I the Changing Structure and Function of Arthropod Food Webs in a Warming Arctic by Amanda

The Changing Structure and Function of Arthropod Food Webs

in a Warming Arctic

Amanda May Koltz

University Program in Ecology Duke University

Date:______Approved:

______Justin Wright, Supervisor

______William Morris, Chair

______Dean Urban

______Robert Dunn

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Program in Ecology in the Graduate School of Duke University

2015

ABSTRACT

The Changing Structure and Function of Arthropod Food Webs

in a Warming Arctic

Amanda May Koltz

University Program in Ecology Duke University

Date:______Approved:

______Justin Wright, Supervisor

______William Morris, Chair

______Dean Urban

______Robert Dunn

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Program in Ecology in the Graduate School of Duke University

2015

Abstract

Environmental changes, such as climate change, can have differential effects on species, with important consequences for community structure and ultimately, for ecosystem functioning. In the Arctic, where ecosystems are experiencing warming at twice the rate as elsewhere, these effects are expected to be particularly strong. A proper characterization of the link between warming and biotic interactions in these particular communities is of global importance because the tundra’s permafrost stores a vast amount of carbon that could be released through decomposition as greenhouse gases and alter the global rate of climate change. In this dissertation, I examine how arthropod communities are responding to warming in the Arctic and how these responses might be affecting ecosystem functioning.

I first address the question of whether and how long-term changes in climate are affecting individual groups and overall community structure in a high-arctic arthropod food web. I find that increasingly warm springs and summers between 1996-2011 differentially affected some arthropod groups and that this led to major changes in the relative abundances of different trophic groups within the arthropod community.

Specifically, spring and summer warming are associated with higher absolute abundances of herbivores and parasitoids and lower relative abundances of detritivores within the community. These changes are particularly pronounced in heath sites,

suggesting that arthropod communities in dry habitats are more responsive to climate change than those in wet habitats. I also show that herbivores and parasitoids are sensitive to conditions at subzero temperatures, even during periods of diapause, and that all trophic groups benefit from a longer transition period between summer and winter. These results suggest that the projected winter and springtime warming in

Greenland may have unexpected consequences for northern arthropod communities.

Moreover, the relative increase in herbivores and loss of detritivores may be changing the influence of the arthropod community over key ecosystem processes such as decomposition, nutrient cycling, and primary productivity in the tundra.

Predator-induced trophic cascades have been shown to impact both community structure and ecosystem processes, yet it is unclear how climate change may exacerbate or dampen predator effects on ecosystems. In the second chapter of my dissertation, I investigate the role of one of the dominant tundra predators within the arctic ecosystem, wolf spiders, and how their impact might be changing with warming. Using results from a two-year-long field experiment, I test the influence of wolf spider density over the structure of soil microarthropod communities and decomposition rates under both ambient and artificially warmed temperatures. I find that predator effects on soil microarthropods change in response to warming and that these changes translate into context-specific indirect effects of predators on decomposition. Specifically, while high densities of wolf spiders lead to faster decomposition rates at ambient temperatures,

they are associated with slower decomposition rates in experimentally warmed plots.

My results suggest that if warming causes an increase in arctic wolf spider densities, these spiders may buffer the rate at which the massive pool of stored carbon is lost from the tundra.

Wolf spiders in the Arctic are expected to become larger with warming, but it is unclear how this change in body size will affect spider populations or the role of wolf spiders within arctic food webs. In the third chapter of my dissertation, I explore wolf spider population structure and juvenile recruitment at three sites of the Alaskan Arctic that naturally differ in mean spider body size. I find that there are fewer juveniles in sites where female body sizes are larger and that this pattern is likely driven by a size- related increase in the rate of intraspecific cannibalism. These findings suggest that across the tundra landscape, there is substantial variation in the population structure and trophic position of wolf spiders, which is driven by differences in female spider body sizes.

Overall, this dissertation demonstrates that arctic arthropod communities are changing as a result of warming. In the long-term, warming is causing a shift in arthropod community structure that is likely altering the functional role of these animals within the ecosystem. However even in the short-term, warming can alter species interactions and community structure, with important consequences for ecosystem function. Arthropods are not typically considered to be major players in arctic

ecosystems, but I provide evidence that this assumption should be questioned.

Considering that they are the largest source of animal biomass across much of the tundra, it is likely that their activities have important consequences for regional and global carbon dynamics.

vii

Dedication

This dissertation is dedicated to my parents, stepparents, and husband.

viii

Contents

Abstract ...... iv

List of Tables ...... xii

List of Figures ...... xiv

Acknowledgements ...... xvi

1. Introduction ...... 1

1.1 Species and community responses to climate change ...... 1

1.2 Ecological consequences of climate change in the Arctic ...... 1

1.3 Arthropods as a model system for understanding effects of climate change on biological communities ...... 3

1.4 Objectives ...... 5

2. Differential response to climate change among arthropods is altering the structure of arctic communities ...... 7

2.1 Introduction ...... 7

2.2 Methods ...... 11

2.2.1 Study sites ...... 11

2.2.2 Climate data ...... 11

2.2.3 Arthropod data collection ...... 12

2.2.4 Analyses ...... 15

2.3 Results ...... 16

2.3.1 Climate change over the study period ...... 16

2.3.2 Links between arthropod abundances and environmental predictors ...... 18

2.3.3 Links between variability in community structure and environmental predictors ...... 18

2.4 Discussion ...... 25

3. Predator effects on a soil food web can buffer carbon losses in a warmer Arctic ...... 31

3.1 Introduction ...... 31

3.2 Methods ...... 34

3.2.1 Experimental design ...... 34

3.2.2 Measures of community structure and function ...... 36

3.2.2.1 Sampling the surface and belowground community ...... 37

3.2.2.2 Decomposition ...... 37

3.2.2.3 Soil moisture ...... 38

3.2.3 Statistical analyses ...... 38

3.3 Results ...... 39

3.3.1 Effects of wolf spider density and temperature on microarthropods ...... 39

3.3.2 Effects of wolf spider density and temperature on decomposition ...... 41

3.4 Discussion ...... 44

4. Larger female body sizes lead to increased cannibalism rather than higher recruitment in arctic wolf spiders ...... 50

4.1 Summary ...... 50

4.2 Results and discussion ...... 52

4.3 Methods ...... 61

4.3.1 Field sampling ...... 61

4.3.2 Sample processing ...... 63

4.3.3 Stable isotopes ...... 63

4.3.4 Analyses ...... 64

Conclusions ...... 67

Appendix A ...... 71

Comparison of wolf vs. wolf spider biomass ...... 71

Calculation of wolf spider biomass/km2 ...... 71

Calculation of grey wolf biomass/km2 ...... 72

References ...... 73

Biography ...... 92

List of Tables

Table 1. Results of principal components analysis of climate variables with varimax rotation. Main contributors for each principal component (>0.50) are in bold. Summer and spring measures are for year t, winter measures are Oct.-Dec. in year t-1 and Jan.- Apr. in year t, and fall measures are from year t-1. Values for cumulative frost during the previous fall were square root transformed prior to analyses; Cumulative degree days include temperatures from January-August of study year...... 17

Table 2. Results of mixed effects models of summertime abundances of each of the most common arthropod Orders at Zackenberg as predicted by our principal components and habitat type. Fixed effects are habitat type and the four principal components; Plot and Year were included as random effects. We generally considered Acari and Collembola as detritivores, Hemiptera and Lepidoptera as herbivores, Araneae as predators, Hymenoptera as parasitoids, and Diptera as mixed feeders. Acari, Hemiptera, Hymenoptera, and Lepidoptera abundances were log transformed; Collembola abundances were square root transformed...... 23

Table 3: Results of mixed effects models showing the interactive effects of wolf spider density and experimental warming on surface-active and belowground detritivores (Collembola and Oribatid mites). Microarthropod densities were log transformed according to Osborne (2002), and degrees of freedom were 21 for all models...... 41

Table 4: Results of mixed effects model showing the interactive effects of wolf spider density and experimental warming on decomposition (arcsine square root transformed proportion of litter mass loss) at the soil surface and belowground. Degrees of freedom were 19 for both models...... 43

Table 5. Results of linear mixed effects models predicting A) total number of spiders / m2, B) total number of juveniles, and C) total number of juveniles at Toolik and Imnavait. Female size is the average female body size per sampling grid. Site was included as a random effect in all models, and the numbers of juvenile densities were log transformed. The number of adult female spiders was a non-significant predictor in each model...... 53

Table 6. Relative abundances of the different wolf spider species at each of the three study sites, based on samples of adult females...... 53

Table 7. Stable isotope signatures, number of replicates, life stages, and species names of wolf spiders and Collembola (means ± SE) from the three study sites. Wolf spider levels

xii

of δ13C and δ15N are corrected by the mean Collembola measures from the site where they were collected. Collembola were not identified past Subclass...... 57

Table 8. Day of year (DOY) of sampling dates and the start and end of the 2012 growing season at each of the study sites...... 62

xiii

List of Figures

Figure 1. Summertime abundances of herbivores (Hemiptera, Lepidoptera), and parasitoids (Hymenoptera) as predicted by the second and third principal components from our PCA (colder winter and spring temperatures; warmer summer and fall temperatures)...... 19

Figure 2. Summertime abundances of detritivores (Acari and Collembola), mixed- feeding flies (Diptera) and predators (Araneae) as predicted by the second and third principal components from our PCA (colder winter and spring temperatures; warmer summer and fall temperatures). Relationships were not significant for any arthropod groups plotted here...... 20

Figure 3. NMDS ordination of annual arthropod communities from Zackenberg, Greenland between 1996-2011. Communities are color-coded by habitat type: Wet fen communities are in green, arid heath in black, and mesic heath in gray. These habitat- specific communities are delimited by 95% confidence ellipses with the same colors. Panel a) Significant climate principal component predictors are overlaid as correlation vectors, whereby the arrow shows the direction of the gradient, and the length of the arrow is proportional to the correlation between the climatic variable and the ordination (WarmSum = PC3: Warmer summer and fall temperatures; Cold Winter = PC2: Colder winter and spring temperatures). Abbreviations in black denote the centroids of each of the analyzed arthropod groups (Col=Collembola, Aca=Acari, Dip=Diptera, Lep=Lepidoptera, Ara=Araneae, Hem=Hemiptera, and Hym=Hymenoptera). Panel b) Arrows indicate the change in average NMDS scores for communities in each habitat type between 1996-2001 and 2005-2011...... 22

Figure 4: Experimental study site in the northern foothills of the Brooks Range of Alaska (site labeled as Toolik Field Station). The study site is at 68°38’N and 149°43’W, sits at an elevation of 760 m, and is 240 km north of the Arctic Circle. Map by Toolik GIS...... 35

Figure 5. Effects of wolf spider density and temperature on Collembola. Points are mean treatment effects; Error bars are standard errors. Blue lines are from ambient temperature plots, and red lines are from experimentally heated plots. Solid lines represent a significant interaction between the treatments...... 40

xiv

represent a significant interaction between the treatments. Decomposition is reported as proportional litter mass loss and has been arcsine square root transformed...... 42

Figure 7. Hypothesized effects of high wolf spider densities on Collembola and litter at the soil surface and belowground under A) ambient and B) artificially warmed temperatures. Solid lines are the potential direct effects and dotted lines are the hypothesized indirect effects. Indirect effects on litter indicate rate of decomposition (positive symbol=faster decomposition)...... 45

Figure 8. Study sites near Toolik Lake (68.6175 N, 149.6033 W; elevation 745 m), Atigun Gorge (68.4530 N, 149.3647 W; elevation 808 m), and Imnavait Creek (68.6202 N, 149.3407 W; elevation 853 m) on the North Slope of Alaska. Map by Toolik GIS...... 51

Figure 9. Juvenile wolf spider density as predicted by A) the density, and B) the average body size of adult female P. lapponica. Data shown here are from sampling grids at Toolik and Imnavait...... 54

Figure 10. Mean ± SE of δ13C and δ15N values for A) adult female P. lapponica from all three study sites and B) juveniles from Toolik and Imnavait. Isotope values were standardized by site-specific mean values of Collembola. The dendrograms and asterisks denote significant differences in δ13C and δ15N, as determined by one-way ANOVA and a Tukey HSD post-hoc tests...... 56

Acknowledgements

This project would not have been possible without the help of my field and lab assistants. My deepest thanks to all of Team Spider -- Kiki Contreras, Samantha Walker,

Sarah Meierotto, Nick LaFave, and Nell Kemp -- for the many hours you devoted to this research and for your wonderful company. Thank you also to Gabriel Sneed for lab assistance; Greg Selby and Rod Simpson for all your guidance in field and lab techniques; and Jon Karr for carrying out the stable isotope analyses.

I would like to gratefully acknowledge my adviser Justin Wright for taking a chance and giving me the freedom to explore my interests. I thank Rob Dunn for exposing me to the world of tiny animals and for his uncanny sense of knowing when I needed encouragement. I also acknowledge Bill Morris for his very thorough manuscript reviews, which improved this research and to Dean Urban for introducing me to multivariate statistics. I’m grateful to my lab mates, Marissa Lee, Aspen Reese,

Cari Ficken, Greg Ames, Rachel Mitchell, Jenny Wang, and Bonnie McGill, who carefully read my grant applications and chapter drafts and provided valuable feedback. In addition, I’d like to acknowledge the support staff for the Duke Department of Biology and Program in Ecology and in particular, Jim Tunney, Meg Stephens, and Anne Lacey.

Thank you to my collaborators Drs. Toke T. Høye and Niels M. Schmidt and all the field and museum staff at Zackenberg Research Station and the Department of

xvi

Bioscience at Aarhus University, Denmark, who collected and identified the hundreds of arthropod samples that provided the data for Chapter 2.

My time at the Toolik Field Station was an important part of my graduate experience, and I’d like to thank all my friends and colleagues from Toolik for providing such a wonderful intellectual and supportive community. In particular, several PIs at

Toolik Field Station have been instrumental in my development as a scientist, including

Laura Gough, Natalie Boelman, Jennie McLaren and John Moore. Gus Shaver and the

Terrestrial LTER provided some of the initial funding that allowed me to start my work at Toolik. Special thanks to Ashley Asmus for the many hours of bug discussions and to

Jason Stuckey for his assistance every summer and for his truck and cabin loans. Thom

Walker, Chad Diesinger, Jeb Timm and Brett Biebuyck provided invaluable science support.

This work could not have been accomplished without funding from the National

Science Foundation, the National Parks Service, the National Geographic Society

Committee for Research and Exploration, Conservation Research and Education

Opportunities International (CREOi), Aarhus University in Denmark, the Earth and

Space Foundation, the North Carolina Wildlife Federation, the Kappa Delta Foundation

(with special thanks to Winifred Hill Boyd), the Arctic Institute of North America, The

Explorers Club, the Lewis and Clark Fund through the American Philosophical Society,

Duke University, and the Arctic Long-Term Ecological Research Site. I would also like to

xvii

thank Katherine Goodman Stern, who sponsored a Duke dissertation writing fellowship that I was extremely lucky and grateful to receive.

Finally, I am grateful to all my friends and loved ones, especially my parents, brothers, and Devin Bridgen. Most of all, I’d like to thank Carlos Botero for his love, support, patience, and jungle stories.

xviii

1. Introduction

1.1 Species and community responses to climate change

Environmental change alters the structure and functioning of terrestrial ecosystems (Chapin et al. 1997). For example, a large number of studies have documented individual and population-level responses to altered climatic conditions

(Root et al. 2003, Walther 2004, Post et al. 2009, Gilg et al. 2012) and have shown that species-specific responses to climate change can have cascading effects through food webs (Tylianakis et al. 2008, Walther 2010, Gilbert et al. 2014). Despite general agreement that species interactions across trophic levels will be affected by climate change, only a handful of studies have explored how this phenomenon might ultimately affect ecosystem functioning (e.g., reviewed in Schmitz 2013). Because community-scale dynamics influence important ecosystem processes such as primary production, nutrient cycling, and decomposition (Moore et al. 1988, Wardle 2002, Chapin III 2012), this gap in our knowledge can drastically impair our ability to predict the overall effects of climate change.

1.2 Ecological consequences of climate change in the Arctic

Understanding the effects of climate change on community and ecosystem processes is especially critical in the Arctic, the region experiencing the fastest warming of any biome on the planet (Serreze et al. 2000, ACIA 2005, Hinzman et al. 2005,

Trenberth 2007). This is particularly concerning, because the warmer temperatures and longer growing seasons in the Arctic may result in increased rates of microbially- mediated decomposition of the vast amounts of carbon stored in northern permafrost soils (Schuur et al. 2009). Increases in decomposition would lead to greater releases of greenhouse gases, such as carbon dioxide and methane, into the atmosphere and potentially accelerate the rate of global climate change (Schuur et al. 2009). Accordingly, work on the effects of arctic warming has focused on plant and microbial community responses to warmer temperatures and greater resource availability (due to deeper permafrost thaw, e.g., Davidson and Janssens (2006)). However, the extent to which these microbial responses may be affected by the responses of organisms at other levels in the food web has been largely overlooked. This broader view is important, because we know that the composition and activity of the microbial community can be modified by micro- and macro- invertebrates in the soil (Moore et al. 1988, Beare et al. 1992, de

Ruiter 2006, Lenoir et al. 2007) and that soil invertebrates themselves are regulated by generalist predators, such as spiders (Kajak 1993, Wise et al. 1999, Scheu 2001b). Yet it is currently unclear the extent to which responses to warming by species outside the producer and decomposer communities may cause fundamental changes in community dynamics that could affect key ecosystem processes in the Arctic.

1.3 Arthropods as a model system for understanding effects of climate change on biological communities

The work I present here addresses the response of arthropods to climate change in the Arctic, with a particular focus on generalist-feeding spiders. Arthropods are widely recognized for their utility in aiding the detection of ecosystem responses to environmental change given their abundance, diversity, short lifespans, functional importance in ecosystems, and sensitivity to environmental perturbation. Moreover, compared to more temperate regions, the diversity of arthropods in the Arctic is greatly reduced. Thus, the food webs are relatively simple and provide a tractable system for linking the effects of climate change on species, communities and ecosystem function.

In this thesis, I pay particular attention to generalist-feeding wolf spiders for several reasons. First, organisms at higher trophic levels are thought to be the most sensitive to changes in their habitat or environment (e.g., Petchey et al. 1999, Voigt et al.

2003), and thus, generalist predators may be particularly responsive to the warming occurring in the Arctic. A couple recent studies from high-arctic Greenland suggest this to be the case in at least two different spider species, whereby spider body sizes are growing larger in response to earlier springtime snowmelt (Høye et al. 2009, Bowden et al. 2014). Secondly, spiders – and wolf spiders in particular – are among the dominant arthropod groups (Wyant et al. 2011, Bolduc et al. 2013, Asmus et al. unpublished data) and sources of terrestrial animal biomass on the tundra (Appendix A). Furthermore, wolf spiders are a ubiquitous predator across the entire arctic biome (Dondale and

Redner 1990), which suggests that any influence they have over ecosystem processes could be relevant at the regional scale. Lastly, evidence from temperate systems indicates that generalist arthropod predators can be heavily linked to belowground food webs and can affect the distribution of biomass within these systems (i.e., the food web structure), sometimes even with cascading consequences for ecosystem functions like primary productivity, nutrient cycling, and decomposition (Wise et al. 1999, Scheu

2001a, Scheu 2001b). In the Alaskan Arctic, the biomass of wolf spiders exceeds the sum of all potential surface-dwelling herbivore or detritivore prey (Asmus et al., in prep,

Koltz et al., in prep). The unique structure of this arctic food web suggests that wolf spiders acquire their resources from a combination of the aboveground and belowground systems. Moreover, the large build-up of detrital matter in the tundra creates a habitat in which the boundary between the aboveground and belowground systems is less distinct and in which the effects of spider predation may be particularly strong (Kaspari and Yanoviak 2009). All of these factors suggest that if climate change alters the abundance or behavior of these key predators, there may be cascading consequences for arctic ecosystems. Within the literature, there are limited examples of ecosystem effects of warming-induced changes in predation pressure (e.g., Barton et al.

2009). However, especially in the short-term, warming can clearly enhance the top-down effects of aquatic and terrestrial predators (Barton and Schmitz 2009, O'Connor et al.

2009, Hoekman 2010, Rall et al. 2010), with the potential for affecting ecosystem-level

processes. Characterizing the ecological role of wolf spiders and whether warming will alter their influence over food web dynamics is an essential step in understanding the interplay between the biotic and abiotic consequences of climate change in arctic ecosystems.

1.4 Objectives

Understanding how organismal responses to climate change fit into a broader ecological context requires a multipronged approach in order to both evaluate overall trends and to identify the roles of key players within the community. This dissertation addresses the question of how communities in the arctic are responding to warming and how this might be affecting ecosystem functioning. I address these questions in three complimentary ways. First, using a 15-year data set from high-arctic Greenland, I investigate how long-term changes in climate patterns have affected individual groups and overall community structure in the arthropod food web (Chapter 1). Next, I explore the ecological role of wolf spiders in the Alaskan Arctic and how this role may be changing under warming. Specifically, I discuss results from a field experiment that tested the influence of wolf spiders over soil community structure and decomposition under different temperatures (Chapter 2). Lastly, I explore differences in population structure, juvenile recruitment, and trophic positions in three populations of wolf spiders in the Alaskan Arctic. I conclude this dissertation with a discussion of several

broad themes that have emerged from this work and their potential implications for arctic ecosystems.

2. Differential response to climate change among arthropods is altering the structure of arctic communities

2.1 Introduction

The accelerated warming of the Arctic provides us with a valuable opportunity to learn about the general implications of climate change for the structure and function of biological communities. Temperatures in the Arctic have increased almost twice as fast as the average global increase over the past 100 years (Callaghan et al. 2004, ACIA

2005, IPCC 2007), and its ecosystems are proving to be sensitive to this warming

(Hinzman et al. 2005, Post et al. 2009, Gilg et al. 2012). Although an abundance of studies has demonstrated the strong effects of climate change on a range of arctic organisms

(e.g. Quinlan et al. 2005, Post et al. 2009, Wookey et al. 2009, Gilg et al. 2012, Iverson et al. 2014, Luoto et al. 2014), it is becoming increasingly clear that species-specific responses can be fairly idiosyncratic (Høye et al. 2014), and that our ability to extrapolate species-level findings to the level of communities may be limited. However, a better understanding of community-level responses to climate change is essential, because the structure and composition of communities determine how ecosystems function (Chapin et al. 1997).

In spite of the dramatic change in climate that the Arctic has experienced in the recent past, the few studies that have explored general responses to this phenomenon

have yielded little evidence of major shifts in community structure. For example, although warming experiments have shown some site-specific community-wide changes in plant diversity (Walker et al. 2006, Elmendorf et al. 2012a), a surprising number of long-term experimental (Hudson and Henry 2010, Hill and Henry 2011) and observational studies (Vittoz et al. 2009, Prach et al. 2010, Daniëls et al. 2011, Schmidt et al. 2012) have found little-to-no overall change in arctic and alpine plant communities. In particular, compared to the changes observed in low arctic communities (Tape et al.

2006, Pouliot et al. 2008, Forbes et al. 2010), ecosystems in the High Arctic still appear to be relatively stable (Hollister et al. 2005, Hudson and Henry 2010, Prach et al. 2010,

Elmendorf et al. 2012a). However, this apparent resilience could simply be an artifact.

For example, the typically long life spans and slow developmental times of many arctic species (Bliss 1971, Mark et al. 1985, Büntgen et al. 2015), often a product of selection imposed by extremely short growing seasons, could conceivably result in longer time lags between environmental stress and population decline. Similarly, the large inter- annual climatic variability that characterizes extreme environments, such as the Arctic, may obscure temporal trends in population dynamics and complicate the statistical detection of population changes. The latter issue is particularly challenging in arctic data sets because there is some evidence that communities in extreme environments tend to be less dynamic and to exhibit very slow succession rates (Svoboda and Henry 1987,

Lamb et al. 2011). The use of long-term data sets, which are rare for arctic ecosystems

(Magurran et al. 2010), could aide in resolving these issues.

Arthropods have long been recognized as a model group for detecting organismal responses to climate change because of their short life spans and the strong effects that temperature can have on their life histories. As a point of comparison, while tundra plants often live from decades to centuries (Bliss 1971, Mark et al. 1985, Morris and Doak 1998, Büntgen et al. 2015), the life spans of most arctic arthropods are much shorter, typically spanning from a few months to several years (Strathdee and Bale 1998,

Danks 2004). Nevertheless, there are conflicting predictions on how arthropods will respond to warming in cold environments (Hodkinson et al. 1998, Nielsen and Wall

2013). On one hand, higher temperatures could reduce population numbers through heat stress (Block et al. 1994), desiccation (Hodkinson et al. 1998), phenological mismatches (Høye et al. 2013), or forced relocation to cooler habitats (Parmesan 2006).

On the other hand, warmer temperatures could benefit species by alleviating thermal stress (Bale and Hayward 2010) and lengthening the growth season, which can facilitate accelerated growth or maturation and potentially result in additional cohorts within the year (Strathdee et al. 1993, Strathdee and Bale 1998, Bale and Hayward 2010). In addition, warming could indirectly facilitate resource acquisition through changes in plant community composition (i.e. food quality) (de Sassi et al. 2012), increased rates of

primary productivity (i.e. food quantity) (e.g. (Richardson et al. 2002), or altered availability of substrate and habitat space (Markkula 2014).

The BioBasis Monitoring Programme at Zackenberg Research Station has consistently monitored climate variables and arthropod communities in northeastern

Greenland since 1996 (Schmidt 2012). This dataset is uniquely suited for the study of community-wide responses to climate change, because it contains information on all trophic levels within the local arthropod community. Here we quantify changes in climate patterns at Zackenberg for the period of 1996 to 2011, we explore how these changes affected arthropod groups in different trophic levels, and we evaluate the extent to which food webs in the three main habitat types present in the landscape (wet fen, mesic heath, and arid heath; see Elberling et al. (2008)) have experienced changes in arthropod community structure as a result of taxonomic-specific responses to climate change. The climate parameters we consider in our analyses include broad estimates of summer warmth and exposure to cold during the previous spring, winter and fall. In addition, we consider the timing and duration of the previous fall and spring seasonal transitions in the annual cycle, because the dramatic changes in temperature that characterize these transition periods in the Arctic are potentially critical for arthropod survival (Young and Block 1980, Danks 1992, Hodkinson et al. 1998).

2.2 Methods

2.2.1 Study sites

Zackenberg Research Station is located in high-arctic northeast Greenland

(74°28'N; 20°34'W). The area is characterized by a continental climate with cold winters and generally dry conditions, and there is continuous permafrost with a maximum active layer depth of 20-100 cm. Mean summertime air temperatures typically vary between 3°C and 7°C (Hansen et al. 2008). Over the study period of 1996-2011,

Zackenberg had a mean annual temperature of -9°C, with the majority of positive average temperatures falling within the summer months of June, July, and August

(Jensen 2012).

2.2.2 Climate data

We used hourly data on ambient air temperature from the Zackenberg climate station (downloaded from zackenberg.dk/data, accessed April 15, 2014) to calculate climate conditions for each summer and its preceding spring, winter, and fall. We calculated the cumulative number of degree days – the sum of daily mean air temperatures above 0 °C (Jensen 2012) – between January and August as our measure of activity time available to the arthropods. Positive temperatures occur almost exclusively from June-August, so we consider the number of degree days to be a measure of summertime warmth. Conversely, we estimated the degree of cold exposure over the previous year by computing the sum of daily mean air temperatures below 0 °C during

the previous fall (September), winter (October-April), and spring (May). We also used standardized temperature thresholds associated with seasonal freeze-thaw cycles

(Sulkava and Huhta 2003) to detect the onset and duration of fall and spring.

Specifically, we defined the onset of spring as the first day of the year for which the temperature reached 2°C and the end of spring to be the last date prior to summer when the temperature dropped to -2°C (the period typically spanning the month of May). In measuring the start of spring, we ignored two rare winter warming events in 2005 and

2007, during which Zackenberg briefly reached above-zero temperatures in January but later maintained seasonably cold temperatures until mid-April (Jensen 2012). Similarly, we quantified the start of fall as the first date toward the end of summer in which the temperature dropped to -2°C and the end of fall as the last date after that in which temperatures reached 2°C (typically spanning the month of September). Spring and fall duration are the number of days between the onset and end date of these periods.

2.2.3 Arthropod data collection

Arthropods were monitored weekly from 1996 to 2011 (samples from 2010 were lost in transit from Greenland) (Jensen 2012). Weekly sampling began each spring after snowmelt and continued through the end of August each year. Sampling plots were located in three different habitat types: mesic heath (two plots), arid heath (two plots), and wet fen (one plot) (see Høye and Forchhammer 2008b). All plots were located within 600 meters of the climate station and were operational during the entire study

period, with the exception of one arid heath plot in which monitoring started in 1999.

Each plot was 10 × 20 m and contained eight pitfall traps between 1996 and 2006 and four traps in half the original plot (5 × 20 m) between 2007 and 2011. Capture numbers within plots were standardized across years by transforming specimen counts to individuals per trap per day. Trapping began in the spring once the snow within each plot had melted. Pitfall traps were filled with water and detergent and left out for one week at a time, and all captured specimens were subsequently stored in 70% ethanol.

Samples were sorted and counted by personnel from the Department of Bioscience at

Aarhus University, Denmark. While pitfall traps do not sample the entirety of the arthropod food web (e.g. soil and foliage-dwelling arthropods), they do capture the arthropod community that is active at the soil surface, which is the bulk of arthropod biomass on the tundra (Gough et al. 2012, Koltz et al., in prep). Here we group specimen counts taxonomically by Order, except for Collembola and Acari, which were only identified to Subclass.

Although weekly counts were available in our plots, we chose instead to analyze sampling data on an annual timescale for two reasons. First, the combination of accelerated growing seasons in the Arctic, as well as the extremely variable seasonal dynamics between different arthropod groups, means that the arthropod community can have a very different structure from week-to-week (e.g. Høye and Forchhammer

2008b). Second, variable weather conditions in a given week, such as solar radiation, can

have a large influence on the quantities and types of animals caught (Høye and

Forchhammer 2008a, Bolduc et al. 2013). Using the total number of arthropods caught over the entire growing season helps minimize these sampling issues between years. For simplicity, we refer below to cumulative annual catches as ‘abundances’ and explicitly assume that among animals frequently caught in pitfall traps, the capture numbers are representative of the proportions of each animal group within the community.

We focused our analysis on the well-represented arthropod groups within the community, which we defined as those groups that were present in at least 85% of our annual plot samples. The animal Orders that were sufficiently represented in our sample based on this criterion were Acari (Subclass), Araneae, Hemiptera, Hymenoptera,

Diptera, Lepidoptera, and Collembola (Subclass). Bees were excluded from analyses, because they usually had large numbers of mites on them, which would skew the counts of surface-dwelling mites. In terms of the functional feeding groups of these arthropods within the Zackenberg ecosystem, Collembola (springtails) and Acari (mites) are generally recognized as detritivores, Araneae (spiders) as predators, Hemiptera (true bugs) and Lepidoptera (moths and butterflies) as herbivores, Diptera (flies and mosquitoes) as mixed feeders, and Hymenoptera (e.g., wasps) as parasitoids

(Hodkinson and Coulson 2004, Olesen et al. 2008, Várkonyi and Roslin 2013, Wirta et al.

2015).

2.2.4 Analyses

We used linear regression models to explore how climate variables changed individually over the 15-year study period. Because the climate variables included in our study exhibit high levels of correlation, we avoided statistical artifacts associated with multicollinearity by reducing them to a smaller number of composite predictors via principal components analysis, PCA, in R (RCoreTeam 2013, Reville 2015). Climate variables were normalized when necessary (Osborne 2002), centered, and scaled prior to

PCA.

We tested for the potential effects of climate on arthropod abundances by fitting separate mixed effects models for each taxonomic group using habitat type and the composite climate parameters derived from PCA as fixed predictors and both sampling plot and study year as random effects. These mixed models were fitted using the package lmerTest (Kuznetsova et al. 2013, Bates et al. 2014). Counts for taxonomic groups were either log or square root transformed as necessary in order to conform to the assumptions of linear models (Osborne 2002).

We also performed non-metric multidimensional scaling (NMDS) on the arthropod abundance data to explore how the structure of communities varied in response to changes in climate variables. NMDS is a technique for community analysis that finds an n-dimensional ordination of the taxonomic groups that best describes the

Bray-Curtis dissimilarity in community composition between plots while minimizing

“stress,” a measure of badness of fit (McCune 2002). We ran the NMDS with the Vegan package for R (Oksanen 2013) using the metamds function with random starting configurations (maximum of 200 random starts to reach a convergent solution), and we selected the number of ordination axes to keep via visual inspection of a scree plot of stress values (McCune 2002). We then tested whether compositional variation at the community level was related to our environmental predictors by fitting the ordination scores to habitat type and the composite climate parameters with Vegan’s envfit function (Oksanen 2013).

2.3 Results

2.3.1 Climate change over the study period

We found that the number of cumulative degree days increased significantly in

Zackenberg from 1996 to 2011 (F1,13=11.41, p=0.005, r2=0.468) and that below-zero springtime temperatures became progressively warmer during the same period

(F1,13=8.477, p=0.0121, r2=0.395). There was also a marginal increase in fall temperatures

(F1,13=4.247, p=0.0599, r2=0.246) but no evidence of significant changes in wintertime temperatures or in the duration and timing of fall and spring (all p-values > 0.145).

Principal component analysis with varimax rotation revealed four underlying components of climate in Zackenberg (“Later, shorter spring”, “Colder winter and spring”, “Warmer summer and fall”, and “Longer fall”) that account for 87% of the total variability in climate data. A longer summer (or later start to fall) one year prior to the

year of sampling was also positively associated with the previous fall and current summer being warmer and with having a colder winter and spring. The relative contributions of each climate variable to the different components derived from PCA are presented in Table 1.

Table 1. Results of principal components analysis of climate variables with varimax rotation. Main contributors for each principal component (>0.50) are in bold. Summer and spring measures are for year t, winter measures are Oct.-Dec. in year t-1 and Jan.-Apr. in year t, and fall measures are from year t-1. Values for cumulative frost during the previous fall were square root transformed prior to analyses; Cumulative degree days include temperatures from January-August of study year.

Rotated component Eigenvalue % Variation Cumulative % variation 1 2.46 0.25 0.25 2 1.49 0.22 0.47 3 2.04 0.25 0.73 4 0.92 0.14 0.87

Warmer Later, shorter Colder winter summer spring & spring & fall Longer fall Climate variable (RC1) (RC2) (RC3) (RC4) Start of spring 0.96 0.07 0.11 0.07 Spring duration -0.93 0.03 -0.28 -0.15 Start of prior fall 0.06 0.54 0.71 -0.35 Fall duration 0.16 0.12 -0.04 0.94 Cum. degree days 0.25 -0.14 0.89 -0.13 sqrt(Prior fall cum. frost) -0.16 0.31 -0.72 -0.2 Winter cum. frost 0.31 0.78 -0.3 0.11 Spring cum. frost -0.15 0.87 -0.04 0.08

2.3.2 Links between arthropod abundances and environmental predictors

We found that habitat type is an important predictor of the abundance of most taxonomic groups. In particular, the wet fen habitat contained relatively more predators

(Araneae) and flies (Diptera) and fewer herbivores (Lepidoptera and Hemiptera) than the heath sites (Table 2). There were also fewer Acari in the mesic heath than the arid heath sites (Table 2). In terms of climate, we found that the duration of the previous fall was positively associated with the abundance of most taxonomic groups (Table 2). In addition, we observe that herbivores (Hemiptera and Lepidoptera) and parasitoids

(Hymenoptera) decrease in abundance when exposed to colder winters and springs and increase in abundance with warmer summers and falls (Table 2; Fig. 1). These temperature effects were not detected in detritivores (Acari, Collembola), flies (Diptera) or predators (Araneae) (Table 2; Fig. 2).

2.3.3 Links between variability in community structure and environmental predictors

The non-metric multidimensional ordination (NMDS) of arthropod community composition resulted in a two-axis solution with a Kruskal's stress value of 0.18.

Kruskal's stress is a measure of fit, and values < 0.2 are generally considered appropriate

(Minchin 1987, Clarke 1993). For simplicity, we present a graphical summary of the results of this analysis in Figure 2. NMDS confirms that mesic and arid heath communities (gray and black points in Fig. 3A) are similar to each other and that they

differ significantly from the wet fen community (green points; p=0.001; r2=0.33). These differences include a larger representation by Diptera, Araneae and Acari in wet fen habitats (Fig. 3).

Figure 2. Summertime abundances of detritivores (Acari and Collembola), mixed-feeding flies (Diptera) and predators (Araneae) as predicted by the second and third principal components from our PCA (colder winter and spring temperatures; warmer summer and fall temperatures). Relationships were not significant for any arthropod groups plotted here.

NMDS also indicates that arthropod communities experienced significant changes in composition at Zackenberg during our 15-year period of sampling and that these changes were linked to increasing temperatures across all seasons. In particular, community composition was correlated with warmer summers and falls (p<0.002; r2=0.18) and warmer winter and spring temperatures (p=0.015; r2=0.13). The nature and direction of the significant associations between the NMDS community scores and the climate parameters are depicted through vectors in Fig. 3A. Our results indicate that as temperatures became warmer over all seasons, community composition shifted towards having greater relative abundances of herbivores (Hemiptera and Lepidoptera) and parasitoids (Hymenoptera) and lower relative abundances of detritivores (Collembola and Acari).

Temporal shifts in community composition were more pronounced in arid and mesic heath habitats than in the wet fen. For example, between the first five years (1996-

2001) and the last five years (2006-2011) of the study period, the average NMDS scores of mesic and arid heath communities changed twice as much as those from the wet fen community. This calculation was robust to comparisons between all possible windows of consecutive five-year periods. To visualize these differences in community trajectories and rates of change among habitat types, we plotted vectors of the average ordination score for each habitat type in the first five years of sampling to the average score in the last five years of sampling (Fig. 3B).

(a)'

WarmSum'

Cold' Winter'

(b)'

length of the arrow is proportional to the correlation between the climatic variable and the ordination (WarmSum = PC3: Warmer summer and fall temperatures; Cold Winter = PC2: Colder winter and spring temperatures). Abbreviations in black denote the centroids of each of the analyzed arthropod groups (Col=Collembola, Aca=Acari, Dip=Diptera, Lep=Lepidoptera, Ara=Araneae, Hem=Hemiptera, and Hym=Hymenoptera). Panel b) Arrows indicate the change in average NMDS scores for communities in each habitat type between 1996-2001 and 2005-2011.

Order Fixed effects terms Coefficient SE df t p-value

Acari Intercept 4.949 0.278 16 17.778 0.000 *** Mesic heath -1.426 0.202 5 -7.043 0.00134 ** Wet fen 0.474 0.246 4 1.928 0.120 Longer fall 0.480 0.260 15 1.845 0.0847 . Late, short spring 0.084 0.260 15 0.323 0.751 Warm sum. & fall -0.001 0.260 15 -0.006 0.996 Cold winter & spring 0.153 0.260 15 0.589 0.565

Araneae Intercept 85.692 11.908 10 7.196 0.000 *** Mesic heath 4.790 12.068 4 0.397 0.711 Wet fen 86.696 14.635 4 5.924 0.00431 ** Longer fall 29.594 9.959 15 2.972 0.00942 ** Late, short spring 4.075 9.927 15 0.411 0.687 Warm sum. & fall 8.430 9.905 15 0.851 0.408 Cold winter & spring -1.846 9.894 15 -0.187 0.855

Collembola Intercept 14.979 2.916 8 5.136 0.00105 ** Mesic heath -0.536 3.621 5 -0.148 0.888 Wet fen -3.925 4.430 5 -0.886 0.418 Longer fall 3.711 1.527 15 2.430 0.0283 * Late, short spring 0.278 1.525 15 0.182 0.858 Warm sum. & fall -0.660 1.524 15 -0.433 0.671 Cold winter & spring 1.186 1.523 15 0.779 0.448

Diptera Intercept 216.447 33.703 25 6.422 0.000 *** Mesic heath -9.719 28.800 57 -0.337 0.737 Wet fen 324.759 34.905 57 9.304 0.000 *** Longer fall 96.179 30.425 15 3.161 0.00643 ** Late, short spring -7.017 30.362 15 -0.231 0.820 Warm sum. & fall -14.370 30.317 15 -0.474 0.642 Cold winter & spring -2.424 30.296 15 -0.080 0.937

Hemiptera Intercept 2.330 0.254 6 9.182 0.000 *** Mesic heath 0.564 0.329 5 1.714 0.150 Wet fen -1.040 0.401 5 -2.593 0.0514 . Longer fall 0.358 0.137 15 2.602 0.0203 * Late, short spring 0.172 0.137 14 1.262 0.227 Warm sum. & fall 0.471 0.136 14 3.459 0.00377 ** Cold winter & spring -0.350 0.136 14 -2.575 0.0220 *

Hymenoptera Intercept 2.443 0.191 9 12.783 0.000 *** Mesic heath 0.063 0.225 5 0.281 0.790 Wet fen -0.025 0.274 5 -0.091 0.931 Longer fall 0.283 0.137 15 2.066 0.0570 . Late, short spring 0.150 0.136 14 1.099 0.290 Warm sum. & fall 0.444 0.136 14 3.264 0.00558 ** Cold winter & spring -0.364 0.136 14 -2.681 0.0178 *

Lepidoptera Intercept 2.005 0.134 26 15.019 0.000 *** Mesic heath -0.106 0.115 57 -0.923 0.360 Wet fen -0.718 0.139 57 -5.156 0.000 *** Longer fall 0.235 0.120 15 1.952 0.0694 . Late, short spring 0.069 0.120 15 0.575 0.574 Warm sum. & fall 0.400 0.120 15 3.336 0.00444 ** Cold winter & spring -0.262 0.120 15 -2.191 0.0445 *

2.4 Discussion

We have shown that among the eight climate parameters explored in this study, only spring and summer temperatures increased significantly over a period of 15 years at Zackenberg. In addition, we showed that these increases in spring and summer temperatures were associated with major axes of change in the composition of local arthropod communities, leading to significant changes in community structure over time. These changes were most pronounced in dry habitats, indicating that there is substantial heterogeneity in the community-wide responses to climate change even across short spatial scales.

Notably, although NMDS ordination indicates that the relative abundances of all trophic groups within tundra habitats have been affected by changes in spring and summer temperatures, mixed models on individual taxonomic groups only detect significant effects of climatic changes on the absolute abundances of herbivores and parasitoids. This apparent discrepancy could potentially be explained in at least two different ways. First, environmental factors not measured in this study could be driving changes in the relative abundances of some taxonomic groups. For example, Collembola abundance may be more responsive to soil moisture (which is likely to be correlated with temperature in water limited habitats) than to temperature itself (Hodkinson et al.

1998). While such indirect effects could in principle be detectable with ordination techniques, they may be too weak to achieve statistical significance in linear models that 25

do not explicitly include moisture as a predictor. Second, it is possible that taxonomic groups that do not appear to respond directly to warming are instead responding to changes in species interactions driven by temperature change. For example, some species of arctic spiders are becoming larger as a product of longer growing seasons

(Høye et al. 2009, Bowden et al. 2014), and this change could result in increased predation pressure on detritivores (Wise 2004, Koltz and Wright this thesis, Chapter 2).

Similarly, the increasing number of herbivores in Zackenberg could affect the quality and quantity of litter (Chapman et al. 2003), thereby affecting food sources for detritivores. Regardless of the actual source of discrepancy between the results of the mixed models and NMDS, our analyses confirm that studies that focus on taxon-specific responses to climate change may fail to detect important changes in the relative abundance of different trophic groups within the community.

Our findings indicate that community-level responses to climate change can be patchy across the landscape. For example, despite the fact that similar changes in climate have occurred throughout the entire Zackenberg area, there are habitat-specific differences in the effects of warming on arthropod community structure. The differential responses observed between wet (fen) and dry (heath) sites (Elberling et al. 2008) are consistent with earlier claims that warming can elicit different responses in habitats that vary in moisture availability (Coulson et al. 1996). Notably, although arthropod communities in drier habitats appear to be more responsive to climate change than those

in wet habitats (Fig. 3B), the opposite appears to be the case for plants (Elberling et al.

2008, Elmendorf et al. 2012b, Schmidt et al. 2012), suggesting a potential feedback loop that could magnify spatial heterogeneity in the effects of climate change. For example, although wet fen habitats experienced a substantial increase in plant productivity over the same period as our study (Tagesson et al. 2010, Tagesson et al. 2012), the abundance of herbivores remained largely stable (Table 2). This suggests a proportional decrease in herbivory, assuming that rates of herbivory scale with the abundance of herbivores.

Conversely, although plant communities in drier heath habitats have been relatively resistant to warming (Elmendorf et al. 2012b, Schmidt et al. 2012), herbivore abundance has increased in those sites (Fig. 3) leading to potentially higher herbivory rates. Overall, the dissimilarities in plant and arthropod community changes suggest that herbivore densities in heath habitat are not currently resource limited.

The findings from our community-level analysis are consistent with other work that has predicted that aboveground and soil-dwelling arthropods might respond differently to warming (Hodkinson et al. 1998) and that warming will disproportionately benefit herbivores (e.g. de Sassi and Tylianakis 2012). Because plant primary productivity is so low in the Arctic relative to temperate and tropical ecosystems, herbivory has traditionally been thought to exert little pressure on plant communities (Jefferies et al. 1994). However, more recent work has shown that vertebrate herbivores can influence how warming affects arctic plant communities with

important consequences for carbon exchange (Post and Pedersen 2008, Sjögersten et al.

2008, Speed et al. 2010, Cahoon et al. 2012, Sjögersten et al. 2012, Olofsson et al. 2013).

While less attention has been paid to changes in baseline herbivore pressure by arthropods, our findings are consistent with modeling efforts by Wolf et al. (2008) that suggest that impacts on vegetation by non-outbreak herbivores will increase with the predicted increases in temperature. Moreover, we may have even underestimated the herbivore response to warming, because our trapping method was not focused on sampling the foliage community, which is largely made up of herbivores. Increases in herbivore abundances are likely reducing the small, albeit increasing amount of carbon that is annually fixed and retained in this arctic ecosystem (Tagesson et al. 2012, but see

Mosbacher et al. 2013).

The ecosystem implications of having relatively fewer surface-active detritivores in these arthropod communities are harder to predict. Soil animals are commonly found to enhance decomposition (Standen 1978, Swift MJ 1979, Seastedt 1984, González and

Seastedt 2001) and nutrient cycling (Persson 1983, 1989; Setälä & Huhta 1991). In this case, lower detritivore densities may translate to slower decomposition on the tundra, which could slow the rate at which the large amounts of stored carbon are lost from permafrost soils. At the same time, fewer plant-available nutrients could limit primary productivity (Setälä and Huhta 1991) and slow the rate at which carbon is fixed.

However, predicting future impacts of warming on detritivore-mediated soil processes

are inherently difficult, because they largely depend on the specific composition of local detritivore and microbial communities (A'Bear et al. 2012). The uncertainty surrounding the influence of arctic detritivores over ecosystem processes under different environmental conditions highlights the need for more studies focused on the functional role of these animals.

Even though summer is the active season for most arctic arthropods, we found that climate conditions during the fall, winter, and spring preceding the sampling were also important determinants of community structure. Our results therefore suggest that arthropods are sensitive to conditions at subzero temperatures and that the projected winter and springtime warming in Greenland (Stendel et al. 2007) may have unexpected consequences for northern arthropod communities. Specifically, the magnitude and direction of change in arthropod community composition may be larger and slightly different than those predicted by changes in summertime temperatures alone (Fig. 3A).

While most arthropods are in diapause during the coldest months of the year, even slight temperature increases at this time may have effects on the community, because both the temperature and the length of time of exposure to extreme cold can be important determinants of arthropod survival (Tenow and Nilssen 1990, Bale 1996,

2002). In addition, longer springs and falls, which imply higher numbers of freeze-thaw events, are thought to be potential sources of stress for arthropods as they transition into or out of diapause (Coulson et al. 2000, Bale et al. 2001, Bale and Hayward 2010).

However, our data indicate that at least for fall, the benefits of a longer fall appear to outweigh the potential stress of repeated freeze-thaw events. This could be due to the increased foraging opportunities prior to winter diapause (Konestabo et al. 2007) or to reduced mortality with a longer preparatory transition period from summertime activity to wintertime diapause.

In conclusion, we have shown that climate change has differentially affected some arthropod groups in high-arctic Greenland and that this has led to major changes in the relative abundances of different trophic groups within the arthropod community, particularly in dry habitats. Because the structure of the biological community is an important determinant of ecosystem functions (Chapin et al. 1997), these findings suggest that the influence of arthropod communities over processes such as decomposition, nutrient cycling, and primary productivity may also be changing in

Greenland as a consequence of climate change. Given that the Arctic is a reservoir for approximately half of the global soil organic carbon (Smith et al. 2004, McGuire et al.

2009, Tarnocai et al. 2009), the patterns we document here could have important implications not only for regional but also for global carbon dynamics. Future studies at higher taxonomic resolution would be able to assess the generality of responses of each functional group and whether biotic interactions like competition and facilitation will exacerbate these observed community shifts.

3. Predator effects on a soil food web can buffer carbon losses in a warmer Arctic

3.1 Introduction

It is becoming increasingly clear that individual and population-level responses to altered climatic conditions can result in effects that cascade through food webs

(Tylianakis et al. 2008, Walther 2010, Gilbert et al. 2014). However, despite general agreement that trophic interactions will be affected by climate change, surprisingly few studies have explored the ways in which climate-modified trophic cascades might affect ecosystem functioning (e.g., reviewed in Schmitz 2013). Because community-scale dynamics can influence important ecosystem processes such as productivity, nutrient cycling, and decomposition (Chapin III 2012), this gap in our knowledge can seriously impair our ability to predict the overall effects of climate change.

The Arctic stores more than one third of the world’s terrestrial carbon (AMAP

2011) and is warming faster than any other biome on Earth (Serreze and Barry 2011).

Understanding how warming will affect arctic ecosystem processes is especially critical, because the potential feedbacks between the release of this stored carbon and the atmosphere mean that even small changes in arctic systems can have important consequences at a global scale. For example, microbially-mediated decomposition is likely to increase with warmer arctic temperatures (Schuur et al. 2008). Due to the vast amounts of carbon stored in permafrost soils, higher arctic decomposition rates could

increase the concentration of atmospheric greenhouse gases, such as carbon dioxide and methane, and potentially accelerate the rate of global climate change (McGuire et al.

2006), even if changes in decomposition rates are only a fraction of a percent.

Prior studies have assessed the feedbacks between arctic terrestrial ecosystems and climate change by focusing primarily on the balance between plant community and microbial responses to warmer temperatures (reviewed in Wookey et al. 2009).

However, the extent to which these microbial responses may be affected by responses by organisms at other levels in the arctic food web has been largely overlooked (but see

Ruess 1999, Sistla et al. 2013). Yet the composition and activity of microbial communities are often modified by soil micro- and macro- invertebrates (Moore et al. 1988), which themselves can be regulated by higher-level predators (Scheu 2001a).

Wolf spiders are active hunters at the soil surface whose prey can include herbivores, other predators, and litter-dwelling detritivores (Wise et al. 1999, Scheu

2001a, Wise 2006). Wolf spider activity is also known to have indirect effects on key ecosystem processes, including either increasing or decreasing productivity (Snyder and

Wise 2001) and decomposition rates (Lawrence and Wise 2000, 2004, Lensing and Wise

2006). At our study site in the Alaskan Arctic, wolf spiders outweigh larger predators, such as wolves, by several orders of magnitude (Appendix A), arguably making them the dominant predators of the tundra community. Moreover, wolf spider biomass at this site is greater than that of all their potential aboveground herbivore prey combined

(Asmus and Gough personal communication). Such a disproportionate ratio of spider to aboveground herbivore biomass suggests that these predators obtain a significant portion of their energy from belowground prey (which are typically detritivores) and are therefore able to influence belowground carbon dynamics in the tundra.

Recent studies indicate that arctic wolf spiders are responding to climate change:

Høye et al. (2009) showed that wolf spiders from high-arctic Greenland are becoming larger and more fecund (Marshall and Gittleman 1994) in response to earlier snowmelt dates. Bigger and more abundant spiders could bring about stronger predation pressure on detritivores and an associated reduction in decomposition rates (Lawrence and Wise

2000). Alternatively, increased spider predation on other predators within the soil food web could indirectly increase detritivore densities and result in faster decomposition.

Thus, climate-induced shifts in spider feeding ecology could have important and far- reaching consequences for arctic community dynamics and, more generally, for global ecosystem processes.

Given their important ecological role and the fast pace at which arctic environments are changing, arctic wolf spiders and the relatively simple food web to which they belong provide us with an ideal system to study how ecosystem functioning can be affected by alterations in species interactions induced by climate change. Here, we explore the extent to which arctic wolf spiders mediate the response of the detrital community to global warming. Using a fully factorial design, we experimentally test

whether higher temperatures and the expected associated increase in predator density will affect lower trophic levels and, ultimately, decomposition rates. Although arctic wolf spiders are primarily surface predators, we explore their influence at both the soil surface and belowground because changes in predator presence and microclimatic conditions can affect detritivore movement patterns and activity throughout the entire soil profile (Faber and Joosse 1993, Frampton et al. 2001). This study improves our understanding of how climate change-induced effects on predators can cascade through other trophic levels, change critical ecosystem functions, and lead to climate feedbacks with important global implications.

3.2 Methods

3.2.1 Experimental design

Our study took place from early June 2011 through late July 2012 near Toolik

Field Station (68°38’N and 149°43’W, elevation 760 m), a well-studied area of tundra on the North Slope of Alaska (Fig. 4). We explored the effects of spider density and temperature on soil community structure and decomposition rates through a fully factorial experiment. Our experimental design consisted of 30 plots distributed among five blocks of undisturbed moist acidic tussock tundra (Bliss and Matveyeva 1992). Plots were 1.5 meters in diameter and were enclosed with aluminum flashing that was buried

20 cm belowground and stood 20 cm above the soil surface in order to contain detritivore communities and maintain experimental wolf spider densities.

Within a given block, we randomly assigned plots to six spider density/warming treatments. For the warming treatment, we placed ITEX (International Tundra

Experiment) open-topped passive warming chambers over half of the plots during the summer season only. This ITEX method increases mean air temperature by 1-2°C

(Marion 1993). Spider density treatments were: 1) removal of all wolf spiders; 2) ambient spider density, and 3) enriched wolf spider density. Enriched plots received enough

additional spiders at the start of each summer to bring wolf spider densities to approximately double the early season average density of control plots. We continued to check and remove individuals from the removal plots throughout each summer. Visual inspection and live pitfall trapping at the end of the summer seasons verified that we successfully manipulated wolf spider densities during both years of the experiment. At the end of the 2011 season, high-density plots had significantly more spiders than the removal and ambient density plots. However, ambient density plots did not have significantly more spiders than the removal plots (ANOVA: F2,27=5.79, p=0.008). In 2012, spider densities differed significantly according to their pre-assigned treatments

(ANOVA: F2,27=18.93, p=<0.001). Because of variation in spider density within and between treatments (likely due to self-regulation and imperfect exclusion), at the end of the experiment, plots were assigned to two post-hoc density treatments. Treatments included low spider density (n=13) or high spider density (n=17), based on whether plots were below or above the average density of 1.2 spiders/m2 in non-manipulated plots at the end of the experiment.

3.2.2 Measures of community structure and function

We took measurements of surface and belowground community structure and function at the end of the second summer. In order to better understand how wolf spider influence changes within the vertical profile of the litter and soil systems, we distinguish between the effects of spiders on the soil surface and the deep organic horizon (herein

referred to as “belowground”) habitats. Following the protocol of Sistla et al. (2013), we defined the belowground habitat as the layer of soil organic matter from 5 cm below the surface to the mineral soil horizon (average 10.4 cm deep).

3.2.2.1 Sampling the surface and belowground community

Our sampling of lower trophic levels focused on the dominant fungal-feeding microarthropods in moist acidic tundra — i.e., Collembola and Oribatid mites. Both types of microarthropods are known to affect decomposition (Moore et al. 1988) and have been extensively studied within the tundra soil ecosystem (e.g. Hodkinson et al.

1998, Moore 2012). Collembola, in particular, are known to be important prey of wolf spiders in temperate detrital food webs (Kajak 1995). We measured microarthropod densities in the surface and belowground habitats during the second summer by using

Berlese funnels to extract the animals from two mid-season (June 20) and (July 17) soil samples per plot (average sample volume: 330 cm3). In subsequent analyses, we used the average microarthropod densities for each plot from these two samplings.

3.2.2.2 Decomposition

We measured decomposition using litter bags containing standing dead graminoid (Eriophorum vaginatum) litter collected in June 2010. Litter was dried at 40° C for 48 hours before being placed in litter bags with 3 mm mesh size to allow access by most arthropods. Paired litter bags were placed in each plot, with one on the soil surface and one buried in the deep organic horizon. At the conclusion of the experiment, we

manually removed accumulated soil, new plant material, and microarthropods from the decomposed leaves in the litter bags before drying the leaves at 40° C for 72 hours.

Surface and belowground decomposition rates are expressed as the proportion of dry litter mass loss that occurred over the 14 months of the experiment and were arcsine square root transformed prior to analyses.

3.2.2.3 Soil moisture

Soil moisture is known to affect polar microarthropod densities (Hodkinson et al.

1998) and their influence over decomposition (Aerts 2006, Wall et al. 2008), so we accounted for this abiotic driver by taking measures of soil moisture at three locations within each plot at the beginning, middle, and end of the second summer season using a

HydroSense portable soil moisture probe (Campbell Scientific, Logan, Utah, USA). We used the average percent moisture from these nine measurements as our measure of soil moisture in all analyses.

3.2.3 Statistical analyses

We analyzed surface and belowground data sets separately with linear mixed effects models estimated through the lmer function of the nlme package (Pinheiro 2014) in R (RCoreTeam 2013). In some cases, dependent variables were log-transformed following Osborne (2002) in order to conform to the assumptions of linear models. The random effect in our mixed models was treatment block. For all analyses, we began with a fully parameterized model and proceeded to simplify it by excluding non-significant

interaction terms followed by non-significant main effects one at a time. We first fitted separate models for surface Collembola, surface mite, belowground Collembola, and belowground mite densities to assess the potential interactive effects of our spider density and temperature treatments on the detritivores. Soil moisture was also included as a continuous independent predictor in each of these models. Next, we fitted separate models for surface and belowground decomposition including an interaction between the spider density and temperature treatments. In these decomposition models, we also included soil moisture, Collembola densities, and mite densities as covariates in the analyses. Because densities of Collembola and mites were correlated with one another

(surface: r = 0.696, p = < .001; belowground: r = 0.63, p = < .001), we explored the potential for multicollinearity through variance inflation factors (VIF). The VIFs of both

Collembola and mites were below the generally accepted cut-off of two (Zuur et al.

2010), so we included both as predictors in the decomposition models.

3.3 Results

3.3.1 Effects of wolf spider density and temperature on microarthropods

Wolf spider density had different effects on surface densities of Collembola at different treatment temperatures (Fig. 5; Table 3). At ambient temperatures, the higher densities of spiders reduced Collembola densities, as might be expected if spiders were consuming Collembola. However, in artificially warmed plots we observed the opposite effect: plots with high spider densities had more surface Collembola than plots with low

spider densities. We did not observe changes in densities of belowground Collembola in response to the spider density or temperature treatments (all p-values > 0.1; see

Supplementary Material). Plots with higher levels of soil moisture had lower densities of

Collembola at the surface and belowground (Table 3).

We did not detect any significant differences in mite densities under any of the temperature or spider density treatments either at the surface or belowground (all p- 40

values > 0.3; Table 3). However, like the Collembola, mite densities were negatively related to soil moisture (Table 3).

Response Fixed effects terms Estimate SE t P Soil surface Intercept 0.373 0.073 5.094 0 Collembola Soil moisture -0.004 0.002 -2.312 0.031* Warming 0.121 0.070 1.718 0.1005 Spider density 0.065 0.086 0.759 0.4561 Warming x Spider density -0.250 0.112 -2.227 0.037* Belowground Intercept 0.472 0.089 5.284 0 Collembola Soil moisture -0.007 0.002 -3.614 0.0016** Warming -0.120 0.086 -1.406 0.174 Spider density -0.161 0.105 -1.536 0.139 Warming x Spider density 0.219 0.137 1.601 0.124 Soil surface Intercept 1.071 0.202 5.307 0 Mites Soil moisture -0.010 0.004 -2.407 0.0253* Warming 0.064 0.193 0.334 0.742 Spider density -0.192 0.237 -0.812 0.426 Warming x Spider density -0.042 0.309 -0.136 0.893 Belowground Intercept 0.521 0.135 3.853 0.0009 Mites Soil moisture -0.006 0.003 -2.357 0.0282* Warming -0.037 0.073 -0.509 0.616 Spider density 0.103 0.099 1.041 0.310 Warming x Spider density -0.076 0.130 -0.588 0.563

3.3.2 Effects of wolf spider density and temperature on decomposition

At the soil surface, decomposition rates were not explained by our experimental treatments, by soil moisture, or by densities of microarthropods (all p-values > 0.2; Fig. 6; 41

Table 4). Although there was a trend toward slower surface decomposition in plots with more wolf spiders, spider density was not a significant predictor of surface litter mass loss in our experiment.

Figure 6. Effects of wolf spider density and temperature on decomposition rates. Points are mean treatment effects; Error bars are standard errors. Blue lines are from ambient temperature plots, and red lines are from experimentally heated plots. Solid lines represent a significant interaction between the treatments. Decomposition is reported as proportional litter mass loss and has been arcsine square root transformed.

Conversely, plots with more belowground Collembola were associated with slower belowground decomposition (Table 4). Belowground decomposition was also influenced by the temperature treatment, with faster decomposition rates observed in

artificially warmed plots (Fig. 6). In addition, after accounting for these effects, we saw that the indirect effect of wolf spiders on belowground decomposition differed according to temperature treatment (Table 4; Fig. 6). In ambient temperature plots, high spider densities caused decomposition to occur more rapidly belowground. However, heated plots with high spider densities experienced slower belowground decomposition than those with low spider densities. Soil moisture and mite densities were not significant predictors of belowground decomposition.

Response Fixed effects terms Estimate SE t p Soil surface Intercept 0.530 0.055 9.558 0 litter mass loss Soil moisture 0.000 0.001 0.254 0.803 log(mite density) -0.057 0.047 -1.208 0.242 log(collembola density) 0.049 0.130 0.376 0.711 Warming -0.004 0.035 -0.106 0.916 Spider density 0.030 0.044 0.667 0.513 Warming x Spider density -0.017 0.063 -0.272 0.789 Belowground Intercept 0.622 0.052 11.928 0.000 litter mass loss Soil moisture 0.000506 0.000903 0.560 0.582 log(mite density) 0.0572 0.0682 0.839 0.412 log(collembola density) -0.194 0.084 -2.296 0.0332* Warming -0.039 0.032 -1.227 0.235 Spider density -0.107 0.0404 -2.650 0.0158* Warming x Spider density 0.153 0.0521 2.940 0.0084**

3.4 Discussion

We have shown that arctic wolf spiders affect the densities of fungal-feeding microarthropods and that these effects differ by temperature treatment. Specifically, our results indicate that while high densities of wolf spiders have a negative impact on surface Collembola under ambient temperatures, similarly high densities of spiders have a positive effect on these microarthropods under warmer conditions (Fig. 5). Despite no evidence of wolf spider effects on belowground Collembola, the spider-initiated changes in surface Collembola led to changes in overall Collembola densities within the soil profile. Furthermore, we demonstrate that wolf spiders influence belowground decomposition rates but that the magnitude and direction of this effect is also temperature-dependent (Fig. 6, Fig. 7). Based on these findings, we propose that the temperature-related effects of wolf spiders on overall densities of Collembola may be indirectly responsible for the opposite effects of the spiders on decomposition rates under ambient versus artificially warm temperatures.

Prior studies have concluded that experimental warming in dry arctic ecosystems has detrimental effects on Collembola, and that this is likely a product of reduced moisture (Hodkinson et al. 1998). However, Collembola density actually increased under drier conditions in our experiment. Moreover, soil moisture levels did not affect our measures of decomposition. These somewhat atypical results (Aerts 2006) highlight that decomposition is not water limited in wetter arctic sites (Moorhead and

Reynolds 1993) and that excessively high levels of water saturation in an already wet environment are not conducive to microarthropod activity. In addition, they emphasize the need to be cautious when generalizing the effects of climate change across the Arctic, 45

which, while superficially homogenous is actually heterogeneous with regard to the abiotic variables most relevant to arctic organisms.

Overall, our results are consistent with the idea that top predators can have important indirect effects on decomposition rates. In accordance with previous work in temperate ecosystems (Wise 2004), we found that under ambient conditions, predation by arctic wolf spiders has negative effects on surface-dwelling Collembola (Fig. 5).

Detritivores such as Collembola and mites play an important ecological role in decomposition through their consumption of detritus and fungi (e.g. Moore et al. 1988).

In our experiment, higher densities of Collembola were associated with slower belowground decomposition rates. Thus, our findings from the ambient temperature treatment can be interpreted as a classic trophic cascade, where higher densities of a top predator (wolf spiders) suppress intermediate consumers (Collembola) at the soil surface, release the fungal community from predation, and indirectly result in faster belowground decomposition rates.

While other studies have observed evidence of wolf spiders affecting decomposition at the soil surface (Kajak 1995, Wise et al. 1999, Lawrence and Wise 2000,

2004, Lensing and Wise 2006), it is clear that the strongest effects of spiders in our experiment were observed belowground. Given that wolf spiders typically hunt at the soil surface, this finding indicates that these predators are capable of affecting ecosystem function beyond their immediate foraging habitat, perhaps by affecting the vertical

migration behaviors of Collembola or other intermediate predators within the soil profile (Faber and Joosse 1993, Frampton et al. 2001, Wu et al. 2014). Notably, high spider densities alone caused an 18% increase in belowground decomposition over the course of our experiment. Because wolf spiders are an extremely widespread predator across the Arctic (Dondale and Redner 1990), this type of cascading effect on decomposition may be common in northern latitudes.

Intriguingly, our findings also show that under certain conditions, wolf spiders can act as a buffer that modulates the effects of warming on arctic community structure and decomposition. Specifically, Fig. 6 shows that there are no temperature-driven increases in decomposition rates under high spider densities. Thus, given recent reports of the potential for higher spider fecundity under warmer arctic temperatures (Høye et al. 2009), our results indicate that although tundra decomposition rates are likely to increase under warmer temperatures (Billings 1987, Schuur et al. 2008), they may not increase to their fullest potential if wolf spider numbers also increase.

In terms of mechanism, our experiment suggests that the details of food web dynamics can be particularly important in determining the ultimate effects of warming on decomposition. It has been suggested that rising temperatures may strengthen top- down effects of predators on food webs, which could, in turn, strengthen the indirect effects of predators on ecosystem function (Barton et al. 2009, Lang et al. 2014).

However, while we did find a significant effect of spiders on decomposition in

artificially warmed plots, this effect appears to be due to weakened, not strengthened top-down predator effects on the soil community. Specifically, high spider densities were associated with increased overall abundances of Collembola in our plots (i.e.,

Collembola density increased at the soil surface and stayed the same belowground). We therefore conclude that the slower decomposition rates observed under high spider densities in our warm treatment are likely to be the product of higher potential for consumption of fungi by Collembola.

The unexpected positive effect of wolf spiders on the density of Collembola under warming suggests that these predators induce different types of trophic cascades under different climatic conditions (see Lensing and Wise 2006). For example, given that warmer temperatures increase wolf spider activity (Ford 1977), warming may increase overall predation rates (Kruse et al. 2008). Hence, at low densities, having more active spiders may suppress Collembola populations more effectively. However, at high densities, an increase in spider activity could instead result in a greater number of antagonistic interactions with other predators (Lang et al. 2012). Strong predator- predator interactions can relax the collective effect of the predator community on their prey, thereby resulting in increased prey populations (Finke and Denno 2003). In addition, warmer temperatures may induce shifts in the species compositions of microarthropod communities (Hodkinson et al. 1998) that could change predator-prey interactions (Toft 1999) and spider-related effects on decomposition. Thus, when

applying these findings to other systems and localities in the tundra and beyond, we caution that in the end, it will be the balance between predator and prey-specific responses to warming, as well as the actual rate of warming, which will ultimately determine the influence of food webs on future carbon dynamics.

In conclusion, this study provides evidence that arctic wolf spiders are capable of providing an important ecosystem service by indirectly buffering the rate of carbon release from permafrost soils. Specifically, the combined effect of increased wolf spider densities and warmer temperatures may ultimately release fungivorous microarthropods from predation pressure, enabling higher Collembola consumption of fungi, and result in slower decomposition rates. In our experiment, a realistic increase in the density of spiders under warming contributed, by itself, to over a 5% reduction in the expected decomposition rates of a warmer Arctic under current spider densities.

This result is of particular significance considering that a third of the world’s terrestrial carbon is currently stored in the Arctic permafrost (AMAP 2011). Thus, our findings indicate that even the smallest and arguably least charismatic arctic predator can have disproportionate effects on large-scale, globally relevant ecosystem processes.

4. Larger female body sizes lead to increased cannibalism rather than higher recruitment in arctic wolf spiders

4.1 Summary

Population structure (the number of individuals in different age and sex classes) and juvenile recruitment are key determinants of the viability of natural populations. All else being equal, bigger populations are expected to have more reproductive females, and in the case of invertebrates, larger females are expected to be more fecund (Marshall and Gittleman 1994). I investigated population structure and juvenile recruitment in wolf spiders (Pardosa lapponica) at three sites of the Alaskan Arctic that naturally differ in mean spider body size. Contrary to expectations, I found that the density of reproductive females is not correlated with total population density, and that juvenile abundance actually decreases where females are larger. Through stable isotope analysis,

I show that these unexpected results are likely to be explained by a size-related increase in cannibalism: where adult spiders are larger (and juvenile survival is lower), females occupy higher trophic positions, as evidenced by enrichment in δ15N. This finding has major implications for both regional and global carbon dynamics. Specifically, because arctic wolf spiders are becoming larger with the longer growing seasons brought on by climate change (Høye et al. 2009), a shift in spider diet toward higher consumption of smaller conspecifics could potentially diminish predation pressure on the soil 50

microarthropods involved in decomposition (Wise et al. 1999, Lawrence and Wise 2000,

2004, Wise 2004, Koltz and Wright this thesis, Chapter 2). Thus, the effects of climate change on wolf spider size and diet could ultimately alter decomposition rates and carbon release from soils in a region that currently store half of the global soil carbon pool (Smith et al. 2004, McGuire et al. 2009, Tarnocai et al. 2009).

4.2 Results and discussion

The number of offspring that animals produce and their survival are two key components of population health and stability. Among invertebrates, bigger animals tend to have more offspring (Simpson 1993, Marshall and Gittleman 1994, Bowden and

Buddle 2012b), and for an animal of a given body size, the more females in a population, the more juveniles there typically are. I explored these key demographic relationships in arctic wolf spiders at three sites of the Alaskan tundra, namely near Toolik Lake, Atigun

Gorge and Imnavait Creek (Fig. 8). Surprisingly, although bigger female spiders are known to produce more offspring, I found that areas with larger females actually contain fewer juveniles (Table 5). Furthermore, the number of females in an area is not predictive of overall spider population size (Table 5).

Geographic variation in spider species composition (Table 6) could influence these findings, so I repeated the analysis using only samples from Toolik and Imnavait, where wolf spider communities are similar, with > 95% of all adult (and presumably juvenile) individuals belonging to P. lapponica. Even with this conservative approach, I found similar results to those obtained in the original model across all species and sites:

Areas with larger female P. lapponica have fewer juvenile spiders, regardless of the number of females (Table 5; Fig. 9).

Predictor Estimate SE df t p-value A) Total density Intercept 8.517 1.085 16 7.853 <0.0001 *** (individuals/m2) Female size -1.321 0.449 16 -2.942 0.00958 ** Sampling date -0.0241 0.003 16 -8.789 <0.0001 ***

B) Juvenile density Intercept 4.6110 1.712 16 2.694 0.01598 * (all sites) Female size -2.534 0.762 16 -3.326 0.00428 ** (all species)

C) Juvenile density Intercept 6.0385 1.731 11 3.489 0.00507 ** (Toolik & Female size -3.219 0.784 11 -4.108 0.00174 ** Imnavait) (P. lapponica)

Table 6. Relative abundances of the different wolf spider species at each of the three study sites, based on samples of adult females.

Atigun Toolik Imnavait

Arctosa insignata (Thorell 1972) 0.05 0.00 0.02 Pardosa lapponica (Thorell 1972) 0.22 0.97 0.95 Pardosa podhorskii (Kulczyn'ski 1907) 0.68 0.00 0.02 Pardosa sodalis (Holm 1970) 0.00 0.03 0.00 Pardosa tesquorum (Odenwall 1901) 0.05 0.00 0.00

A negative correlation between female size and juvenile numbers cannot be explained by variation in female condition, because larger females are expected to produce more rather than less offspring (Simpson 1993, Marshall and Gittleman 1994,

Bowden and Buddle 2012b). Similarly, this pattern is unlikely to be explained by a possible preference by parasitic wasps for larger females (Bowden and Buddle 2012a), because egg sac parasitism rates do not differ between sites (Imnavait = 16%, Toolik =

14% (GLM with binomial errors, p = 0.817)), despite spiders from the Imnavait site being significantly larger (average female body size: Imnavait = 2.217 mm, Toolik = 2.164 mm

(One-tailed t-test: t = -1.830, df = 45.281, p = 0.0369)).

Figure 9. Juvenile wolf spider density as predicted by A) the density, and B) the average body size of adult female P. lapponica. Data shown here are from sampling grids at Toolik and Imnavait.

An alternative explanation is that the negative effect of female size on juvenile numbers is a product of increased reliance on intraspecific cannibalism as female wolf spiders become larger (Rypstra and Samu 2005, Brose et al. 2006). To test this hypothesis,

I measured the stable isotope ratios (2002) of 13C/12C (which is indicative of the basal resources of the food web) and 15N/14N (which is indicative of trophic position) in adult female P. lapponica at Imnavait and Toolik. A size-related increase in cannibalism rates is expected to result in higher levels of δ15N (i.e., indicating a higher trophic position) but not necessarily of δ13C (if the base of the food web remains the same). As predicted, I found that female trophic position is significantly higher at Imnavait (Tukey’s HSD, p<0.001; Fig. 10; Table 7), where average female body size is larger than at Toolik. Levels of δ13C were similar at both sites (Tukey’s HSD, p=0.115). One possibility for the difference in levels of δ15N is that females from Imnavait could feed on other predatory arthropods more frequently than females at Toolik. However, this behavior does not explain the fewer number of juveniles at Imnavait. Thus, the site differences in levels of stable isotopes are consistent with the hypothesis that the negative effects of female body size on juvenile abundance are driven by increased rates of intraspecific cannibalism.

As opposed to the annual life cycles of wolf spiders in temperate habitats, wolf spiders in northern latitudes take up to two years to become reproductively active

(Buddle 2000). Such an extended life history means that during the growing season, there are two to three different size classes of individuals of the same species occupying similar habitat (Dondale 1961). This large variation in the size structure of the population provides cannibalistic opportunities for both adults on juveniles and larger juveniles on smaller individuals. Moreover, in some wolf spider populations, cannibalistic behavior appears to be equally or more common among juveniles as in

adults (Ponsard and Arditi 2000, McNabb et al. 2001, Wise 2006 and references therein).

In order to determine whether cannibalism is restricted to adults or also prevalent in juveniles, I measured the carbon and nitrogen stable isotope ratios of juvenile spiders at

Toolik and Imnavait. As with the adult data, I found that although juveniles do not significantly differ in δ13C at these sites (Tukey’s HSD, p=0.0676), they have significantly higher levels of δ15N at Imnavait than at Toolik (Tukey’s HSD, p<0.001) (Fig. 10B). These findings indicate that juveniles from Imnavait are also in a higher trophic position and may rely more heavily on cannibalism or other forms of intraguild predation than those from Toolik.

Table 7. Stable isotope signatures, number of replicates, life stages, and species names of wolf spiders and Collembola (means ± SE) from the three study sites. Wolf spider levels of δ13C and δ15N are corrected by the mean Collembola measures from the site where they were collected. Collembola were not identified past Subclass.

Site Organism Life stage N δ15N δ13C Atigun wolf spider Juv 10 3.89 ± 0.823 0.765 ± 0.448 Toolik wolf spider Juv 10 4.553 ± 0.541 1.355 ± 0.769 Imnavait wolf spider Juv 10 6.08 ± 0.943 1.967 ± 0.631 Atigun P. lapponica Adult 8 4.113 ± 1.051 0.6625 ± 0.804 Toolik P. lapponica Adult 15 4.66 ± 0.568 1.371667 ± 0.467 Imnavait P. lapponica Adult 4 6.525 ± 0.492 2.067 ± 0.542 Atigun P. podhorskii Adult 8 5.838 ± 1.022 0.55 ± 0.969 Atigun Collembola - 4 1.2 25.775 ± 1.0275 (N=1) Toolik C ollembola - 4 0.767 ± 1.474 25.825 ± 0.585 (N=3) Imnavait C ollembola - 3 0.7 ± 0.566 26.467 ± 1.007 (N=2)

When compared to adults, and after accounting for site differences, juveniles from Toolik and Imnavait exhibit significantly less enrichment in δ15N (mixed effects model: t = -2.076, df = 61.16, p = 0.0421). This result indicates that while cannibalism and/or intraguild predation do occur in juveniles in some sites, it is likely more prevalent in adults. Levels of δ13C do not differ between juveniles and adults (mixed effects model: t = -1.119, df = 61.62, p = 0.268), meaning that the base of the food web is consistent across all life stages.

Overall, these findings suggest that intraspecific cannibalism in P. lapponica is size-dependent, and that any ecological changes that result in larger females may also result in a greater threat of predation to the smaller juvenile classes. In harsh environments like the Arctic, where growing seasons are typically short, selection for animals to consume high quality resources that maximize growth and or reproductive potential is likely to be strong (Elser et al. 2000, Jensen et al. 2011). These conditions are conducive to the evolution of cannibalism because conspecifics can be high-quality food sources that closely match an individual’s nutritional needs (Wildy et al. 1998, Snyder et al. 2000, William F. Fagan et al. 2002). It is plausible that larger individuals engage in this behavior more often because of potentially different nutritional demands and their higher chances of capturing and killing conspecifics (Rypstra and Samu 2005, Brose et al.

2006).

Female P. lapponica at Atigun are smaller than at Toolik (One-tailed t-test: t = -

3.625, df = 15.997, p = 0.00114), making them the smallest of all three sites. The levels of

δ15N in these Atigun females are not different than those from Toolik (Tukey’s HSD, p =

0.225). However, they are less enriched than those from Imnavait (Tukey’s HSD, p<0.0001), as would be expected from a positive correlation between cannibalism and body size. Nevertheless, the Atigun data remind us that other ecological factors may also play a role in determining spider feeding behavior. For example, levels of δ13C in female P. lapponica are significantly lower at Atigun than at the other two sites (One-way

ANOVA and Tukey’s multiple-comparison test, p = 0.00208; Fig. 10A; Table 7), suggesting that these individuals — or their prey — are feeding on something different than their conspecifics at Imnavait and Toolik. This difference in basal resources at

Atigun makes the comparison of spider trophic positions more difficult. In addition, the

Atigun wolf spider community is dominated by P. podhorskii, not P. lapponica. The low abundance of P. lapponica at this site (22%; Table 6) indicates that interspecific interactions may potentially limit P. lapponica’s food choices and trophic level when compared to the other sites. These findings emphasize the importance of considering ecological context when exploring geographic patterns of variation in the feeding ecology and/or population dynamics of these important arctic predators.

The results from this study suggest that across the tundra landscape, there is significant variation in the population structure of wolf spiders, which is driven by

differences in female spider body sizes. In female wolf spiders, body size is well documented as being a phenotypically plastic trait which is responsive to both environmental conditions (Høye et al. 2009) and prey availability (Reed and Nicholas

2008). The Arctic region has a high degree of habitat heterogeneity where even over small temporal and spatial scales, animals can experience a wide range of environmental conditions (Stow et al. 2004, Elmendorf et al. 2012a). Site-specific differences in wolf spider body sizes and population structures suggest that there is also variation in predator pressure by wolf spiders on lower trophic levels across the Arctic. Specifically, predation pressure may vary negatively with body size if cannibalism is supplementing the diets of larger spiders and there are, as a result, fewer hunting juveniles.

These findings have important implications for our understanding of the potential response of arctic ecosystems to climate change. Recent research has shown that the longer growing seasons produced by recent climate change have resulted in larger wolf spiders in high-arctic Greenland (Høye et al. 2009). Current theory suggests that larger body sizes should increase female fecundity (Marshall and Gittleman 1994), which may result in larger spider populations in the Arctic. These larger populations may in turn intensify predation pressure on detritivores (Wise 2004), indirectly releasing the microbial community from predation, and result in a trophic cascade that increases decomposition rates (Lawrence and Wise 2004, Koltz and Wright this thesis, Chapter 2).

However, our findings suggest that climate-related increases in spider body size could

instead result in a shift toward increased rates of intraspecific cannibalism. This shift in feeding behavior could potentially relieve the detritivores of some predation pressure and indirectly slow decomposition rates (Koltz and Wright this thesis, Chapter 2), thereby reducing the loss of stored carbon from the tundra.

In conclusion, I have shown that processes that cause an increase in wolf spider body size can result in higher cannibalism rates among female arctic wolf spiders and that this behavioral response can outweigh the well-known fecundity benefits of larger females and result in unexpected population structures. These insights call into question the basic assumption that larger and more abundant females will lead to higher numbers of offspring within a population. Future studies may benefit from exploring how these results will affect the population viability of arctic wolf spiders and how these behavioral responses are ultimately affecting local ecosystem processes and global carbon dynamics.

4.3 Methods

4.3.1 Field sampling

I sampled wolf spiders at three sites (Atigun, Toolik, and Imnavait) on the North

Slope of Alaska during the summer of 2012 (Fig. 8). The sites are geographically close and experience similar daily environmental fluctuations but vary in their timing of spring snowmelt and fall snow accumulation. To account for the seasonal differences between sites and their potential effects on spider phenology (Høye and Forchhammer

2008b), I sampled spiders approximately two weeks, one month, and two months past snowmelt at each site. Atigun was only sampled at one and two months past snowmelt.

Sampling dates and the dates of spring snowmelt and fall snow accumulation for each site are provided in Table 8.

Table 8. Day of year (DOY) of sampling dates and the start and end of the 2012 growing season at each of the study sites.

Spring Fall snow Sample Sample Sample Site snowmelt cover 1 2 3 Atigun 138 281 NA 164 192 Toolik 152 270 165 183 206 Imnavait 155 281 172 190 213

I sampled the wolf spider communities using grids of pitfall traps with the traps placed a meter apart from one another. Two weeks and one month past snowmelt, I sampled each site using a single 10x10 meter sampling grid. At two months past snowmelt, I sampled the sites with four 5x5 meter grids of pitfall traps; each grid was separated by at least thirty meters. Weather is known to affect wolf spider activity and catch densities (Høye and Forchhammer 2008a), so I only sampled on sunny days. Pitfall traps contained 75% ethanol and were left out for 24 hours during each sampling period, except during the sampling that occurred one month past snowmelt at Imnavait. In that case, traps were left out for 48 hours due to unforeseen poor weather; spiders from those samples were excluded from density comparisons between sites. I also hand-collected 62

gravid female wolf spiders from each site on several occasions throughout the summer order to estimate rates of egg sac parasitism.

4.3.2 Sample processing

To determine wolf spider body sizes at each site, I measured the carapace width

(Hagstrum 1971, Pickavance 2001) of every adult female (N=193) and juvenile (N=791) using digital calipers (Diesella, Kolding, Denmark). I identified all female specimens to species according to (Dondale and Redner 1990), except for Pardosa concinna and Pardosa lapponica, which I classified as the same species (Sim et al. 2014). All samples were stored in 70% ethanol until further processing. While ethanol storage may affect carbon stable isotope ratios in invertebrates (Sticht et al. 2006), I assume that any effects of ethanol storage were similar across samples and therefore should not change the interpretation of the results.

4.3.3 Stable isotopes

In order to compare trophic positions between spider communities, stable isotope analyses were performed on a subset of juvenile wolf spiders and adult female

P. lapponica from each site and on several adult female P. podhorskii from Atigun (Table

7). I also measured the stable isotope ratios of a subset of Collembola that were removed from pitfall traps to use as site-specific isotopic baselines. I chose Collembola as the isotopic baseline here, because they are thought to be an important prey source of wolf spiders (Wise 2004), and as detritivores, they should reflect the differences in δ13C

between sites. Prior to isotope analyses, I dried the animals for two days at 60 °C and homogenized the spiders with a mortar and pestle. Because of their small size, up to 13

Collembola were pooled per sample.

Stable isotope ratios of animals were determined at the Duke Environmental

Stable Isotope Laboratory (DEVIL) using a Carlo-Erba NA1500 elemental analyzer feeding a Thermo Finnigan Delta Plus XL continuous flow mass spectrometer system. Ratios were calculated as δX in per ml (vs. atmospheric N2 for 15N and vs. VPDB for 13C) as ((Rsample− Rstandard)/Rstandard) x 1000, whereby R represents the heavy to light isotope ratio (i.e. 13C/12C or 15N/14N) and X is the target isotope. Reference materials from

The National Institute of Standards and Technology (NIST) and the U.S Geological

Survey (USGS), as well as internally calibrated standards were used for 3-point normalization of raw isotope data. The ratio of standards to samples analyzed was approximately 1/6.

4.3.4 Analyses

All statistical analyses were performed in R (RCoreTeam 2013). To determine whether the number and size of adult female wolf spiders were related to total population size, I used the lmer function of the lmerTest package (Kuznetsova et al.

2013, Bates et al. 2014) to fit a linear mixed effects model of the total number of wolf spiders across all species that were caught within each sampling grid to the number and average body size of female wolf spiders caught during the same time period from that

grid. Next, to test potential effects of these female measures on juvenile populations, I fit a mixed effects model of the number of juveniles to the number and average body size of females from each sampling grid. In order to account for differences in community composition of wolf spiders between sites, I restricted the female data in this second model to the species Pardosa lapponica and only included samples from Toolik and

Imnavait, where communities are dominated by P. lapponica (Table 6). This conservative approach also accounts for the fact that morphological identification of juvenile spiders is often unreliable due to the lack of visible external sex organs. For all mixed effects models, I included sampling date as a continuous covariate and site as a random effect. I reduced the models by eliminating non-significant fixed effects one at a time. Numbers of spiders (total, female, and juvenile) were corrected by the size of the sampling grid, and in the second model, the number of juveniles was log-transformed in order to conform to the assumptions of linear models. I estimated the R-squared value of the association between adult female and juvenile population numbers using methods described in (Nakagawa and Schielzeth 2013, Johnson 2014). I tested for differences in female body size between sites with one-way ANOVA and Tukey HSD post-hoc tests and compared rates of egg sac parasitism between Toolik and Imnavait by fitting a binomial GLM of parasitoid wasps in egg sacs (presence/absence) to sampling site.

Stable isotope values of δ13C and δ15N for each wolf spider are given as the differences to the mean signature values of Collembola from the spider’s sampling site. I

tested for site-level variation in levels of δ15N and δ13C in adult female P. lapponica and juveniles using one-way ANOVAs, followed by Tukey HSD post-hoc tests. In order to test for variation in stable isotope ratios between juveniles and female P. lapponica, I fit a linear mixed effects model of δ15N, with life stage as the main effect and site as a random effect and then repeated the process with δ13C as the dependent variable.

Conclusions

In this dissertation, I examined the effects of climate change on arctic arthropods across several levels of biological organization: species, populations, and communities.

Characterizing the role of arctic arthropods within the broader ecosystem is an essential step in helping us to identify the conditions under which climate change may alter their effects on key ecosystem processes. My dissertation integrates results from three complementary approaches, including the use of a long-term data set, a two-year manipulative experiment, and a comparative study. Several broad themes pertaining to climate change and the role of arthropods within arctic ecosystems have emerged from my research efforts.

First, while the short growing seasons and long life spans of many species in the

Arctic often make detecting community-wide changes challenging, the research presented here highlights the fact that arctic arthropods are sensitive to climatic warming both in the short- and long-term. For example, I found that the structure of the entire arthropod community shifted in response to warming over a 15-year period in the

High Arctic (Chapter 1). While herbivores and parasitoids responded positively to warming, the relative representation of detritivores within the community declined.

Furthermore, my experimental results demonstrate that short-term warming over just two growing seasons can change the top-down effects of predators and result in a restructuring of the soil community (Chapter 2). Together, these findings suggest that

arthropod communities and trophic interactions are already changing in conjunction with warming in the Arctic and that there are potential consequences for the broader arctic food web (Tulp and Schekkerman 2008, Bolduc et al. 2013, McKinnon et al. 2013) and ecosystem (e.g., Wolf et al. 2008, Mosbacher et al. 2013, Sistla et al. 2013).

Secondly, arthropods likely play an important role in mediating the effects of warming on ecosystem functioning in the Arctic. The finding that current arthropod food webs may contain relatively more herbivores and fewer detritivores than those in the recent past (Chapter 1) has direct relevance for the amount of carbon that remains fixed and/or stored in arctic ecosystems. Moreover, my experimental results show that warming can alter predator-prey dynamics such that the indirect effects of predators on ecosystem functions also change. For example, while decomposition occurs faster in places with higher densities of wolf spiders, under artificial warming, the activity of these same spiders results in slower decomposition relative to places with no spiders

(Chapter 2). The Arctic is a reservoir for half of the world’s terrestrial carbon (Smith et al. 2004, McGuire et al. 2009, Tarnocai et al. 2009), so these results have important implications for the rate at which this stored carbon may be released to the atmosphere.

If wolf spider populations increase under warming as predicted (Høye et al. 2009), we may expect them to provide an important ecosystem service by slowing rates of decomposition on the tundra. However, because there is likely variation in the extent to which wolf spiders depend on the belowground food web for resources (Chapter 3), we

must exercise caution in attempting to generalize the degree to which spiders influence carbon dynamics across the tundra landscape.

There is likely a high level of variation in the strength and nature of the effects of climate change throughout the Arctic. Despite it being an extreme environment, the heterogeneity in climatic and microhabitat conditions across this region mean that there are very distinct habitat and community types. My work from high-arctic Greenland demonstrates that communities in certain habitat types are more responsive to warming than others (Chapter 1). Moreover, in Alaska, site-specific variation in feeding behavior within the same predator species (Chapter 3) suggests that warming may have differential effects on predators and on predator-induced trophic cascades, even between sites within close proximity to one another. In order to fully understand the link between species, communities, and ecosystems in light of climate change, it may be particularly important to consider both the strength of environmental drivers and the legacy of site-specific conditions.

Climatic warming is occurring faster in the Arctic than anywhere else on the planet, and understanding the consequences of these changes is one of the greatest conservation challenges of the century. Addressing these consequences is particularly relevant in the Arctic, where species are under the most pressure to adapt to climate change, and where increased rates of decomposition of carbon stored in permafrost soils could create positive feedbacks to climate change. The work presented in this

dissertation will enhance the predictability of responses by specific functional groups, communities, and ecosystem processes to changes in predation and environmental conditions.

Appendix A

Comparison of wolf vs. wolf spider biomass

We estimate that the biomass of wolf spiders is roughly 95 times more than the biomass of grey wolves within the vicinity of Toolik Field Station on the North Slope of

Alaska.

Calculation of wolf spider biomass/km2

As our measure of wolf spider density/km2, we used the average density in non- manipulated plots of the end of our experiment (1.2 spider/m2). In order to estimate the average biomass of a single wolf spider, we created a size-mass model using 38 wolf spiders that we collected near our study site during the 2012 growing season. For each of these spiders, we measured the carapace width (Hagstrum 1971, Pickavance 2001) using digital calipers (Diesella, Kolding, Denmark) and then dried the spiders at 60°C for three days before weighing them to the nearest 0.001 mg. We fit a linear model with no intercept (y=mx+0) to these biomass and size data, whereby y was the square root transformed measure of biomass, m was the model coefficient, and x was carapace width

(y = (0.99304x + 0)2; R2 = 0.958). Next, we fit this carapace width-biomass model to an additional 400 wolf spiders that we had collected during the same period and whose carapace widths we had also measured. We calculated the average dry biomass of an individual wolf spider (across all life stages) as 4.279 mg and estimate that there are

approximately 5135 g/km2 of wolf spider biomass in the general area around Toolik

Field Station.

Calculation of grey wolf biomass/km2

Our estimate of the density of grey wolves on the North Slope of Alaska comes from a 2008 aerial survey that was done by Alaska Fish and Wildlife, in which there was evidence of 4.4 wolves per 1000 km2 (Carroll 2009). We took a conservative approach by recalculating wolf density within the suitable habitat over the same area by removing the area that was occupied by major rivers and water bodies using the USGS National

Hydrography Dataset (http://nhd.usgs.gov/data.html). After accounting for these major water bodies, the density of grey wolves becomes approximately 4.4 wolves per 975.7 km2 of terrestrial habitat. Assuming that average biomass for a wolf is 40 kg

(Environment-Yukon 2014) and that the dry weight of terrestrial mammals is approximately 30% of the wet weight, our estimate of grey wolf biomass on the Alaskan tundra is 54.12 g/km2.

References

A'Bear, A. D., L. Boddy, and T. Hefin Jones. 2012. Impacts of elevated temperature on the growth and functioning of decomposer fungi are influenced by grazing collembola. Global Change Biology 18:1823-1832.

ACIA. 2005. Arctic Climate Impact Assessment Cambridge University Press, Cambridge, UK.

Aerts, R. 2006. The freezer defrosting: global warming and litter decomposition rates in cold biomes. Journal of Ecology 94:713-724.

AMAP. 2011. Snow, water, ice and permafrost in the Arctic (SWIPA). Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway.

Asmus, A., N. Boelman, J. Wingfield, and L. Gough. unpublished data.

Asmus, A., and L. Gough. personal communication.

Asmus, A., A. Koltz, G. Shaver, and L. Gough. , in prep. A 24-year press fertilization experiment restructures arthropod communities with no net effects on biomass or abundance.

Bale, J. S. 1996. Insect cold hardiness: A matter of life and death. European Journal of Entomology 93:369-382.

Bale, J. S. 2002. Insects and low temperatures: from molecular biology to distributions and abundance. Philosophical Transactions of the Royal Society B: Biological Sciences 357:849-862.

Bale, J. S., and S. A. L. Hayward. 2010. Insect overwintering in a changing climate. The Journal of Experimental Biology 213:980-994.

Bale, J. S., M. R. Worland, and W. Block. 2001. Effects of summer frost exposures on the cold tolerance strategy of a sub-Antarctic beetle. Journal of Insect Physiology 47:1161-1167.

Barton, B. T., A. P. Beckerman, and O. J. Schmitz. 2009. Climate warming strengthens indirect interactions in an old-field food web. Ecology 90:2346-2351.

Barton, B. T., and O. J. Schmitz. 2009. Experimental warming transforms multiple predator effects in a grassland food web. Ecology Letters 12:1317-1325.

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2014. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7.

Beare, M. H., R. W. Parmelee, P. F. Hendrix, W. Cheng, D. C. Coleman, and D. A. Crossley. 1992. Microbial and Faunal Interactions and Effects on Litter Nitrogen and Decomposition in Agroecosystems. Ecological Monographs 62:569-591.

Billings, W. D. 1987. Carbon balance of Alaskan tundra and taiga ecosystems: past, present and future. Quaternary Science Reviews 6:165-177.

Bliss, L. C. 1971. Arctic and Alpine Plant Life Cycles. Annual Review of Ecology and Systematics 2:405-438.

Bliss, L. C., and N. N. Matveyeva. 1992. Circumpolar arctic vegetation. Pages 59–89 in J. R. Chapin FS III, Reynolds JF, Shaver GR and Svoboda J, editor. Arctic Ecosystems in a Changing Climate: An Ecophysiological Perspective. Academic Press, San Diego.

Block, W., N. R. Webb, S. Coulson, I. D. Hodkinson, and M. R. Worland. 1994. Thermal adaptation in the Arctic collembolan Onychiurus arcticus (Tullberg). Journal of Insect Physiology 40:715-722.

Bolduc, E., N. Casajus, P. Legagneux, L. McKinnon, H. G. Gilchrist, M. Leung, R. I. G. Morrison, D. Reid, P. A. Smith, C. M. Buddle, and J. Bêty. 2013. Terrestrial arthropod abundance and phenology in the Canadian Arctic: modelling resource availability for Arctic-nesting insectivorous birds. The Canadian Entomologist 145:155-170.

Bowden, J., R. Hansen, K. Olsen, and T. Høye. 2014. Habitat-specific effects of climate change on a low-mobility Arctic spider species. Polar Biology:1-10.

Bowden, J. J., and C. M. Buddle. 2012a. Egg sac parasitism of Arctic wolf spiders (Araneae: Lycosidae) from northwestern North America. Journal of Arachnology 40:348-350.

Bowden, J. J., and C. M. Buddle. 2012b. Life history of tundra-dwelling wolf spiders (Araneae: Lycosidae) from the Yukon Territory, Canada. Canadian Journal of Zoology 90:714-721.

Brose, U., T. Jonsson, E. L. Berlow, P. Warren, C. Banasek-Richter, L.-F. Bersier, J. L. Blanchard, T. Brey, S. R. Carpenter, M.-F. C. Blandenier, L. Cushing, H. A. Dawah, T. Dell, F. Edwards, S. Harper-Smith, U. Jacob, M. E. Ledger, N. D. Martinez, J. Memmott, K. Mintenbeck, J. K. Pinnegar, B. C. Rall, T. S. Rayner, D. C. Reuman, L. Ruess, W. Ulrich, R. J. Williams, G. Woodward, and J. E. Cohen. 2006. Consumer-Resource Body-Size Relationships in Natural Food Webs. Ecology 87:2411-2417.

Buddle, C. M. 2000. Life history of Pardosa moesta and Pardosa mackenziana (Araneae, Lycosidae) in Central Alberta, Canada. Journal of Arachnology 28:319-328.

Büntgen, U., L. Hellmann, W. Tegel, S. Normand, I. Myers-Smith, A. V. Kirdyanov, D. Nievergelt, and F. H. Schweingruber. 2015. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. Journal of Ecology 103:489-501.

Cahoon, S. M. P., P. F. Sullivan, E. Post, and J. M. Welker. 2012. Large herbivores limit CO2 uptake and suppress carbon cycle responses to warming in West Greenland. Global Change Biology 18:469-479.

Callaghan, T. V., B. Lars Olof, Y. Chernov, T. Chapin, T. R. Christensen, B. Huntley, R. A. Ims, M. Johansson, D. Jolly, S. Jonasson, N. Matveyeva, N. Panikov, W. Oechel, G. Shaver, and H. Henttonen. 2004. Effects on the Structure of Arctic Ecosystems in the Short- and Long-Term Perspectives. Ambio 33:436-447.

Carroll, G. M. 2009. Unit 26A wolf management report. Juneau, AK.

Chapin, F. S., B. H. Walker, R. J. Hobbs, D. U. Hooper, J. H. Lawton, O. E. Sala, and D. Tilman. 1997. Biotic Control over the Functioning of Ecosystems. Science 277:500- 504.

Chapin III, F. S., Matson, Pamela A., Vitousek, Peter. 2012. Principles of Terrestrial Ecosystem Ecology. 2nd edition. Springer, New York.

Chapman, S. K., S. C. Hart, N. S. Cobb, T. G. Whitham, and G. W. Koch. 2003. Insect herbivory increases litter quality and decomposition: An extension of the acceleration hypothesis. Ecology 84:2867-2876.

Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18:117-143.

Coulson, S. J., I. D. Hodkinson, C. Wooley, N. R. Webb, W. Block, M. R. Worland, J. S. Bale, and A. T. Strathdee. 1996. Effects of experimental temperature elevation on high-arctic soil microarthropod populations. Polar Biology 16:147-153.

Coulson, S. J., H. P. Leinaas, R. A. Ims, and G. Søvik. 2000. Experimental manipulation of the winter surface ice layer: the effects on a High Arctic soil microarthropod community. Ecography 23:299-306.

Daniëls, F. J. A., J. G. de Molenaar, M. Chytrý, and L. Tichý. 2011. Vegetation change in Southeast Greenland? Tasiilaq revisited after 40 years. Applied Vegetation Science 14:230-241.

Danks, H. V. 1992. Arctic Insects as Indicators of Environmental Change. 1992 45.

Danks, H. V. 2004. Seasonal Adaptations in Arctic Insects. Integrative and Comparative Biology 44:85-94.

Davidson, E. A., and I. A. Janssens. 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440:165-173. de Ruiter, P., Moore JC , Wolters V. 2006. Dynamic Food Webs: Multispecies Assemblages, Ecosystem Development and Environmental Change. Elsevier, Amsterdam. de Sassi, C., O. T. Lewis, and J. M. Tylianakis. 2012. Plant-mediated and nonadditive effects of two global change drivers on an insect herbivore community. Ecology 93:1892-1901. de Sassi, C., and J. M. Tylianakis. 2012. Climate Change Disproportionately Increases Herbivore over Plant or Parasitoid Biomass. PLoS ONE 7:e40557.

Dondale, C., and J. Redner. 1990. The wolf spiders, nursery web spiders, and lynx spiders of Canada and Alaska: Araneae: Lycosidae, Pisauridae and Oxyopidae. Insects and Arachnids of Canada Handbook. Agriculture Canada, Ottawa.

Dondale, C. D. 1961. Life histories of some common spiders from trees and shrubs in Nova Scotia. Canadian Journal of Zoology 39:777-787.

Elberling, B., M. P. Tamstorf, A. Michelsen, M. F. Arndal, C. Sigsgaard, L. Illeris, C. Bay, B. U. Hansen, T. R. Christensen, E. S. Hansen, B. H. Jakobsen, and L. Beyens. 2008. Soil and Plant Community-Characteristics and Dynamics at Zackenberg. Pages 223-248 in T. R. C. B. E. M. C. F. Hans Meltofte and R. Morten, editors. Advances in Ecological Research. Academic Press.

Elmendorf, S. C., G. H. R. Henry, R. D. Hollister, R. G. Björk, A. D. Bjorkman, T. V. Callaghan, L. S. Collier, E. J. Cooper, J. H. C. Cornelissen, T. A. Day, A. M. Fosaa, W. A. Gould, J. Grétarsdóttir, J. Harte, L. Hermanutz, D. S. Hik, A. Hofgaard, F. Jarrad, I. S. Jónsdóttir, F. Keuper, K. Klanderud, J. A. Klein, S. Koh, G. Kudo, S. I. Lang, V. Loewen, J. L. May, J. Mercado, A. Michelsen, U. Molau, I. H. Myers- Smith, S. F. Oberbauer, S. Pieper, E. Post, C. Rixen, C. H. Robinson, N. M. Schmidt, G. R. Shaver, A. Stenström, A. Tolvanen, Ø. Totland, T. Troxler, C.-H. Wahren, P. J. Webber, J. M. Welker, and P. A. Wookey. 2012a. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecology Letters 15:164-175.

Elmendorf, S. C., G. H. R. Henry, R. D. Hollister, R. G. Bjork, N. Boulanger-Lapointe, E. J. Cooper, J. H. C. Cornelissen, T. A. Day, E. Dorrepaal, T. G. Elumeeva, M. Gill, W. A. Gould, J. Harte, D. S. Hik, A. Hofgaard, D. R. Johnson, J. F. Johnstone, I. S. Jonsdottir, J. C. Jorgenson, K. Klanderud, J. A. Klein, S. Koh, G. Kudo, M. Lara, E. Levesque, B. Magnusson, J. L. May, J. A. Mercado-Diaz, A. Michelsen, U. Molau, I. H. Myers-Smith, S. F. Oberbauer, V. G. Onipchenko, C. Rixen, N. Martin Schmidt, G. R. Shaver, M. J. Spasojevic, o. E. orhallsdottir, A. Tolvanen, T. Troxler, C. E. Tweedie, S. Villareal, C.-H. Wahren, X. Walker, P. J. Webber, J. M. Welker, and S. Wipf. 2012b. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nature Clim. Change 2:453-457.

Elser, O’Brien, Dobberfuhl, and Dowling. 2000. The evolution of ecosystem processes: growth rate and elemental stoichiometry of a key herbivore in temperate and arctic habitats. Journal of Evolutionary Biology 13:845-853.

Environment-Yukon. 2014. Grey Wolf.

Faber, J. H., and E. N. G. Joosse. 1993. Vertical distribution of Collembola in a Pinus nigra organic soil. Pedobiologia 37:336-350.

Finke, D. L., and R. F. Denno. 2003. Intra-guild predation relaxes natural enemy impacts on herbivore populations. Ecological Entomology 28:67-73.

Forbes, B. C., M. M. Fauria, and P. Zetterberg. 2010. Russian Arctic warming and ‘greening’ are closely tracked by tundra shrub willows. Global Change Biology 16:1542-1554.

Ford, M. 1977. Metabolic costs of the predation strategy of the spider Pardosa amentata (Clerck) (Lycosidae). Oecologia 28:333-340.

Frampton, G. K., P. J. Van den Brink, and S. D. Wratten. 2001. Diel activity patterns in an arable collembolan community. Applied Soil Ecology 17:63-80.

Gilbert, B., T. D. Tunney, K. S. McCann, J. P. DeLong, D. A. Vasseur, V. Savage, J. B. Shurin, A. I. Dell, B. T. Barton, C. D. G. Harley, H. M. Kharouba, P. Kratina, J. L. Blanchard, C. Clements, M. Winder, H. S. Greig, and M. I. O'Connor. 2014. A bioenergetic framework for the temperature dependence of trophic interactions. Ecology Letters 17:902-914.

Gilg, O., K. M. Kovacs, J. Aars, J. Fort, G. Gauthier, D. Grémillet, R. A. Ims, H. Meltofte, J. Moreau, E. Post, N. M. Schmidt, G. Yannic, and L. Bollache. 2012. Climate change and the ecology and evolution of Arctic vertebrates. Annals of the New York Academy of Sciences 1249:166-190.

González, G., and T. R. Seastedt. 2001. Soil Fauna and Plant Litter Decomposition in Tropical and Subalpine Forests. Ecology 82:955-964.

Gough, L., J. C. Moore, G. R. Shaver, R. T. Simpson, and D. R. Johnson. 2012. Above- and belowground responses of arctic tundra ecosystems to altered soil nutrients and mammalian herbivory. Ecology 93:1683-1694.

Hagstrum, D. W. 1971. Carapace width as a tool for evaluating the rate of development of spiders in the laboratory and the field. Annals of the Entomological Society of America 64:757-760.

Hansen, B., C. Sigsgaard, L. Rasmussen, J. Cappelen, J. Hinkler, S. Mernild, D. Petersen, M. Tamstorf, M. Rasch, and B. Hashold. 2008. Present-Day Climate at Zackenberg. Pages 111-147 in H. Meltofte, T. Christensen, B. Elberling, M. Forchhammer, and M. Rasch, editors. High-Arctic Ecosystem Dynamics in a Changing Climate.

Hill, G. B., and G. H. R. Henry. 2011. Responses of High Arctic wet sedge tundra to climate warming since 1980. Global Change Biology 17:276-287.

Hinzman, L., N. Bettez, W. R. Bolton, F. S. Chapin, M. Dyurgerov, C. Fastie, B. Griffith, R. Hollister, A. Hope, H. Huntington, A. Jensen, G. Jia, T. Jorgenson, D. Kane, D. Klein, G. Kofinas, A. Lynch, A. Lloyd, A. D. McGuire, F. Nelson, W. Oechel, T. Osterkamp, C. Racine, V. Romanovsky, R. Stone, D. Stow, M. Sturm, C. Tweedie, G. Vourlitis, M. Walker, D. Walker, P. Webber, J. Welker, K. Winker, and K. Yoshikawa. 2005. Evidence and Implications of Recent Climate Change in Northern Alaska and Other Arctic Regions. Climatic Change 72:251-298.

Hodkinson, I. D., and S. J. Coulson. 2004. Are high Arctic terrestrial food chains really that simple? – The Bear Island food web revisited. Oikos 106:427-431.

Hodkinson, I. D., N. R. Webb, J. S. Bale, W. Block, S. J. Coulson, and A. T. Strathdee. 1998. Global Change and Arctic Ecosystems: Conclusions and Predictions from Experiments with Terrestrial Invertebrates on Spitsbergen. Arctic and Alpine Research 30:306-313.

Hoekman, D. 2010. Turning up the heat: Temperature influences the relative importance of top-down and bottom-up effects. Ecology 91:2819-2825.

Hollister, R. D., P. J. Webber, and C. E. Tweedie. 2005. The response of Alaskan arctic tundra to experimental warming: differences between short- and long-term responses. Global Change Biology 11:525-536.

Høye, T., and M. Forchhammer. 2008a. The influence of weather conditions on the activity of high-arctic arthropods inferred from long-term observations. BMC Ecology 8:8.

Høye, T., and M. Forchhammer. 2008b. Phenology of high-arctic arthropods: effects of climate on spatial, seasonal and inter-annual variation. Adv Ecol Res 40:299 - 324.

Høye, T. T., Anne Eskildsen, Rikke R. Hansen, Joseph J. Bowden, Niels M. Schmidt, and W. D. Kissling. 2014. Phenology of high-arctic butterflies and their floral resources: Species-specific responses to climate change Current Zoology 60:243- 251.

Høye, T. T., J. U. Hammel, T. Fuchs, and S. Toft. 2009. Climate change and sexual size dimorphism in an Arctic spider. Biology Letters 5:542-544.

Høye, T. T., E. Post, N. M. Schmidt, K. Trojelsgaard, and M. C. Forchhammer. 2013. Shorter flowering seasons and declining abundance of flower visitors in a warmer Arctic. Nature Clim. Change 3:759-763.

Hudson, J. M. G., and G. H. R. Henry. 2010. High Arctic plant community resists 15 years of experimental warming. Journal of Ecology 98:1035-1041.

IPCC. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

Iverson, S. A., H. G. Gilchrist, P. A. Smith, A. J. Gaston, and M. R. Forbes. 2014. Longer ice-free seasons increase the risk of nest depredation by polar bears for colonial breeding birds in the Canadian Arctic. Proceedings of the Royal Society B: Biological Sciences 281.

Jefferies, R. L., D. R. Klein, and G. R. Shaver. 1994. Vertebrate Herbivores and Northern Plant Communities: Reciprocal Influences and Responses. Oikos 71:193-206.

Jensen, K., D. Mayntz, S. Toft, D. Raubenheimer, and S. J. Simpson. 2011. Nutrient regulation in a predator, the wolf spider Pardosa prativaga. Animal Behaviour 81:993-999.

Jensen, L. M. 2012. Zackenberg Ecological Research Operations, 17th Annual Report, 2011. Roskilde, DCE – Danish Centre for Environment and Energy, Aarhus University, Denmark.

Johnson, P. C. D. 2014. Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models. Methods in Ecology and Evolution 5:944-946.

Kajak, A. 1995. The role of soil predators in decomposition processes. European Journal of Entomology 92:573-580.

Kajak, A., K. Chmielewski, M. Kaczmarek, and E. Rembialkowska. 1993. Experimental studies on the effect of epigeic predators on matter decomposition process in managed peat grassland. Polish Ecological Studies 17:289–310.

Kaspari, M., and S. P. Yanoviak. 2009. Biogeochemistry and the structure of tropical brown food webs. Ecology 90:3342-3351.

Koltz, A., and J. Wright. this thesis, Chapter 2. Predator effects on a soil food web can buffer carbon losses in a warmer Arctic.

Koltz, A. M., A. Asmus, L. Gough, R. Simpson, G. Shaver, K. Holfelder, and J. C. Moore. in prep. A severe wildfire restructures tundra food webs.

Konestabo, H. S., A. Michelsen, and M. Holmstrup. 2007. Responses of springtail and mite populations to prolonged periods of soil freeze-thaw cycles in a sub-arctic ecosystem. Applied Soil Ecology 36:136-146.

Kruse, P. D., S. Toft, and K. D. Sunderland. 2008. Temperature and prey capture: opposite relationships in two predator taxa. Ecological Entomology 33:305-312.

Kuznetsova, A., P. B. Brockhoff, and R. H. B. Christensen. 2013. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version:2.0-0.

Lamb, E. G., S. Han, B. D. Lanoil, G. H. R. Henry, M. E. Brummell, S. Banerjee, and S. D. Siciliano. 2011. A High Arctic soil ecosystem resists long-term environmental manipulations. Global Change Biology 17:3187-3194.

Lang, B., B. C. Rall, and U. Brose. 2012. Warming effects on consumption and intraspecific interference competition depend on predator metabolism. Journal of Animal Ecology 81:516-523.

Lang, B., B. C. Rall, S. Scheu, and U. Brose. 2014. Effects of environmental warming and drought on size-structured soil food webs. Oikos 123:1224-1233.

Lawrence, K. L., and D. H. Wise. 2000. Spider predation on forest-floor Collembola and evidence for indirect effects on decomposition. Pedobiologia 44:33-39.

Lawrence, K. L., and D. H. Wise. 2004. Unexpected indirect effect of spiders on the rate of litter disappearance in a deciduous forest. Pedobiologia 48:149-157.

Lenoir, L., T. Persson, J. Bengtsson, H. Wallander, and A. Wirén. 2007. Bottom–up or top–down control in forest soil microcosms? Effects of soil fauna on fungal biomass and C/N mineralisation. Biology and Fertility of Soils 43:281-294.

Lensing, J. R., and D. H. Wise. 2006. Predicted climate change alters the indirect effect of predators on an ecosystem process. Proceedings of the National Academy of Sciences 103:15502-15505.

Luoto, T., S. Brooks, and V.-P. Salonen. 2014. Ecological responses to climate change in a bird-impacted High Arctic pond (Nordaustlandet, Svalbard). Journal of Paleolimnology 51:87-97.

Magurran, A. E., S. R. Baillie, S. T. Buckland, J. M. Dick, D. A. Elston, E. M. Scott, R. I. Smith, P. J. Somerfield, and A. D. Watt. 2010. Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends in Ecology & Evolution 25:574-582.

Marion, G. M., Henry, G. H. R., Mølgaard, P., Oechel, W.C., Jones, M. H., and Vourlitis, G. 1993. Passive techniques for manipulating field temperatures in tundra ecosystems. Hanover, NH.

Mark, A. F., N. Fetcher, G. R. Shaver, and F. S. C. Iii. 1985. Estimated Ages of Mature Tussocks of Eriophorum vaginatum along a Latitudinal Gradient in Central Alaska, U.S.A. Arctic and Alpine Research 17:1-5.

Markkula, I. 2014. Permafrost dynamics structure species compositions of oribatid mite (Acari: Oribatida) communities in sub-Arctic palsa mires. 2014.

Marshall, S. D., and J. L. Gittleman. 1994. Clutch Size in Spiders: Is More Better? Functional Ecology 8:118-124.

McCune, B. a. J. B. G. 2002. Analysis of Ecological Communities. MjM Software, Gleneden Beach, Oregon, USA.

McGuire, A. D., L. G. Anderson, T. R. Christensen, S. Dallimore, L. Guo, D. J. Hayes, M. Heimann, T. D. Lorenson, R. W. Macdonald, and N. Roulet. 2009. Sensitivity of the carbon cycle in the Arctic to climate change. Ecological Monographs 79:523- 555.

McGuire, A. D., F. S. Chapin, J. E. Walsh, and C. Wirth. 2006. Integrated Regional Changes in Arctic Climate Feedbacks: Implications for the Global Climate System*. Annual Review of Environment and Resources 31:61-91.

McKinnon, L., E. Nol, and C. Juillet. 2013. Arctic-nesting birds find physiological relief in the face of trophic constraints. Sci. Rep. 3.

McNabb, D. M., J. Halaj, and D. H. Wise. 2001. Inferring trophic positions of generalist predators and their linkage to the detrital food web in agroecosystems: a stable isotope analysis. Pedobiologia 45:289-297.

Minchin, P. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69:89-107.

Moore, J. C., and P.C. de Ruiter. 2012. Energetic Food Webs: An Analysis of Real and Model Ecosystems. Oxford University Press, Oxford, UK.

Moore, J. C., D. E. Walter, and H. W. Hunt. 1988. Arthropod Regulation of Micro- and Mesobiota in Below-Ground Detrital Food Webs. Annual Review of Entomology 33:419-435.

Moorhead, D. L., and J. F. Reynolds. 1993. Effects of Climate Change on Decomposition in Arctic Tussock Tundra: A Modeling Synthesis. Arctic and Alpine Research 25:403-412.

Morris, W., and D. Doak. 1998. Life history of the long-lived gynodioecious cushion plant Silene acaulis (Caryophyllaceae), inferred from size-based population projection matrices. American Journal of Botany 85:784.

Mosbacher, J., N. Schmidt, and A. Michelsen. 2013. Impacts of eriophyoid gall mites on arctic willow in a rapidly changing Arctic. Polar Biology 36:1735-1748.

Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133-142.

Nielsen, U. N., and D. H. Wall. 2013. The future of soil invertebrate communities in polar regions: different climate change responses in the Arctic and Antarctic? Ecology Letters 16:409-419.

O'Connor, M. I., M. F. Piehler, D. M. Leech, A. Anton, and J. F. Bruno. 2009. Warming and Resource Availability Shift Food Web Structure and Metabolism. Plos Biology 7.

Oksanen, J., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O'Hara, R.B., Simpson, G., Solymos, P., Stevens, M., and H. Wagner 2013. vegan: Community Ecology Package. R package version 2.0-10.

Olesen, J. M., J. Bascompte, H. Elberling, and P. Jordano. 2008. Temporal dynamics in a pollination network. Ecology 89:1573-1582.

Olofsson, J., M. te Beest, and L. Ericson. 2013. Complex biotic interactions drive long- term vegetation dynamics in a subarctic ecosystem. Philosophical Transactions of the Royal Society B: Biological Sciences 368.

Osborne, J. 2002. Notes on the use of data transformations. Practical Assessment, Research & Evaluation 8.

Parmesan, C. 2006. Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics 37:637-669.

Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin. 1999. Environmental warming alters food-web structure and ecosystem function. Nature 402:69-72.

Pickavance, J. R. 2001. Life-cycles of four species of Pardosa (Araneae, Lycosidae) from the island of Newfoundland, Canada. Journal of Arachnology 29:367-377.

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. and R Core Team. 2014. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-117.

Ponsard, S., and R. Arditi. 2000. What can stable isotopes (δ15N and δ13C) tell about the food web of soil macro-invertebrates? Ecology 81:852-864.

Post, D. M. 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83:703-718.

Post, E., M. C. Forchhammer, M. S. Bret-Harte, T. V. Callaghan, T. R. Christensen, B. Elberling, A. D. Fox, O. Gilg, D. S. Hik, T. T. Høye, R. A. Ims, E. Jeppesen, D. R. Klein, J. Madsen, A. D. McGuire, S. Rysgaard, D. E. Schindler, I. Stirling, M. P. Tamstorf, N. J. C. Tyler, R. van der Wal, J. Welker, P. A. Wookey, N. M. Schmidt, and P. Aastrup. 2009. Ecological Dynamics Across the Arctic Associated with Recent Climate Change. Science 325:1355-1358.

Post, E., and C. Pedersen. 2008. Opposing plant community responses to warming with and without herbivores. Proceedings of the National Academy of Sciences 105:12353-12358.

Pouliot, D., R. Latifovic, and I. Olthof. 2008. Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006. International Journal of Remote Sensing 30:149-168.

Prach, K., J. Košnar, J. Klimešová, and M. Hais. 2010. High Arctic vegetation after 70 years: a repeated analysis from Svalbard. Polar Biology 33:635-639.

Quinlan, R., M. S. V. Douglas, and J. P. Smol. 2005. Food web changes in arctic ecosystems related to climate warming. Global Change Biology 11:1381-1386.

Rall, B. C., O. Vucic-Pestic, R. B. Ehnes, M. Emmerson, and U. Brose. 2010. Temperature, predator-prey interaction strength and population stability. Global Change Biology 16:2145-2157.

RCoreTeam. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Reed, D. H., and A. C. Nicholas. 2008. Spatial and temporal variation in a suite of life- history traits in two species of wolf spider. Ecological Entomology 33:488-496.

Reville, W. 2015. psych: Procedures for Personality and Psychological Research. Northwestern University, Evanston, Illinois, USA.

Richardson, S. J., M. C. Press, A. N. Parsons, and S. E. Hartley. 2002. How do nutrients and warming impact on plant communities and their insect herbivores? A 9-year study from a sub-Arctic heath. Journal of Ecology 90:544-556.

Root, T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rosenzweig, and J. A. Pounds. 2003. Fingerprints of global warming on wild animals and plants. Nature 421:57-60.

Ruess, L., Michelsen, A., Schmidt, I. K., & Jonasson, S. 1999. Simulated climate change in subarctic soils: responses in nematode species composition and dominance structure. 1:513-526.

Rypstra, A. L., and F. Samu. 2005. Size dependent intraguild predation and cannibalism in coexisting wolf spiders (Araneae, Lycosidae). Journal of Arachnology 33:390- 397.

Scheu, S. 2001a. Plants and generalist predators as links between the below-ground and above-ground system. Basic and Applied Ecology 2:3-13.

Scheu, S. a. H. S. 2001b. Multitrophic interactions in decomposer food webs. Pages 223– 264 in a. B. A. H. T. Tscharntke, editor. Multitrophic Interactions in Terrestrial Systems. Cambridge University Press, Cambridge.

Schmidt, N. M. 2012. BioBasis Manual. Conceptual design and sampling procedures of the biological monitoring programme within Zackenberg Basic. . Department of Bioscience, Aarhus University.

Schmidt, N. M., D. K. Kristensen, A. Michelsen, and C. Bay. 2012. High Arctic plant community responses to a decade of ambient warming. Biodiversity 13:191-199.

Schmitz, O. J. 2013. 4.17 - Terrestrial Food Webs and Vulnerability of the Structure and Functioning of Ecosystems to Climate. Pages 213-222 in R. A. Pielke, editor. Climate Vulnerability. Academic Press, Oxford.

Schuur, E. A. G., J. Bockheim, J. G. Canadell, E. Euskirchen, C. B. Field, S. V. Goryachkin, S. Hagemann, P. Kuhry, P. M. Lafleur, H. Lee, G. Mazhitova, F. E. Nelson, A. Rinke, V. E. Romanovsky, N. Shiklomanov, C. Tarnocai, S. Venevsky, J. G. Vogel, and S. A. Zimov. 2008. Vulnerability of Permafrost Carbon to Climate Change: Implications for the Global Carbon Cycle. BioScience 58:701-714.

Schuur, E. A. G., J. G. Vogel, K. G. Crummer, H. Lee, J. O. Sickman, and T. E. Osterkamp. 2009. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459:556-559.

Seastedt, T. R. 1984. The Role of Microarthropods in Decomposition and Mineralization Processes. Annual Review of Entomology 29:25-46.

Serreze, M. C., and R. G. Barry. 2011. Processes and impacts of Arctic amplification: A research synthesis. Global and Planetary Change 77:85-96.

Serreze, M. C., J. E. Walsh, F. S. Chapin, T. Osterkamp, M. Dyurgerov, V. Romanovsky, W. C. Oechel, J. Morison, T. Zhang, and R. G. Barry. 2000. Observational

Evidence of Recent Change in the Northern High-Latitude Environment. Climatic Change 46:159-207.

Setälä, H., and V. Huhta. 1991. Soil Fauna Increase Betula Pendula Growth: Laboratory Experiments With Coniferous Forest Floor. Ecology 72:665-671.

Sim, K. A., C. M. Buddle, and T. A. Wheeler. 2014. Species boundaries of Pardosa concinna and P. lapponica (Araneae: Lycosidae) in the northern Nearctic: morphology and DNA barcodes. 2014 3884:10.

Simpson, M. R. 1993. Reproduction in two species of arctic arachnids, Pardosa glacialis and Alopecosa hirtipes. Canadian Journal of Zoology 71:451-457.

Sistla, S. A., J. C. Moore, R. T. Simpson, L. Gough, G. R. Shaver, and J. P. Schimel. 2013. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497:615-618.

Sjögersten, S., R. van der Wal, and S. Woodin. 2012. Impacts of Grazing and Climate Warming on C Pools and Decomposition Rates in Arctic Environments. Ecosystems 15:349-362.

Sjögersten, S., R. van der Wal, and S. J. Woodin. 2008. Habitat type determines herbivory controls over CO2 fluxes in a warmer arctic. Ecology 89:2103-2116.

Smith, L. C., G. M. MacDonald, A. A. Velichko, D. W. Beilman, O. K. Borisova, K. E. Frey, K. V. Kremenetski, and Y. Sheng. 2004. Siberian Peatlands a Net Carbon Sink and Global Methane Source Since the Early Holocene. Science 303:353-356.

Snyder, W. E., S. B. Joseph, R. F. Preziosi, and A. J. Moore. 2000. Nutritional Benefits of Cannibalism for the Lady Beetle Harmonia axyridis (Coleoptera: Coccinellidae) When Prey Quality is Poor. Environmental Entomology 29:1173-1179.

Snyder, W. E., and D. H. Wise. 2001. Contrasting trophic cascades generated by a community of generalist predators Ecology 82:1571-1583.

Speed, J. M., S. J. Woodin, H. Tømmervik, and R. van der Wal. 2010. Extrapolating herbivore-induced carbon loss across an arctic landscape. Polar Biology 33:789- 797.

Standen, V. 1978. The Influence of Soil Fauna on Decomposition by Micro-organisms in Blanket Bog Litter. Journal of Animal Ecology 47:25-38.

Stendel, M., J. Hesselbjerg Christensen, G. Aðalgeirsdóttir, N. Kliem, and M. Drews. 2007. Regional climate change for Greenland and surrounding seas. Part I: Atmosphere and land surface Danish Meteorological Institute Copenhagen.

Sticht, C., S. Schrader, and A. Giesemann. 2006. Influence of chemical agents commonly used for soil fauna investigations on the stable C-isotopic signature of soil animals. European Journal of Soil Biology 42, Supplement 1:S326-S330.

Stow, D. A., A. Hope, D. McGuire, D. Verbyla, J. Gamon, F. Huemmrich, S. Houston, C. Racine, M. Sturm, K. Tape, L. Hinzman, K. Yoshikawa, C. Tweedie, B. Noyle, C. Silapaswan, D. Douglas, B. Griffith, G. Jia, H. Epstein, D. Walker, S. Daeschner, A. Petersen, L. Zhou, and R. Myneni. 2004. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sensing of Environment 89:281-308.

Strathdee, A., and J. Bale. 1998. Life on the edge: insect ecology in arctic environments. Annu Rev Entomol 43:85 - 106.

Strathdee, A. T., J. S. Bale, W. C. Block, S. J. Coulson, I. D. Hodkinson, and N. R. Webb. 1993. Effects of temperature elevation on a field population of Acyrthosiphon svalbardicum (Hemiptera: Aphididae) on Spitsbergen. Oecologia 96:457-465.

Sulkava, P., and V. Huhta. 2003. Effects of hard frost and freeze-thaw cycles on decomposer communities and N mineralisation in boreal forest soil. Applied Soil Ecology 22:225-239.

Svoboda, J., and G. H. R. Henry. 1987. Succession in Marginal Arctic Environments. Arctic and Alpine Research 19:373-384.

Swift MJ, H. O., Anderson JM. 1979. Decomposition in terrestrial ecosystems. University of California Press, Berkeley, CA.

Tagesson, T., M. Mastepanov, M.P. Tamstorf, L. Eklundh, P. Schubert, A. Ekberg, C. Sigsgaard, T.R. Christensen, and L. Ström. 2010. Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008. Biogeosciences Discussions 7:1101-1129.

Tagesson, T., M. Mastepanov, M. P. Tamstorf, L. Eklundh, P. Schubert, A. Ekberg, C. Sigsgaard, T. R. Christensen, and L. Ström. 2012. High-resolution satellite data reveal an increase in peak growing season gross primary production in a high- Arctic wet tundra ecosystem 1992–2008. International Journal of Applied Earth Observation and Geoinformation 18:407-416.

Tape, K. E. N., M. Sturm, and C. Racine. 2006. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Global Change Biology 12:686-702.

Tarnocai, C., J. G. Canadell, E. A. G. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov. 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles 23:GB2023.

Tenow, O., and A. Nilssen. 1990. Egg Cold Hardiness and Topoclimatic Limitations to Outbreaks of Epirrita autumnata in Northern Fennoscandia. Journal of Applied Ecology 27:723-734.

Toft, S. 1999. Prey Choice and Spider Fitness. Journal of Arachnology 27:301-307.

Trenberth, K. E., Jones, P.D., Ambenje, P., Bojariu, R. et al. 2007. Observations: surface and atmospheric climate change. Pages 235–336 in Q. D. Solomon S, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, editor. Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, NY.

Tulp, I., and H. Schekkerman. 2008. Has Prey Availability for Arctic Birds Advanced with Climate Change? Hindcasting the Abundance of Tundra Arthropods Using Weather and Seasonal Variation. Arctic 61:48-60.

Tylianakis, J. M., R. K. Didham, J. Bascompte, and D. A. Wardle. 2008. Global change and species interactions in terrestrial ecosystems. Ecology Letters 11:1351-1363.

Várkonyi, G., and T. Roslin. 2013. Freezing cold yet diverse: dissecting a high-Arctic parasitoid community associated with Lepidoptera hosts. The Canadian Entomologist 145:193-218.

Vittoz, P., C. Randin, A. Dutoit, F. Bonnet, and O. Hegg. 2009. Low impact of climate change on subalpine grasslands in the Swiss Northern Alps. Global Change Biology 15:209-220.

Voigt, W., J. Perner, A. J. Davis, T. Eggers, J. Schumacher, R. Bährmann, B. Fabian, W. Heinrich, G. Köhler, D. Lichter, R. Marstaller, and F. W. Sander. 2003. Trophic levels are differentially sensitive to climate. Ecology 84:2444-2453.

Walker, M. D., C. H. Wahren, R. D. Hollister, G. H. R. Henry, L. E. Ahlquist, J. M. Alatalo, M. S. Bret-Harte, M. P. Calef, T. V. Callaghan, A. B. Carroll, H. E. Epstein, I. S. Jónsdóttir, J. A. Klein, B. Magnússon, U. Molau, S. F. Oberbauer, S. P. Rewa, C. H. Robinson, G. R. Shaver, K. N. Suding, C. C. Thompson, A. Tolvanen, Ø. Totland, P. L. Turner, C. E. Tweedie, P. J. Webber, and P. A. Wookey. 2006. Plant community responses to experimental warming across the tundra biome. Proceedings of the National Academy of Sciences of the United States of America 103:1342-1346.

Wall, D. H., M. A. Bradford, M. G. St. John, J. A. Trofymow, V. Behan-Pelletier, D. E. Bignell, J. M. Dangerfield, W. J. Parton, J. Rusek, W. Voigt, V. Wolters, H. Z. Gardel, F. O. Ayuke, R. Bashford, O. I. Beljakova, P. J. Bohlen, A. Brauman, S. Flemming, J. R. Henschel, D. L. Johnson, T. H. Jones, M. Kovarova, J. M. Kranabetter, L. E. S. Kutny, K.-C. Lin, M. Maryati, D. Masse, A. Pokarzhevskii, H. Rahman, M. G. SabarÁ, J.-A. Salamon, M. J. Swift, A. Varela, H. L. Vasconcelos, D. O. N. White, and X. Zou. 2008. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Global Change Biology 14:2661-2677.

Walther, G.-R. 2010. Community and ecosystem responses to recent climate change. Philosophical Transactions of the Royal Society B: Biological Sciences 365:2019- 2024.

Walther, G. R. 2004. Plants in a warmer world. Perspectives in Plant Ecology Evolution and Systematics 6:169-185.

Wardle, D. A. 2002. Communities and ecosystems: linking the aboveground and belowground components. Princeton University Press.

Wildy, E. L., D. P. Chivers, J. M. Kiesecker, and A. R. Blaustein. 1998. Cannibalism Enhances Growth in Larval Long-Toed Salamanders, (Ambystoma macrodactylum). Journal of Herpetology 32:286-289.

William F. Fagan, Evan Siemann, Charles Mitter, Robert F. Denno, Andrea F. Huberty, H. Arthur Woods, and James J. Elser. 2002. Nitrogen in Insects: Implications for

Trophic Complexity and Species Diversification. The American Naturalist 160:784-802.

Wirta, H. K., E. Weingartner, P. A. Hambäck, and T. Roslin. 2015. Extensive niche overlap among the dominant arthropod predators of the High Arctic. Basic and Applied Ecology 16:86-92.

Wise, D. H. 2004. Wandering spiders limit densities of a major microbi-detritivore in the forest-floor food web. Pedobiologia 48:181-188.

Wise, D. H. 2006. Cannabalism, food limitation, intraspecific competition, and the regulation of spider populations. Annual Review of Entomology 51:441-465.

Wise, D. H., W. E. Snyder, P. Tuntibunpakul, and J. Halaj. 1999. Spiders in Decomposition Food Webs of Agroecosystems: Theory and Evidence. Journal of Arachnology 27:363-370.

Wolf, A., M. Kozlov, and T. Callaghan. 2008. Impact of non-outbreak insect damage on vegetation in northern Europe will be greater than expected during a changing climate. Climatic Change 87:91-106.

Wookey, P. A., R. Aerts, R. D. Bardgett, F. Baptist, K. A. Bråthen, J. H. C. Cornelissen, L. Gough, I. P. Hartley, D. W. Hopkins, S. Lavorel, and G. R. Shaver. 2009. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Global Change Biology 15:1153-1172.

Wu, X., J. N. Griffin, and S. Sun. 2014. Cascading effects of predator–detritivore interactions depend on environmental context in a Tibetan alpine meadow. Journal of Animal Ecology 83:546-556.

Wyant, K. A., M. L. Draney, and J. C. Moore. 2011. Epigeal Spider (Araneae) Communities in Moist Acidic and Dry Heath Tundra at Toolik Lake, Alaska. Arctic, Antarctic, and Alpine Research 43:301-312.

Young, S. R., and W. Block. 1980. Experimental studies on the cold rolerance of Alaskozeles antarcticus. Journal of Insect Physiology 26:189-200.

Zuur, A. F., E. N. Ieno, and C. S. Elphick. 2010. A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution 1:3-14.

Biography

Amanda May Koltz was born in Binghamton, NY on November 2, 1983. After high school, she spent a year as a Rotary Youth Exchange student in Tournon, France before attending Cornell University. She earned her Bachelor of Science degree in

Biological Sciences (magna cum laude) in 2007, with a concentration in Ecology and

Evolutionary Biology and distinction in research.

Upon graduation, Amanda was a Fulbright Fellow at the University of Pavia,

Italy, where she did bioacoustics and behavioral research on mass strandings of beaked whales. Prior to coming to Duke in 2009, Amanda also spent time as a research assistant at the University of Groningen, in the Netherlands, and at North Carolina State

University.

While at Duke, Amanda was awarded several fellowships to support her doctoral work, including a National Science Foundation Graduate Research Fellowship,

George Melendez Wright Climate Change Fellowship through the National Parks

Service, and a Katherine Goodman Stern Fellowship. In addition, she received research funding from the National Geographic Society Committee for Research and Exploration, a NSF Doctoral Dissertation Improvement Grant, the NSF Office of Polar Programs, a

National Parks Service Discover Denali Research Fellowship, Conservation Research and Education Opportunities International (CREOi), Aarhus University in Denmark,

Western Ag Innovations, Earth and Space Foundation, North Carolina Wildlife 92

Federation, Kappa Delta Foundation, Arctic Institute of North America (AINA), The

Explorers Club, Lewis and Clark fund (American Philosophical Society), and the Arctic

Long-Term Ecological Research Site.

Amanda served as the graduate student representative to the Toolik Field Station

Steering Committee, as the Science Liaison at Toolik Field Station, as vice-president of the Duke Chapter of Sigma XI Scientific Research Society and as a representative to the

U.S. State Department Young Leaders Dialogue Conference in Prague. While at Duke, she also spent a significant amount time doing scientific outreach with underrepresented students in the Durham community, with rural and Alaska Native students in Alaska, and in Polar Science education with middle and high school teachers through the NSF- funded program PolarTREC (Teachers and Researchers Exploring and Collaborating).

Amanda is a member of the Ecological Society of America, the Entomological Society of

America, AAAS, and the Association of Polar Early Career Scientists.