae families. Fabaceae family presents a taproot system depth and branched, while the Poaceae family is characterized by fasciculate root system. The tested species, belongs to both these families and they are listed in Table 3. Table 3. Tested species FABACEAE Medicago sativa Trifolium pretense Lotus corniculatus

POACEAE Lolium perenne Festuca pratensis Poa pratensis

The grass species were sown in each period with the measure recommended in the packaging (about 10g/m2). Neither nutrients nor water (over the rainfall) was added: that is because the aim was to evaluate their growth in the real environmental conditions. 

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

 

 

Medicago sativa grows in drought condition for long period, in optimal climate condition of 20-25 °C, in soil where the pH ranges between 6.5 and 8. The aerial part of the plant reaches length of 80-90 cm. Vegetative repose is consequence of the cold temperature (5° C). Trifolium pratense lives in fresh climate with sufficient water availability, it doesn‟t prefer to high temperature but is more resistant to the cold climate. The aerial part reaches height of 50-70 cm, with numerous stalks. Lotus corniculatus lives in drought soil for long period in soil with acid pH in clay soils. The aerial part varies between 50 and 90 cm. Lolium perenne grow very well in fresh climate but is very sensible to the cold or hot temperature and to the drought conditions. The optimal temperature is 18-20° C. The aerial part has a mean value of 50-80 cm, generally Lolium perenne presents an high density of plants. Festuca pratensis grows in plain, hills and mountain places, is resistant to the cold climate but is very weak in drought condition and high temperature. Poa pratensis is a perennial plant, typical of the temperate regions, growing 30-70 cm tall.

APPARATUS AND METHODS In-Situ Shear Tests The root soil reinforcement provided by the tested grass species was determined carrying out direct in-situ shear tests (Comino and Druetta, 2009; Comino et al., doi:10.1016/ j.still.2010.06.006) (Fig. 4). The shear surface was imposed at a depth of 0.1 m. Fig. 5 shows

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a schematic drawing of the used apparatus. The shear box (0.30 x 0.30 x 0.10 m of size) runs along two guide rails of a sheet frame (1.20 m long and 0.66 m large). The shearing force is applied by an hydraulic jack (driven by a power plant) seated between the box and the frame. A load cell (top scale value of 2.0 ton) is positioned between the shear box and the hydraulic jack, in order to quantify the force needed to shear the clod, while a slide-wire potentiometer is used to measure the displacements. The data during the tests were recorded by a data logger. Lubricating oil was put along the guide rails for reducing the friction with the shear box (highest values of friction: 2% of the strength acquired by the load cell). All the system was made closed to the soil with four pile shoes, 900 mm long. In alternative to the shear box in some tests a different device was used, characterized by a steel plate with guide rails, allowing to measure the additional shear strength provided by both the basal (singularly quantified by the apparatus with the shear box) and the lateral root resistance.

Figure 4. Shear test equipment.

The trials had to reach the soil slipping not later than the 3 minutes, for avoiding overtensions in the root-soil system and for recreating artificially a soil slipping with a speed near to the real condition, when (in presence of a storm or constant rain event typical of the Alpine environment during spring and autumn) the soil saturation can be achieved, and the phenomenon primed with a speed included between 1 cm/min and 30 cm/min. The speed trials was controlled by the power plant, varying the oil pressure of the hydraulic jack from 0 bar to 10 bar.

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Figure 5. Schematic drawing of the used apparatus.

The trials were made eight months and sixteen months after the autumnal seeding: usually they were 8 trials/specie for what concern the measure of basal resistance, 3 trials/specie for what concern the value of lateral and basal resistance. Some trials were considered not correct for external factors (presence of gravel or old roots into the soil, bad function of the data recorder because of the air moisture…). The shear tests made with rooted samples are compared directly with the data of soil in absence of roots acquired in the same day and in the same test site (that were considered the landmark). In this study, every shear test result, for values of stress and strain, was plotted. Shear time, peak shear resistance, shear displacement, root area, soil moisture, average increase in peak shear strength and average increase in displacement due to roots are calculated. During the test realization in situ, the root area ratio (RAR) was measured. Root area ratio is defined as the fraction of the soil cross sectional area occupied by roots per unit area (Gray and Laiser, 1982). After the clod failure, it has been possible to separate the part of the clod above the shear plane. Using a cylinder sampler, some portions of the clod surface were isolated. The roots crossing the shear plane (0.1 m of depth) were counted and their diameters were measured by calipers. The thinnest roots considered for the evaluation of RAR had a diameter of 0.1 mm. The total numbers of roots that cross the shear plane were divided in classes of diameter to evaluate the contribution given to the soil reinforcement.

Scientific Validation The apparatus described above was verified with a scientific validation. It was used for determining the peak angle of internal friction of a sand whose grain size distribution and

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relative density were known. As a normal shear box, it was settled on a sand level, the box was filled up with the same compressed sand and it was made to run on, for eight trials with increasing weight on the sand included. The results are shown in Fig. 6. It can be observed that the angle of the tangent of the parabola that interpolates the points is about 41° (the resting angle of the sand was 38°).

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Figure 6. Validation test of the equipment using uniform coarse sand with grain size and relative density known (8 trials). The resulting peak angle of internal friction of the sand (φP, angle between tangent parabola and the x axis) was about 41°, in accordance with Schmertmann diagram values (φP included between 38° and 46°).

Figure 7. Schmertmann diagram values (φP included between 38° and 46°). Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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This value is in accordance with that of uniform coarse sand shown in the Schmertmann diagram (referred to peak angle of internal friction – Fig. 7) in presence of a relative density of 50-60%: for uniform coarse and dense sand φP is included between 38° and 46° and φCV is included between 30° and 34°. Thus the sand used for this comparison test confirms that the model is proper for a quick but reliable use for in situ tests.

Experimental Results In situ were carried out four different sets of shear tests:    

Test 0 (pre-apparatus) Test 1 (June - 2007) Test 2 (April - 2008) Test 3 (May - 2009)

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Test 0 (Pre-Apparatus) During September and October 2006, a set of tests was carried out in a preliminary way, in order to study some aspects of soil-root behavior during in situ shear tests. This approach was propaedeutic for the creation of a standard model for these kind of trials. They were made eight months after the seeding, testing Fabaceae species (Medicago sativa, Trifolium pratense, Lotus corniculatus) and a commercial Grass mix. Prior to the test, trenches were excavated around a soil block, approximately 0.25 m square (500 x 500 mm) and 300 mm deep, to isolate it from the surrounding soil. In some cases the trench was created only on the uphill and the downhill side of the sample, for measuring the basal and the lateral root resistance. The trench on the uphill side of the block was just wide enough to accommodate an handpowered jack (Fig. 8) used for the loading against a wood plate (300 mm large, 200 mm high). A load cell (top scale value of 2.0 ton, located between the axis and the hand-powered jack) and a slide-wire potentiometer (top scale value: 625 mm) were used to quantify the force needed to shear the soil sample and its displacement. A heavy stone was used as contrast of the transversal load. All the acquired data were entered in a data recorder. The shear surface was imposed at a depth of 200 mm and the maximum displacement measurable with the slide-wire potentiometer was about 650 mm, which proved to be sufficient for the complete soil failure, both in case of absence of roots and in case of presence of roots. The trials had to reach the soil slipping no later than the 3 minutes, for avoiding overtensions in the root-soil system and for recreating artificially a soil slipping with a speed near to the real condition, when the soil saturation can be achieved (during a storm or constant rain –typical in the Alpine environment during Spring and Autumn), and the phenomenon primed with a speed included between 1 cm/min and 30 cm/min. During these shear tests, some difficulties were encountered because of the contrast (the stone and any other object were insufficient), the linearity of the system composed by the

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hand-powered jack, the load cell and the wood plate, the thrust of the hand-powered jack that was too much influenced by the operator attitude, the data capture (sometime too dispersive).

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Figure 8. Field shear test pre apparatus.

The data obtained with rooted clods are compared with the data of no-rooted clods acquired in the same day and in the same place. The mean values of peak shear resistance, shear displacement, root area ratio (RAR), soil moisture, average increase in peak shear strength and average increase in displacement due to roots are calculated have been listed in (Table 4). As written before, Medicago Sativa, Lotus corniculatus, Trifolium pratense and a commercial grass mix were tested. In the rooted samples, the shear plane created at a depth of 0.2 m did not present a clear level because the system was not still and had not a adequate contrast. The data acquired were similar to a cloud of points. It can be identified with some difficulties the point where shear strength happened, at what time, and what is its respective shear displacement. Site A had a moisture of 20.00 % and gave the highest number of valid trials (37.62 % of the total). The positive effects of the roots were clear: for example the rooted samples with Medicago sativa showed a mean increase in peak shear strength over the non-rooted samples of 118% (measure of basal resistance). The average percentage increase in displacement was included between 2 % and 138 %. At the end of the shear tests, the roots found at the bottom of the sample were usually unthreaded (diameter smaller than 2.5 mm): the roots did not mobilize entirely their shear resistance. The grass mix showed the bigger difference between the values obtained from tests that measured basal resistance and basal and lateral resistance: it grows more than twice (from 5.2 kPa to 11.8 kPa). This happened because grass mix is composed also by Poaceae plants that have fasciculate roots, with a diameter smaller than Fabaceae taproot, but more widespread in soil.

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Elena Comino, Paolo Marengo and Valentina Rolli Table 4. Mean data results acquired in Test pre-apparatus, October 2006

Average Peak shear Shear Root area increase in Site Grass species strength displacement ratio peak shear [kPa] [mm] (RAR) strength due to roots [%] non rooted 4.5 ± 1.8 21.7 ± 25.8 Medicago sativa 9.8 ± 3.5 43.6 ± 11.2 118 0.004 Medicago sativa ** 15.4 ± 2.4 38.3 ± 13.3 242 Lotus corniculatus 5.2 ± 0.9 22.2 ± 7.1 0.001 16 Site A Grass mix 5.2 ± 2.2 24.7 ± 16.8 16 0.001 Grass mix ** 11.8 ± 2.6 40.8 ± 15.1 162 Trifolium pratense 8.8 ± 4.3 51.7 ± 20.3 96 0.002 Trifolium pratense ** 10.6 ± 3.2 41.7 ± 24.4 136 non rooted 7.0 ± 2.0 42.9 ± 31.0 Medicago sativa 7.5 ± 1.6 87.3 ± 57.6 0.004 7 Site B Lotus corniculatus 5.5 64.3 0.001 -21 Grass mix 3.4 ± 0.7 48.1 ± 29.5 0.001 -51 Trifolium pratense 6.9 ± 3.0 27.4 ± 10.8 0.002 -1 non rooted 2.4 ± 1.0 9.2 ± 7.3 Medicago sativa 7.8 ± 2.6 14.9 ± 9.1 0.004 225 Site C Lotus corniculatus 8.7 35.2 0.001 262 Grass mix 5.0 ± 1.6 61.5 ± 49.1 0.001 108 Trifolium pratense 5.3 ± 0.7 46.7 ± 37.1 0.002 121

Average increase in displacement due to roots [%] 101 76 2 14 88 138 92 103 50 12 -36 62 283 568 408

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** Measured basal and lateral resistance.

Site B had a moisture of 18 % and the number of tests carried out was 20 % (limited by the system of measure and the very dry summer). Both the sample with Lotus corniculatus and grass mix and Trifolium pratense do not show any particular increase in shear resistance, because of short roots. Only the samples with Medicago Sativa present an average increase of 7 % in shear strength. The average increase in displacement due to roots was positive for Medicago Sativa (103 %), Lotus corniculatus (50 %) and grass mix (12 %), negative only for Trifolium pratense (-36 %). Also in the site C, the number of valid trials was limited (25 %), with a value of soil moisture of 29.60 %. The rooted samples showed a significant increase in shear resistance (between 121 % and 262 % respect to the no - rooted samples) and displacement (between 62 % and 568 %).

Test 1 – June 2007 The trails (Comino and Druetta, 2009) were made with species belonging to Poaceae families (Table 5). In the rooted samples, the shear plane (depth of 0.1 m) was observed to assume a level form beneath the shear box: this is due to the fact that the system weight is widely enough for these sort of tests. These tests were made during and after rainfalls, so that the Authors could act in situations similar to those found before the generation of a landslide on a mountain slope. The data acquired (Fig. 9) are well interpolated by a line. It can be

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identified exactly the point where shear strength happened, at what time, and what is its respective shear displacement.

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Figure 9. In situ shear test of a clod rooted with Lotus curniculatus.

In site A the valid trials were the 85 %; some of them were considered unacceptable because the high value of moisture in the air interfered with the data recorder. The average soil moisture had a high value: 48.88 %. In this site the rooted samples showed a mean increase in peak shear strength over the non-rooted samples was 267 % for the Lolium perenne (basal resistance) and 325 % for the Festuca pratensis (basal resistance). Evaluating the root area (best value: 0.024 % for the Festuca pratensis), it is noticed that only a part of the tensile strength of the single root was mobilized. The average increase in displacement due to roots were always positive, but seeing that the roots had diameter smaller than 0.5 mm the increase has value included between 14.9 mm (149 %) and 45.0 mm (450 %). Observing the data obtained from tests that measured basal and lateral resistance, it is identified a great increase in peak shear strength both in the case of Festuca pratensis and in the case of Lolium perenne. In site B the soil moisture was 31.20 %. The number of tests that could be carried out was limited only by a dry winter (70 %). The particular characters of the soil in this site (thick layers, low adhesion between soil and root) influenced the species survived (only Lolium perenne) and the shear tests. The trials gave results different one from another. There was an increase in peak shear strength (395 % is the percentage increase in the basal resistance), but a decrease in displacement (-81 % respect to the non-rooted sample). The moisture in the soil was low even with the rainy days. The site was not included any longer in the tests. In site C the valid trials were the 75 %; the others were invalid not for a bad working of the apparatus but for a difficult growth of the grass species selected. The average soil moisture was 36%. The rooted samples had an increase in shear resistance (4.1 kPa the average increase for the Festuca pratensis, 12.2 kPa for the Lolium perenne with a value of root area of 0.026 %). Only in two tests the peak in shear strength was lower than the nonrooted values: obviously the roots did not exceed sufficiently the shear plane. As noticed before, for the rooted samples the maximum shear resistance coincided with a greater

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displacement: the increase was 8.6 mm for the Festuca pratensis (93 %) and 34.9 for the Lolium perenne (379 %). Table 5. Mean data results acquired in June 2007 Average Peak shear Shear increase in Shear time Root area strength displacement peak shear [s] [%] [kPa] [mm] strength due to roots [%]

Name of Grass species the area

Site A

Site B

Site C

non rooted 33.5 ± 7.1 1.2 ± 0.4 10.0 ± 2.7 average Festuca pratensis 38.0 ± 0.5 5.1 ± 1.6 24.9 ± 5.9 Festuca pratensis** 56.1 ± 10.3 11.7 ± 0.6 67.0 ± 33.4 Lolium perenne 80.8 ± 17.6 4.4 ± 0.3 55.0 ± 22.8 Lolium perenne** 77.8 ± 16.4 10.5 ± 0.4 72.2 ± 3.3 non rooted -average 21.8 ± 8.5 1.9 ± 0.7 25.8 ± 7.9 Lolium perenne 22.4 ± 16.4 9.4 ± 3.1 4.8 ± 0.6 Lolium perenne** 18.7 ± 8.6 10.9 ± 3.5 15.0 ± 0.9 non rooted 20.7 ± 1.4 8.2 ± 0.3 9.2 ± 5.2 average Festuca pratensis 66.3 ± 38.8 12.2 ± 3.1 17.8 ± 12.2 Festuca pratensis** 125.7 ± 9.2 17.8 ± 1.1 49.5 ± 13.8 Lolium perenne 202.0 ± 45.2 20.4 ± 3.4 44.1 ± 25.0 Lolium perenne** 92.1 ± 9.2 20.8 ± 0.8 47.3 ± 3.2

0.024 0.012

0.008

0.020 0.026

Average increase in displacement due to root [%]

325 875 267 775

149 570 450 622

395 474

-81 -42

49 117 149 154

93 438 379 414

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** Measured basal and lateral resistance.

(c)

(d)

Figure 10. Experimental site (site b - Belvedere-) (a) and clod before (b) and after the shear tests (c, d). Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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Test 2 – April 2008 The trails were made 8 months after the seeding with species of Fabaceae family (Table 6, Comino and Druetta, 2009) and 16 months after the seeding with species of Poaceae family (Table 7, Comino and Druetta, 2010). Also in this period the tests were carried out after spring rain events, but the moisture in the soil showed lower values than the past years: 23.60 % in Site A and 28.47 % in Site C. These data influenced in particular the tests made in Site A in no - rooted soil because the included gravel had a superior resistance in a drier soil (greater friction among the particles): in fact the results were increased of four times than those obtained in 2007. In site A the valid trials were the 95.4 % (two trials were not calculated because of a too fast push of the hydraulic jack). Medicago sativa increased the strength of 19 % (9.3 kPa – the worse increase among the Fabaceae), and the displacement of 1,755 %. Lotus corniculatus showed a massive development of the roots (root area = 0.102 %) and the best results in strength increase: 46 % (11.4 kPa). Trifolium pratense increased the strength of 17 % and the displacement of 1,994 % (37.7 mm, the best shear displacement). The data obtained from tests measuring basal and lateral resistance showed values very similar to those acquired in the tests measuring basal resistance. In site C the valid trials considered valid were the 95.4 % (two trials were not calculated because of a too dry soil in the no - rooted tests). Medicago sativa and Trifolium pratense showed very similar values in peak shear strength (about 11 kPa) and root area. The great difference lives in the shear displacement: the root of Trifolium pratense had a lateral growth. Such as in site A Lotus corniculatus showed the best values in root area (0.030 %), increase in shear strength (162 %) and in general the best resisting attitude. Concerning Festuca pratensis, the values of root area were 0.082 % in Site A and 0.054 in Site C. In Site A it showed a peak shear strength (basal resistance) of 16.6 kPa, with an increase over the non-rooted samples of about 113 %; in Site C this average value was 14.5 kPa (202 %). The percentage average increase in displacement was included between 945 % (20.9 mm - Site C) and 1,544 % (29.6 mm - Site A). The data obtained from tests measuring basal and lateral resistance showed values very similar to those acquired in the tests measuring basal resistance. This detail can signalize their lacking lateral growth in a long period and the importance of increasing those kind of tests number. The values of root area of Lolium perenne were analogous in the two places: 0.044 % in Site A and 0.047 % in Site C. Its peak shear strength (basal resistance) was 19.9 kPa in Site A (increase of about 155 %) and 14.6 kPa in Site C (204 %). The average increase in displacement was 622 % in Site A (13.0 mm) and 735 % in Site C (16.6 mm). In both sites this specie needed the cut of the aerial part (in total, four different times) that grew up over 60 cm and colonized the neighboring areas (root maximum length: 15 cm). In Site A the data about basal and lateral resistance were lower than basal ones; in site C there were no valid data because of technical problems with the hydraulic jack which stopped the trials. Poa pratensis confirmed its slow germination and its difficulty to grow up during dry period: it grew up only in Site A. The low increasing in the peak shear strength is meaningful of its contribute in the soil, despite the remarkable root area respect the other grass species (0.086 %). The peak shear strength was 12.8 kPa (64 %) and the shear displacement 14.5 mm (706 %)

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In loco the authors found wild Lolium (Lolium spp.), that was tested. It showed the greater values in peak shear strength (22.6 kPa – 371 %), shear displacement (22.8 mm – 1,040 mm) and root area (0.094 %). Obviously, this fact is normal because the period passed from its germination is longer than the seeding made in November. Table 6. Mean data results acquired in April 2008 (Fabaceae species)

Name of the area

Site C

Site A

Grass species

non rooted soil Medicago sativa Medicago sativa** Lotus corniculatus Lotus corniculatus** Trifolium pratense Trifolium pratense** non rooted soil Medicago sativa Medicago sativa** Lotus corniculatus Lotus corniculatus** Trifolium pratense Trifolium pratense**

Peak shear strength [kPa] 4. 8 ± 1.9 11.2 ± 2.1 10.3 ± 1.85 12.7 ± 2.8

Shear displacement [mm] 2.0 ± 1.33 11.9 ± 8.7 14.9 ± 4.9 17.4 ± 12.8

13.9 ± 0.71 28.25 ± 9.7 10.7 ± 2.7

0.69 0.304

18.0 ± 3.8

12.9 ± 4.3

230.5 ± 4.8

7.83 ± 0.6 9.27 ± 3.1 14.40 ± 2.3 12.09 ± 5.2

1.1 ± 0.9 32.23 ± 12.2 22.78 ± 5.1 16.05 ± 7.1

16.69 ± 0.6 20.07 ± 8.7 9.42 ± 3.6

Root area [%]

0.105

0.0694 0.1016

35.13 ± 18.6

13.09 ± 1.9 45.64 ± 10.27

0.032

Average increase in peak shear strength due to roots [%]

Average increase in displacemen t due to root [%]

134.8 117 166

494 643 765

192

1305

124

793

170

921

18 84 54

2830 1970 1359

113

1724

116

3093

67

4049

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** Measured basal and lateral resistance

Table 7. Mean data results acquired in April 2008 (Poaceae species) Average Average Peak shear Shear increase in increase in Name of Shear time Root area Grass species strength displacement peak shear displacement the area [s] [%] [kPa] [mm] strength due to due to roots roots [%] [%] non rooted 8.5 ± 1.3 7.8 ± 0.1 1.8 ± 0.1 Festuca pratensis 12.9 ± 4.4 16.6 ± 6.2 29.6 ± 14.3 113 1,544 0.082 Festuca 14.2 ± 1.4 103 2.344 pratensis** 15.8 ± 3.7 44.0 ± 5.4 Site A Lolium perenne 12.0 ± 8.5 19.9 ± 8.6 13.0 ± 6.2 155 622 0.044 Lolium perenne** 23.1 ± 10.6 15.3 ± 3.8 25.3 ± 7.7 96 1,305 Poa pratensis 16.1 ± 6.2 12.8 ± 3.1 14.5 ± 4.1 0.086 64 706 non rooted 3.8 ± 1.0 4.8 ± 2.3 2.0 ± 0.5 Festuca pratensis 4.8 ± 1.7 14.5 ± 1.9 20.9 ± 8.9 202 945 0.054 Festuca 213 Site C 17.8 ± 10.7 15.9 ± 5.0 26.0 ± 23.9 1,200 pratensis** Lolium perenne 4.0 ± 1.0 14.6 ± 2.1 16.6 ± 3.5 0.047 204 735 Lolium spp 8.6 ± 3.6 22.6 ± 7.3 22.8 ± 5.7 0.094 371 1,040

** Measured basal and lateral resistance

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Test 3 – May 2009 The trails were made 8 months after the seeding with Festuca pratensis, Lotus corniculatus and Trifolium pratense in Site A. All the three tested species increased the shear strength, comparing no – rooted clods , of about 35 %. The data (listed in Table 8) referred to the average increase in peak shear strength due to roots obtained measuring both basal and lateral resistance showed values higher than those acquired in the tests measuring only the basal resistance, varying between 46 % (Trifolium pratense) and 82 % (Festuca pratensis). Lotus corniculatus showed a massive development of the roots (root area = 0.22 %), as shown in the tests carried out in June 2008. Table 8. Mean data results acquired in May 2009

Site

Grass species

non rooted Festuca pratensis Festuca pratensis ** Site A Lotus corniculatus Lotus corniculatus ** Trifolium pratense Trifolium pratense **

Average Average Peak shear Shear Root areaincrease in increase in strength displacement ratio peak shear displacement [kPa] [mm] (RAR) strength due to due to roots roots [%] [%] 5.2 ± 1.1 1.8 ± 0.7 7.2 ± 2.5 9.5 ± 5.6 37 18 0.010 9.5 ± 2.2 22.8 ± 5.0 82 61 7.2 ± 3.8 27.6 ± 17.0 37 245 0.022 8.1 ± 2.4 22.5 ± 3.3 56 181 7.1 ± 1.0 12.0 ± 6.7 36 50 0.005 7.6 ± 0.7 27.8 ± 18.5 46 247

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** Measured basal and lateral resistance.

MODELS DESCRIPTION Scientific community widely studied the effects generating by roots on hill slopes. Numerous theories have been developed, but the most appreciated, and later studied, is based on the concept that roots can increase the soil shear strength. The experimental tests have been also supported by models, trying to reproduce the roots behavior during superficial slice of the soil. The most important models are known as: Wu et al. (1979) (W&W model) and of Pollen and Simon (2005) (FBM model). Wu et al. (1979) model, considers the shear strength increase due to the roots as a cohesion term in the Mohr – Coulomb failure criteria: s = c‟ + σ‟tanΦ‟ + cR     

s is the effective soil shear strength, c‟ the soil cohesion, σ‟ the effective normal stress on the shear plane, Φ‟ the effective soil friction angle cR the additional cohesion due to the presence of roots.

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(1)

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Numerous hypotheses involve Wu e al. model: roots are considered as cylindrical, flexible and elastic fibres, oriented perpendicularly to the slip plane. The model is based on the concept that when a shear tension induces a soil displacement, generating friction between roots surface and soil grains, roots deform and mobilize their tensile strength. The tensile strength can be translated into the tangential component that counterbalances the shear force and the normal components that increase the confining pressure. Assuming the MohrCoulomb equation as shear criterion and that the soil friction angle is not affected (Waldron, 1977), the additional root cohesion can be estimated as: cR = tR(sinδ + cosδtanφ‟)  tR is the average mobilized tensile strength of roots per unit area of soil  δ is the angle of root distortion in the shear zone.

(2)

Gray and Leiser (1982) generalized the Waldron (1977) model to the case where roots may be oriented at any angle relative to the failure plane. The mobilized tensile strength of roots per unit area can be calculated as: n A  t R   TRi  Ri   A  i 1

(3)

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where (𝑇𝑅𝑖 ) is the tensile strength of a single root (i), (ARi/A) the fraction of the soil cross section occupied by a single root (i) and n the number of roots in the considered soil cross section. TR is correlated with root diameter (d) by a power law equation (Gray and Sotir, 1996): TR = αd-β 

(4)

α and β are empirical constants depending on species.

A successive problem was the determination of the term in brackets of Eq. 2. Wu et al. (1979) have showed that it ranges between 1.0 and 1.3 kPa with normal variations in δ and φ‟ (40 – 90° and 25 – 40° respectively). Later it changed in 1.15 (Waldron, 1977) or 1.2 (Wu et al., 1979). Docker and Hubble (2008) have reported, for riparian vegetation from New Zealand, values of the term (sinδ + cosδtanφ‟) less than 1 (around 0.75). The term (sinδ + cosδtanφ‟) for common values of δ and φ‟ can be generally considered a factor k’ (Bischetti, 2009) and Eq. 2 can be simplified as: cR = k’ tR

(5)

In the present research another model have been considered, based on the study carried out by Pollen and Simon (2005), that infer as Wu‟s model overestimates the results because it assumes that all roots crossing the shear plane break at the same time (Bischetti et al., 2009). Pollen and Simon (2005) proposed the Fiber Bundle Model (FBM), built on the concept that roots have different maximum strength values and therefore break at different points as a load is applied to the soil.

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The FBM model was applied in the present work through a numeric algorithm elaborated by the authors (Comino et al. doi:10.1016/j.still.2010.06.006). The algorithm is based on the flowchart presented by Pollen and Simon (2005). The criterion adopted for load distribution is the Equal Load Sharing (ELS) or Global Load Sharing (GLS) among the roots. It means that each intact root of the bundle takes up an equal portion of the applied load. Roots fail when the portion of the total load value exceeds the single root tensile strength, then the same load is redistributed on the intact remaining roots. When the load redistribution does not cause further roots to break the initial load has to be increased. The failure process ends when all the roots are broken by the applied load. In this research the fibres are modelled as a “static” bundle where the fibres strength is independent of time (Bischetti et al., 2009). Field and laboratory testing (Pollen and Simon, 2005) has shown that the smallest roots tend to break preferentially at the onset of soil shearing. In the simulations carried out by Thomas and Pollen-Bankhead (2009), if load was apportioned equally to each root crossing the failure plane, the smallest roots broke first and the largest roots broke last. Conversely, the inverse power relation between root tensile stress and diameter (Eq. 4) means that if load was apportioned to the roots so that equal stress was applied to each root, the largest roots broke first and the smallest roots broke last. Therefore, to correctly model the dynamics of a soil– root matrix, the results obtained by Thomas and Pollen-Bankhead (2009) suggest that load should be apportioned equally to roots in a FBM (ELS or GLS). Wu et al. 1979 and FBM models have to be implemented through few parameters referring to the root system characteristics. The principal is the root tensile strength, considered by both models, as input for the root reinforcement prediction. The measure of root tensile strength has been carried out by laboratory tests, using an equipment and a methodology described below.

LABORATORY TENSILE STRENGTH TESTS One of the important mechanical characteristics of roots is the tensile strength. Soils, on the other hand, are strong in compression and weak in tension. A combined effect of soil and roots results in a reinforced soil (De Baets et al. 2008). When shearing the soil, roots mobilize their tensile strength whereby shear stresses that develop in the soil matrix are transferred to the root fibers via interface friction along the root length (Gray and Barker 2004) or via the tensile resistance of the roots. As highlighted before, root tensile strength testing is an important step to evaluate root reinforcement. Tensile strength tests were carried out on single root samples, that were dug out from the soil during the field work period (shear strength tests on soil blocks) by softly removing the soil from the clod soil–roots using jets of water. Root samples of 80 – 100– 120 mm length were stored in a 15 % alcohol solution (Meyer and Gottsche, 1971; Bischetti, 2009) and then tested in the laboratory. For each root the diameter was measured (using a digital callipers of 0.01 mm resolution) along its length in three different positions, in order to assign a representative value of the diameter corresponding to the breaking point (after the tensile strength test). The diameter measures were not taken at the extremities where the sample was clamped.

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The tensile strength tests were conducted using an experimental device designed and built by the authors (Comino and Marengo, doi:10.1016/j.catena.2010.06.010). The methodology to carry out the tests accords to the procedures described by Bischetti et al. (2003), and Tosi (2007). The experimental device consists of a modified bench vise with a 500 N load cell (0.5 N of resolution) connected to a steel fork where is fixed the first root end. The other extremity of the root sample is clamped by a steel fork anchored to the basement. Both root extremities are fixed by non-serrated clamps (Fig. 11) in order to avoid damages to the fixing points, thus affecting the tensile strength measure (Mattia et al., 2005). Clamping is the most critical issue when measuring root strength. The most often reported and experienced problem with clamping is that the grips damage the root structure, inducing rupture of roots at the position of clamping. Tests where the roots broke near or at the position of clamping are to be considered invalid. The clamps used in the present research are connected to the two steel plates and consist of a support with two small wheels where the root sample extremity is positioned.

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Figure 11. Tensile test equipment.

Tensile strength tests were characterized by a linear constant speed of 10 mm min -1 (Mattia et al., 2005). Tensile force was measured by the load cell, and displacement by a slide–wire potentiometer. The data were registered by a data–logger. The tensile breaking force values (TS) were calculated dividing the tensile force at failure (maximum registered load) by the cross sectional area of the root (considering the nearest one to breaking point of the three measured diameters). This procedure to obtain the tensile breaking force is justified by the alignment between force direction and root position (Tosi, 2007).

Results of Laboratory Tests Tensile strength tests were carried out on 100 single root specimens of five grass species studied:     

Lotus corniculatus Trifolium pratense Medicago sativa Festuca pratense Lolium perenne

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Some roots that showed slipping or rupture in the clamped points (Cofie and Koolen, 2001) were accounted as invalid tests. Root tensile strengths (TS) decreased nonlinearly with increasing diameter (d) for all the five grass species tested. The relationship between TS and d can be described by a power law equation 4 (Gray and Sotir, 1996; Genet et al., 2005; Mattia et al., 2005; Tosi 2007). The α and β values of the power law equation of the five tested species are listed in Table 9. The tensile strengths (TS) recorded for L. corniculatus (Fig. 12) vary between 1.54 MPa (root specimen with 0.93 mm diameter) and 19.76 MPa (d = 0.34 mm). The power law relationship between tensile strengths and diameters is characterized by an equation (Eq. 4) with a value for α of 3.52 and a value for β of 1.41. The TS registered values for T. pratense (Fig. 13) vary between 3.14 MPa (1.66 mm) and 50.86 MPa (0.27 mm). M. sativa (Fig. 14) is characterized by a TS range of 4.76 (0.14 mm) MPa and 335.05 (0.97 mm) MPa. Concerning the two tested grass species belonging to the Poaceae family, tensile strength values of F. pratensis (Figure 15), (α = 2.58 and β = 2.01) range between 372.04 MPa and 6.09 MPa, while L. Perenne (Figure 16) is characterized by a maximum value of 365.29 MPa (0.10 mm) and a lowest value of 3.16 MPa (0.65 mm). As shown in most of the graphs reported in Fig. 14 to 16, the laboratory data of three tested species (M. sativa, L. perenne and F. pratensis) are well interpolated by a power law regression if compared to other studies, as, for example, Mattia et al. (2005) and Tosi (2007). The other two species, Lotus corniculatus and Trifolium pratense (Fig. 12 and Fig. 13) show lower correlations with coefficients of determination (R2) that range between 0.30 and 0.40.

Figure 12. Root tensile strength [MPa] vs Root diameter [mm] for L.corniculatus (Comino et al., doi:10.1016/j.still.2010.06.006).

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Figure 13. Root tensile strength [MPa] vs Root diameter [mm] for T. Pratense (Comino et al., doi:10.1016/j.still.2010.06.006).

Figure 14. Root tensile strength [MPa] vs Root diameter [mm] for M. Sativa (Comino et al., doi:10.1016/j.still.2010.06.006).

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Figure 15. Root tensile strength [MPa] vs Root diameter [mm] for F. Pratensis (Comino et al., doi:10.1016/j.still.2010.06.006).

Figure 16. Root tensile strength [MPa] vs Root diameter [mm] for L. Perenne (Comino et al., doi:10.1016/j.still.2010.06.006).

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COMPARISON BETWEEN MODELS AND FIELD RESULTS After the estimation of the tensile strength by laboratory tests and the RAR values measured on the clods tested in situ, it has been possible to implement both the models presented above. The soil reinforcement calculated by the models has been compared with the value obtained by the experimental test provided in situ. The comparison presented in Table 9 is referred to the species tested during April 2008. Table 9. Comparison between the in situ and models results for the tests realized in April 2008

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Species Lotus corniculatus Trifolium pratense Medicago sativa Festuca pratensis Lolium perenne

Wu results [kPa] 6.0 8.5 17.0 10.0 14.3

FBM results [kPa] 3.3 3.4 11.6 9.9 12.2

In situ results [kPa] 10.2±3.4 7.6±2.8 7.8±2.3 8.9±1.9 8.6±2.3

The models over-predicted root reinforcement measured in field tests for three species: M. sativa, F. pratensis and L. perenne. Many rooted clods showed, after the failure (direct shear tests), some not broken roots (mainly with diameter larger than 1 mm) involved in slipping phenomena. The slipped roots did not contribute to the soil reinforcement with their ultimate tensile strength, while both the models consider that all the roots crossing the shear plane break during the direct shear test. Presumably the models hypothesis about the perfect anchorage between root and soil could not ever be valid in the case of grass roots and shallow shear surface (10 cm). Slipped root depth observed in the situ tests mainly reach lengths of 24 cm under the shear surface. The low anchorage depth below the shear surface and the geotechnical characteristics at 10 cm depth do not guarantee the perfect adhesion between soil and root. As a consequence of both these reasons, the models can overestimate the field results. Concerning L. corniculatus and T. pratense, the FBM model under-predicted root reinforcement measured in situ. Since the FBM model (as well as the Wu et al. model) simulates the root reinforcement effect considering only the tensile failure mode, the underprediction depends on the low values of root tensile strength. As showed L. corniculatus and T. pratense are characterized by values of tensile strength lower than M. sativa, F. pratensis and L. perenne (comparing root samples with the same diameters).

ROOT – SOIL: COMPLEX SYSTEM As showed in Table 9 models results do not fit very well the in situ measurements. As described above the root reinforcement depends on many other parameters not considered in the models, such as chemical and physical characteristics of the soil. The subsequent part of our research concerns the study of those parameters that played a role in situ shear tests. Several studies showed that vegetation affects slope stability influencing both hydrological processes and mechanical structure of the soil (Schiechtl, 1991; Wu, 1999; Schmidt et

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al., 2001; Roering et al., 2003; Bischetti, 2003; Florineth, 2005; Osman and Barakbah, 2006; Pollen, 2006). In our research we took in consideration:  

species-specific characteristics of the plants, environmental site-specific characteristics, such as soil texture and structure, aeration, moisture, temperature, competition with other plants.

In 2007 before seeding grass species seven replicate samples of each soil were collected with a pedologic probe and analyzed. The samples were dug out at a superficial depth (included between 0 and 30 cm, that is root grass depth). From November 2007 to October 2008 in Site A and Site C the moisture values at a depth of 30 cm was measured weekly with the use of a pedologic probe. These samples were introduced in a 100° C oven for 24 h and the weight difference was listed.

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Moisture The beneficial hydrological impacts of vegetation on bank stability include vegetative canopy interception, which decreases the amount of water available for infiltration, and extraction of water from the root zone which lowers the local phreatic surface (Selby, 1993), and increases matrix suction within streambanks (Simon and Collison, 2002). The hydrologic disadvantages of vegetation on soil stability are related to soil infiltration characteristics both at the soil surface, and deeper within the soil profile. At the surface, canopy interception and stem flow tend to concentrate rainfall locally around the stems of plants, creating locally higher pore-water pressures (Durocher, 1990). The presence of stems and roots at the soil surface also act to increase infiltration rate and capacity, at times accelerating the delivery of water at depth into the slope or bank by creating preferential flow paths (Simon and Collison, 2002). The extent to which preferential flow affects bank stability depends on properties of the bank materials and the stratigraphy of the slope. An appropriate knowledge of the groundwater trend is essential in the evaluation of the slope stability. This knowledge is deepened with the individuation of the temporal and spatial distribution of the neutral pressures. This argument was studied previously through water uptake model by many authors (Gardner, 1964; Landsberg and Fowkes, 1978; Rowse et al., 1978; Iwata et al., 1988; Lafolie et al., 1991; Bruckler et al., 1991; Chen and Lieth, 1992; Clausnitzer and Hopmans, 1994; Thornley, 1996; Bengough, 1997; Doussan et al., 1998a; Wu et al., 1999; Collins, 2001) developing two different approaches: one approach is to model water flow inside the root branching structure, assuming that the soil is and remains fully saturated; the other calculates the changes in soil moisture conditions assuming that the pressure inside the root branching structure is constant and uniform. Thus, continuous, accurate measurements of variations in soil-water content in the laboratory, glasshouse, and field are essential means of investigating soil-water consumption by plants and its role in irrigation scheduling and efficiency. Such studies require frequent measurements at specific locations in the soil profile for days, weeks, and even entire seasons.

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Many methods of automating soil–water status measurements have been described. A common, indirect method of determining water content, especially for agricultural needs, is to measure the soil water potential (Armstrong et al., 1985; Lowery et al., 1986) with tensiometers or gypsum blocks. Many studies on different scales have measured soil moisture with the TDR method (Topp et al. 1980; Topp and Davis, 1985; Dalton and van Genuchten, 1986; van Wesenbeeck and Kachanoski, 1988; Zegelin et al., 1989; Baker and Allmaras, 1990; Heimonvaara and Bouten, 1990; Herkelrath et al., 1991; Noborio, 1996; Green and Clothier, 1999). In the field study presented here, moisture content variation in two sites placed in the Italian Alpine environment was measured through a weekly analysis, sampling the soil along a year (November 2007 – November 2008) with a probe. The transient-water flow in the soil and its uptake by roots between rainfall events were monitored and analyzed, considering also the evapotranspiration phenomenon.

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Moisture Monitoring From November 2007 to October 2008 in Site A and Site C the moisture values at a depth of 30 cm was measured each week, through a pedologic probe. The Authors took samples either in rootless and rooted soil (with Lolium perenne) every Friday morning at about 10 A.M.. These samples were hermetically closed in a plastic bag and sealed inside. Then they were introduced in a 100° C oven for 24 h and the weight difference was listed. So the moisture value was drawn. Lolium perenne is a grass species that grows spontaneously in this area. It is sensible to dryness and high temperature, sensible to coldness, bears high moisture in the soil, soil pH included between 6 and 7, and has fasciculate roots. The saturation value was calculated following the directives contained in AASHTO T 191/86, T 205/86; ASTM D 1556/82, D 2167/84; BS 1377. First of all γd (dry density, [g/cc]) was obtained with this formula: γd = 100  γ / (100+w)

(6)

where: γ=density in natural condition w=corresponding moisture contents Then saturation was calculated using the value of porosity, soil unit weight, water and void volume. The raininess value was taken from the rain gauge located in Luserna San Giovanni by the ARPA Piedmont. It recorded the data daily and they are available on the official site of this regional Agency for the environmental protection. It is necessary to consider the value of evapotranspiration (i.e. the loss of water from the soil by direct loss plus loss through evaporation of moisture by plants growing on the soil) in this area. A recent study made by the Politecnico di Torino evaluated this data (Viglione, 2004), using FAO Penman-Monteith equation. It determines the daily evapotranspiration referred to ARPA station in Luserna San Giovanni.

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Results of Moisture Monitoring

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The samplings were made between November 2007 and October 2008. In twelve months this area was hit by a violent storm in the end of May, with a consequent flood (it can be seen by the data registered by the rain gauge). Despite this event, the annual precipitation measured (814.6 mm) was lower than the mean data of the last 20 years (1,092.3 mm). Site A – From the physical analysis, the soil of this site showed a dry density γd equal to 1.006 g/cm3, a voidage “n” of 0.62 and a void index “e” of 1.63 . The mean data of the saturation along the year for the soil with roots was 32.08% (with a standard deviation of ± 7.35%); for the soil with no roots was 33.20 % (± 7.20%) (Fig. 17). For the rooted soil the low value of saturation was registered the 5th of September (17.40%), after the spring months and almost one month with no rain (August, with a mean temperature of 20.4 °C). The highest value was recorded the 27th of June (45.90%), after the May flood (277.4 mm in few days) and during rainfall event, that can usually happened during spring time. The not-rooted soil showed the lowest saturation value the 5th of September (19.80%) and the highest value the 30th November (44.40%). During the weeks comprised between April and August the saturation of the rooted soil showed higher value than that of not-rooted one. In the rest of years the saturation of the soil with no roots was always greater. Site C – The physical analysis showed that this soil had a dry density γd equal to 1.149 g/cm3, a voidage “n” of 0.57 and a void index “e” of 1.31 . The mean data of the saturation for the soil with roots was 53.21% (± 7.43%); for the soil with no roots was 53.60% (± 9.10%) (Fig. 18).

Figure 17. Saturation and rain trend during 2008 in site A. Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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The rooted soil had the low value of saturation the 15th of August (39.30%). The highest value was recorded the 4th of January (66.00%), in presence of a thin layer of snow and ice of about 10 cm. The not-rooted soil showed the lowest saturation value the 25th of April and the 15th of August (about 19.00%) and the highest value the 4th January (44.40%). The saturation of the rooted soil had higher value than not-rooted in two different periods: the weeks comprised April and July and during September and October.

Figure 18. Saturation and rain trend during 2008 in site C.

The graphs show a tendency in the answer of the soil to the rainfall and a correlation between the soil moisture and the rainfall. The saturation showed always the same increasing or decreasing trend with the not-rooted or rooted soil, according to the atmospheric conditions. These attitudes dependant on many factors, such as the particle-size compositions of the soils, natural and dry density, voidage, void index, local evapotranspiration, mean temperature, air humidity, roots distribution and their species. During this year of observations there were heavy rainy events during the end of April, May and the beginning of June: the sum of the fallen rain was 504.2 mm (about the 64.00% of the total). Also September, as usually, was a rainy month (72.0 mm). During May, June and September the water in the soil was minimized by high evapotranspiration values (between 102.2 and 121.3 mm/month). Site A – Between September and April the moisture in the soil was strongly influenced by the rain: every rain events increased it remarkably. For what concern the trend slope, the roots did not have evident effects with the suction. The trend after the rain events did not show a steeper slope than that of soil with no roots. The action of the roots was clear in the general trend, in so far the saturation of this soil was always lower.

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Between May and September the trend of saturation in the two kind of soil was overturned: except a short parenthesis during May storm, the roots kept a bigger percentage of water in soil. It is interesting to notice that after the storm the soil with no roots had immediately the pores saturated, meanwhile in the soil with roots the rain passed into the soil through the slits created by roots. After the rainy days, the water in the soil with no roots evaporated in the atmosphere from the superficial layers. The moisture in the soil with roots show a crescent trend, until it quickly decreased when there was no more outstanding rainfall. The role played by the roots was self-evident. Site C – The soil is more compact than Site A, so in general it can be said that the movement of water rain were eased by the fracture created by the thin roots (where attended), or kept difficult in the no-rooted soil by the strong grain adhesion. The saturation between November and February is strongly influenced by the presence of snow and an ice layer. It is the period where the moisture in the soil with no roots is higher than that of the soil with roots. The permanent presence of water (even if in different physical status) in the surface created a situation where there was its slow and gradual penetration slowly into the compact and not-rooted soil. This water stagnated in the first 30 centimeters of height, and obviously the wintry temperature and the evapotranspiration values did not cause its evaporation. In the area with roots the saturation was lower until the first April days. Then, in the rest of year observation the moisture registered in the soil with roots is higher. The water of rain events (or the heavy storm) did not penetrate the not-rooted soil, eroding it and leaving furrows on the surface. A clear example is represented during the days after the May flood: the saturation in this kind of soil increased very slightly in spite of great quantity of fallen water. Also in September and October the summer trend was carried on, except few days: the roots and the evapotranspiration kept the soil more oxygenate, with an explicit benefit for it, under a pedological, geotechnical and agricultural point of view.

RELATIONSHIP BETWEEN CHEMICAL-PHYSICAL SOIL PARAMETERS AND HERBACEOUS SPECIES DEVELOPMENT Site A Medicago sativa roots, after 8 months, showed an area with a medium value, among those estimated); grain size and pH were favorable, but it suffered the lack of rain even if had the properties of bearing the dryness, and exposition to the sun was excessive in spite of it would be the more resistant forage that to drought because its root system is able to reach great depths. Medicago sativa is a considerable consumer of water: it consumes 700-800 liters to form a kilogram of dry matter. Trifolium pratensis roots, after 8 months, had one of the lowest area (0.033 %). In this site it found adverse chemical and physical characteristics of soil, basic pH and calcareous soil, which enabled its development increased. Lotus corniculatus roots had the highest value of root area (0.102%), even if the pH was too subalkaline. Soil grain size was favorable.

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Poa pratensis found difficulties in the developing in Site A because it does not tolerate the acidity of the soil (unfavorable pH), it has a low tolerance to salinity. Festuca pratensis had a proportional augment of the root area as the time passed by (after 8 months: 0.024 %; after 18 months: 0.082 %), in spite of a unfavorable pH value. Grain size was favorable. The development of these grass species is due to favorable pH and to the presence of chemical parameters such as high quantities of phosphorus and potassium. Its development depends on its high resistance to the presence of moisture, and to a favorable period of spring and autumn transition. Lolium perenne root area ranged from 0.012% to 0.044%. Such as Festuca pratensis, it had a great development thank to the pH and to the presence of phosphorus and potassium.

Site B Three species were sown at the Site B: Figure 5 shows that the physical and chemical characteristics of this area allowed only a scarce development of Lolium perenne. The structure of the soil, characterized by impermeable and compacted layers, that did not allow the development of the roots and the water rain infiltration, has influenced this issue. From a chemical point of view the low concentrations of chemical elements is an additional crucial aspect of the non-development of grass species. Lolium perenne has a low resistance of drought, and it is good especially for fresh land.

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Site C Medicago sativa root had a low value of area after 8 months the root area was 0.007 %, due especially to high moisture in the air and in the soil (the site is located near a river). The soil most suitable for Medicago sativa is composed by medium mixture of clay and it is wellstructured and deep, (so it does not hinder the deepening of the roots) and it does not tolerate the acidity. The features described above reflect those of Site C, and for this reason that Medicago sativa was the plant that was less developed. Grain size and pH were favorable, but the chemical characteristics of the soil were hostile. Lotus corniculatus showed the highest percentage of root area and the highest peak shear strength among the three Leguminosae. In fact Lotus corniculatus resists excess of moisture better than Medicago sativa and Trifolium pratensis. At the same time it is characterized by resistance to the dry period, therefore it is able to provide, even in not optimal conditions, a good harvest in summer. The thermal limits of Lotus corniculatus are approximately those of Medicago sativa, to which is comparable entirely for resistance to the cold. This feature and its famous tolerance towards a certain acidity of the soil makes the Lotus corniculatus a species suitable to be grown in organic soils also in mountain. Festuca pratensis found favorable pH and grain size, so it showed a regular increase of the root area during the months. It allowed the growth of native species, and, interacting with them. Lolium perenne development had related characteristics of Festuca pratensis: continuous increase of root area during the 18 months, high development. On the contrary of Festuca pratensis, it showed an excessive growth in the aerial part: it was a negative attribute, because

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this fact underlined that it needs a continue care. Thanks to the closely fasciculate roots, it showed great shear strength both in 2007, and in 2008 (analogous to those of Festuca pratensis). Lolium spp. was a weed and spontaneous species grown up in “Site C”. “Site B” soil was not favorable for the tested species. In “Site C” the species that showed the best mechanical properties and the greatest root growth were and Lolium perenne and Lotus corniculatus after 8 months from the seeding, and Lolium perenne after 18 months from the seeding.

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CONCLUSION This research has shown that grass roots increase the shear strength of Alpine soil, its displacement, delaying the phenomenon of soil slipping and reducing the soil susceptibility to erosion phenomena. The results is more appreciable proportionally to the number of roots that cross the shear plane and their diameters. Nevertheless the complex root – soil system is influenced by many parameters that make difficult to establish a rule in the soil root reinforcement prediction. The study has emphasized the relation between moisture, chemical elements characterizing soil and pH. Moisture is the first element that influenced the germination and development of grass species. The second important aspect is the presence of chemical elements in the soils. An excess nitrogen in the soil determines very green leaves and lack of roots. High concentration of nitrogen in soil (such as “Site B” and “Site C”) causes great development of shoots and a limited growth of roots, instead low concentration of nitrogen delay the root development. High concentrations of potassium in soils improves some characteristics of Gramineae species, such as resistance to drought by reducing the transpiration, and improving the development and ramification of roots. In our study Festuca pratensis, Lolium perenne and Poa pratensis showed the most developed root system. The comparison between direct shear tests data and models outcomes showed different results for the five tested species, although both the models predicted the order of magnitude of the additional cohesion measured in situ. W&W and FBM models consider, as contribution to the soil reinforcement, the root tensile strength at failure. As a consequence both the models outcomes are strongly dependent on the root tensile strength associated to each root crossing the shear plane. A correct evaluation of RAR and of the root diameter distribution assume a crucial role in estimating root cohesion, even if small roots generally involve a greater degree of uncertainty in measuring their diameter and in the evaluation of the correct number of roots crossing the shear plane. The high root density, the mean root diameter distribution, the bio-mechanical characteristics of the root (not-woody) and the difficulty to investigate some sensitive parameters (i.e. the distortion angle of sheared roots and the initial root orientation relative to the failure plane) requested by the models, represent critical point to the correct root reinforcement estimation by the models. The methodology carried out in the present research

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allows to realize easily situ tests. For this reason, reinforcement estimation provided by grass species could be more reliable if directly measured by shear tests on rooted clods. The obtained results can be useful for the improvement of soil bioengineering techniques in the Alpine environment, with widespread potential applications, for example in the ski runs, river banks or slopes running along roads. An understanding of the effectiveness of vegetation in protecting the soil surface against erosion is not only scientific and environmental interest, but can be of great practical value in land management. In this direction it is very important to select autochthonous species that can well develop in the Alpine environment, in order to prevent erosion with implications for sustainable mountain management and environmental protection.

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Topp G. C., Davis J. L. and Annan A. P., 1980. Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resour. Res. 16, 574–582. Topp G. C. and Davis J. L., 1985. Time-domain reflectometry (TDR) and its application to irrigation scheduling. Adv. Irrigation 3, 107–127. Vahabi J. and Nikkami D., 2008. Assessing dominant factors affecting soil erosion using a portable rainfall simulator. Int J Sed Res 23, 376-386 Van Wesenbeeck I. J. and Kachanoski R. G., 1988. Spatial and temporal distribution of soil water in the tilled layer under a corn crop. Soil Sci. Soc. Am. J. 52, 363–368 Waldron L. J., 1977. The shear resistance of root-permeated homogeneous and stratified soil. Soil Sci. Society of Am. J. 41, 843-849. Viglione Alberto, 2004. Stima dell‟evapotraspirazione media mensile sul territorio piemontese. Working Paper 2004-01, 21-27. Wu, T.H., Mckinnell, W.P., Swanston, D.N., 1979. Strength of tree roots and landslides on Prince Of Wales Island, Alaska. Can Geotech J. 16, 19 –33. Wu J., Zhang R. and Gui S., 1999. Modeling soil water movement with water uptake by roots. Plant Soil 215, 7–17. Zegelin S. J.,White I. and Jenkins D. R., 1989. Improved field probes for soil water content and electrical conductivity measurement using time domain reflectometry. Water Resour Res 25, 2367–2376.

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Chapter 3

ESTIMATION OF THE GROWING SEASON LENGTH IN ALPINE AREAS: EFFECTS OF SNOW AND TEMPERATURES Arvid Odland Telemark University College, Norway

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ABSTRACT Alpine areas have frequently been characterized by having a very short growing season length (GSL). A major problem with such statements is that GSL has no generally accepted definition. This also makes comparisons between results from regional studies difficult. The objective of this study has been to quantify differences in GSL from different study plots based on three different definitions of the start of the growing season, and to discuss their ecological significances. The start of the growing season is here intended reflect the timing of initiation of plant growth and the end when plants have ended their annual growth period. During the spring and summer of 2004, patterns of snow-melt and increases in soil temperature were investigated at 187 study plots from three mountain areas. The study plots were selected to cover major environmental and vegetation gradients from the forest limit ecotone up to mountain summits (1400 m). Air temperature for each plot was interpolated from the nearest meteorological station using a lapse rate of 0.6 oC per 100 m. Start of the growing season was defined in three different ways: (1) Julian day when air temperature the 5 first consecutive days in spring was higher than 5 oC, (2) Julian day of snow-melt, and (3) Julian day when the soil threshold temperature exceeds 6 oC. The end of the growing season was defined as the date when average air temperature (2 m level) for the last 10 consecutive days was higher than 5 oC. Based on these definitions, GSL varied more than three months between different plots. Air temperature data and snow-melt data were generally considered to be poor predictors for the start of the growing season. On exposed sites with a sparse snow cover the soil was frozen, and a period of more than 2 months was needed to reach the soil threshold temperature while in lee sides and snow beds, less than 6 days were required. GSL decreased in average by approximately 6 days per 100 m increase in altitude, and it decreased from oceanic to continental areas. The most continental area generally had an earlier date of snow-melt and a considerable longer period (25.3 days ±19.2) between snow-melt date and the soil threshold date compared to the two more oceanic sites (12.0

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Arvid Odland days ±7.0 and 13.0 days ±16.0 respectively). The study was performed in 2004 during which both spring and autumn temperatures were higher than normal and the estimated GSL are probably longer than during a “normal” year. The variation in GSL as defined by either date of snow-melt or day when the soil temperature threshold was reached varied more than three months in the forest limit ecotone (Northern Boreal zone) and the low alpine zone while in the middle alpine zone the variation was generally less than one month.

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1. INTRODUCTION In general terms, the growing season has been defined as the number of days in a year plants can grow (Brinkmann 1979), and length of the growing season (GSL) is often considered to be a very important factor limiting vascular plant life in high alpine and arctic habitats (Billings & Bliss 1959, Kudo 1991,1992, Molau 1996, Ladinig & Wagner 2005). Larcher (1980) maintains that plants in mountains of the temperate zone have 3-5 months for growth and reproduction in the alpine belt, and 1-3 months in the nival belt depending on relief and snow accumulation in winter. Unfortunately, however, no universal definition of the growing season exists (Walther & Linderholm 2006), and often it is described in general terms such as: “Growing season is about 180 days” or “Growing season lasts 60-90 days” without any definition of the start and the end of the period. In ecological and phenological studies, GSL is often assumed to be equal with number of days without snow cover (e.g. Kudo 1991, 1996, Nagy & Grabherr 2009), or it is quantified purely on the basis of air temperature data, i.e. number of days with temperatures above a certain value or as a day degree sum above a threshold value (e.g. Tuhkanen 1980, 1984, Holmgren & Tjus 1996). In such studies the fact that vascular plants do not start to develop on frozen soil is not taken into consideration. In many studies (e.g. Holtmeier 2003 and Rabenhorst 2005) it is shown that, in general, soil temperatures below 5 oC will seriously impede biological activity. A historical review of previously used definitions of GSL used in Europe has been given by Gensler (1946, cited in Lauscher 1988). Phenological data, air temperature thresholds and snow layer duration have all been used to quantify GSL. More than 30 years ago, however, Brinkmann (1979) concluded that GSL is not the simple climatic indicator it was been assumed to be, and that it is sensitive to the particular definition used. Use of different ecological factors to define the start or the end of the growing season have resulted in controversies in delimitation of bioclimatic zones, for instance in N. Europe (see discussion in Karlsen et al. 2006). The need for a critical review of the term “GSL” has become more urgent the last years because most models for future changes predict increasing GSL in most parts of the world as a result of global warming, particularly at high latitudes and altitudes. Most plants need a certain season length and a certain amount of “heat” during this period to complete their annual life cycle, and it is therefore not surprising that date of snowmelt and air temperatures often have been used to define the growing season (Moen 1999, Theurillat & Schlüssel 2000, Karlsen et al. 2005). The circumboreal climatic-phytogeographical regions defined by Tuhkanen (1980, 1984) were based on interpolation from monthly mean temperatures with a threshold of 5 oC, and the total variation in GSL (arctic

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and alpine areas excluded) extended from 80 to 225 days. In the alpine forest limit ecotone GSL was estimated to lie between 100 and 150 days, and it was longest in oceanic climates. Major variation in snow cover duration is typical for most alpine areas, and start of the growing season is therefore often defined by the timing of snow-melt (Knight et al. 1977, Tomaselli 1991, Niedzwiedz 1992, Kudo 1991, 1996, Woolgrove & Woodin 1994, Galen & Stanton 1995, and Fosaa et al. 2004). Defined in this way, GSL in snow-bed vegetation is typically very short while in exposed sites with little or no snow it will be very long. There are, however, several examples of unsuccessful attempts of “temperature data” and “snowmelt data” to predict phenological events in the spring. In most cases, delayed plant development in the spring or early summer has been explained as effects of frozen soil. According to Körner (2003), effects of frozen ground and soil temperature have been reported much less frequent than air temperatures although it is not obvious that these should be less important. Previous studies (Holway & Ward 1965, Bliss 1971, Chapin III et al. 1979, Tranquilini 1979, Sveinbjörnsen 1993, Wielgolaski 1999) have demonstrated that the timing of photosynthetic activity and the earliest phenophases of alpine plants were more strongly controlled by soil temperature than by air temperature. In general, vascular plants will not start to develop in the spring until a certain soil temperature has been reached, independent of previous air temperatures or cumulative day degrees. Biologically-based definitions of the start of the growing season are at present often based on interpretation of satellite-images as NDVI-values (Normalised Difference Vegetation Index) interpreted from images from polar orbiting meteorological satellites. The spectral reflectance response characteristic for green vegetation, soil and water are used to create the index (Prock & Körner 1996, Karlsen et al. 2006, 2007, Fontana et al. 2008). Based on NDVI-values, Karlsen et al. (2006) estimated that GSL in Fennoscandia ranged from less than 90 days in high mountain areas to more than 150 days in lowland areas. A major challenge in biologically-based definitions of the growing season is to select the “correct” indicator species that may be representative for a group of species, a specific vegetation type, or a landscape area. Cooper et al. (2011) found that the earliest phenophases of plant development was first observed 3 weeks after snow-melt in High Arctic tundra meadows and heaths. There was a difference of approximately 5 weeks between the timing of green up after snow-melt between 13 plant species. Compressed growing seasons and length of the reproductive period led to a reduced reproductive success in some of the study species. There were fewer flowers, fewer plots with dispersing seeds, and lower germination rates. Depending on species and climatic conditions, the minimum time period for seed development have been reported to range from 22 to 70 days (Billings & Bliss 1959, Kudo 1991, Stenström & Molau 1992, Wagner & Reichegger 1997, Ladinig & Wagner 2005). Wielgolaski (1974) maintained a strong need for linking the start of the growing season to measurements of assimilation, respiration and biomass at the tundra sites. A defined phenological stage of specific indicator species or the average of several species could then be used to define the start of the growing season, but in an environmentally heterogeneous alpine landscape several indicator species or groups of species have to be defined. Recently, Linderholm (2006) and Walther & Linderholm (2006) evaluated different indices used for calculating the thermal growing season and they stated that dates for the start and the end of the growing season may be seen as an integration of all environmental factors which affect plant growth.

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1.1. Conceptual Model According to Körner (2003), there is a strong decrease in the length of the growing season (GSL) from the equator (more than 300 days) to 80 degrees north (fewer than 60 days), and likewise one should also assume a strong general decrease in GSL with increasing elevation. Since the last glaciations, plant communities have developed on sites where the growing season on average has been sufficiently long and sufficiently warm. At a certain altitude we may assume that GSL is too short and too cold for plants to fulfil their life-cycles (Fig. 1).

Altitudinal gradient (m a.s.l.)

HA or Nival zone

MA GSL Duration of the period when plant growth is possible 50

100

150

200

LA Forest limit NB 250

300

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Julian day Figure 1. Conceptual model for the variation in GSL along an altitudinal gradient separated into a frequently used vegetation zonation. Theoretically, GSL may vary from more than 200 to 0 days a year. When the GSL is below a certain limit (hatched area), we may assume that only scattered plants will be found in microclimatic favourable sites. The limit between northern boreal (NB) and low alpine zone (LA) follows the alpine forest limit, and the limit between low alpine (LA) and middle alpine zone (MA) lies where continuous vegetation stands are absent. Similar diagrams are shown by Pisek (1963), Fægri (1972), Ozenda (1988, based on data from Schröter 1926), Niedzwiedt 1992, and Krautzer et al. (2004).

Start of the growing season at high altitudes may be delayed in the spring because of much snow and low melt rates due to low temperatures and/or deep frozen soil. End of the season in the autumn may be expected to start early at high elevation due to low temperatures, early frost, and early snow accumulation. The minimum GSL associated with different plant communities and for plants to complete their annual life cycle are generally not known. The total area where a closed vegetation carpet has been developed decreases strongly with altitude, and at high elevations (High alpine or the Nival zone), only scattered plants are found (cf. Ellenberg 1996, Moen 1999). According to Fægri (1972), this is mainly a result of a decrease in available sites where plant may find suitable growth conditions. Snow thickness must be sufficiently thick to protect plants and soil from freezing and at the same time snow thickness should melt early enough for the plants to get a sufficiently long growing season. In addition, plants must receive a sufficient amount of heat during this period. Towards the nival

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zone (above 3100 m in the Alps), GSL is generally less than 30 days both as defined by snow layer duration (Pisek 1963) and by air temperatures (Winkler & Moser 1967).

1.2. Main Aims The main aims of the study were to quantify variations in GSL based on different criterias for the start of the season. Differences were calculated for four different scales: 1. Sample plots representing a major gradients in mountain landscapes (n = 187) (see sampling methods) 2. Plant communities (representing mainly differences in snow cover duration) (n = 13) 3. Altitudinal (vegetation) zones (representing a complex elevation gradient) (n = 3) 4. Three study areas (representing mainly a climatic gradient) (n = 3)

2. STUDY AREAS, MATERIAL AND METHODS

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The data has been sampled within three study areas (Fig. 2) situated along a west-east (oceanic-continental) gradient (Fig.1, Table 1). According to Moen (1999), the western area A (Røldal) lies in the transition between the markedly oceanic section and the slightly oceanic section, the middle area B (Haukeli) in the slightly oceanic section, and the eastern C (Imingfjell) representing a transition between oceanic and continental areas (indifferent section).

Figure 2. Geographic position for the three study areas in S Norway.

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Table 1. Study areas and climate. Climate data from the nearest meteorological stations, supplied by the Norwegian Meteorological Institute. Temperature measurements during 2004 and deviations from the normal period (1961-1990) are calculated. Normal date for snow accumulation estimated from Bjørbæk (1979). * = Normal monthly data could not be calculated

Northing Easting No of samples Highest mountain (m) Altitudinal range below (-) or above the forest limit (m) Climatic forest limit (m) Lower limit for the Mid alpine zone (m) Nearest meteorological station DNMI station number Station altitude (m) Distance from study sites (km) Annual precipitation (cm) Normal day for snow accumulation at 800 m Normal day for snow accumulation at 1200 m July mean temperature oC January mean temperature oC January mean deviation 2004 February mean deviation 2004 March mean deviation 2004 April mean deviation 2004 May mean deviation 2004 June mean deviation 2004 July mean deviation 2004 August mean deviation 2004 September mean deviation 2004 October mean deviation 2004

A-Røldal 59o 47' N 06 o 49' E 43 1479 -199 473 970 1240 Midtlæger 46510 1079 5-10 1250 315 296

B-Haukeli 59 o 46' N 07 o 11' E 96 1476 -151 384 1080 1350 Vågsli 33890 821 0-15 840 311 294

C-Imingfjell 60 o 09' N 08 o 35' E 48 1392 -120 240 1150 1420 Dagali II 29790 828 20-30 550 309 286

8,6 -6,4 -0.7 +1.5 +1.0 +2.5 +0.4 -0.7 -0.6 +2.4 +1.4 -0.2

* * * * * * * * * * * *

11.0 -10,0 -0.5 +3.6 +1.7 +3.1 +1.7 -0.5 -0.8 +1.7 +1.3 -0.4

2.1. Sampling Methods A stratified random sampling procedure was applied for the selection of sample plots. Within homogenous stands, a 2 x 2 m plot was randomly selected for data sampling. A homogenous stand is a defined area of vegetation that shows no obvious variation in the spatial distribution or relative abundance of at least the major species present and where there is only a small variation in growth substrate. It was attempted to cover a wide variety of plant communities and to maximize environmental differences between the sampling sites. The main gradients included in the sampled data are:

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Altitude: Northern boreal zone (NB) up to the middle alpine zone (MA) Snow layer duration: snow beds to exposed ridges (fell fields) Edaphic richness: Mesotrophic to oligotrophic heaths (wetlands were omitted) Climate: Western to Eastern Norway (decreasing oceanity)

The geographic position of all study plots was recorded by a GPS, and it was therefore possible to locate the plots even when they were covered by snow. Altitude, slope degree, and aspect were measured for all sites, and a complete list of vascular plants, mosses and lichens and their cover degree in percentage was measured. During the spring of 2004, mean snow thickness and soil temperatures were measured in all sites when visited (5-10 days interval depending on weather conditions). Some of the sites were exposed (without a snow cover) when they were visited the first time in the beginning of April (day 91). The actual Julian day of snow-melt could therefore be much earlier for these plots. Soil temperature (5 cm below surface) was measured at each quadrat corner, and an average soil temperature was calculated. Air temperature for each study plot was interpolated from the nearest meteorological station using a lapse rate of 0.6 oC/ 100 m increase in altitude. Climatic data from the study areas are given in Table 1.

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2.2. Classification of Vegetation Based on the Sampling Plots The vegetation plots were classified by the use of the TWINSPAN program (Hill 1979). TWINSPAN is a hierarchical, divisive program separating clusters (vegetation groups or types) at different hierarchical levels. Here two levels have been considered: 4 main groups and 16 vegetation types. More detailed floristic and environmental characteristics are given in Odland & Munkejord (2008a). Group 1 includes plots from exposed sites with very little snow dominated by chionophobous species (fell field communities). 6 different types (A-D) were separated: Types A-D are dominated by lichens and ericaceous dwarf-scrubs or grained. Plots included in type E are mainly found at higher altitudes. Here Juncus trifidus, Nardus stricta, Carex bigelowii and Salix herbacea are common but lichens and ericaceous species are less abundant. Group 2 included plots from snow accumulation slopes dominated by snow cover demanding species (lee-side communities). Four different types were separated: Types F with a high abundance of Vaccinium myrtillus, Deschampsia flexuosa and Juniperus communis. Type G is characterized by Betula pubescens, Salix glauca and S. lapponum. Nardus stricta and Salix herbacea are common in type H, and Salix lapponum, Carex brunnesens, Rumex acetosa and Anthoxanthum nipponicum in type I. Group 3 includes plots from early snow bed vegetation dominated by graminoids, small herbs (forbs), Salix herbacea and snow-bed mosses. Type M mainly located at higher elevations has high abundance of Juncus trifidus, Loiseleuria procumbens, Harrimanella hypnoides, and several snow-bed mosses. Group 4 includes plots from late snow bed vegetation. Types N – P are chionophilous communities dominated by snow-bed mosses, particularly Anthelia juratzkana, Marsupella

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brevissima and Polytrichastrum sexangulare. Type N and O have high abundance of Salix herbacea, while type P has more Carex rufina and Warnstorfia exannulata. The types represent common previously described vegetation communities from Scandinavia, and they reflect mainly differences in snow thickness, snow duration and altitude (described and discussed more in detail by Odland & Munkejord 2008a). Plots within each of these types have previously been shown to represent fairly equal ecological conditions, and these were used to compare variation in GSL between different growth sites and vegetation types.

2.3. Vegetation Zones Separation of the mountain areas into vegetation zones follows criteria described by Kilander (1960) and Moen (1999). The climatic forest limit is defined as an imaginary line drawn between the uppermost forest stands where there appears to be no growth restriction due to unfavourable edaphical, topographical conditions, or cultural effects. The Northern boreal zone (NB) includes the uppermost forest stands, in Scandinavia mainly dominated by Betula pubescens. The climatic birch forest zone makes the transition to the low alpine zone (LA) which extends 250- 270 m upwards. Salix shrubs, Vaccinium spp. heaths, tall herb meadows, Cladonia-dominated exposed heaths, and mires are common in the NB and the LA zone. The middle alpine zone (MA) may be found up to approximately 500 m above the forest limit. Here the vegetation is mainly dominated by graminoid dominated heaths and snow beds, but large areas have only bare rocks and stones.

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2.4. Three Study Areas The three study areas are separated by approximately 150 km in west-east direction, representing a climatic gradient (Fig. 1 and Table 1). In addition to climatic differences, there are also differences in vegetation types and flora. Type A and B (most exposed site vegetation) were rare or missing in the two westernmost areas, and type N, O, and P (late snow-beds) were not found in the easternmost area (C).

2.5. Definitions and Abbreviations Study sites: A representative plot (2x2 m) was randomly selected for each of the selected homogeneous vegetation stands. Here both floristic and environmental data were sampled. RelAlt: In this comparative study, where data from areas with different climatic conditions are included, altitude was given as m above or below the climatic birch forest limit. It is generally assumed that the natural, climatic forest limit integrates local thermal conditions in such a way that it occurs at equal temperatures at both local and regional scales (Mook & Vorren, 1996, Odland, 1996, Körner, 1998, Körner et al., 2003, Körner & Paulsen, 2004). Julian day: Day number as calculated from January 1.

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SGSSnow: Start of growth season defined as the Julian day when average (4 measurements in each quadrat corner) snow cover depth was zero (date of snow-melt). The actual day was estimated by interpolation if the snow had melted completely between two visits. SGSSoilT: Start of growth season was defined as the Julian day when average (4 measurements in each quadrat corner) soil temperature exceeded 6 oC. The actual day was estimated by interpolation if the temperature threshold had been reached between two visits. SGSAirT: Start of growth season was defined as the Julian day when the mean of the first five consecutive days in the spring exceeded 5 oC. EGS: End of growth season was defined as the first Julian day when mean air temp for ten consecutive days dropped below 5 oC for the first time. ΔDays: Difference in days between different definitions GSL: Growth season length was the number of days between SGS (based on different definitions) and the EGS GSDD: Effects of warmth was quantified as growing season day-degree of effective temperature sum. It is the cumulative number of degrees above a certain threshold during a defined period. W = Σ (t – tk) where t = daily mean temperature, tk = threshold value for the growing season (0 oC and 5 oC threshold temperatures).

3. RESULTS

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3.1. Air Temperatures During 2004 Temperature data were interpolated from the nearest meteorological station to the position of the climatic forest limit. As indicated in Table 1, there was a relatively strong climatic gradient between the study areas from the oceanic influenced western area (A) with high precipitation, low summer temperatures and high winter temperatures, to the more continentally influenced eastern area (C). Differences between mean and minimum temperatures increased from area A to area C. Number of days when the average daily temperature lied below 0 oC or 5 oC in the study areas is given in Table 2. The climate data (Fig. 3 and Table 3) shows that sub-zero temperatures occurred frequently at the forest limit level during the growing season, especially in the most continental area. The lowest temperatures occurred during day 144 in all study areas. Fig. 3a-c shows variation in mean average and mean minimum temperatures during 2004 (January 1 – October 31) with Lowess smoother lines (degree of smoothing = 0.5, number of steps = 2). The average trends in mean and minimum temperatures during 2004 show some variation between the three study areas. The Julian day when smoothed air temperature exceed 0 oC at forest limit was reached earlier in Røldal (day 98) compared with Haukeli (day 102) and Imingfjell (day 108), and the same pattern was shown for the day when the daily mean temperature exceed 5 oC (day 121, 140, and 150 respectively).

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A

Mean daily temperature Minimum temperature

20 15

Temperature C

10 5 0 -5 -10 -15 -20 0

B

50

100

150 Julian day

200

250

300

Mean daily temperature Minimum temperature

20 15

Temperature C

10 5 0 -5 -10 -15 -20 -25 -30

C

50

100

150 200 Julian day

250

300

250

300

Mean daily temperature Minimum temperature

20 15 10

Temperature C

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0

5 0 -5 -10 -15 -20 -25 -30 0

50

100

150 200 Julian day

Figure 3. Variation in mean and minimum temperatures during 2004 (January 1 – October 31) with Lowess smoother lines (degree of smoothing = 0.5, number of steps = 2). All temperature values were interpolated to the forest limit altitude in the different study areas: A = Røldal, B = Haukeli, C = Imingfjell. Approximate numbers of days when the smoothed lines are higher than 0 oC or 5 oC are given in Table 2.

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The smoothed mean daily temperature trends during 2004 (Fig. 3) indicates that the number of days with temperatures above 5 oC at the forest limit altitude increased from ca 120 days in Røldal, 110 days at Haukeli, to 100 days in Imingfjell. The air temperature data thus indicate a potential earlier start and longer GSL in western area compared with the eastern area (Table 2).

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Table 2. Climate data calculated for the forest limit altitude in the study areas. Growing season length is calculated from mean air temperature data (GSLAirT) (1: threshold 5 oC, and 2: Based on the smoothed mean temperature data < 0 oC, see Fig.3). Growing season degree days (GSDD) during GSL1 were calculated on the basis of 0 and 5 oC temperature thresholds. GSDDSoilT = Growing season degree days calculated from the date when the soil temperature threshold (6 oC) was reached in forest stands (Type G) and with 0 and 5 oC as temperature thresholds

Forest limit (m) GSLAirT 1 (days) GSLAirT 2 (days) GSDDAirT (5) GSDDAirT (0) GSDDSoilT(5) GSDDSoilT(0) GS Mean July GS Mean max July temperature July mean temperature July mean max temperature Frost sum Days 74-159

>85

MA

12

403

76-106

30

76-119

43

NB

46

-76

80-115

35

>67-155

>88

LA

44

70

79-119

40

>71-159

>88

MA

6

299

95-113

18

101-129

28

NB

22

-57

61-130

50

>61-151

>90

LA

23

125

68-111

43

>68-158

>90

MA

3

232

79-106

27

93-129

36

B

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C

ΔGSLAirT 70

ΔSGSSnow >66-156

ΔGSLSnow >90

Table 4. Average and variation (SD = Standard deviation) in start of the growing season within the three study areas: SGSSoilT = Start of the growing season based on the average Julian day when soil temperature >6 oC. SGSSnow = Start of the growing season based on the sites were completely melted. ΔGSL = average number of days between SGSSnow and SGSSoilT. SGSAirT = Start of the growing season based on the average Julian day when mean air temperature was higher than 5 oC (data in Fig. 4)

SGSSoilT SGSSnow ΔGSL SGSAirT

A Røldal 151.1 ± 19.6 139.6 ± 23.8 11.5 ± 7.1 126 ± 1

B Haukeli 153.5 ± 8.9 140.1 ± 21.5 12.4 ± 16.2 127 ± 1

C Imingfjell 152.8 ± 12.7 128.0 ± 26.0 24.8 ± 18.6 127 ± 1

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Estimation of the Growing Season Length in Alpine Areas 100 120 140 160 180

A

Snow melt

500

Soil temp.

MA

400 Relative altitude (m)

97

300 200

LA

100 0

NB

-100 -200 100 120 140 160 180

B

100 Snow melt

400

Relative altitude (m)

Julian day 120

140

160

180

Soil temp.

MA

300 200

LA

100 0

NB

-100

100

120

140

160

180

C

Julian day 100 120 140 160 180

250

Snow melt

Soil temp.

MA

200 Relative altitude (m)

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-200

150

LA

100 50 0 -50

NB

-100 100 120 140 160 180

Julian day

Figure 4. Altitudinal variations in the start of the growing season based on Julian day of snow-melt and the day when the soil threshold temperature (6 oC) was reached (Soil temp.). Dotted lines indicate altitudinal trends. A = Røldal, B = Haukeli, C = Imingfjell. See also Table 3.

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3.3. Variation in GSL between Different Vegetation Types

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Variation in mean GSL between the different vegetation types is shown in Table 5. The results show that the different SGS-definitions have major importance for the estimation of GSL. Based on air temperature, the variation in GSL was very small and mainly related to altitude. Based on the soil temperature threshold, the variation was in average 45 days, and based on date of snow-melt, it was more than 80 days. The difference in GSL was particularly large for the exposed fell-field communities situated in the NB and LA zones (Type A-D), and relatively small for the snow bed communities (group 3 and 4). Extreme snow beds melted later in the season close to the forest limit than at high altitudes, but due to higher air temperatures, plants growing at low altitudes could still receive enough “heat” to complete growth during the short season. Table 5 shows that NB birch forest communities, LA shrubs (type G), and exposed lichen heaths (type A-E) had all a GSLSoilT mostly between 100 and 110 days even though the lichen heaths were free from snow more than two months earlier than the other types. Early snow beds dominated by graminoids (type J, K, L) had a GSLSnow around 90 days while late snow beds and MA Juncus trifidus heaths (type E) had a GSLSnow around 80 days, with type O as the most extreme. Table 5. Estimated variation in growing season length (GSL-averages) calculated for the 4 main vegetation groups and the 16 plant communities. RelAlt = average relative altitude. SGSAirT = average Julian day when air temperature exceeded 5 oC, SGSSoilT = average Julian day when soil temperature in the plots exceeded 6 oC, SGSSnow = average Julian day when all snow had melted in the plots. EGSAirT = End of growing season is based in the average Julian day when air temperature falls below 5 oC. GSLsoil = number of days between GSE and GSLsoil. GSLsnow = number of days between GSE and GSLsnow, ΔGSL = difference between GSLST and GSLSnow Grp

1

2

3

4

Type RelAlt SGSAirT SGSSoilT SGSSnow EGSAirT GSLAirT GSLSoilT GSLSnow ΔGSL A 39 126 150 94 251 125 101 157 >56 B 90 126 146 104 250 124 104 146 >42 C 43 126 140 104 251 125 111 147 >36 D -63 125 142 111 254 129 112 143 >31 E 224 127 154 142 247 120 93 105 12 F -63 125 146 128 254 129 108 126 18 G -106 124 141 126 255 131 114 129 15 H 91 126 154 148 250 124 96 102 6 I 13 125 147 137 252 127 105 115 10 J 32 125 156 150 251 126 95 101 6 K -17 125 164 160 253 128 89 93 4 L 109 126 156 151 249 123 93 98 5 M 287 128 167 159 245 117 78 86 8 N 104 126 173 166 250 124 77 84 7 O 342 128 174 166 243 115 69 77 8 P 82 126 164 161 250 124 86 89 3

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The altitudinal variation in GSL for the different types is shown in Fig. 5. The average altitudinal decrease in GSL per 100 m increase in elevation is 8.6 days based on soil temperature, 11.8 days based on date of snow-melt, and 3.5 days based on air temperature. Variation in GSLAirT snows hardly any trend, neither between the different types nor with altitude. The altitudinal variation in GSLSnow shows major variation, and this relationship has low statistical significance compared with the variation in GSLSoilT.

Length of growing season (days)

160

GSLAirT GSLSoilT GSLSnow

140

120

100

80

60

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-100

0

100 200 Relative altitude (m)

300

400

Figure 5. Altitudinal variation in growing season length (GSL) for the studied vegetation types based on different criteria‟s for start of the growing season. The altitudinal trend for GSL based on snow data show major variation, and the relationship has low statistical significance. The linear equations are: GSLSoilT = 102 – 0.086*RelAlt, r2 = 63.1, p < 0.0001, GSLSnow = 121 – 0.118*RelAlt, r2 = 32.2, p < 0.0220, GSLAirT = 127 - 0,035* RelAlt, r2 = 99.2 p < 0,0001.

3.4. Variation in GSL between Different Zones and Areas Average values for GSL were estimated on the basis of values for all study plots within each of the three vegetation zones. In the LA zone, the exposed vegetation plots were often free from snow before Julian day 90, while in the MA zone the plots were not free from snow before day 130. The soil temperature threshold was in most cases not reached until day 130 in the LA zone, and until day 140 close to the summits (MA). In extreme snow beds (group 4), the snow cover lasted until day 170-180, and here the soil temperature threshold was reached a few days after snow-melt. Average values for GSL were estimated for all sites within the three different vegetation zones in each of the three study areas (Table 6). The estimations were based on three different SGS definitions but the end of the season was the same. The results show three main trends for the variation in GSL: (1) a decrease with altitude, (2) a decrease from west to east, and (3) a strong variation according to how SGS was defined. GSL based on a soil temperature threshold gave generally a much shorter season than based on air temperature data or day of snow-melt.

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Table 6. Variation in average growing season length (GSL) between the different study areas and zones based on different criteria for the start of the growing season: SGSAirT date for the first 5 consecutive days when mean air exceed 5 oC, SGSSoilT = date when soil temperature threshold is reached, SGSSnow = Date of snow-melt. EGS = End of season (see definition). Growing season length (GSL) is the difference between SGS and EGS. ΔGSL = difference in GSL based on date of snow-melt and the soil temperature threshold. A = study area (1 = Røldal, 2 = Haukeli, 3 = Imingfjell), Zone (NB = Northern boreal, LA = Low alpine, MA = Middle alpine), No = number of samples, RelAlt = average Relative altitude in m above or below the climatic forest limit for the study sides

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A Zone No RelAlt SGSAirT SGSSoilT SGSSnow NB 15 -102 125 139 126 249 123 110 246 119 93

GSLSoilT ΔGSL 117 >9 97 13 82 11

2

NB LA MA

46 44 6

-76 70 299

126 127 128

152 155 156

126 105 95

109 94 89

>17 11 6

3

NB LA MA

22 23 3

-57 125 232

126 127 127

152 154 157

121 121 108

97 94 84

>24 37 24

There were only minor differences in GSL based on SGSAirT or SGSSoilT, but the timing of SGSSnow was much earlier in the continental site and consequently the GSL was longer. In the continental area the ΔGSL was much higher than in the two oceanic sites. It also shows that SGSAirT in average occurred at the same date (around June 1.) in all sites, but the mean date for snow-melt was significantly earlier in Imingfjell than in the two westernmost areas. The average difference in time period between snow-melt and the date when the soil threshold was reached was twice as long in the continental area (Imingfjell) as in the two oceanic areas. Mann-Whitney tests showed that the differences between Røldal and Haukeli were not statistically different (p>0.05). GSLSoilT was not statistically different between the three study areas, but both GSLSnow and ΔGSL in the Imingfjell area were statistically different from both Røldal and Haukeli (p < 0.05) (Table 4). Altitudinal trends in GSL based on different definitions and for all study sites together are shown in Fig. 6. In average, the altitudinal decrease in GSLSoilT was 5.6 days/ 100 m, while the decrease in GSLSnow was 6.5 days/ 100 m, and based on air temperature (GSLAirT) the decrease was 3.0 days/ 100 m. The relationship based on snow-melt data was, however, approximate since the Julian day of snow-melt represents in some cases a maximum value (they were exposed already when they were visited for the first time in the beginning of April).

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Growing season length (days)

140

101

GSLSoilT GSLAirT GSLSnow

130 120 110 100 90 80 -100

0

100 200 Relative altitude (m)

300

400

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Figure 6. Altitudinal trends in growing season length (GSL) based on different definitions for the start of the growing season (see Table 6). All sites within the different vegetation zones in all study areas are here included. The total variation at different altitudes (vegetation zones) is indicated by the vertical lines. In average, the altitudinal decrease in GSL based on the soil temperature, air temperature or date of snow-melt are 5.6 days (r2 = 77.2), 3.0 days (r2 = 62.9) or 6.5 days (r2 = 83.8) respectively (all statistically significant, p < 0.05).

Average cumulative day degree sum (GSDD) for all sites within each vegetation zone was calculated for three different GSL definitions (Table 7). For all zones, the GSDD was higher when calculated based on GSLAirT or on GSLSnow compared with GSLSoilT. The average altitudinal trends in decreasing GSDD (0 and 5 oC threshold) based on soil thresholds are shown in Fig. 7. Table 7. Average cumulative degree-days during the growing season (GSDD) based on air temperature thresholds (Th.) of 0 or 5 oC for different vegetation zones and study areas. Zone NB LA MA NB LA MA NB LA MA

Area

1

2

3

GSDDAirT GSDDSnow Th. 0 Th. 0 1134 1099 888 829 671 605 1060 1003 841 775 697 638 974 961 818 818 719 682

GSDDSoilT GSDDAirT GSDDSnow Th. 0 Th. 5 Th. 5 1053 1086 1053 979 786 743 552 499 468 933 988 944 715 799 766 600 555 531 848 906 897 706 727 727 598 614 580

GSDD5SoilT Th. 5 1022 714 441 898 716 492 820 641 522

The altitudinal decrease in GSDD based on the 0 oC threshold was higher than when based on the 5 oC threshold, and the difference increased with increasing altitude. GSDD at Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

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the forest limit position was 875 based on a 0 oC threshold and 818 based on a 5 oC threshold, and the average altitudinal lapse rates for GSDD were 107 for the 5 oC threshold and 87 for 0 o C threshold per 100 m. Use of the 5 oC threshold gave the highest statistical significance.

Growing season Day-degrees (GSDD)

1100

GSDD0 GSDD5

1000 900 800 700 600 500 400 300 -100

0

100 200 Relative altitude (m)

300

400

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Figure 7.The altitudinal trends in decreasing growing season degree days (0 and 5 oC threshold) for each zone within all study areas based on the soil threshold are shown. The linear equations are: GSDD (0 oC threshold) = 875 – 0.87*RelAlt, r2 = 69.2, p < 0.005, GSDD (5 oC threshold) = 818 – 1.07*RelAlt, r2 = 92.8, p < 0.0001

4. DISCUSSIONS Alpine plants have evolved different strategies to be able to live and reproduce under stressful environmental conditions, and in high mountain areas the most critical environmental factors have generally been assumed to be low summer temperatures, frost, and a short growing season (Bliss 1971, Billings & Mooney 1968, Körner 2003). We may assume that there is a critical limit for both the GSL and heat received during this period (GSDD), but these limits are still mostly unknown. GSL as low as 20-32 days have previously been reported from arctic and alpine areas (Appendix). On Devon Island in the High Arctic, Svoboda (1977) found that most plants completed their growth cycle within 5060 days regardless of the snow-melt date. Cooper et al. (2011) found that the variation in seed dispersal varied from approximately 40 to 90 days after snow-melt. Average temperature for the warmest month has often been used as a proxy for the heat received as it often has been used to explain the alpine forest limits. Studies have indicated that vascular plants both in high alpine and arctic areas may be limited to areas where the mean temperature of the warmest month lie between 1.0 and 3.0 oC (Rannie 1986, Karlsen & Elvebakk 2003, Odland 2009), and possibly such limits may also be explained in terms of short GSL and low GSDD.

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In environments with a continuous variation in ecological factors, it is always difficult to define both a start and an end of the growing season, and in addition there will always be annual variations. Coupled with different definitions for the start of the growing season, we should expect major variation in previous estimations of the GSL. This is also evident from the review given in the Appendix. The present study shows also considerable variation in GSL (more than 80 days during 2004). Strengths and weaknesses of the different criteria used to quantify the GSL are discussed below.

4.1. Climate Conditions During 2004

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Climatic and ecological data sampled a particular year may deviate from a “normal year” which at present is defined by the period from 1961-1990. According to data from meteorological stations close to the study areas, mean temperatures for March, April and May were 1-3 oC higher than normal values, while June and July were ca 0.5 – 0.8 oC colder than normal (Table 1). This caused the snow to disappear 15 – 25 days earlier than normal (unpublished data). We may assume that also the soil temperature thresholds were reached earlier, and thus the measured GSL during 2004 were longer than during a “normal year”. Even though date of snowmelt may vary from year to year, the pattern of melting has been shown to remain remarkably consistent among micro sites (e.g. Gjærevoll 1956, Billings & Bliss 1959, Kudo 1991, Stanton et al., 1994). Also the autumn 2004 was warmer than normal (Table 1), and together with the warmer spring the estimated GSL were longer than they would be during a normal year, probably within the range of 20-30 days depending on site type, altitude, and study area. The relative differences between different types measured during 2004 will, however, probably more or less the same also during a normal year.

4.2. Environmental Variables Associated with the Start of the Growing Season As indicated in Fig. 4 and Table 5, there was a great variation in SGS between the study plots investigated sites in all three study areas, particularly for Julian day of snow-melt. Possible environmental and biological factors associated with the start of the growing season are discussed below:

4.2.1 Air Temperature The nature of the biological mechanisms associated with the triggering of bud-burst in spring remains poorly known. However, a large body of data has shown temperature to be the primary factor for most species, with other factors, including soil moisture, solar radiation, and photoperiod playing a secondary role (Bennie et al. 2010). A number of quantitative analyses have investigated the relationship between spring air temperatures and the timing of bud-break of deciduous trees. In boreal conditions, simple models where the initiation of bud development was a function of an accumulated temperature sum above a given threshold have in some cases given surprisingly good results (e.g. Häkkinen et al. 1998, Hannerz 1999, Huelber et al. 2006). It is here essential to bear in mind that deciduous trees are growing on

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sites with a moderate snow cover and where the soil is not frozen (Odland & Munkejord 2008a). Plant metabolism and growth is closely linked to the temperature regime but when the effect of mean temperature for plant growth is studied, two important facts have to be considered: (1) the relationship between temperature and growth is not linear (Went 1950, Skre 1972, Tuhkanen 1984, Dahl 1998, Wielgolaski 1999), and therefore an increase in temperature from 20-21 oC will have a much higher effect on plant growth than an increase from 5-6 oC. Consequently relationships between plant growth or plant distribution and mean temperature values are not accurate. The daily temperature amplitude is normally greater in areas with a continental climate compared with oceanic areas, and possible effects of extreme temperatures will not be shown when average temperatures are used. In a continental climate, there will normally be some hours with high temperatures during the day and low temperatures during the night, and consequently the “effective” temperature sum will be higher than in a continental than in an oceanic area even though they have the same mean temperature (Skre 1972). This may partly explain why the average GSDD in the studied vegetation zones is associated with higher values in the oceanic sites as compared with the continental site (Table 7). (2) There is a certain soil temperature threshold for plant development (e.g. Körner 200), and therefore high air temperatures have no effect on plant development before a certain soil temperature threshold has been reached. According to Körner (2006), knowledge of such thresholds is critically important for modelling. After the soil temperature threshold has been reached, however, air temperatures are the main drivers for plant development. Frequently air temperature- based definitions of GSL have been used in phenological and ecological studies. A certain temperature threshold (often 0 or 5 oC) has been defined, and then the number of “degree-day” (GSDD) has been calculated for a particular area or altitude (e.g. Skaugen & Tveito 2004, Wilson & Nilsson 2009). It has been suggested that this variable seems to be an excellent expression for the influence of temperature on the growth and other life processes of the dominants in the vegetation, such as trees, and presumably many other plants as well (Sjörs et al. 2004). Heikinheimo & Lappalainen (1997) maintained: “In high latitudes, the use of 5 oC as a threshold for spring-time development is justified on the basis that soil remains frozen (or even snow-covered) until the air temperature has permanently risen well above freezing”. Similarly, Diekmann (1996) found that flowering phenology in deciduous forests was better predicted by the use of a 5 oC rather than a 0 oC threshold, but even then he found that the model performed better for the late-flowering species than for the early-flowering species. Tuhkanen (1980) defined the growing season as that period over which the daily mean air temperature remains above 5 oC. According to this definition, GSL decreased from south to north, in average by 4-5 days/ 100 m increase in altitude, and it was longer in coastal areas than in inland areas at the same latitude. In general, start of the growing season defined by air temperatures above 5 oC resulted in a GSL mostly less than 100 days for areas above the forest limit and between 100 and 150 days in the zone below (NB). Tuhkanen (1984) maintained, however, that the GSL often was actually shorter than denoted by this definition, especially due to frost, heavy snow and frozen soil. Karlsen et al. (2008) found on the basis of NDVI-analyses that a 5 oC threshold to indicate the onset of the growing season was useful in most of the study areas in northern boreal and low alpine areas.

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Average values for GSDD (with 0 or 5 oC threshold) within different vegetation zones (Table 7) are in accordance with values calculated from different mountain areas in Fennoscandia by Karlsen et al. (2006) and Førland et al. (2004). According to Kudo & Suzuki (1999), early flowering alpine plants were generally associated with less than 100 GSDD for flowering after snow-melt, intermediate species with 100-200 GSDD, and late-flowering species with more than 200 GSDD (air temperature threshold was 5 oC). This study shows that an air temperature based definitions of the GSL alone in an alpine area has a limited value because it corresponded only roughly to the period when the plants are biologically active, but a 5 oC threshold appears to be more useful than a 0 oC threshold. Also calculation of the GSDD should be based on a 5 oC threshold.

Frost Frost is a possibility at all times of the year at high altitudes, at the beginning and end of the growing season an interchanging frost climate generally predominates (Arenson 2002). As shown in Fig 3 and Table 2, air temperatures below freezing occurred frequently during the growth season in all sites (as measured at the forest limit level) during 2004. Frost events and total frost sums increased strongly from west to east (Table 2). Consequently, if freezing temperatures should be used to determine the GSL in these study areas, it would have a length of only a few days. Frost events during the growing season may be serious to frost-sensitive species, but most alpine plants appear to tolerate freezing temperatures (-5 to -10) during the growing season (Körner 2003), and the measured minimum temperatures during 2004 were not lower than this (Table 2). However, it should be remembered that the minimum temperatures are measured 2 m above ground. According to Billings (1974) and Körner (2003), frost during the growth season is the first environmental filter that species have to pass to become high-elevation. Freezing temperatures can occur both early and late in the growing season, suggesting that, from an evolutionary point of view, the freezing resistance of high-elevation plants should be high both early and late in the growing season (Sierra-Almeida et al. 2009). Although alpine and arctic plants are generally able to replace frost aborted shoots and leaves within the same growing season, frost damage may seriously reduces plant growth and fecundity in the short term, and may even become lethal if occurring several years in a row as plant resources may be depleted (Wipf et al. 2009). This study indicates that estimation of the GSL based on frost events would probably not be useful. Frost may be an important variable in autecological studies, but probably not to define the length of the growing season within an area or within a vegetation zone. 4.2.2. Soil Temperature The relationship between soil- and air temperatures is often complex, and this makes it difficult to estimate and describe variations in soil temperatures based on air temperature measurements only. Soil temperature can be both much higher and much lower than the air temperatures above. Vegetation cover, soil surface albedo and frozen soil are the main factors determining the deviation between soil- and air temperatures in alpine sites. Shi et al. (2008) studied differences between air- and soil temperatures during a 117 days growing season at the tree limit (4500 m) in the eastern Himalayas. At this site there was a delay of about 2 months between the date when air temperature exceeded 0 oC and the date when soil temperature exceeded 6.6 oC. During most of the growing season there is, however, often a

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close relation between air- and soil temperatures. DA Walker et al. (2001) found that differences between soil-surface- and air temperature at 120 cm were rarely greater than 1-3 o C. Gehrig-Fasel et al. (2008) showed that from all tree-line indicators evaluated, soil seasonal mean temperature had the lowest variation irrespective of the investigated indicator period length. It was therefore considered to best describe the position of the alpine tree-line. Start of the growing season has here been defined as the Julian day when average soil temperature exceeds 6 oC. This threshold has earlier been found to be associated with the initiation of a period with a high growth rate for many plant species (Odland 1995, Karlsson & Weih 2001, Walker et al. 2001, Bjerketvedt et al. 2003). In northern and alpine areas, several studies have shown that net growth and/or nutrient uptake was very poor at soil temperatures lower than 5-7 oC (Mueller 1970, Anderson & McNaughton 1973, Kaspar & Bland 1992, Karlsson & Nordell 1996, Rabenhorst 2005, Körner 2006), and it has previously been demonstrated that growth and phenology of alpine plants may be more strongly controlled by soil temperature than by air temperature (Holway & Ward 1965, Bliss 1971, Chapin III et al. 1979, Sveinbjörnsen 1993, Schwarz et al. 1997). According to Pregitzer et al. (2000), rate of root growth increased with temperature if not other factors limited photosynthesis and respiration, and they hypothesized that in temperate and boreal environments, the initiation and rate of root growth in the spring were directly related to cumulative heat sum in the soil. Alvarez-Uria & Körner (2007) found that the critical temperature for significant root growth was ca 6 oC, which was close to the worldwide mean soil temperature at climatic tree-lines. Different soil temperature thresholds, measured at different soil depths have been used in ecological studies (Körner 2006, Appendix). Körner et al. (2003) found that soil temperature variation in alpine grasslands during the season appeared to be quite similar across Europe. Exceptions were northern areas where there could be frozen soil, and therefore a long period of time was needed before the soil temperature threshold was reached. They defined the growing season by a critical daily mean soil temperature of >3.2 oC at 10 cm depth, and this roughly corresponded to a mean air temperature of zero degrees, irrespective of the actual length of the season (12 months at the Equator and 2.5 months at sub-polar latitudes). Soil temperatures can be highly variable over very small distances, being dependent upon snow cover, type of vegetation cover and the heath capacity of the soil (Dahl 1957, Larcher 1995). Increase in soil temperature is highly dependent on the thickness of frozen soil, and this is again depends on interactions between thickness of the snow layer and low air temperatures. Early snow cover limits intensive and deep freezing of the soil, and a recent study (Schimel et al. 2004) focused on the importance of early snow accumulation during the autumn and its insulating effect preventing extensive soil freezing. Timing of the establishment of a permanent snow cover in the autumn has therefore a major importance in relation to presence or absence of permafrost. Early establishment of snow cover can make permafrost disappear, even with a relatively thin cover. Permafrost may, however, survive when snow cover starts after the middle of December even with a snow thickness > 1.0 m (Zang et al. 2001). Both the present and previous studies show that a soil temperature threshold should be considered as the main factor associated with the start of the growing season in boreal, arctic and alpine areas. Which soil temperature threshold measured at which depth that gives the best predictive power for the initiation of plant growth in the spring should be verified by detailed studies in the future. During the end of the growing season there will, however, often

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be minor differences between soil- and air temperatures, so both variables can probably be used to define the EGS.

4.2.3. Snow The ecological gradient generated by the spatial patterns of “snow” represents several correlated variations in many environmental factors that highly influence GSL (Miller 1982, Stanton et al. 1994, Walker et al. 2001). According to Evans & Fonda (1990), alpine community patterns can be expressed as a function of the depth and duration of snow cover and their influence on soil moisture, soil temperature, and the length of the growing season. Length of the snow-free period has frequently been used as an environmental variable to explain the variation in mountain vegetation, but both the present paper and previous studies have shown that this variable corresponds only roughly to a biologically based definition of GSL. Some species show positive net assimilation and may start to grow before snow-melt and under as much as 50-100 cm of snow if there is no soil frost (Billings & Bliss 1959, Billings & Mooney 1960, Walker et al. 2001), and some tree species start photosynthesis in spring still being snow covered (Holtmeier 2003). On the other hand, may plants survive in a dormant stage on exposed sites without a snow layer and with a frozen soil during most parts of the winter. Problems associated with the use of snow data only to estimate GSL can be illustrated by a quote of Cooper (1986): “Because snow-free areas may not occur every year, and because there are few snow accumulation areas, the difference in growing season length between the earliest snow-free and latest melting sites seem to be about 2 months. By comparison in an area such as the Colorado Front Range some sites are snow-free all winter and other sites do not become snow-free until late July or August. Thus a difference up to 4 months in length of growing season exists.” There may be major variation in thickness of the snow pack and its duration in alpine areas, and annual variations in date of snowmelt between 10 and 30 days in permanent study sites have been reported (Kudo & Ito 1992, Walker et al. 1995, Molau 1996, Kudo 1996). According to Ellenberg (1996), the period of snow cover per year for alpine plant communities may range from 0-2 months on exposed sites to 9-10 months in snow beds. Kärenlampi (1972) estimated that the snow cover at study sites in northern Finland generally disappeared about day 140, and a stable snow cover was formed about Oct. 20, thus the snowfree period was 150 days. Kudo (1996) found that the snow-free period ranged in his study sites from 69 to 117 days, and the magnitude of variation during a three year period was between 12 and 22 days for the study plots. Earlier published results from Scandinavian mountains have shown that the date of snowmelt on sites with a vegetation cover varied between “earlier than day 90 - 100” and “later than day 190 – 220” (Gjærevoll, 1956, Dahl, 1957, Molau, 1996), and in extreme years snow beds (dominated by bryophytes) may not be exposed at all. According to Schuler et al. (2006), mean number of days per year with snow cover > 50% in Norway varied from 0 to 365, and in alpine areas snow cover lasted more than 225 days, but some less in the continental parts The date for the first snow fall in the autumn is probably of minor importance since the plants have by then finished important biological processes for that season and both soil and air temperatures may already have dropped below the threshold. The first snow may also often melt before a permanent snow cover is established later in the autumn. In average, it

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appears to be small differences as to when snow starts to accumulate in the studies mountain areas (cf. Table 1). According to Holtmeier (2003) and Schimel et al. (2004), however, the early snow period during the autumn may be important because it controls the wintertime soil microclimate. Early heavy snow insulates the soil but allows the soil temperature to drop to conditions cold enough to shut down significant winter activity. Schimel et al. (2004) therefore suggested that autumn should be considered to be the beginning of the functioning of the system for the rest of the year. But the drop in air temperatures during the autumn will also generally initiate plant dormancy and represent an end of the vegetative period. Euskirchen et al. (2006) found strong connections between decreases in snow cover, increases in permafrost degradation, earlier thaw, later freeze, and a lengthened growing season. These dynamics substantially influence changes in carbon fluxes, including enhanced respiration and productivity. Such enhancements yielded increases in vegetation carbon, but overall decreases in soil carbon. According to Baptist & Choler (2008), the carbon-uptake period in cold ecosystems was primarily determined by the snow-cover duration. They demonstrated that the snow-induced changes in the GSL had the highest impact on the seasonal gross primary production. It may be concluded that SGS defined by date of snow-melt is generally a poor estimate on the initiation of plant growth in alpine areas. Snow is a very important environmental factor in alpine areas but mainly indirectly through its effect on soil temperatures.

4.2.4. Photoperiod General effects of photoperiod control on growth initiation in the spring and growth cessation and dormancy in the autumn have been difficult to find, but in some studies significant effects were evident. According to Inouye (2000, 2008), strong effects of photoperiod, which protects plants from starting too early and subsequently suffering from frost damage may be expected for plants in exposed habitats, while the timing of snow-melt could be the key factor for plant development in snow-beds. Deviating results were, however, found by Hülber et al. (2010) when they studied effects of temperature sums, time of snowmelt and photoperiod on alpine plant flowering phenology. Their results showed that temperature was the overwhelming trigger, and impact of photoperiod was particularly weak for the flowering phenology of snow bed species. Photoperiodic sensitivity of many alpine plant species safeguards against premature dehardening during warm spells in early spring, but temperature often has a stronger influence on vegetative and reproductive phenology later in the spring when days are longer. According to Keller & Körner (2003), high-elevation species may develop shortly after release from dormancy regardless of photoperiod and about half of the studied alpine taxa flowering were affected by photoperiod. According to Heide (1993) it is well documented that in most woody species, growth cessation and dormancy are induced by decreasing daylength during late summer and autumn. Photoperiodic behaviour is often modified by temperature and appears important only within a certain temperature range. White et al. (1997) showed that extremely low temperatures induced senescence, while at warm temperatures, growth continued regardless of photoperiod. In this study where all study sites lie almost at the same latitude, and possible effects of day length and photoperiod have therefore not been considered.

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4.2.5. Phenology and Vegetation Index (NDVI) The average SGSSoilT (start of the growing season based on soil temperature) recorded in this study appears to be in accordance with previous result of Wielgolaski & Kärenlampi (1975) from a neighbouring area (Hardangervidda mountain plateau). They found that the date of the first bud-break occurred on average about May 30th (Julian day 150) in the subalpine (NB) birch forests, day 154 in the lichen heaths, and between day 159 and 163 in the meadows. Similarly, SGS at the Finnish sites were later, estimated to be on average between day 158 and 161 in the forests and two or three days delayed in the mountain heaths. Typical alpine plants have evolved various strategies to adjust their life cycle to a short growing season in a cold climate (Billings 1974, Billings & Mooney 1968, Körner 2003). Rapid flowering after snow and ice is a phenomenon that is common to both alpine and arctic floras (Crawford 2008). As an example, Saxifraga oppositifolia may flower 5-8 days after being released from snow (Larl & Wagner 2006). This ability to produce flowers quickly is dependent on the possession of pre-formed flowering buds developed during the previous growing season. Other species start their development late in the summer, and consequently the difference in GSL between different species may be grate. Alpine and Arctic plants have different timing of their phenological stages in the spring (e.g. Cooper et al. 2011), and therefore it is difficult to select a “reference plant” that could be used as a general indicator for the start of the growing season. In some cases, dominant and common species have been selected (Karlsen & Elvebakk 2003, Karlsen et al. 2005), and this gives probably a start date for the growing season which is in accordance with the date when soil temperature reach a certain threshold. Furthermore, the starting dates of early spring vegetative and generative phenophases may respectively differ with the district of growth and latitude because of growth pattern and day-length (Wielgolaski 1974). In addition ecotypic differences may exist between populations from different latitudes or altitudes (e.g. Prock & Körner 1996, Wielgolaski & Karlsen 2007). In terms of biology, the best way to define the start of the growth period is probably to use phenological data, but this method is often time consuming if sites covering a large area are included. Karlsen & Elvebakk (2003) used the flowering beginning of Cassiope tetragona to define the start of the growing season, which according to Sørensen (1941) represents the mean flowering time of all the species in the region. This view was, however, not confirmed by the studies of Cooper et al. (2011) where Cassiope tetragona was on average found to flower approximately 2 weeks after most of the other plants studied. Field observations of the general senescence of the vegetation in the lower parts of ridges, or the first measured period of one day or more with mean daily temperatures around oC were by Karlsen & Elvebakk (2003) used to estimate the end of the growing season. For sites where direct observations on the start and the end of the growing season were lacking, estimates were performed by comparison with meteorological data from the nearest weather station. A similar method was used by Karlsen et al. (2005) to define the growing season start in NE Norway, but then phenology of the common species Cornus suecica and Vaccinium myrtillus were used. The timing of the end of the growing season was defined when these two species had 50 % autumn colour of their leaves. Cooper et al. (2011) defined an active growing season length as number of weeks between green-up and 50 % senescence. In their study sites on Svalbard (Arctic meadows and heaths), green-up for 13 studied plants occurred on average 3 weeks after snowmelt, flowering 5 weeks after snowmelt, and 50 % senescence 9.5 weeks after snowmelt. Thus, the

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active growing was on average approximately 45 days. There was a difference in mean greenup of approximately 20 days between the studied species, and the mean difference in the timing of senescence was similar. The variation in date of flowering showed, however, a larger variation (almost 6 weeks). Some of the studied species showed quite similar timing of green-up and senescence while a few had a large deviation to the mean. Such studies give important informations about which species that could be used as general indicators to quantify average SGS, EGS and GSL within a relatively homogeneous landscape. At present, a common method to define the start of the growing season has been defined by the timing of when the NDVI value (Normalized Difference Vegetation Index images derived from satellite images) exceeds a certain threshold (e.g. White et al. 1997). In strongly heterogeneous areas this method does not work, and a modified method have been developed (e.g. Høgda et al. 2007). Also the end of the season has been defined by the use of NDVIvalues. According to Høgda et al. (2007), a correlation of 0.60 between passing 5 °C at autumn and NDVI values was found. Karlsen et al. (2008) estimated the start and end of the growing season in N Fennoscandia based on the onset and 50 % yellowing of birch leaves. In most of the study areas, onset of leafing started between day 140 and 160, and ended between day 250 and 260. The start of the growing season for the NB zone based on a soil temperature threshold (Table 6) was here a few days earlier, and this may partly be explained to be a result of the high spring temperatures during 2004. Buus-Hinkler et al. (2006) used snow cover and NDVI data to develop models to calculate end-of-winter snow accumulation, snow-cover-depletion, and net vegetative activity in 16 different melting (growing) seasons between 1988 and 2004. At Zackenberg the end-ofwinter snow accumulation showed significant inter-annual variability, whereas the end-ofwinter snow cover distribution remained similar from year to year. A comparison between snow cover and NDVI distribution revealed that vegetative vigour in the Zackenberg area was primarily linked to the initiation time of the snow-free period rather than air temperature. This indicated that in some Arctic regions increases in winter precipitation (snow) might be as or even more crucial for the ecosystem than the increased temperatures projected by the majority of General Circulation Models (GCMs), and the results of Cooper et al. (2011) also verified this suggestion. Use of NDVI vegetation indices to estimate GSL in large study areas has obviously many advantages, but at local scales the method has not been used yet. It would, however, be interesting to compare results from detailed field studies with maps generated from NDVI vegetation indices. The main challenge will probably be to select the best indicator species to be used.

4.3. Environmental Variables Associated with the End of the Growing Season Senescence at the end of the growing season is a normal consequence of the aging process and will occur even when the supply of essential elements is maintained. In perennial vascular plants, leaf senescence is an integrated set of physiological processes responsible for conserving reusable resources prior leaf death (Thomas & Stoddart 1980). Three main environmental factors are often reported to be associated with the end of the growing season:

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(a) decreasing air temperatures, (b) frost and first snowfall, and (c) decreasing day length. At local scales, however, variation in day length can not be used to explain differences in the end of the growing season between different sites. McGraw et al. (1983) maintained that it is surprising that only few studies have addressed the question of environmental control of leaf senescence in tundra plants because the onset of leaf senescence marks the end of a growing season. They found that the onset of senescence in July 1981 represented a growing season shortened by 15 to 30 days in an already short season lasting between 44 and 87 days (depending on snow cover, cf. Miller 1982) at the midelevation site, probably as a result of low temperatures. They concluded that the observed early senescence (in July 1981) was mainly a response to overall low temperatures. According to Wielgolaski & Kärenlampi (1975), the time of the autumn colours clearly shows when decreasing photosynthesis could be expected to take place in various species. They maintain that the vegetation period at the Norwegian alpine sites ended about September 20th (Julian day 263) and about two weeks later in birch forests. Autumn colouring in alpine areas varied between Julian day 222 and Julian day 232 during the period from 1970 to 1973. These dates are 20-30 days earlier than the end of season as defined in this study (Tables 5,6). End of the growing season is often defined by a threshold air temperature in a predefined number of days, as has also been done in this study. Previous studies have shown that air temperatures drop below freezing earlier than soil temperatures, and the low temperature threshold is reached earlier in the air compared with the soil in the autumn (Gehrig-Fasel et al. 2008). Bootsma (1994) defined the EGS as the 5-day weighted air mean temperature falling below 5.5 °C while Jones & Briffa (1995) defined it as the last 4-day sequence above the 5 °C threshold. Later Jones et al. (2002) revised the definition so that the EGS was defined as the last 5-day period with temperatures above 5 °C occurring after frost. Carter (1988) defined the end as the 10-day running mean when daily temperatures dropped below 5 °C, and Pudas et al. (2008) defined the end to occur when temperature dropped permanently below 5 °C. In a few studies, the EGS has been defined by a soil temperature threshold (Appendix). In general we may assume that the variation in soil temperature (measured at a depth of 5 cm) follows the air temperatures during early autumn and that freezing will first be experienced by the above-ground plant tissues. According to Mertens et al. (2001), soil temperatures reach their seasonal maximum in the early autumn, and thus they continue to stimulate respiration even after air temperatures have already decreased greatly, possibly mediating an after-effect of warm periods. Temperature control of root elongation under experimental conditions has been demonstrated for a number of species (Richards et al. 1952). Shaver & Billings (1977) found that late season cessation in root elongation rate in Dupontia and Eriophorum was controlled by decreasing day-length, but Carex species were not affected by day-length. Events of frost and snow-fall frequently occur during late summer. In some studies, the season end has been defined by the first snow fall in the autumn (Kozlov & Berlina 2002). Larl & Wagner (2006) described heavy snow falls which covered plants for several weeks each year during a three year study period. This was, however, followed by warmer periods when the snow melted. Probability of frost during the season and the end of the season increases normally from oceanic to continental areas (Tuhkanen 1984, Moen 1999). Frost sums based on normal monthly temperatures increase both with increasing altitude and increasing continentality and this was also evident in the present study areas (Table 2). As indicated in Fig. 3, selection of first event of autumn frost as a criterion for end of GS would

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in 2004 result in a very short GSL, especially in the eastern area (C). It can therefore be concluded that timing of the first frost events or first snow fall should not be used as a general criteria to define the end of the growing season. Temperature thresholds, or even better, data on plant senescence should rather be used.

4.4. Variation in Growing Season Length The study shows major variation in estimated GSL on different scales:    

Between different vegetation types (plots from different growing sites) Along altitudinal gradients Between different areas (oceanity-continentality gradient) Differences related to effects of different criteria used to define the start and the end of the growing season

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Results of both the present and previous studies (Appendix) have estimated that GSL variations to range from 20 to150 days in mountain areas. Consequently it is a challenge for ecologists to explain this great variation in terms of plant biology.

4.4.1. Differences between Oceanic and Continental Areas In general, there is an increase in GSL from the western oceanic areas to the more continental area, but the difference appears to decrease with increasing elevation (Tables 3, 6, Fig. 3). The difference in GSL (ΔGSL) is particularly large when the start of the growth season is defined either by date of snow-melt or by the soil temperature threshold, especially in the most continental area (Imingfjell) (Table 3, 5). This should mainly be explained to be a result of a thin or missing snow cover resulting in deep frozen soil and therefore a longer time period is needed before the soil temperature threshold is reached in a continental area. The increasing time interval between SGSSnow and SGSSoilT from the oceanic areas to the continental area (Table 3) is in accordance with the results of Wielgolaski & Kärenlampi (1975). Comparing an oceanic site with a continental site they stated: “There is a shorter period from snow-melt to bud-break at the Norwegian than at the Finish sites, causing an earlier growth start at most of the Norwegian sites in spite of later snow-melt.” Tuhkanen (1984) reports a general difference in GSL of 50 days between coastal areas (oceanic climate) and inland areas (continental climate) in the northern boreal zone. Similar trends were also shown by Walther & Linderholm (2006). Differences in GSLSoilT between the most oceanic area and the continental area were in average 20 days, 3 days, and -2 days respectively for the NB, LA, and MA zones (Table 6). Based on the other definitions, the differences were smaller. At high elevation, the variation in GSL was relatively small which was probably due to stable snow cover on the vegetation plots preventing the soil to freeze. Average GSDD during the growing season decreased from the oceanic- to the continental area (Table 7). This may partly be explained to be a result of the non-linear relationship between plant growth and air temperature. In a continental climate, the daily temperature amplitude is greater than in an oceanic climate, resulting in relatively high maximum temperatures during mid-day. The same effect has been shown for the forest limit

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temperatures in oceanic versus continental climates (Tuhkanen, 1980, Odland 1996, Holtmeier 2003).

4.4.2. Altitudinal Variation Measured altitudinal variations in GSL may primarily be explained to be results of decreasing temperatures and their effect on precipitation, snow thickness, and rate of snowmelt. Relationships between air temperature data and phenological studies have most often been based on interpolated data measured from neighbouring meteorological stations. Interpolated (or most often extrapolated) temperature values can, however, only be used to indicate possible relationships between temperature and plant phenology, growth and plant distribution partly because the values are measured 2 m above the ground and in shadow within climate huts. Temperature interpolation along altitudinal gradients have been shown to be a difficult enterprise that includes several possible sources of error (e.g. Körner 2007, Wundram et al. 2010), despite this, interpolated temperature data have frequently been used to both delimitation of bioclimatic zones and in phenological studies. Pudas et al. (2008), found a 1 °C increase in mean air temperature in May corresponded to an advancement of bud burst by 4.7-4.8 days and flowering by 0-3.7 days, depending on the species. A delay of bud burst between 3 and 8 days for each 100 m increase in altitude are reported from different areas in Fennoscandia by the use of different methods (Karlsen et al. 2007). A five-day shift in autumnal phenology approximately corresponds to 1 °C shift in temperature (Kozlov & Berlina 2002), and consequently there should theoretically be an 8day difference in phenology from the forest limit to the LA/MA transition. Increasing elevation is in general associated with a strong decrease in total vegetation cover, and mountain summits have large areas with bare rock and stones. This may be explained to be a result of reduced distribution of available sites where the environmental conditions during the summer are favourable for plant growth. According to Fægri (1972), low temperatures and unfavourable snow conditions strongly constrain vegetation development on high mountains. Several studies show that max snow thickness is often highest near the altitudinal forest limit decreasing toward the mountain summits where much of the snow is blown away and deposited in depressions (Niedzwiedz 1992, Doležal & Šrutek 2002, Liptzin & Seastedt 2009). Due to low melt rates during spring and early summer, total area covered by snow increase with altitude due to slow snow-melt (Tong et al. 2008). Lapse Rate Variation In ecological studies along elevation gradients, it is essential to select “the correct” lapse for altitudinal temperature interpolations. Lapse rates have been shown to vary both with altitudinal range, degree of oceanity, topography (especially mountain height), season, and type of measurement (maximum, minimum or mean values) (Green & Harding 1980, Haugen & Brown 1980, Barry 1992, Rolland 2003, Richardson et al. 2004, Crawford 2008). Effects of mountain height on both climate and plant distribution have previously been shown by Barry (1992), Dahl (1998), Holtmeier (2003), Körner (2003), Odland (2008, 2010). According to Dodson & Marks (1997), the rate at which air cools with elevation change varies from about -0.98 (for dry air) to about -0.4 (warm saturated air) oC/ 100 m increase in altitude, and similar results were also found by Richardson et al. (2004). According to Richardson et al. (2004), previous studies have shown that the altitudinal lapse rate in annual

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heath sums (GSDD) has been estimated to be -1.3 and -0.95 oC/ 100 m with a temperature threshold of 0 or 5 oC, respectively. Here, the commonly used lapse rate of -0.6 oC/ 100 m has been used, but close to the studied mountain summits, the altitudinal temperature decrease is probably much stronger. This is evident from studies showing that the vertical gradient in mean air temperature is nonlinear and that the lapse rate is particularly high close to mountain summits. According to Barry (1992), the air over a mountain is affected by radiative and turbulent heat exchange. These processes modify the temperature structure over the massif so that lapse rates on a mountain slope may differ from those in the free atmosphere according to the time of day. Mook & Vorren (1996) showed that the variation in temperature conditions along mountain slopes deviated from the general atmosphere lapse rate, and the altitudinal trend did not follow a linear trend. It was measured to be as high as -1.2 oC/ 100m close to a mountain summit. Duane et al. (2008) found that the tree-line weakened the lapse rate gradient and that the snow-ice line at the mountain summit enhanced it. Between 5470 and 5880 m a.s.l., the mean temperature lapse rate was -1.03 oC/ 100 m while the average lapse rate for the total elevation gradient was -0.51 oC/ 100 m. As a conclusion they maintained: “Thus extrapolation of summit climate from lower sites would be dangerous, confirming the need for detailed climate monitoring at a variety of elevations on such a mountain.” Consequently we should assume that the adiabatic lapse rate will be strongly affected by the general mountain height, especially close to the mountain summits, and therefore the actual GSL experienced by the plants may be shorter than is calculated by linear temperature lapse rates. A possible non linear elevation trend in lapse rate may be a result of the so-called Massenerhebung effect (mass elevation effect) which refers to the phenomenon where the mountain sizes ameliorate the physical environment. The effect is most noticeable where large mountains are massed together. In the centre, isotherm level rise and create a more continental climate, usually with reduced cloud level to retreat to higher altitude. Accordingly, the forest limits and the uppermost alpine vegetation can therefore be found at higher altitudes on large mountains than on small mountains (Fang et al. 1996, Crawford 2008, Odland 2010). Linear altitudinal trends in air temperatures as shown in Fig. 7 and Table 5 may therefore not give a correct picture on the GSL at high altitudes in the studied areas, and values for GSDD may be lower than shown in Fig. 7 and Table 7, especially close to the summits (MA zones). The altitudinal soil temperature lapse rate may be difficult to calculate because of frozen soil at high altitudes, but a value of -0.54 oC/ 100 m have been reported by Richardson et al. (2004). Green & Harding (1989) found that the altitudinal gradient of soil temperature varied predictably throughout the year, with the gradient largest in summer around -1.0 oC/ 100 m. Wundraum et al. (2010) found a general decrease of daily mean soil temperatures by altitude in all study sites with an average from 0.4 to 0.6 oC/ 100 m, but with pronounced variability. The study of Mook & Vorren (1996) showed that the altitudinal variation in soil temperature followed a non-linear trend, and close to the summits, the soil temperature lapse rate could be as high as -2.0 oC/ 100 m. Estimation of GSDD at high altitudes (mountain summits) based on extrapolation from temperature measurements at lower altitudes with the use of a “normal” lapse rate of -0.6 oC/ 100 m gives probably far too height values (e.g. in the MA zones in Table 7). The lapse rate both for air- and soil temperature should probably be between -1.0 and -2.0 oC/ 100 m. So far,

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there are, unfortunately, few studies available where effects of mountain height affect the temperature lapse rate.

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Variation in Snow Cover Duration and Snow-melt Number on months without snow cover in the Alps have been found to decrease from approximately 9 months at 600 m to zero in the nival zone (3200-3400 m), and the snow melts approximately one month later in N-facing slopes as compared with S-facing slopes (Pisek 1963). According to the map published by Bjørbæk (1979), the altitudinal trend in date for normal accumulation of snow in the autumn occurred in average 4-5 days earlier for each 100 m increase in altitude in the two westernmost areas and 5-6 days earlier in the easternmost mountain (Table 1). Ozenda (1988) reports similar gradients (3.8 days/ 100 m) from the Alps. This difference indicates a stronger lapse rate in the studied mountain areas compared with the Alps. Snow-melt rate is mainly a function of air temperature, and Inouye & McGuire (1991) found a significant linear relationship between degree days (above 0 oC) and snow-melt, where 1.16 degree day was required to melt 1 cm of snow. Aizen et al. (1997) calculated an average daily snow-melt rate of 1.77 cm during a study period with average air temperatures. The duration of snow cover has been reported to increase with increasing elevation, mostly in the range of 8 – 11 days/ 100 m (e.g. Rychetnik, 1987, Niedzwiedz 1992, Benniston et al. 2003). Mean gradient of snow cover duration with elevation has in Canada been calculated to be 3.8, 4.3, and 11.6 days/ 100 m for the snow onset, snowmelt, and entire year, respectively (Tong et al. 2008). For 18 selected Swiss sites, Beniston et al. (2003) found a significant linear relationship between snow duration days and altitude (r = 0.97) from approximately 300 days at 2400 m to 30 days at 500 m, representing an average decrease rate of 14 days/ 100 m. Variation in GSL With increasing elevation, plants will meet a gradually shorter GSL. This is evident from the gradual decrease in vegetation cover and number of vascular plants with increasing elevation, and more and more of the mountain surface consists of bare rocks and stones (Körner 2003). The decreasing trends in GSLSoilT is here calculated be on average 8.6 days/ 100 m (Fig. 5) or 5.6 days/ 100 m (Fig. 6) which is close to the GSL lapse rate of 5.9 days/ 100 m estimated by Kakubari (1991). According to Skre (1972), the duration of the growing season in NW Europe decreases with 8 days/ 100 m. As previously indicated by Fægri (1972), Ozenda (1988) and Niedzwiedt (1992), the length of the period when plant growth is possible approaches 0 days toward high altitudes (Fig. 1). At present it is difficult to calculate the GSL and thermal conditions during this period because of uncertainty related to the temperature lapse rates, particularly near alpine summits. If we assume that GSL at the forest limit is 100 days, and that there is an altitudinal lapse rate in GSL of 8 days/ 100 m, a GSL of 0 would theoretically be reached approximately 1250 m above the climatic forest limit based on a linear relationship between lapse rate and elevation. If we assume that the minimum GSL requirement for development of a vegetation stand is 30 days, the altitudinal limit would lie 875 m above the forest limit. Due of effects of snow and mountain height (Massenerhebung effects), the 0 level would probably be reached at lower levels in the study areas because the mountains are relatively small. As shown by Shanks (1956) and Mook & Vorren (1996), there is a significant negative relationship between

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altitude and soil temperature which at high elevation is best explained by a non-linear trend. In south central Norway, where the mountains are higher (2469 m), the transition between MA and HA zone (upper limit for vegetation stands) lie at approximately 1800 m (Moen 1999) which is 580 m higher than the forest limit. In the Alps, this limit would be reached approximately at 3500 m (Ozenda 1988), i.e. approximately 1200 m above the forest limit. A recent broad scale comparison of geographic variation in the alpine climate show that there are differences between the far north, the extremely oceanic sites (Scotland) and the Mediterranean, but seasonal mean temperatures are quite similar, and local snow distribution rather than latitude controls thermal minima experiences by roots and below ground apical meristems in winter (Körner et al. 2003).

4.4.3. GSL at the Forest Limit Environmental condition at the climatic forest limits has been one of the most popular study objects in the world, and many have attempted to explain their distribution limits in terms of environmental variables, especially temperature or “heat” (e.g. Holtmeier 2003, Körner 2006). The climatic forest limit has here been used as a reference line to which the altitudinal positions of the study plots have been related. It has previously been assumed that both the polar and altitudinal position of the climatic forest limit has a bioclimatic characterisation (Jobbagy & Jackson 2000, Körner & Paulsen 2003, Holtmeier 2003, Nagy 2006). Both growing season length and temperature conditions during this period are important variables for the differentiation of functional tissues of overwintering plant organs of forest trees, and for Betula ermanii a minimal length of 98 days at 2450 m was sufficient for its survival, while at 2800 m a considerable reduction to 55 days was considered to be critical (Gansert 2004). According to Körner (2006), the growing season mean soil temperature found at tree-line covered a surprisingly narrow range of 5 to 8 oC, mostly between 6 and 7 oC with a global mean of 6.7 oC for 30 locations. GSLSoilT has been found to be globally highly variable, but north of 40 degree latitude, it lied around 100 days. Daily mean air temperature at the treeline has been reported to exceed 5 oC for about 100 days and is above 10 oC for ca 35 days (Körner 2003). A mean July temperature around 10 oC has previously been suggested, but studies have shown that in oceanic areas higher average temperatures are needed, and in continental lower temperatures are needed (Skre 1972, Odland 1996). Forest stands are mainly restricted to sites with a max snow depth between 90 and 120 cm (Odland & Munkejord 2008a) and the snow within forest stands had in average melted between Julian day 122-127 (beginning of May) (Odland & Munkejord 2008b). This study shows that the altitudinal position of the climatic forest limit (transition between the NB and the LA zone) is most often associated with a GSL between 100 and120 days, both defined by the soil temperature threshold and an air temperature threshold of 5 oC, and GSL decreases from west to east. GSL was increased by up to 26 days using date of snow-melt as the start of the growth season. As shown in the Appendix, GSL at the forest limit from different parts of the World show mostly a variation between 80 and 120 days, but in some studies it has been reported to be much longer. Holmgren & Tjus (1996) showed that use of different temperature threshold resulted in major variations in forest limit GSL in N Sweden. Use of number of number of days with air temperatures above 0 °C increased the average GSL by more than 80 days compared with a 6 °C threshold.

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GSDDSoilT sums for the forest limits varied between 800 and 900 (Fig. 7), with highest values in the two western areas (Table 7). Fairly similar values have been found in other studies. Karlsen et al. (2005) estimated that the birch forest limit to occur at about 980 GSDD (0 oC threshold), the northern boreal zone had values mostly between 980 and 1100, and the arctic shrub tundra had values between 880 and 980 GSDD. From Greenland, Karlsen & Elvebakk (2003) found a variation in GSDD (threshold 5 oC) from in 267 (northern Arctic tundra) to 680 (arctic shrub-tundra). Malyshev (1993) calculated sum of temperatures for days with stable temperatures exceeding 0 or 5 oC for 26 forest limit areas in N Asia which gave values of 876 ± 169 and 742 ± 192 respectively. Parts of this variation can probably be explained by the assumption that there is a linear relationship between growth and temperature, and that temperature conditions during the study period deviated from a “normal year”.

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4.5. Changes in Growing Season Length as a Result of Global Warming Increasing GSL during recent decades in Scandinavia data have been reported in many studies based on climatic or phenology data (e.g. Holmgren & Tjus 1996, Wilson & Nilsson 2009). Ecological consequences of increased GSL are already remarkable (Walther & Linderholm 2006), and the main causes have been assumed to be associated with higher temperatures and earlier snowmelt resulting in higher effective temperature sums or accumulated GSDD (Sparks & Menzel 2002, Linderholm 2006, Walther & Linderholm 2006, Pudas et al. 2008). Several papers have also predicted continuous increases in the future, resulting in prolonged GSL (Rammig et al. 2010). According to Robeson (2002), variation in GSL can be a useful climatic indicator with several important climatologic applications. In a review, Linderholm (2006) reported that GSL from N Europe to have increased by up to 20 days during the last 30 years based on NDVI-data. According to Carter (1998), GSL in the lowlands of the Nordic countries have increased in average from 170 days in 1890 to over 180 days in 1997 (based on a temperature threshold of 5 oC). Skaugen & Tveito (2004) predicted that GSL in alpine areas in Norway may increase by up to 87 days (the highest mountains) within 2050 while Førland et al. (2004) project that the growing season may increase by 3-4 weeks in the Nordic Arctic. Linderholm (2006) reports that GSL in N Europe has increased by up to 20 days during the last 30 years, all based on increased air temperatures. Wilson & Nilsson (2009) found that on the studied mountain top in N Sweden, GSL had increased so much that in 2007 it enjoyed the same GSL as an altitude 275 m lower did 20 years later. The GSL had increased by 28 % at in the forest limit area (780 m) and by 175 % on the summit (1280 m). Smith et al. (2004) found that GSL in evergreen conifer forest had increased by 5.1 ± 2.9 days/decade, and in the North American tundra it had increased with 5.4 ± 3.1 days/decade. Amount of snow generally increases with elevation since rate of precipitation mostly increase with elevation and the temperatures decrease. Trivedi et al. (2007) estimated that a 1 o C increase in temperature corresponds to a 33-day reduction in snow cover at 750 m in Scotland, and they predict a strong reduction in snow cover in the future as a result of global warming. Model simulations (Euskirchen 2006) show decreased snow cover and permafrost stability from 1960 to 2100, and a trend towards an earlier thaw date of frozen soils and the

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onset of the growing season in the spring by approximately 2-4 days from 1988-2000. Although regions with the longest snow cover duration displayed the greatest increase in growing season length, these regions maintained smaller increases in productivity. Climate models indicate increasing precipitation and higher temperatures in most parts of Scandinavia. Future GSL changes in mountain areas will depend on the balance between increased precipitation and increased winter temperatures and how this will affect snow accumulation and thereby soil freezing. Models indicate that length of snow cover duration will be reduced, at least at intermediate altitudes (Hantel et al. 2000, Dye 2002, Wielke et al. 2004, Trivedi et al. 2007, and Brown & Mote 2009). Schuler et al. (2006) project that during the end of this century, the start of the snow accumulation may start 3-4 weeks later and the snow cover period will end 1-7 weeks earlier. The changes will get smaller with increasing altitudes and towards the coast. In N Fennoscandia, the snow-free period will probably be extended by the end of this century, with more than 30 days even at high elevations (Dankers & Christensen 2005). A recent study by Seidel et al. (2009) shows that caution is needed when extrapolating climate change trends from other mountains or proximate lower elevation climate data to upper elevations. They found support to the conclusion that some mountains may only weakly follow regional low elevation surface climatic trends and may exhibit resistance to climatic warming with elevation. Factors may include temperature inversions and sufficient atmosphere availability to result in frequent cloud or fog exposure on the upper slopes. Pudas et al. (2008) found significant changes in climate and phenophases between 1997 and 2006 in Finnish Lapland. The effective temperature sum had increased on average by 17.7 day degrees/year, while the maximum snow depth had decreased by 3.5 cm/year and the timing of snow-melt had advanced by 1.4 days/year. The spring phenophases advanced on average by 1-2 days/year in the case of most of the species studied resulting in a lengthening of their growth period. Several modelling studies predict that climatic warming will be accompanied by greatly increased risk of spring frost damage to native woody species in the boreal and temperate zones (Linkosalo et al. 2006, Bennie et al. 2010). By the use of NDVI-data combined with phenology data on Betula pubescens, Karlsen et al. (2009) found that there had been a linear increasing trend for all Fennoscandia of 0.27 days/year earlier onset of the growing season, a 0.37 day/year later end of the growing season, and a 0.64 day/year longer growing season between 1982 and 2006. Mapping the EGS showed less correlation with field phenology data and indicated some uncertainty. The southern and oceanic regions showed a trend of about a 1 day/year longer growing season, in contrast to the alpine and northern continental regions which showed either no trend or a slightly shorter growing season. Future changes in snow cover and its effects on the GSL may decide the faith of plants and vegetation types that are associated with narrow environmental niches in alpine areas. So far there are, however, few studies on changes in snow cover in alpine areas. Most studies predict shorter snow season, but also increasing duration of the snow cover has been shown (Borgstrøm 2001).

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CONCLUSIONS 

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Due to the uneven distribution of snow in alpine areas, it is impossible to give a general characterization of GSL. Sites (vegetation types) separated by only a few metres may experience a difference in GSL of more than two months. In the NB and the LA zones, the variation in GSL between the study sites was more than 90 days while at higher altitudes the variation was mostly less than 30 days. A variation in GSL greater than 100 days has been estimated for the studied plots (vegetation types). The variation is partly results of variable ecological conditions at different altitudes (vegetation zones), between vegetation types, between areas (climatic regions), and different criteria for the start of the growing season. For a particular site, a difference in GSL of more than 60 days may was found if the start of the growing season was defined by date of snow-melt or the day when a soil temperature threshold of 6 oC was selected. The difference was greatest on exposed sites with frozen soil, while on sites with a thick and stable snow cover the differences were mostly less than a week. In general, the difference increased from the western to the eastern area. With increasing elevation, there is a decrease in GSL (in average between 3 and 12 days/ 100) depending on the definition used. Snow cover thickness and duration are very important environmental factors in alpine areas, but they are poor predictors for the initiation of growth in the spring, and thereby for the GSL. The effect is mainly indirect thru the insulation effect of snow preventing soil freezing. Average GSLSoilT for the four main vegetation groups described were: Group1 (fell field): 104 days, Group 2 (lee-side) 106 days, Group 3 (early snow-bed) 89 days, and Group 4 (late snow-bed) 77 days. Both the present result and several previous studies show that the position of the forest limit is associated with a GSL around 100 days based on both soil temperature threshold of 6 oC data and air temperatures data with a threshold of 5 oC . On average, the growing season started earlier in the oceanic area compared with the eastern continental area, and GSL was longest in the oceanic area. Start of the growing season based on a soil temperature threshold of 6 oC in the spring and low air temperatures in the autumn was found most useful for the estimation of the GSL. A literature review shows that the limit for development of vegetation stands both in alpine and Arctic areas appear to be associated with a minimum GSL of approximately 30 days both, as defined by snow layer duration and air temperatures higher than 5 oC. GSL should be used by care because it does not give any information about temperature conditions during the actual period. Heat received may be quantified as growing season degree days. Estimation of temperature data and GSDD for sites close to mountain summits is unreliable due to uncertainty about temperature lapse rates.

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ACKNOWLEDGMENT Reviewer: Elisabeth J. Cooper. Department of Arctic and Marine Biology, University of Tromsø, N-9037 Tromsø, Norway.

APPENDIX

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Measurements of growing season length (GSL) as presented in previous publications. Duration of GSL has been based on: S= snow data, ST = soil temperature data, AT = air temperature data, P = phenology data, and NDVI = vegetation index calculated from interpretation of satellite images GSL 44-62 24-44

Def. S S

60

S

50

S

53

S

70 30-70 80-150 105-126

S S S S

71-104 S

88-121 S 121 S 117±22 S 104±19 S 89±14

S

75±12

S

69±13

S

147±30 S 97±30 S 84 ± 41 S

Area Svalbard Svalbard

Reference

Comments

Crawford (1997)

Variation from 1991 to 1995 Cushion plant, Devon I. Bliss (1997) Sedge-moss, (a) Weighted average based upon aerial extent Devon I. of the two ecosystems Total Lowland, Devon I.(a) Sedge moss, Barrow Arctic Nagy & Grabherr (2007) Variation with altitude Boreal Mid-thawing site, Period between snowmelt in the spring and Alps Ladinig & daily mean temperatures below freezing or the Wagner (2005) formation of a continuous snow cover in Late-thawing site, autumn. Alps (Variation during three years) N Sweden, Sites along a snow gradient. Period between N Sweden, Summit Molau (1996) date of snow-melt and timing of continuous plateau snow cover (mean of 4 years) Japan, Snow-bed, 1910 m Japan, Snow-bed, 1890 m Japan, Snow-bed, Kudo (1996) Average snow free days during a 4 year period 1880 m Japan, Snow-bed, 1790 m Japan, Snow-bed, 1790 m Alps, Alpine site, 2300 m Subnival site 2650 m Larl & Wagner Subnival site 2650 m (2006) Snow free period. Average for three years.

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GSL 115±18 100±18 85±13 71±12 65±13 52±7 32 88 50

Def. S S S S S S S S S

35

S

109 107 87 83 52 44

S S S S S S

87

S

Area Reference Comments N Japan, -A N Japan, -B N Japan, -C Taisetu Mountains: site A-F along gradients in Kudo & Ito snow duration. N Japan, -D (1992) N Japan, -E Snow free days, average for 3 years. N Japan, -F Japan, Snow-bed Kudo & Suzuki (1999) Japan, Fell-field Colorado, Edge of Galen & snow-bed Stanton (1995) Colorado, Late snowbed Fell-field Dry meadow DA Walker et Colorado, USA Moist meadow al. (2001) Wet meadow Colorado, Snow bed Alaska, Forb-grass Miller (1982) Time between date of snowmelt and 15. August zone when most species in the area were clearly Alaska, Fellfield becoming senescent.

>200 100-200 5.5 °C, The start of the season Gansert (2004) requires that 5 consecutive days surpass the actual temperature threshold, and the end of the period require that 5 consequtiveconsecutive days are lower than the threshold Turner & Blaser (1977), Ellenberg Air temperature threshold 5 oC (1996)

100-110 AT

Treelines

150-180 95-105 80-100 95-125 60-95 20-60

AT0 AT5 AT6 AT AT AT

Treeline, N Sweden Treeline N Sweden N Sweden, Treeline N Sweden, 780 m N Sweden,1005 m N Sweden, 1280 m

16 89 90 107 148 152

AT AT AT AT AT AT

3 oC Increasing GSL since 1987 (average increases during 20 years) Holmgren Tjus (1996)

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Estimation of the Growing Season Length in Alpine Areas GSL 70-80 30-50 100 80 H”, i.e. the lower and higher populations have less diversity than those at intermediate levels; 2) “L < M < H”, i.e. the lower populations have less diversity; 3) “L > M > H”, i.e. the lower populations have greater diversity; and 4) “L = M = H”, i.e. no significant change with altitude. Some published studies amongst those considered here compared explicitly the genetic diversity within populations from different altitudes. For example, Zhao et al. (2006) investigated the genetic diversity of five Kobresia species from the eastern Qinghai-Tibetan Plateau and found no significant correlation between diversity and altitude. In contrast, using allozyme markers, Liu et al. (2006) found that the genetic diversity of Sophora moorcroftiana increased significantly with altitude in terms of expected heterozygosity (Hs) but not observed heterzygosity (Ho). For other species which the authors did not perform such statistical analysis, we plotted the diversity value against altitude, based on data in the original papers, and looked for possible non-linear patterns (i.e. L < M > H). Where the data suggested a linear increase or decrease, statistically analyses were performed to examine the possible correlation of variability with altitude. Our results revealed that most species show no significiant correlation between diversity and altitude (Figure 3), suggesting the existance of other factors that affect genetic diversity more strongly than altitude. Another point is that, in most cited studies, populations from different altitudes are collected from areas that are also far apart, often from different mountains. In other words, the difference of genetic diversity may represent the combined effects of both vertical and horizontal gradients.

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Yupeng Geng, John Cram and Yang Zhong

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2. Genetic Differentiation between Populations Knowledge of genetic structure, i.e. the distribution of diversity within and between populations of a species, is important for the conservation of alpine species because it provides useful insights into how the species may respond to climate changes. For example, if a large proportion of the diversity resides within populations, this would seem good for in situ conservation of alpine species for at least two reasons: 1) the local populations may have high evolutionary potential and thus increase their chances to pass through the environmental filter caused by changed selection regimes; and 2) a large proportion of diversity within populations usually means effective gene exchange between populations, which would help the warm-adapted alleles in low altitude populations to spread into higher populations and thus decrease the risk of local extinction by warming. A commonly used statistical parameter for genetic differentiation is Gst (Nei, 1973), which provides a measure of the proportion of the total diversity occurring between populations. The values of Gst (or st, an analogue of Gst based on AMOVA) for 19 species endemic to the Qinghai-Tibetan Plateau are presented in Table 1. Most species show considerable genetic differentiation between populations, with Gst ranging from 0.1066 to 0.727 and st ranging from 0.011 to 0.773. The mean value (Gst = 0.300, Φst = 0.481) is largely comparable to the average for short-lived perennial plant species (Gst = 0.32, Φst = 0.41) and higher than that for long-lived perennial plants (Gst = 0.25, Φst = 0.19) (Nybom, 2004). Generally, several endangered and/or medical plants including the genera Rhodiola, Pedicularis, Lamiophlomis, Swertia, and Anisodus show high genetic differentiation between populations, which may be partitially ascribed to their fragmented habitats and shrinking population size because of overexploitation (Liu et al. 2006). An exception is the highly endangered Pinus squamata, in which both limited genetic diversity within populations and low genetic differentiation between populations were found as a result of extremely small population size (Zhang et al. 2005). In contrast, several widespread plants like Androsace tapete and Kobresia species, whose life histories are similar to those of long-lived trees, have relatively low genetic differentiation between populations (Geng et al. 2008). Several studies also investigated the genetic differentiation between populations in a spatial context. One of the most widely considered models is isolation-by-distance. In this case the genetic differentiation between populations is predicted to be quantitatively correlated with the corresponding geographic distance (Wright 1943). A Mantel test can be used to examine the correlation between genetic distances and geographic distances (Mantel et al. 2003). For example, Liu et al. (2006) investigated the spatial genetic structure of ten populations of Sophora moorcroftiana along the Brahmaputra River (known within Tibet as Yarlung Zangbo River) and reported a significant correlation between genetic and geographic distances (r2 = 0.50, p = 0.002). Similar findings were reported in Anisodus tanguticus (r = 0.345, p = 0.020), Rhodiola crenulata (r = 0.677, p = 0.006), and Lamiophlomis rotata (r = 0.688, p = 0.001). In contrast, no significant correlations were found in Androsace tapete (r = 0.042, p = 0.446), Elymus sibiricus (r = 0.744, p = 0.993), and Megacodon stylophorus (r = 0.531, p = 0.146). The lack of significiant correlations between genetic and geographical distances in the last three species suggests that the isolation-by-distance model cannot explain the spatial genetic structure of populations of alpine plant species in the Qinghai-Tibetan

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Plateau. It is notable that some of the assumptions of the isolation-by-distance model may be invalid in the case of Tibetan plateau. Specifically, the model assumes continuous and homogeneous populations, and ignores the effects of habitat characteristics and demographic variation within the range of a species (Slatkin and Maruyama 1975, McRae 2006). Nevertheless, most plants in the Qinghai-Tibetan Plateau occur within a limited range of altitudes, resulting in a belt along land at those altitudes. Given the complex geomorphology and interlaced valleys (i.e. low altitude areas), the distributions of most alpine plants are not spatial continously. Recently, a more refined model of isolation-by-resistance has been proposed, which may be more useful in explaining the spatial genetic structure of alpine plants in the Tibetan plateau.

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3. Spatial Genetic Structure on a Large Scale: Effects of Landscape Barriers The Qinghai-Tibetan Plateau has a few outstanding features that make it very different from its counterparts elsewhere in the world. Firstly, it occupies an extremely large area (i.e. 2.5 million km2) and spans considerable latitude and longitude ranges (i.e. N 25-40, E 74104). It is not surprising that the plateau possesses highly diverse vegetation types, including not only vertical zones between different altitudes on the same mountain but also horizontal zones crossing different latitudinal or longitudinal areas (see The Vegetation of Tibet, Institute of Botany at the Chinese Academy of Sciences, 1988). Alpine plants on the Tibetan plateau may respond to climate warming by both upward migration along the same mountain, and northward migration in some areas (e.g. northern Tibet) where many plants are continuously distributed. Secondly, many major landscape features (e.g. ridges with peaks of more than 5000m and valleys with basins below 4000 m) run west-east across the plateau, and present significant barriers to the northward migration of alpine plants whose habitats are usually constrained between altitudes of 4000 and 5000m. Thus, knowledge of spatial genetic structure at a large scale, especially across major landscape features, is essential for predicting the response of alpine plants to climate change. Among the outstanding landscape features of the southern Qinghai-Tibetan Plateau is the Brahmaputra River, which is the largest and longest river on the Tibetan plateau and forms a huge west-east valley, about 1,500 km in length and 200 km in maximum width. Several studies have investigated the effect of the Brahmaputra River on the spatial genetic structure of alpine plants endemic to Tibet. Using ISSR markers, Xia et al. (2007) investigated the genetic structure of Rhodiola chrysanthemifolia, in which five populations were collected to the south and five to the north of the Brahmaputra River. AMOVA revealed that the genetic variation between populations located in the south and north side of the Brahmaputra River was only 4.6%, and most variation (73.1%) was found among populations within regions, suggesting limited genetic differentiation across the Brahmaputra River.

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Figure 3. The change of genetic diversity (measured as Hs or I) with altitude in alpine plants endemic to the Qinghai-Tibetan Plateau. The correlation between genetic diversity and altitude is not significant in most species. Only studies including more than six populations are analyzed.

Similarly, Liu et al. (2006) used isozyme markers and analyzed ten populations of the endemic shrub Sophora moorcroftiana along the Brahmaputra River. Althought they did not explicitly test the effect of the river on the genetic differentiation among populations of this species. The genetic distances presented in Table 6 of Liu et al. (2006) and the results of

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cluster analysis (Figure 3 in Liu et al. 2006) give no indication of genetic discontinuity across the Brahmaputra River. The lack of genetic differentiation across landscape features may result from extensive current and/or past gene exchanges between populations located in different geographical regions. The relative importance of the two factors (current versus historical gene exchanges) can be inferred through the analysis of gene flow at different spatial scales. For example, in a recent study, Geng et al. (2008) explored the spatial genetic structure of Androsace tapete at both fine-scale (several meters) and landscape-scale (hundreds of km). On a fine scale, Androsace tapete showed significant genetic spatial autocorrelation within a short distance (less than 10 m), suggesting limited current gene dispersal via pollen and/or seeds. On a landscape scale, however, the Brahmaputra River played a weak role in shaping the spatial population structure of this species. The contrasting results of spatial genetic structure at different scales suggest that historical gene exchanges, rather than current gene flow, might have played an important role in shaping the genetic structure of this species across the landscape features like the Brahmaputra River. Besides the Brahmaputra river, a few other landscape barriers (e.g. the Nianqingtanggula and Tanggula mountains) were also involved in the studies of genetic structure in alpine plants endemic to the Qinghai-Tibetan Plateau (Xia et al. 2005). More studies are needed to make a fuller assessment of the effects landscape barriers on the spatial genetic structure of alpine species in the Qinghai-Tibetan Plateau.

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CONCLUSIONS The population genetics of alpine plants endemic to the Qinghai-Tibetan Plateau have received increasing attention in recent years, as these species are among the most senstive to climate change. Despite significant progress reviewed here, several challenges remain for better management and conservation. Generally, whether or not alpine plants can survive the ongoing climate changes will largely depend on their ability to disperse into suitable new habitats, and on their ability to adapt to the changed environment in situ through rapid evolution (Pulido and Berthold 2004). As the dispersal abilities of alpine plants are often difficult to measure using traditional methods, indirect methods of population genetic analysis based on neutral molecular markers represent a promising alternative to assess the gene flow between populations. However there are few studies of this sort. In addition, the knowledge of current vertical and horizontal genetic differentiation can provide important insights into long-term gene exchange and historical dispersal patterns of alpine plants, which are useful for better prediction of their probable responses to future climate change. In addition, efforts are needed to assess the adaptive potentials in alpine plant populations in more accurate ways. Although the neutral molecular markers (e.g. ISSR and RAPD) are most widely used to measure the genetic diversity within natural populations with the assumption of the existance of positively correlationship between marker diversity and the additive genetic variance. However, this assumption has not been tested rigorously, and neutral markers may fail to detect genetic differentiation of great adaptive significance. Thus, there is a need for more well-designed research in Tibet, using both neutral molecular markers and quantitative traits with explicitly ecological significance, to examine the amount and distribution of genetic variation along the altitudinal gradients and across the horizontal zones.

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ACKNOWLEDGMENTS We would like to thank Dr. Tashi Tersing and other colleagues at Tibet University for their help in field investigation and Dr. Yidong Lei, Dr. Jimei Liu, Dr. Qingbiao Wang, Dr. Li Wang and Dr. Liyan Zeng for their assistance in experiments and data analyses. Special thanks to Professors Suhua Shi and Shaoqing Tang for their guidence and support to our studies on plant genetic diversity. This work was supported by Shanghai Science and Technology Committee (07XD14025), China Postdoctoral Science Foundation (200801171 and 20070410163), and Doctoral Fund of Ministry of Education of China (200802461047).

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Liu, J. M., Wang, L., Geng, Y. P., Wang, Q. B., Luo, L. J. & Zhong, Y. (2006). Genetic diversity and population structure of Lamiophlomis rotata (Lamiaceae), an endemic species of Qinghai-Tibet Plateau. Genetica, 128, 385-394. Liu, Z. M., Zhao, A. M., Kang, X. Y., Zhou, S. L. & Lopez-Pujol, J. (2006). Genetic diversity, population structure, and conservation of Sophora moorcroftiana (Fabaceae), a shrub endemic to the Tibetan Plateau. Plant Biology, 8, 81-92. Lu, J. Y., Yang, X. M. & Ma, R. J. (2008) Genetic diversity of clonal plant Polygonum viviparum based RAPD in eastern Qinghai-Tibet Plateau of China. Journal of Northwest Normal University, 44, 66-72. Ma, X., Zhang, X. Q., Zhou, Y. H., Bai, S. Q. & Liu, W. (2008). Assessing genetic diversity of Elymus sibiricus (Poaceae: Triticeae) populations from Qinghai-Tibet Plateau by ISSR markers. Biochemical Systematics and Ecology, 36, 514-522. McRae, B. H. (2006). Isolation by resistance. Evolution, 60, 1551-1561. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853-858. Nei, M. (1973). Analysis of gene diversity in subdivided populations. Proceedings of the National Academy of Sciences, USA, 70, 3321-3323. Nybom, H. (2004). Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Molecular Ecology, 13, 1143-1155. Ohsawa, T. & Ide, Y. (2007). Global patterns of genetic variation in plant species along vertical and horizontal gradients on mountains. Global Ecology and Biogeography, 17, 156-163. Pulido, F. & Berthold, P. (2004). Microevolutionary response to climatic change. In: Moller et al (Eds) Effects of climatic change on birds. Elsevier, Amsterdam, 151-184. Qin, D. H. (1998). The glaciers and ecological environments of the Qinghai-Tibet Plateau. China Tibetology Publisher, Beijing. Slatkin, M. & Maruyama, T. (1975). The influence of gene flow on genetic distance. American Naturalist., 109, 597-601. Selkoe, K. A. & Toonen, R. J. (2006). Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters, 9, 615-629. Wu, C. Y. (1988). Hengduan Mountain flora and her significance. Journal of Japanese Botany, 63, 297-311. Wu, S. G, Yang, Y. P. & Fei, Y. (1995). On the flora of the alpine region in the QinghaiXizang (Tibet) plateau. Acta Botanica Yunnanica, 17, 233-250. Weng, E. S. & Zhou, G. S. (2006). Modeling distribution changes of vegetation in China under future climate change. Environmental Modeling and Assessment, 11, 45-58. Xia, J. & Guo Y. H. (2006). ISSR analysis for genetic diversity of Pedicularis dunniana. Journal of Wuhan Botanical Research, 24, 565-568. Xia, T., Chen, S. L., Chen, S. Y. & Ge, X. J. (2005). Genetic variation within and among populations of Rhodiola alsia (Crassulaceae) native to the Tibetan Plateau as detected by ISSR markers. Biochemical Genetics, 43, 87-101. Xia, T., Chen S. L., Chen, S. Y., Zhang, D. F., Zhang, D. J., Gao, Q. B. & Ge, X. J. (2007). ISSR analysis of genetic diversity of the Qinghai-Tibet Plateau endemic Rhodiola chrysanthemifolia (Crassulaceae). Biochemical Systematics and Ecology, 35, 209-214. Xu, W. X. & Liu, X. D. (2007). Response of vegetation in the Qinghai-Tibet Plateau to global warming. Chinese Geographical Science, 17, 151-159.

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Zhang, D. F., Chen, S. L., Chen, S. Y., Zhang, D. J. & Gao, Q. B. (2007). Patterns of genetic variation in Swertia przewalskii, an endangered endemic species of the Qinghai-Tibet Plateau. Biochemical Genetics, 45, 33-50. Zhang, Z. Y., Chen, Y. Y. & Li, D. Z. (2005). Detection of low genetic variation in a critically endangered Chinese pine, Pinus squamata, using RAPD and ISSR markers. Biochemical Genetics, 43, 239-249. Zhao, Q. F., Wang, G., Li, Q. X., Ma, S. R., Cui, Y. & Grillo M. (2006). Genetic diversity of five Kobresia species along the eastern Qinghai-Tibet Plateau in China. Hereditas, 143, 33-40. Zheng, W., Wang, L. Y., Meng, L. H. & Liu J. Q. (2008). Genetic variation in the endangered Anisodus tanguticus (Solanaceae), an alpine perennial endemic to the Qinghai-Tibetan Plateau. Genetica, 132, 123-129. Zhou, Z. Q., Shao, Q. Q. & Jiang, X. C. (1984). Comparison of karyotype and chromosome N-banding pattern of Hordeum spontaneum of Qing-Zang Plateau and that of the Middle East. Acta Genetica Sinica, 11, 120-124.

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INDEX # 20th century, 8, 131

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A access, 141, 147 accessions, 193 accommodation, 140, 144 accounting, 82, 145, 153 acid, 3, 6, 8, 39, 42, 44, 45, 54 acidity, 41, 45, 78 adaptation, 125, 157 adaptive radiation, 192 adhesion, 61, 72, 77 adults, 9, 31 advancement, 113 Afghanistan, 192 age, ix, 81, 173, 174, 177, 180, 182, 184, 185 aging population, 148 aging process, 110 agriculture, ix, 3, 125, 155, 158, 159, 162, 172 air pollutants, 3 air temperature, viii, 2, 6, 10, 43, 85, 86, 87, 89, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 122, 123, 124, 125, 126, 127, 131, 132 Alaska, 83, 121, 122, 123, 125, 127, 130 algae, 9, 29, 39, 46 algorithm, 67 alleles, 198 Alpine environment, vii, 47, 48, 49, 55, 58, 74, 80 Alpine-Carpathian-Dinaric region, ix, 173, 176 alternative, 201 altitudinal environmental gradient, vii, 1, 4, 12, 13, 17, 19, 20, 21, 33 amalgam, vii, 1, 4, 17, 22 amplitude, 104, 112

Amsterdam, 203 anchorage, 72 ANOVA, 10, 17 Antarctic, 192, 202 anthropic, 192 aquatic ecosystems, vii, 1, 3, 4 aquatic habitats, 23 Arctic, 192, 202 Asia, 117, 122, 124, 126, 130 assessment, 4, 39, 40, 129, 152, 201 assimilation, 87, 107 assumptions, 199 asymmetry, 186 atmosphere, 77, 114, 118 atmospheric deposition, 3, 36, 45 Austria, 38, 127, 134, 135, 136, 138, 141, 147, 151, 152, 153, 186, 187, 188 authorities, 136, 144, 150 authority, ix, 135, 149, 150 autocorrelation, 201

B back, 193 Balkans, 42 banks, viii, 48, 80 Barents Sea, 177 barley, 192, 193, 202 barriers, 2, 199, 201 base, 2, 128, 137, 144, 152, 162, 176, 180, 181, 185, 194 bauxite, 188 Beijing, 202, 203 benefits, 136, 137, 141, 142, 143, 144, 145, 149, 150 benthic invertebrates, 40, 41 Bhutan, 192 biodiversity, iv, x, 3, 129, 136, 145, 147, 150, 153, 154, 156, 191, 192, 193

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Index

206 biogeography, 37, 128 bioindicators, 4 biological activity, 86 biological processes, 3, 107 biomass, 10, 16, 19, 21, 22, 25, 26, 29, 30, 31, 33, 35, 38, 49, 87 biosphere, 136, 147 biotic, vii, 1, 2, 6, 10, 13, 17, 19, 20, 21, 26, 29, 31, 33, 35 birds, 203 Bosnia, 188 branching, 73 breakdown, 45 breaking force, 68 breeding, 139, 140, 141, 142, 143 Brooks Range, 125 bryophyte, 29, 134

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C CAP, 10 capillary, 53 carbon, 108, 126 case studies, 147, 153 case study, 126, 152, 154 catchments, 2, 4, 8, 12 Caucasus, 42 Central Europe, vii, 1, 6, 16, 151 challenges, 201 changing environment, 128 chemical, 2, 3, 6, 16, 23, 30, 36, 38, 43, 44, 49, 50, 52, 72, 77, 78, 79 chemical characteristics, 38, 50, 78 Chile, 132 China, 177, 191, 192, 193, 202, 203, 204 chloroplast, 194 chromosome, 193, 204 circulation, 128 classes, 10, 14, 24, 56 classification, 5, 26, 39, 44, 127, 194 climate, vii, viii, 1, 2, 3, 4, 5, 6, 30, 36, 37, 38, 39, 40, 41, 42, 43, 45, 47, 54, 90, 93, 104, 105, 109, 112, 113, 114, 116, 118, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 139, 156, 157, 159, 163, 192, 197, 198, 199, 201, 203 climate change, vii, 1, 2, 3, 4, 36, 37, 39, 40, 41, 45, 118, 125, 126, 128, 129, 130, 131, 132, 133, 134, 197, 198, 199, 201, 203 climate warming, 199 climates, 87, 113, 127, 133 cluster analysis, 17, 26, 201 clustering, 17 clusters, 91

CO2, 130 coherence, 36 colonisation, 156 colonization, 19, 22, 36 combined effect, 49, 67, 197 combustion, 10 commercial, 58, 59 communities, vii, 1, 2, 4, 13, 38, 43, 45, 88, 89, 90, 91, 92, 98, 107, 127, 129, 132, 133, 156, 157, 186, 194 community, vii, 30, 38, 43, 47, 49, 50, 52, 65, 107, 126, 154 competition, 73 competitors, 152 compilation, 5 complement, 159 complexity, 50 composition, vii, 1, 4, 5, 6, 17, 26, 30, 33, 36, 42, 44, 126, 127, 139, 156, 160, 192 compounds, 41 compression, 67, 187 computer, 81 conceptual model, ix, 135, 136, 139 conductivity, 83 conflict, 144, 145 conifer, 51, 117 conservation, iv, vii, ix, x, 9, 37, 44, 47, 82, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 191, 192, 198, 201, 202, 203 constituents, 140 construction, 48 consumption, 73 contamination, 39 Continental, 112 control measures, 140 controversies, 86 correlation, x, 11, 76, 110, 118, 146, 162, 165, 191, 197, 198, 200 correlations, 69, 198 cost, 136, 149, 150, 194 covering, vii, 1, 5, 109, 136 Croatia, 185 crop, 83, 193, 194 crops, 82, 192, 193, 194, 196 crust, 177, 181, 184, 185 crystallization, 184 cultivation, 139 cycles, vii, 2, 5, 15, 31, 33, 35, 88, 125, 132 cycling, 125 cyst, 43

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Index

207

D

E

data set, 12 database, 152 DCA, 21 decay, 153 decomposition, 13 deforestation, 157, 162 Deforestation, ix, 155 deformation, 187 degradation, 108, 140 demographic change, 148 dendrogram, 17 Denmark, 37 deposition, 3, 6, 8, 36, 39, 42, 45, 130, 184 deposits, 158, 162, 164, 188 depth, 8, 9, 11, 23, 53, 54, 56, 58, 59, 60, 72, 73, 74, 80, 93, 106, 107, 111, 116, 118, 121, 123, 124, 125, 132, 134 destruction, 140, 149 detectable, 148 detection, 41 deviation, 75, 90, 95, 96, 105, 110 diatoms, 5, 38 differentiation, 196, 198, 199, 200, 201, 202 Dinaride Ophiolite Belt, x, 174 direct cost, 141 direct costs, 141 direct observation, 109 directives, 74 discontinuity, 201 discordance, 176 displacement, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 79, 174 distribution, vii, x, 1, 2, 4, 5, 16, 22, 37, 38, 39, 40, 41, 42, 43, 53, 56, 67, 73, 76, 79, 81, 82, 83, 90, 104, 110, 113, 116, 119, 124, 127, 128, 129, 131, 132, 133, 187, 191, 192, 198, 201, 203 diversity, vii, x, 1, 5, 10, 19, 20, 22, 26, 29, 31, 33, 35, 37, 40, 42, 44, 131, 136, 157, 191, 192, 193, 194, 196, 197, 198, 200, 201, 202, 203, 204 DNA, 194, 203 dominance, 10, 13, 14, 15, 17, 23, 24, 31, 35, 160 donations, 144, 149, 150 drawing, 55, 56 drought, 54, 77, 78, 79 dry matter, 77, 131 drying, 36, 80 dykes, 180 dynamic factors, 13

early warning, vii, 1, 4, 44 earth, 192 East Asia, 126 ecological, 192, 201, 203 ecological data, 103 ecologists, 192, 203 ecology, iv, vii, 40, 43, 44, 45, 46, 124, 125, 126, 128, 129, 130, 132, 133, 151, 172 economic activity, 149 economic consequences, 36 economic development, ix, 135, 136, 137, 138, 144, 146, 147, 148, 150 economic efficiency, 152 economic evaluation, 152 economics, vii, 44, 151, 153 ecosystem, ix, 3, 30, 38, 46, 50, 110, 124, 125, 128, 134, 135, 136, 137, 138, 139, 140, 141, 142, 145, 146, 150, 151, 153, 195 ecosystems, 192 education, 141, 149 Education, 202 educational programs, 140 elaboration, 51 electrical conductivity, 83 electricity, 138, 149, 150 elongation, 111, 132 e-mail, 1, 155 employment, 148, 149 endangered species, x, 142, 153, 191, 194, 196, 197 energy, 4, 13, 49 engineering, 48 environment, iv, vii, ix, 1, 2, 4, 12, 13, 23, 29, 31, 36, 37, 44, 47, 48, 49, 55, 58, 74, 80, 114, 125, 126, 128, 131, 132, 134, 141, 142, 155, 157, 158, 162, 164, 172, 194, 201 environmental change, vii, 1, 3, 19, 44, 192 environmental characteristics, 8, 91 environmental conditions, 4, 5, 11, 13, 19, 30, 43, 50, 54, 102, 113, 156 environmental control, 111 environmental economics, 44 environmental factors, 10, 40, 87, 102, 107, 110, 119, 127 environmental protection, 74, 80 environmental stress, 36 environmental variables, 4, 37, 38, 43, 44, 116 equipment, 48, 55, 57, 67, 68 erosion, vii, 5, 47, 48, 49, 79, 80, 81, 83, 141, 176 estimating, 203 EU, 5, 129, 144, 149

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Index

208

Europe, vii, 1, 3, 5, 6, 16, 39, 42, 86, 106, 115, 117, 123, 125, 126, 128, 129, 130, 136, 148, 153, 157, 165, 169, 171 European Commission, 37 evaporation, 74, 77, 126 evapotranspiration, 74, 76, 77 evidence, 150, 162, 165, 172 evolution, ix, 139, 159, 161, 162, 164, 165, 173, 174, 176, 181, 182, 184, 185, 186, 201, 202 expenditures, 144, 149, 150 experimental condition, 111 exploitation, 140 exposure, 118, 157 external shocks, 139 extinction, vii, 1, 22, 37, 136, 139, 198 extraction, 73

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F facies, 181, 187 faith, 118 families, 5, 54, 60 farmers, 147 Faroe Islands, 126 fauna, vii, 1, 4, 5, 6, 13, 15, 16, 17, 18, 19, 22, 23, 26, 27, 31, 32, 34, 36, 37, 39, 40, 45, 136 federal government, 149 fertility, 133 fiber, 82 fibers, 67 field tests, 72 filters, 37 fingerprints, 37 Finland, 5, 41, 42, 107, 123, 125, 127 fires, 159 fish, 4 flexibility, 142 flora, 26, 92, 136, 192, 203 flow, 201, 203 flowers, 87, 109 fluctuations, 3, 11, 13, 30 focusing, 194 food, 2, 4, 13, 19, 29, 30, 31, 36, 37, 139 food chain, 4 food web, 30, 37 forbs, 91, 133 force, 55, 58, 66, 68, 146, 148 forest ecosystem, 137, 145 formation, 120, 132, 161, 176 formula, 74 fractures, 52 fragility, 126 France, ix, 136, 147, 152, 153, 154, 155, 156, 169

freezing, 36, 88, 104, 105, 106, 111, 118, 119, 120, 127 freshwater, 41, 43, 44, 161 friction, 53, 55, 56, 57, 58, 63, 65, 66, 67 frost, 88, 102, 104, 105, 107, 108, 111, 118, 123, 125, 128 funding, ix, 135, 138, 141, 142, 144, 149, 150

G GDP, 148 gene, 194, 196, 198, 201, 203 genetic diversity, vii, x, 191, 192, 193, 194, 197, 198, 200, 201, 202, 203 genetics, x, 191, 201 geography, 133, 193 geology, vii, 4, 6 geometry, 181, 182 Germany, 40, 136, 147, 152, 153, 154, 177 germination, 63, 64, 79, 87, 133 glaciers, 202, 203 global climate change, 4, 45 global warming, x, 36, 86, 117, 191, 192, 203 Global Warming, 191 goods and services, 141 governments, 149 GPS, 91 gracilis, 25 grain size, 52, 56, 57, 77, 78 graph, 17 grass, viii, 47, 49, 50, 54, 59, 60, 61, 63, 68, 69, 72, 73, 74, 78, 79, 80, 81, 121 grasslands, 106, 139 gravity, 49 groundwater, 73 grouping, 26 growing season length (GSL), viii, 85, 99, 100, 101, 120 growth, viii, ix, 5, 29, 30, 54, 61, 63, 78, 79, 80, 82, 85, 86, 87, 88, 90, 92, 93, 98, 102, 104, 105, 106, 108, 109, 112, 113, 115, 116, 117, 118, 119, 124, 125, 128, 129, 130, 131, 132, 133, 139, 155 growth rate, 106 guidelines, 147

H habitat, 13, 23, 36, 40, 44, 127, 136, 138, 139, 140, 141, 142, 143, 192, 199 habitat quality, 141 habitats, 3, 22, 23, 35, 40, 86, 108, 130, 142, 150, 198, 199, 201

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Index haplotype, 194 harvesting, 192 health, 141 height, 51, 54, 77, 113, 114, 115, 131, 157, 159 hemisphere, 125 herbaceous vegetation, viii, 47 herbs, 194, 197 heterogeneity, 4, 17 heterozygosity, 197 hiking trails, 140, 146 history, 2, 165, 168, 169, 172, 176, 180, 185, 197, 202 Holocene, v, ix, 128, 155, 156, 157, 158, 159, 160, 161, 165, 169, 172 host, 136 hotspots, 147, 192, 193, 203 human, ix, 2, 3, 4, 6, 8, 48, 136, 137, 139, 141, 142, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 168 human activity, 2, 161, 163 human perception, 141 humidity, 2, 76, 126, 157, 163 Hungary, 173, 186, 187, 188, 189 hunting, 140 hypothesis, 72 hysteresis, 36

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I Iceland, 41, 133, 184 ideal, 3 identification, 9 images, 87, 110, 120 imitation, 132 in situ, 198, 201 incidence, 17, 26 income, 149 India, 192 indication, 201 indirect effect, 3 individuals, 30, 127, 194, 197 individuation, 73 industry, 148 inflation, 146 infrastructure, 140, 141, 142, 145, 146, 147, 148, 149 initiation, viii, 85, 103, 106, 108, 110, 119, 187 initiation time, 110 insects, vii, 1, 2, 5, 6, 9, 13, 15, 16, 17, 19, 20, 21, 23, 26, 28, 29, 31, 33, 35, 36, 40, 43, 44 insulation, 119 integration, 87, 154 integrity, 151, 158 interface, 67

209

interference, 38, 156, 159 intervention, 138 invasive tree species, ix, 155 invertebrates, 4, 9, 39, 40, 41, 44 iron, 158 irrigation, 73, 83 islands, 181 isolation, x, 191, 198 isozyme, 193, 200, 202 issues, 36, 146 Italy, viii, 40, 43, 44, 47, 50, 80, 82, 136, 145, 147, 148, 151, 153, 169, 186, 187, 188

J Japan, 120, 121, 129 Julian day, viii, 85, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 106, 109, 111, 116 juveniles, 9, 31

K karyotype, 193, 204 Kola Peninsula, 129 Kyrgyzstan, 192

L labor force, 148 laboratory tests, viii, 47, 49, 67, 72, 81 lakes, vii, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 19, 21, 22, 23, 26, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 156 land, 199 landscape, viii, 23, 37, 47, 51, 87, 110, 140, 145, 146, 157, 158, 159, 162, 164, 165, 199, 201, 202 landscapes, 2, 89, 145, 146, 150, 157, 192 large-scale, 193 larvae, 9, 15, 24, 29, 30, 31, 34, 35, 36, 40 larval development, 5 Late Pleistocene, 158 lateral roots, 53 layering, 80 lead, 3, 22 leisure, 140, 151 lichen, 98, 109 LIFE, 144, 149 life cycle, vii, 2, 5, 15, 31, 33, 35, 86, 88, 109, 125 light, 2, 3, 13, 16, 19, 25, 144, 157, 174 light conditions, 13 limestone, 180 linear, 197

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Index

210 livestock, 158, 192 living conditions, 142 local community, 154 local conditions, 158 loci, 202 Lolium perenne, viii, 47, 49, 54, 61, 62, 63, 64, 68, 72, 74, 78, 79 London, 202 longevity, 197 low temperatures, 88, 104, 108, 111, 113 Luo, 203 Luxemburg, 46

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M macroalgae, 26 macroinvertebrate fauna, vii, 1, 4, 6, 13, 19 magnitude, 35, 79, 107, 139 maintenance, 192 major issues, 36 majority, 2, 6, 16, 110, 181 man, 152, 168 management, viii, ix, 37, 47, 80, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 147, 149, 150, 151, 152, 153, 156, 201 mantle, x, 174, 176, 182, 184, 185 mapping, 128 marginal costs, 141 marketing, 147, 148, 149 marsh, 160, 161, 163 mass, 10, 19, 29, 31, 49, 114, 161 materials, 52, 73 matrix, 67, 73 matter, 3, 4, 5, 6, 8, 10, 12, 23, 29, 40, 46, 52, 77, 131, 163 measurement, 9, 11, 80, 83, 113 measurements, 10, 11, 49, 72, 73, 74, 83, 87, 90, 93, 105, 114, 125, 126 mechanical properties, 79 media, 83 medical, 192, 193, 194, 196, 198 medical plant, 192, 193, 194, 198 Mediterranean, 81, 82, 116, 156, 157, 162, 164, 187, 188 melt, viii, 85, 86, 87, 88, 91, 93, 95, 97, 98, 99, 100, 101, 102, 103, 105, 107, 108, 112, 113, 115, 116, 118, 119, 120, 121, 124, 134 melting, 6, 103, 107, 110 melts, 115 merchandise, 144, 149 mercury, 10 metabolism, 5, 104 metallurgy, 157, 159

metals, 3 methodology, vii, 47, 48, 67, 68, 79 micro-charcoal, ix, 155 microclimate, 108 microhabitats, 4 microorganisms, 134 microscope, 9 Middle East, 204 Middle Triassic evolution, ix, 173, 176, 181 Middle-Upper Triassic terrestrial sediments, ix, 173 migration, 2, 199 mineralization, 132, 185 Ministry of Education, 202 Miocene, 174 mitochondrial, 194 mitochondrial DNA, 194 modelling, 104, 118, 125, 134, 177 models, viii, 10, 47, 65, 67, 72, 79, 80, 81, 86, 103, 110, 118, 127, 145, 185, 186, 198 modifications, 165 MODIS, 126, 128, 133 moisture, 49, 50, 56, 59, 60, 61, 63, 73, 74, 76, 77, 78, 79, 80, 82, 103, 107 moisture content, 74 molecular markers, 201 morphology, 50 mosaic, 174 mountain environments, 3 mountains, 197, 201, 203 multiplier, 148 museums, 140

N National Academy of Sciences, 203 national parks, ix, 135, 136, 140, 144, 146, 147, 148, 149, 150, 152, 153 native species, 78 natural, 194, 201 natural resources, 153 nature conservation, ix, 135, 136, 143, 144, 146, 147, 149, 150 NCA, ix, 173, 177 negative relation, 115 Nepal, 192 Netherlands, 40, 42, 45 neutral, 73, 201 New Zealand, 66 nitrogen, 79, 128, 129, 132 no-rooted soil clods, viii, 47 North America, 117 North Sea, 184 Northeast, 196

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Index Northern Calcareous Alps (NCA), ix, 173, 177 North-West Italian territory, vii, 47 Norway, 5, 44, 45, 85, 89, 91, 107, 109, 116, 117, 120, 122, 123, 126, 127, 128, 130, 131, 132, 153 Norway spruce, 127, 132 nuclear, 203 nutrient, 3, 4, 5, 13, 38, 42, 106, 125 nutrient concentrations, 13 nutrients, 2, 16, 54, 130

O oceanic areas, 100, 104, 112, 116 oil, 49, 54, 55, 74, 80, 87, 107 openness, 19 opportunity costs, ix, 135, 136, 141, 145, 149, 150 organic matter, 3, 4, 5, 6, 8, 10, 12, 23, 29, 40, 46, 52 organic soils, 78 organochlorine compounds, 41 organs, 116 overexploitation, 198 overgrazing, 192 ownership, 136, 151 ox, 57 oxygen, 4, 13, 16, 23

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P Pakistan, 192 paleontology, 185 Pan-European scale, vii, 1 parallel, 156 parameter, 198 pastures, 157, 158, 165 peat, 159, 161, 162, 171 pellicle, 51 periodicity, 30 permeability, 49 permit, 164 pH, 8, 17, 52, 54, 74, 77, 78, 79 phosphate, 125 phosphorus, 78 photoperiodism, 129 photosynthesis, 106, 107, 111, 124, 132 phylogenetic, 192 physical characteristics, 72, 77 physical environment, 114 Physiological, 130 physiology, 130, 133 piano, 153 plankton, 41

211

plant growth, viii, 85, 87, 104, 105, 106, 108, 112, 113, 115, 129, 131 plants, vii, viii, x, 52, 54, 59, 73, 74, 85, 86, 87, 88, 91, 98, 102, 104, 105, 106, 107, 108, 109, 110, 111, 114, 115, 118, 125, 128, 129, 130, 131, 133, 134, 141, 146, 149, 157, 162, 191, 192, 193, 194, 196, 197, 198, 199, 200, 201, 203 plasticity, 52 platform, ix, 173, 176, 177, 180, 181, 183, 184, 187, 189 playing, 103 Poaceae family, viii, 47, 49, 54, 63, 69 Poland, 40, 42, 43, 44 polar, 87, 106, 116, 134, 192 policy, 136, 137, 142, 144, 146, 153 policy makers, 136 politics, 154 pollen, 159, 162, 163, 164, 172, 201 pollutants, 3, 46 pollution, vii, 1, 3, 36 ponds, 6, 37, 44 pools, 6 poor, 194 population, vii, x, 37, 43, 136, 139, 142, 144, 148, 150, 191, 192, 194, 197, 198, 201, 203 population size, 139, 142, 197, 198 population structure, vii, x, 191, 192, 201, 203 porosity, 74 positive correlation, 11, 146 potassium, 78, 79 power plants, 141, 146, 149 precipitation, 2, 6, 49, 75, 90, 93, 110, 113, 117, 118, 127, 192 predators, 136, 139, 152 prediction, 201 preparation, iv, 37 preservation, 153 press, 202 private enterprises, 140 probability, 49 probe, 73, 74 project, 4, 6, 8, 37, 46, 117, 118, 144, 149, 151, 159, 185 protected areas, 136, 141, 144, 146, 147, 149, 150, 153 protection, 29, 74, 80, 142, 150, 152, 154 public goods, 146, 154 public support, 136, 137, 138, 142, 149, 150, 151

Q Qinghai-Tibetan Plateau, v, x, 191, 192, 193, 194, 195, 197, 198, 199, 200, 201, 202, 204

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Index

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212

R

S

radiation, 2, 3, 9, 13, 30, 103, 192 rainfall, 49, 54, 73, 74, 75, 76, 77, 83 range, 193, 196, 199 RAPD, 194, 196, 201, 203, 204 reality, 49 recommendations, iv reconstruction, 39, 43, 158, 159, 174 recovery, 6, 36, 158, 186 recreation, 140, 141, 144, 145, 147, 149, 150, 151, 152 Red Sea, 185, 186 redistribution, 67 regional economic impacts, 147, 149 regional economies, 148 regression, 69 reinforcement, viii, 47, 49, 53, 54, 56, 67, 72, 79, 80, 81, 82 reintroduction, 136 relationship, 192 relatives, 192, 193, 194, 196 relevance, 149 relief, 2, 86, 156, 157 remediation, 49 Remote alpine lakes, vii, 1 reproduction, 4, 5, 86, 130, 139 researchers, 176 reserves, 136, 146, 147 resilience, 140 resistance, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 67, 78, 79, 81, 83, 105, 118, 132, 199, 203 resolution, 39, 67, 68, 126 resource availability, 30 resources, 19, 29, 46, 105, 110, 146, 153, 193 respiration, 87, 106, 108, 111, 127 response, 3, 16, 19, 30, 38, 43, 45, 87, 111, 125, 133, 134, 159, 168, 199, 203 restrictions, 141 risk, 118, 125, 192, 198 root, 49, 50, 53, 54, 55, 56, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 77, 78, 79, 80, 81, 82, 83, 106, 111, 124, 129, 132 root growth, 79, 80, 106, 124 root system, 49, 53, 54, 67, 77, 79, 81, 82 roots, viii, 47, 49, 52, 53, 54, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 116, 131 roughness, 49 rowing, 52, 53, 74, 98, 110 runoff, 3, 46 rural development, 152 Russia, 129

salinity, 78, 81 sample, 192 sampling, 194 samplings, 4, 23, 75 Sarajevo, 188 saturation, 55, 58, 74, 75, 76, 77 Scandinavia, 92, 117, 118, 123, 124, 131, 132 science, 41 scientific investigations, 193 scientific papers, 146 scope, 192 sea level, ix, 136, 139, 155, 156, 157, 158, 159, 162, 172, 177, 184 sea-level, 177 seasonal changes, 30 seasonal dynamics, vii, 2, 5, 6, 31, 33, 34 seasonality, 31, 35, 36, 39, 134 sediment, 9, 39, 49, 177 sedimentation, 49, 181, 185, 187 sediments, ix, 4, 6, 23, 29, 39, 45, 156, 173, 174, 176, 177, 180, 181, 182, 184, 188 seed, 87, 102, 133 seeding, 56, 58, 63, 64, 65, 73, 79 seedlings, 128 seeds, 201 senescence, 108, 109, 110, 111, 112, 121, 124, 130, 133 sensing, 125 sensitivity, 3, 30, 40, 45, 108, 127, 142 sensors, 9 sequencing, 194 services, 141, 145, 146, 150, 153 settlements, 163 shade, 157 shallow lakes, 39 Shanghai, 191, 202 shape, ix, 173, 176, 181, 184 shaping, 201 shear, viii, 47, 49, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 72, 78, 79, 80, 81, 82, 83, 185, 189 shear strength, viii, 47, 49, 55, 56, 59, 60, 61, 62, 63, 64, 65, 67, 78, 79, 80, 81, 82 sheep, 157 shelter, 158 shoot, 129 shoots, 79, 105 showing, 114, 165 shrubs, 81, 92, 98 signals, 39 signs, ix, 36, 155 simulation, 82, 124

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Index simulations, 67, 117, 134 Slovakia, v, vii, 1, 6, 38, 39, 41, 42, 43, 44, 46 smoothing, 93, 94 social change, 162 social status, 152 society, 141 software, 10 soil erosion, vii, 47, 49, 83 soil type, 50 solution, 67 Soviet Union, 128 Spain, v, ix, 39, 121, 131, 155, 156, 162, 168 spatial, x, 191, 198, 199, 201, 202 specialists, 23, 37 species richness, 5, 19, 22, 33, 35, 37, 43 Spring, 2, 58 stability, vii, 23, 36, 47, 49, 50, 72, 73, 80, 81, 82, 117 stabilization, 81 stages, 193 stakeholders, ix, 135, 144, 149, 151 standard deviation, 10, 12, 22, 30, 75 state, 139, 140, 141, 143, 176, 192 states, 185 statistical analysis, 197 statistics, 148 steel, 55, 68 stereomicroscope, 9 strategies, 194 stress, 36, 56, 65, 67, 130, 142, 181 stretching, 136 structure, vii, x, 3, 4, 5, 12, 16, 26, 43, 68, 72, 73, 78, 81, 114, 126, 136, 191, 192, 194, 197, 198, 199, 201, 202, 203 Styria, 188 substrate, 8, 9, 13, 16, 17, 23, 26, 29, 90, 157 substrates, 9 succession, 157, 175, 176, 188 Sun, 193 superficial erosion, vii, 47 surface area, 8 survival, 15, 116, 126, 129, 139, 192, 202 susceptibility, viii, 48, 49, 79 sustainability, 152 sustainable development, 153 Sweden, 116, 117, 120, 121, 122, 123, 124, 126, 129, 133 Switzerland, 46, 133, 134, 136, 147, 148, 152, 153, 154 sympathy, 141 synthesis, 82

213

T Tajikistan, 192 Tanzania, 126 target, 42, 142, 143, 144, 147 taxa, 3, 5, 10, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 29, 30, 31, 33, 35, 36, 37, 108, 127, 132, 160, 162, 163, 165 taxes, 141, 144, 145 taxonomic composition, vii, 1, 5, 6, 17, 33 techniques, viii, 48, 49, 80, 194 temperature, vii, viii, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 16, 17, 19, 22, 23, 26, 35, 36, 38, 42, 43, 54, 73, 74, 75, 76, 77, 85, 86, 87, 90, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134 tensile strength, 49, 61, 66, 67, 68, 69, 70, 71, 72, 79, 80, 81, 82 tension, 66, 67 tensions, 55, 58 territory, vii, 47, 48 testing, 58, 67, 80 texture, 49, 73 The Pyrenees, ix, 155, 156 thermophilic species, vii, 1, 22 threatened, 192 Tibet, 191, 192, 193, 196, 198, 199, 201, 202, 203, 204 time series, 126, 148 tissue, 124 tourism, ix, 135, 136, 137, 138, 140, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153 trace elements, 184 trade, 137 trade-off, 137 traits, 129, 201, 202 transgression, 159 transmission, 83 transparency, 2, 36 transpiration, 79, 124 transport, 3, 23, 45, 80, 82 trees, 198 tundra, 8, 87, 111, 117, 124, 125, 130, 132, 133, 134

U UNESCO, 136, 147, 154 uniform, 16, 17, 53, 57, 58, 73 United, 131, 152, 153, 154

Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science

Index

214

United Kingdom (UK), 41, 44, 45, 46, 152, 153, 154, 191 United States (USA), 41, 44, 121, 131, 152, 153, 203 USDA, 52 USSR, 134 UV, 2

V

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validation, 56 valuation, ix, 135, 136, 141, 144, 145, 146, 150, 152, 153 values, 197, 198 variability, 192, 197 variables, vii, 1, 4, 6, 16, 22, 37, 38, 43, 44, 107, 116, 140 variance, 201 variation, x, 13, 30, 35, 66, 73, 89, 95, 97, 103, 105, 107, 112, 113, 116, 124, 125, 130, 132, 157, 191, 192, 194, 197, 199, 201, 202, 203, 204 varieties, 194 vegetation, viii, ix, 2, 4, 8, 12, 42, 47, 49, 51, 52, 53, 66, 72, 73, 80, 81, 82, 85, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 118, 119, 120, 125, 126, 127, 128, 130, 131, 132, 133, 134, 155, 157, 159, 161, 162, 163, 164, 165, 168, 169, 197, 199, 203 vulnerability, 3

W

Washington, 126, 132, 153 wastewater, 3 water, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 16, 19, 23, 26, 35, 36, 38, 40, 43, 45, 46, 49, 51, 52, 53, 54, 67, 73, 74, 76, 77, 78, 80, 81, 82, 83, 87, 124, 132, 134, 139, 141, 145, 150, 157, 163, 180, 181 water absorption, 53, 81 water chemistry, 5, 8, 16, 45 water quality, 4, 49 water resources, 46 water supplies, 2 watershed, 37, 133 web, 2, 30 weight loss, 10 Western Carpathians (WNC), ix, 173, 177 wetlands, 91 wetting, 82 wildlife, 140, 141, 142, 151, 152 wildlife conservation, 152 wood, 58, 59, 137 woodland, 156, 157, 158, 159, 162 worldwide, 49, 106, 129

Y Yugoslavia, 188

Z zoogeographical aspect, vii, 5 zooplankton, 45

Wales, 83

Alpine Environment: Geology, Ecology and Conservation : Geology, Ecology and Conservation, edited by John G. Schmidt, Nova Science