Ecology and Evolution of Physiological Phenotypes in Garter Snakes (Thamnophis Spp.) Eric Joseph Gangloff Iowa State University

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2016 Ecology and evolution of physiological phenotypes in garter snakes (Thamnophis spp.) Eric Joseph Gangloff Iowa State University

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Ecology and evolution of physiological phenotypes in garter snakes (Thamnophis spp.)

Eric Joseph Gangloff

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Major: Ecology and Evolutionary Biology

Program of Study Committee: Anne M. Bronikowski, Major Professor Lance H. Baumgard Brent Danielson Philip Dixon Fredric Janzen

Iowa State University

Ames, Iowa

2016

DEDICATION

With immense gratitude to my family, who have always chased snakes with me.

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TABLE OF CONTENTS

Page

DEDICATION ...... ii

ACKNOWLEDGMENTS ...... v

ABSTRACT………………………………...... vii

CHAPTER 1 INTRODUCTION ...... 1

CHAPTER 2 GEOGRAPHIC VARIATION AND WITHIN-INDIVIDUAL CORRELATIONS OF PHYSIOLOGICAL STRESS MARKERS IN A WIDESPREAD REPTILE, THE COMMON GARTER SNAKE (THAMNOPHIS SIRTALIS) ...... 21

Abstract ...... 21 Introduction ...... 22 Materials and Methods ...... 26 Results ...... 33 Discussion ...... 35 Acknowledgements ...... 40 References ...... 41 Tables ...... 47 Figures ...... 51

CHAPTER 3 HORMONAL AND METABOLIC RESPONSES TO UPPER TEMPERATURE EXTREMES IN DIVERGENT LIFE-HISTORY ECOTYPES OF A GARTER SNAKE ...... 54

Abstract ...... 54 Introduction ...... 55 Materials and Methods ...... 60 Results ...... 69 Discussion ...... 70 Acknowledgements ...... 77 References ...... 78 Tables ...... 87 Figures ...... 90

CHAPTER 4 AMONG-INDIVIDUAL HETEROGENEITY IN BEHAVIOUR AND PHYSIOLOGY INTERACT TO INFLUENCE FITNESS IN A MODEL ECTOTHERMIC AMNIOTE, THE GARTER SNAKE THAMNOPHIS ELEGANS ...... 93

Abstract ...... 93 Introduction ...... 94 Materials and Methods ...... 102 Results ...... 108 Discussion ...... 111 Acknowledgements ...... 118 References ...... 119 Tables ...... 127 Figures ...... 131

CHAPTER 5 SUMMARY AND CONCLUSIONS ...... 135

APPENDIX A DEVELOPMENTAL AND IMMEDIATE THERMAL ENVIRONMENTS SHAPE ENERGETIC TRADE-OFFS, GROWTH EFFICIENCY, AND METABOLIC RATE IN DIVERGENT LIFE-HISTORY ECOTYPES OF THE GARTER SNAKE THAMNOPHIS ELEGANS ...... 137

APPENDIX B FAST- AND SLOW-LIVING SNAKES DIFFER IN INFORMATION-GATHERING AND ACTIVITY IN OPEN-FIELD TESTS ...... 152

ACKNOWLEDGMENTS

First, my family: Becky, James, and Anna, whose immense support and patience made possible this adventure, and all our others. For my mom and dad, who tirelessly supported my pursuits even when they involved bringing home wild animals that sometimes escaped in the house. Mark, who has never held a snake but loves me anyway. Jason, whose passion for science and learning continues to inspire me. My Nina, who thinks that what I do is the greatest thing in the world. And Carol, Bill, Melissa, Matt, Bill, Shayna, Caleb, Ceci, Billy, and Ryan, whose love and support is remarkable.

Thank you to Anne, a superlative scientist and incredible advisor. I am especially grateful for your patience and trusting support through this process. I’ve never had mentor for whom I’ve had more respect.

My committee members, both current and past, have provided invaluable guidance:

Lance Baumgard, Gordon Burghardt, Brent Danielson, Phil Dixon, Fred Janzen, Clint Kelly, and

David Vleck. I am grateful for all that I’ve learned from each of you.

My current collaborators, all of whom are also both mentors and friends: Antonio

Cordero, Vianey Leos-Barajas, Dave Miller, Tonia Schwartz, Amanda Sparkman, and Rory

Telemeco.

My lab mates have both pushed my learning and given friendship: Liz Addis, Kaitlyn

Holden, Dawn Reding, Gaby Palacios, Skiha Parsai, and Tonia Schwartz.

I have had the excellent fortune of working with some dedicated and insightful undergraduate students, who have contributed a great deal to the projects included here: Mitch

Barazowski, Caitlyn Corwin, Merritt Polomsky, Madeline Topf, and Alexander Wendt.

My fellow EEB/EEOB students have made me a better scientist and person, especially

Brooke Bodensteiner, Byran Juarez, Andy Kraemer, Rory Telemeco, Sean Satterlee, Melissa

Telemeco, Rebecca Polich, Alex Walton, and Amy Worthington.

The EEOB Department as a whole is tremendously supportive. I especially acknowledge the mentorship of faculty members Dean Adams, Bill Clark, John Nason, Jean Serb, and Amy

Toth. Clark Coffman has been a tremendously supportive teaching mentor.

And the families and friends who have kept our children safe and happy while I put in long hours and then had to the patience to listen to me talk about it afterward: the Toth-Miguez

Family, the Schmoorte Family, and the Glenn Family. You have made Ames a home for us.

And finally, thank you to Norm and Mira Boyczuk who continue to send me snake- themed birthday cards every year. See what you started?

vii

ABSTRACT

Physiology is the mechanistic link between how an individual organism experiences its external environment and higher-level biological effects. Since natural selection acts on whole phenotypes, understanding the evolution of physiology requires integration with other traits, such as behavior and life-history, at both the phenotypic and genetic levels. Accumulating evidence suggests that within-individual plasticity of physiological and behavioral traits is limited, resulting in stable individual phenotypes and differences among individuals. Using the model systems of two widespread North American vertebrates (the garter snakes Thamnophis sirtalis and T. elegans), the research presented here quantifies physiological variation across levels of biological organization: measures of multiple traits within individuals and repeated measures over time to assess within-individual correlations among markers; comparisons of populations adapted to different environments to assess evolutionary potential; and measures of among- individual differences in physiological and behavioral traits and subsequent consequences to reproductive output and fitness.

In the first study, we measured biomarkers of the stress response (stress hormone: plasma corticosterone; energy mobilization: plasma glucose; immune cell distribution: leukocyte ratios) in T. sirtalis from geographically disparate regions in response to a standardized restraint protocol. Snakes from mountain populations exhibited higher initial baseline concentrations of corticosterone but snakes from plains populations maintained elevated corticosterone and plasma glucose levels for a longer duration before returning to baseline levels, if at all. In the second study, divergent life-history ecotypes of T. elegans elevated corticosterone and glucose concentrations as well as metabolic rate non-linearly at high temperature extremes, while the

viii hormone insulin responded to temperature in a pattern suggesting that it plays a role in the thermal stress response beyond glucose regulation. The ecotypes did not differ in their response, indicating that neither local adaptation nor physiological plasticity allows populations from warmer environments to develop higher thermal tolerances. In the final study, we found that reproductive female T. elegans with matched behavioral and physiological phenotypes (either

‘high reactive-high reactive’ or ‘low reactive-low reactive’) gave birth to offspring in better body condition that, in turn, grew faster and lived longer than offspring from mothers with other phenotype combinations.

CHAPTER I

INTRODUCTION

Physiology provides the mechanistic link between how an organism experiences its environment and higher-level biological effects at the individual, population, species, and community levels. Since natural selection acts on whole phenotypes, understanding the evolutionary significance of physiological traits requires an integration of physiology with other traits at both the phenotypic and genetic levels (Arnold 1987). For example, recent work has examined the within-individual correlations of physiology with life histories (Zera and

Harshman 2001, Ricklefs and Wikelski 2002, Braendle et al. 2011) and behavior (Careau et al.

2008, Réale et al. 2010). This focus on integrated phenotypes has received a recent resurgence in interest (e.g.,

Murren 2012, Woods et al. 2015).

Especially relevant to the work I present here is the integration of behavior, physiology, and life-history traits in the pace-of-life syndrome hypothesis, which posits that these characters will be Fig. 1. Schematic representation of the process through which variation in phenotypes affects fitness outcomes in an correlated, either within or among environment-dependent manner. Behavioral and physiological aspects of the phenotype are influenced by species, along a fast-slow continuum environmental conditions and feed back to affect individual fitness outcomes. Cumulatively, fitness outcomes across individuals bear population-level demographic (Ricklefs and Wikelski 2002, Réale et consequences. In turn, evolutionary feedback shapes fitness outcomes and individual phenotypes through changes in allele frequencies over time.

2 al. 2010). This approach builds on previous work characterizing a continuum of life-history strategies (e.g., r- and K-selection, Pianka 1970) with the explicit goal of integrating behavioral and physiological traits (Réale et al. 2010). In natural environments, these complex suites of integrated traits are shaped by both ecological factors influencing fitness and evolutionary feedback affecting allele frequencies both across populations and within populations across generations. Thus, an understanding of both environment-phenotype interactions and the underlying genetic architecture of covarying traits is necessary to draw conclusions about demographic consequences and population fates (Fig. 1; Pelletier et al. 2009). With the research presented here, I first identify the ecological significance of variation in physiology and then work to explain both the evolutionary history and potential for adaptation of integrated phenotypes in two vertebrate model organisms: the common garter snake (Thamnophis sirtalis) and the western terrestrial garter snake (T. elegans).

Garter snakes (Thamnophis spp.) are a speciose genus of generally small, harmless snakes found across nearly all of North America (Rossman et al. 1986). Over this broad geographic range, these snakes have adapted to a wide array of environmental conditions, food sources, and predation pressures. Within species, there are numerous examples of divergences in life-history strategies (Bronikowski and Arnold 1999, Tuttle and Gregory 2014), thermal ecology (Aleksiuk 1971), prey preference (Arnold 1981, Britt et al. 2006), and antipredator behavior (Herzog and Schwartz 1990, Burghardt and Schwartz 1999). Furthermore, garter snakes are amenable to being kept under laboratory conditions and are viviparous, facilitating their use in numerous studies quantifying the genetic components of variation in a variety of traits (Arnold and Bennett 1984, Garland 1988, Brodie 1993, King et al. 2004) as well as genetic correlations among traits (Brodie 1989). No garter snakes are perhaps better-studied than the system of

Thamnophis elegans metapopulations around Eagle Lake, California. Here, even in the presence of significant gene flow (Manier and Arnold 2005), we find that populations from habitats around the rocky lakeshore and populations in the mountain meadows exhibit divergent life- history ecotypes on the pace-of-life continuum (Bronikowski and Arnold 1999) through both local adaptation and phenotypic plasticity in response to local environments. This divergence has occurred relatively rapidly (within the last 10,000—100,000 years) in response to a number of environmental conditions, including temperature regime (Bronikowski 2000), food availability (Bronikowski and Arnold 1999, Robert and Bronikowski 2010), and predation pressure (Manier et al. 2007, Sparkman et al. 2013). This unique natural laboratory has been studied more-or-less continuously for the past four decades (mark-recapture studies began in

1976), providing a rich background of natural history knowledge and experimental results to inform my tests of how various physiological systems diverge in conjunction with life-history traits under differing selective regimes. Importantly, the same molecular pathways responsible for stress response mechanisms are also implicated in these life-history divergences, which provides important groundwork for characterizing the molecular drivers of this variation (Schwartz and Bronikowski 2011).

How individuals respond to stressors in their environment is an active area of research, with implications for both evolutionary processes (Palacios et al. 2012, Patterson et al. 2014) and conservation (Wikelski and Cooke 2006, Dantzer et al. 2014). Nonetheless, there is much uncertainty about what biomarkers can be used to assess the health status of individuals and, by extension, populations. The most commonly used of these, and perhaps the most uncertain in its interpretation, is concentration of the glucocorticoid hormones cortisol (in mammals and fish) or corticosterone (in rats and reptiles, including birds). Measures of corticosterone (CORT) provide

4 a marker of the activation of the hypothalamus-pituitary-adrenal/interrenal (HPA/HPI) axis, which maintains organismal energy balance generally and modulates energetic allocations during and after an encounter with a stressor specifically (Sapolsky et al. 2000, Wingfield and Kitaysky

2002). Historically, it has been assumed that high CORT concentrations, as measured in blood plasma, feces, saliva, or body tissues such as hair or feathers, indicate that an individual is more

'stressed'. However, the complex role of CORT in regulating energetic balance in both day-to- day functions and stressful situations belies such simple interpretation and has been debated in the literature (Johnstone et al. 2012, Breuner et al. 2013, Schoech et al. 2013). For example,

CORT levels cycle within individuals at daily, seasonal, and yearly time scales (e.g., Romero

2002, Romero and Wikelski 2006, Palacios et al. 2012).

At a molecular level, CORT interacts with two receptors that translocate to the nucleus and act as transcription factors once activated by a ligand: a mineralocorticoid receptor (MR) present at high density and a glucocorticoid receptor (GR) present at lower density (Sapolsky et al. 2000). Furthermore, an additional lower-affinity MR receptor is located in the plasma membrane and binds CORT when CORT is circulating at high concentrations and mediates non- genomic cellular responses in neurons of the hypothalamus and hippocampus which amplify the effect of other stress hormones and can produce rapid behavioral responses (de Kloet et al.

2008). At basal levels, CORT primarily binds with nuclear MRs, but at increased stress-induced concentrations, binds with GRs and plasma membrane MRs to mediate various aspects of the stress response (de Kloet et al. 2008). Additionally, CORT in blood plasma is bound to corticosterone-binding globulins which regulate the amount of CORT that is available to tissues (Breuner et al. 2013). The proportion of bound vs. unbound CORT is then important in determining the downstream effects of the stress response (e.g., Edwards and Boonstra 2015).

Taken together, it is clear that great care must be made in interpreting measures of total CORT concentrations, including the environmental and physiological context in which the measurement was made. Notwithstanding, many studies have shown significant associations between circulating levels of total CORT and stress-related physiological and behavioral phenotypes (e.g.,

Whittier et al. 1987, Moore and Jessop 2003, Robert et al. 2009, Thaker et al. 2010, Angelier and

Wingfield 2013, Vitousek et al. 2014). Therefore, consideration of total CORT levels in relationship to behavior and physiology is warranted, especially when combined with additional indicators of physiological response (see below; Schoech et al. 2013).

Because of the complex role of CORT in energy regulation, there is not a clear relationship between either basal or stress-induced CORT concentrations and fitness (Breuner et al. 2008, Bonier et al. 2009, Crespi et al. 2013). Furthermore, theoretical models of the maintenance of homeostasis across both life-history stages and unpredictable environmental stressors do not agree on a common currency with which to measure stress response or even which biological parameters should be the focus of such studies (Romero et al. 2009, McEwen and Wingfield 2010). Indeed, the state of affairs of general principles and theories of organismal stress response can be described as in the “natural history” phase (Breuner et al. 2013), with much work needed to describe and catalogue aspects of stress response in natural populations.

The research presented here attempts to address this critical need and provide the outcome of a theoretical and empirical framework for measuring and interpreting organismal stress responses through two primary avenues:

(1) The use of multiple biomarkers of stress response to quantify both regulation and

downstream effects of stress response pathways. For example, one of the primary

actions of stress-induced concentrations of CORT is to increase glucose availability to

tissues by initiating processes of glycogenolysis and gluconeogenesis (Sapolsky et al.

2000). Thus, measures of both CORT and glucose concentrations, especially over time,

provide data on both the activation of the HPA axis (CORT) and its downstream effect on

energy mobilization (glucose). Downstream effects of CORT can also be measured by

quantifying the ratio of leukocyte types in blood plasma, since CORT acts to redistribute

leukocytes to tissues where they may be needed during an encounter with a

stressor (Sapolsky et al. 2000, Davis et al. 2008). In reptiles, this is commonly measured

as the heterophil : lymphocyte ratio in whole blood. And finally, glucose is regulated by

overlapping regulatory systems involving multiple pathways, including catecholamines

(adrenaline), CORT, insulin, and glucagon (Strack et al. 1995, Foster and McGarry

1996). Measures of these pathways, for example insulin, can provide an additional

quantification of the regulation of energy homeostasis across stressful conditions. This

combination of biomarkers (CORT, glucose, insulin, leukocytes) provides a more

complete picture of organismal stress response than reliance on a single marker alone and

is essential for describing the functional and ecological significance of variation in

glucocorticoid concentrations (Breuner et al. 2013, Sparkman et al. 2014).

(2) The use of repeated measures over time within individuals. It is not only the

magnitude but the duration of the physiological stress response that bears consequences

for energy balance maintenance (Dickens and Romero 2013, McCormick et al. 2015).

This is especially important when considering the physiology of ectotherms, which have

generally slower metabolic processes and stress responses than more commonly-studied

endotherms (e.g., Palacios et al. 2012). For example, the common protocol in measuring

stress-response in birds is to collect tissue samples 30 minutes after a stressor. Given the

slower response times of reptiles generally, studies that assume similar response curves to

birds may not be measuring stress response at all (e.g., Mell et al. 2016). By

characterizing a stress response curve over time, or at the very least taking measures of

stress biomarkers at repeated time points over the course of an experiment, we can

quantify the efficacy of negative feedback systems that promote a return to basal levels,

describe the relationship among biomarkers across time, and identify ‘wear and tear’ that

may characterize overworked or otherwise inefficient stress response systems (Romero et

al. 2009, McEwen and Wingfield 2010).

The effects of thermal stress deserve special attention in an age of changing climates and warming global temperatures (IPCC 2014). Despite decades of research into thermal biology and response to extreme temperatures, we still lack a general model describing the mechanisms that set thermal tolerance limits in ectotherms (reviewed in Angilletta 2009, Schulte 2015).

Additionally, accumulating evidence suggests that adaptive increases in thermal tolerances are rare due to limited genetic variation within species (Sunday et al. 2011, Huey et al. 2012).

Snakes behaviorally thermoregulate to avoid high temperatures (Scott et al. 1982), thus reducing selection on physiological responses to these temperatures (Huey et al. 2003, Buckley et al.

2015). While these limitations might prevent thermal tolerances from adaptively responding on evolutionary timescales, the sub-lethal effects of increased temperatures still bear longer-term energetic or other physiological consequences and these may be of importantance for understanding population response to climate warming. For example, previous work in the T. elegans system of divergent life-history ecotypes from Eagle Lake demonstrates that populations

8 adapted to warm and cool habitats do differ in some aspects of their thermal response, including growth rates (Bronikowski 2000), metabolic rate (Gangloff et al. 2015), and production of reactive oxygen species (Robert and Bronikowski 2010, Schwartz and Bronikowski 2013). A framework garnering much interest over the past decade is the oxygen and capacity-limited thermal tolerance hypothesis (OCLTT), which proposes the inability of oxygen delivery systems to meet tissue demand as the proximate mechanism limiting ectotherm function at high temperatures (Pörtner 2002, Pörtner and Knust 2007). Much support has been found for this hypothesis in aquatic ectothermic vertebrates (fish), but tests in terrestrial ectotherms have been equivocal. Although oxygen limitation may not be the direct mechanism setting upper thermal tolerance limits, restricted oxygen levels may result in alterations to energy allocation and storage pathways and set performance restrictions. These limitations, in turn, affect competitive interactions and thus have important consequences at the population and species level. Thus, tests which measure a number of physiological response pathways across a range of increasing temperatures are needed to identify potential nodes of divergence and the physiological systems responsible for setting both acute and long-term thermal tolerance limits to organism function and survival.

Variation among individuals is the raw material on which natural selection acts.

Nonetheless, this among-individual heterogeneity was long overlooked by ecological physiologists and behavioral ecologists, largely based on the assumption that responses at the population-level would demonstrate convergence on optimum trait values under given ecological conditions. Work of the past few decades, however, has demonstrated the ecological and evolutionary significance of among-individual variation, both in physiology (Bennett 1987,

Williams 2008) and behavioral ecology (Dall et al. 2004, Sih et al. 2004). Importantly, these

9 frameworks have merged with the recognition that physiological and behavioral traits are necessarily correlated within individuals and interact to affect individual fitness (Arnold 1987,

Arnold 1988, Careau et al. 2008). The pace-of-life syndrome hypothesis synthesizes these expectations and posits that suits of traits likely co-evolve to produce phenotypes that are integrated within individuals with important implications for both ecology and evolution of populations (Réale et al. 2010, Murren 2012) . This covariance of traits can result from pleiotropic effects (e.g., Dantzer et al. 2016) or from correlated selection producing genetic linkage and/or linkage disequilibrium of genes (Falconer and Mackay 1996, Lynch and Walsh

1998). Within this framework, numerous studies have described the consequences to evolutionary dynamics of among-individual heterogeneity, especially in life-history traits (Dochtermann and Gienger 2012, Vindenes and Langangen 2015). Yet still we lack a mechanistic understanding of how selection acts on phenotypes to produce and maintain genetically correlated traits in natural systems, especially in terrestrial ectotherms. Linking physiological responses to higher-order effects, such as demography and population persistence, is essential to both describing and mitigating the impending effects of rapidly changing environments on natural populations (Cooke et al. 2014).

The studies included here attempt to provide such integrative knowledge by combining measures of multiple biomarkers of stress response within individuals, quantifying the response of novel pathways under stress conditions, comparing responses across evolutionarily diverged lineages to quantify adaptive potential of physiological traits, and finally quantifying the fitness consequences of among-individual heterogeneity in physiological and behavioral traits. Taken together, these experiments begin to fill an important knowledge gap connecting how physiological responses vary among individuals and how this variation leads to higher-order

10 effects on populations. It is my hope that these contributions represent an addition to the rich history of physiological and behavioral studies in garter snakes which provide empirical evidence to bolster our understanding of broader questions of organismal ecology and evolution.

Dissertation Organization

Chapter 1: Geographic variation and within-individual correlations of physiological stress markers in a widespread reptile, the common garter snake (Thamnophis sirtalis)

The effects of the external environment on organisms’ long-term viability are largely mediated through physiology. By measuring multiple biomarkers in both basal and stress- induced conditions in snakes from disparate portions of the species range, this study provides data about within-individual correlations of stress response systems as well as the potential flexibility of these systems across environmental conditions. We measured plasma corticosterone concentration, plasma glucose concentration, and heterophil : lymphocyte ratios at nine time points over three days during a standardized restraint protocol. We found that these biomarkers are correlated under basal conditions, become de-coupled as the stress response peaks after three hours, and then become partially correlated again as the stress response declines after three days.

Furthermore, we found that while snakes from Iowa populations had lower baseline indicators of stress, they exhibited a more dramatic and sustained stress response across time in restraint.

Taken together, these data provide insights into the importance of measuring multiple biomarkers over time to properly assess the health status of both individuals and populations.

Chapter 2: Hormonal and metabolic responses to upper temperature extremes in divergent life-history ecotypes of a garter snake

Hormonal pathways mediate energetic trade-offs among organismal functions (Dantzer et al. 2016). Thus, quantifying the hormonal response to specific stressors allows understanding of how these stressors affect both short- and long-term energy balance. This study quantifies the hormonal and metabolic response to near-lethal temperatures in individuals from both the slow- and fast-living ecotypes of T. elegans found around Eagle Lake, CA. As predicted, oxygen consumption, corticosterone concentration, and glucose concentration increase with increasing temperatures, indicating that such temperatures impose high energetic costs to individuals. In a novel finding, we discovered that insulin also responds to temperature, but with a thermal response curve distinct from that of glucose concentration. This suggests that insulin may play a role in the heat stress response in addition to its role in regulating glucose concentrations and maintaining energy balance. Furthermore, we found no evidence that snakes were unable to deliver sufficient oxygen to tissues under heat stress, providing evidence against oxygen limitation as the mechanism that sets thermal tolerance limits in this species. Finally, snakes from the slow- and fast-living ecotypes did not diverge in their thermal response curves, which suggests that these lineages have not adaptively diverged in their thermal tolerance capacities and thus it is unlikely that selection for higher thermal tolerance limits would result in an adaptive response in these populations.

Chapter 3: Among-individual heterogeneity in behaviour and physiology interact to influence fitness in a model ectothermic amniote, the garter snake Thamnophis elegans

By quantifying the cross-generational effects of integrated phenotypes, this chapter demonstrates the central roles of physiology and behavior in bridging cellular-level processes with population-level effects. We identified a striking pattern whereby reproductive females differed along a reactivity continuum in both physiological and behavioral traits, with ‘low reactive’ individuals characterized by lower plasma corticosterone and glucose concentrations and less active behaviors. Snakes with matched behavioral and physiological phenotypes (‘low- reactive’ with ‘low-reactive’ and ‘high reactive’ with ‘high reactive’) gave birth to offspring with more energy stores than the offspring of females exhibiting the opposite combination of traits.

The offspring born in good body condition, in turn, grew more quickly and survived longer, indicating that heterogeneity in maternal phenotypes bears fitness consequences. Importantly, we are able to place this study in the broader context of fluctuating environmental resources across years. This study was conducted in a ‘bad’ year of limited resources, leading to the speculation that in ‘good’ years of high food abundance, where energetic resources are not limited, we might not expect to find a trade-off between maternal phenotypes and energetic allocation to offspring (sensu van Noordwijk and de Jong 1986). Thus, we provide a mechanism by which environmental stochasticity maintains polymorphisms of ‘energetic phenotypes’ within this population.

Appendix 1: Developmental and immediate thermal environments shape energetic trade-offs, growth efficiency, and metabolic rate in divergent life-history ecotypes of the garter snake Thamnophis elegans

This study offers an experimental test of the proximate factors determining energetic allocation trade-offs in garter snake neonates. By raising offspring from both fast- and slow- living ecotypes in thermal conditions designed to mimic field differences in temperature regimes, we were able to quantify the relative influence of genetic background and developmental environment on growth and metabolic rate. We identified a negative correlation between resting metabolic rate and growth efficiency, implying an allocation trade-off between maintenance metabolism and growth. This study also provides empirical support for the recent metabolic- level boundary hypothesis, which posits that the mass-metabolic rate relationship (metabolic scaling) can vary across environmental conditions and activity levels within individuals. We report that scaling exponents decrease with increasing temperatures across the range we measured, indicating that the increased efficiency associated with larger size is most pronounced at temperatures in these snakes’ normal activity range. Taken together, we provide empirical support for trade-offs predicted by classic life-history theory as well as newer theoretical models explaining metabolic scaling relationships.

Appendix 2: Fast- and slow-living garter snakes differ in information-gathering and activity in open-field tests

Within the framework of the divergent life-history ecotypes of T. elegans populations around Eagle Lake, CA, this study tests the correlation of behavioral activity levels (based on tongue-flick rate and movement data) and habituation (change in behaviors over time) in

14 response to a standardized exposure to a novel environment. We find support for theory proposing that animals with faster life-history strategies (i.e., fast growth, high reproduction, short lifespan) should exhibit more active behavioral phenotypes relative to those with slower life-history strategies to facilitate fast growth via resource acquisition. Furthermore, we found that fast-living individuals also change their behaviors more across repeated trials, supporting the prediction that fast-living animals may also be under strong selection to learn and respond more quickly to ecological challenges. This study includes a novel application of hidden Markov models, a statistical clustering tool for use with serially-dependent data, which we use to classify behavioral states based on movement data (Patterson et al. 2008). Taken together, the results of this study support the predictions of the pace-of-life syndrome hypothesis with regard to behaviors in these snakes.

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CHAPTER 2

GEOGRAPHIC VARIATION AND WITHIN-INDIVIDUAL CORRELATIONS OF

PHYSIOLOGICAL STRESS MARKERS IN A WIDESPREAD REPTILE, THE

COMMON GARTER SNAKE (THAMNOPHIS SIRTALIS)

This is a manuscript currently in revision with the peer-reviewed journal Comparative

Biochemistry and Physiology A

Eric J. Gangloff, Amanda M. Sparkman, Kaitlyn G. Holden, Caitlyn J. Corwin, Madeline Topf,

and Anne M. Bronikowski

Author contributions: EJG designed the experiment, analyzed the data, and wrote the manuscript. EJG, AMS, KGH, and CJC conducted experiment. EJG, KGH, CJC, and MT performed lab work. AMB provided guidance in experimental design, data analysis, and interpretation. All authors contributed to manuscript revisions.

Abstract

Characterizing the baseline and stress-induced hormonal, metabolite, and immune profiles of wild animals is important to assess the impacts of variable environments, including human-induced landscape changes, on organismal health. Additionally, the extent to which these profiles are coordinated across physiological systems within individuals remains an important question in understanding how stressors can differentially affect aspects of an individual’s physiology. Here, we present data from wild populations of the common garter snake

(Thamnophis sirtalis) on both baseline and stress-induced biomarkers: plasma corticosterone

(CORT) concentration, plasma glucose concentration, and whole blood heterophil:lymphocyte ratio. Using a standardized restraint protocol with individuals from populations in disparate portions of this species’ range – the Sierra Nevada Mountains of California and the plains of

Iowa – we collected blood plasma samples at nine time points over three days. Both CORT and glucose response curves differed between georegions, with Iowa snakes attaining higher glucose concentration and maintaining elevated CORT and glucose levels for a longer duration.

Additionally, both the total amount and proportional increases of CORT and glucose were lower in larger and therefore older snakes, suggesting ontogenetic shifts in stress perception or response. Within-individual correlation among the three physiological indicators was significant at the time of capture, absent after 3 h in captivity, and partially restored after 3 d in captivity, demonstrating the effect of stress on the relationships among these physiological systems.

Together, these results provide further evidence for the great physiological flexibility of ectothermic tetrapods in maintaining homeostasis across a range of factors.

Introduction

How physiological biomarkers of organismal energy status and homeostatic maintenance respond to stressors remains an important question with implications for both conservation (Baker et al., 2013; Busch and Hayward, 2009; Dantzer et al., 2014; Wikelski and

Cooke, 2006) and evolution (Cox et al., 2016; Crespi et al., 2013; Dantzer et al., 2016; Palacios et al., 2012; Patterson et al., 2014). In both endotherms and ectotherms, organismal homeostasis refers to the physiological processes that hold constant the milieu intérior with the purpose of maintaining steady state functionality across a range of external environments and internal

23 conditions (Bradshaw, 2003; Chrousos, 1998). More recently, this concept has been expanded to include the idea of allostasis, which incorporates variation in set points for internal variables in response to predictable or cyclical events and describes the energetic consequences (“allostatic load”) of deviation from this dynamic equilibrium (McEwen and Wingfield, 2010).

Glucocorticoids, hormones that mediate trade-offs among energetic allocations and maintain homeostasis in response to stressors, have been the primary subject of studies quantifying organismal responses to disturbances to homeostatic conditions (Angelier and Wingfield, 2013;

Sapolsky et al., 2000; Wingfield et al., 1998). Additionally, much work has been done to estimate the correlation between both baseline and stress-induced levels of glucocorticoids and organismal fitness, though this relationship remains unclear (Bonier et al., 2009; Breuner et al.,

2008; Crespi et al., 2013; but see Baker et al., 2013). At both baseline stress-induced levels, variation in glucocorticoids is driven by past experiences, current energetic status, reproductive status, and age (e.g. in snakes, Dayger et al., 2013; Lutterschmidt et al., 2009; Sparkman et al.,

2014; reviewed in Sapolsky et al., 1986). As such, myriad factors must be taken into account when assessing the functional role of these hormones in wild animals, such as when comparing across populations (Eikenaar et al., 2012; Moore et al., 2001) or quantifying the ability of individuals to modulate their stress response (Wingfield, 2013). Higher baseline CORT concentration in wild animals is widely construed to indicate increased levels of physiological stress, but interpretation of CORT levels is not always straightforward. Recent work suggests that stress-induced CORT concentrations may reveal more about an organism’s physiological ability to deal with stressors than do baseline levels (Breuner et al., 2008; Neuman-Lee et al.,

2015; Vitousek et al., 2014). Both the magnitude and the duration of the stress response are

24 important for assessing the functionality of these hormonally-regulated stress response systems (Dickens and Romero, 2013; McCormick et al., 2015; Taff and Vitousek, 2016).

In reptiles, the neuroendocrine stress response is primarily facilitated by the hypothalamic–pituitary–interrenal (HPI) axis, with corticosterone (CORT) acting as the primary glucocorticoid (Norris and Jones, 1987). Glucocorticoid hormones modulate numerous day-to- day functions, including feeding, locomotor activity, energy metabolism, and immune function (reviewed in Landys et al., 2006). In response to a stressor, circulating glucocorticoids increase on an order of minutes to hours after the event and serve to promote recovery from stress and re-establish homeostasis (Bradshaw, 2003; Romero, 2004; Wingfield and Kitaysky,

2002). For both baseline and stress-induced measures, the effects of total plasma CORT may be modulated at multiple nodes, including the relative concentrations of binding globulin (reviewed in Breuner et al., 2013) as well as receptor type and density (reviewed in Angelier and

Wingfield, 2013; Landys et al., 2006; Lema and Kitano, 2013). Nonetheless, measures of total

CORT concentration remain an important tool for assessing physiological status (Schoech et al.,

2013), especially when measured at both baseline and stress-induced levels and in conjunction with other physiological stress biomarkers such as glucose and white blood cell counts (Breuner et al., 2013). After an encounter with a stressor, glucose concentrations are increased rapidly

(within minutes) by actions of catecholamines. This is followed by increases in circulating glucocorticoids, which promote production of glucose via gluconeogenesis and glycogenolysis (Sapolsky et al., 2000). After the HPI axis has responded to a stressor, circulating glucose levels generally positively covary with the glucocorticoid response (e.g., Gangloff et al.,

2016), providing opportunity to measure both the glucocorticoid stress response and its subsequent downstream effects. Additionally, glucocorticoids act to redistribute leukocytes to

25 specific target tissues. For example, heterophil:lymphocyte (H:L) ratios generally increase in response to elevated glucocorticoids and remain stable over longer time scales (Goessling et al.,

2015). However, despite their established functional relationship, H:L ratios and levels of circulating glucocorticoids are not always correlated within individuals (Goessling et al., 2015;

Sparkman et al., 2014). The differential responses of physiological systems to both the intensity and duration of stressors thus underscores the need to use multiple markers when assessing physiological status (Breuner et al., 2013; Goessling et al., 2015).

In this study, we quantified multiple measures of both baseline and stress-induced physiological biomarkers in a widespread terrestrial vertebrate, the common garter snake

(Thamnophis sirtalis). Previous work with this species demonstrates that glucocorticoids cycle daily and seasonally and interact with other hormonal axes to regulate reproductive and feeding behaviors (Lutterschmidt and Maine, 2014; Lutterschmidt and Mason, 2008; Moore and Jessop,

2003; Moore et al., 2000a; Moore et al., 2000b; Moore and Mason, 2001) as well as life-history transitions (Cease et al., 2007). Furthermore, much work has been done on the short-term (< 4 h) glucocorticoid response of T. sirtalis from natural populations, mostly in relation to reproductive behaviors and trade-offs. For example, handling stress results in increased CORT concentrations and reduced testosterone concentrations in males during the reproductive season, but does not alter reproductive behavior (Moore et al., 2001; Moore et al., 2000a). The present study adds to the rich history of quantifying the hormonal stress response in this species by providing multiple measures of the stress response over a longer duration of a stressor. Data on the cumulative stress response over a number of days are important in interpreting stress indicators to assess individual or population-level health status.

Our study has three primary goals that complement previous work with this species: (1) to assess spatial variation in T. sirtalis physiological profiles by comparing multiple populations from disparate parts of this species’ geographic range, (2) to test the relative roles of size, sex, reproductive condition, and time of year in determining both baseline patterns and stress-induced cumulative physiological response, and (3) to quantify the within-individual correlations among multiple physiological indicators (CORT, glucose, H:L ratios) across an extended stress response. By examining both the within-individual relationship of physiological indicators and multiple sources of among-individual variation, this study provides insight into the factors that affect the ability of individuals in natural populations to maintain functionality and energy balance in different ecological contexts.

Materials and Methods

Field Methods

We sampled multiple populations of T. sirtalis from each of two geographic regions

(hereafter “georegions”) representing very different habitat types within the broad geographic range of this species, which extends across much of North America (Ernst and Ernst, 2003). The

California populations are found in mountain meadows on federally managed land in largely undisturbed habitats (elevation 1600 –2100 m). The Iowa populations represent a sampling of both natural areas and impacted sites in temperate grasslands (elevation 250–300 m). All snakes were located in the open or under cover objects and hand-captured at four sites in CA and four sites in IA (Table 1). We conducted fieldwork during daylight hours (between 7 30 h and 17 30 h) and during the active season (June 2013 in CA; May to October 2013 and 2014 in IA). Garter snakes generally mate immediately upon emergence from hibernation in the spring (Rossman et

27 al., 1986), which for these populations occurs at least a month before our field observations.

Furthermore, we did not observe any reproductive behaviors during our fieldwork, indicating that measures on these individuals were likely not conducted during mating season. Upon capture, we first measured snake body temperature using a cloacal thermometer (DeltaTrak

FlashCheck Probe Thermometer, Pleasanton, CA, USA) and then immediately drew a baseline blood sample (50–150 µL) from the caudal vein using a heparinized syringe. All blood samples for baseline measures were collected ≤ 10 min after time of capture, which is before CORT concentration is elevated due to handling stress in a congeneric species (Thamnophis elegans;

Palacios et al., 2012).

For a subset of samples from both georegions (N = 53), we used fresh blood to prepare blood smears, which we fixed with methanol and stained with Wright-Giemsa stain (Fisher

Scientific Inc., Pittsburgh, PA, USA).We stored blood on ice for up to 3 h, then separated plasma by centrifugation, snap-froze in liquid nitrogen, and stored at -80°C until the time of assays.

After blood collection, all snakes were weighed (g), measured for snout-vent length (SVL in mm), and sexed. Females were gently palpated to determine gravidity. Snakes included juveniles and adults (SVL range: 202—820 mm) and did not differ in SVL between the regions (t-test, P =

0.53). Body condition, a measure of size-corrected weight, is known to affect stress response in snakes (e.g., Moore et al., 2001; Palacios et al., 2012). However, such a metric cannot be applied to reproductive females as the developing mass of embryos confounds estimates of tissue energy stores, therefore precluding tests of the influence of body condition on the response of pregnant snakes. As the focus of this study is to examine the relationship among stress response variables within individuals and the potential flexibility of these systems across populations and sex categories, including pregnant females, we are not including body condition as a predictor

28 variable in our models. Using males and non-pregnant females, body condition (calculated as the residual of the log-mass on log-SVL regression; Weatherhead and Brown, 1996), did not differ between the georegions (t-test, P = 0.88).

Capture-Restraint Protocol

A subset of snakes (Table 1) were kept for repeated blood draws following a standardized restraint protocol, which elicits a glucocorticoid response in a variety of reptile and avian taxa (e.g., Moore et al., 2000a; Palacios et al., 2012; Romero, 2002). After collection of the baseline sample, snakes were maintained in breathable nylon bags at ambient temperature and we collected a smaller amount of blood (20-30 µL) at 15 min, 45 min, 90 min, 3 h, and 6 h post- capture. After the 6 h bleed, snakes were transferred to plastic boxes (20 cm × 30 cm × 17 cm) with shredded paper bedding and a water dish, stored in a wooden hutch in the shade at ambient outdoor temperatures. We then collected blood in the mornings on the following three days, generally between 9 00 h and 9 30 h. This protocol resulted in a total of nine samples collected for each snake. Additionally, we prepared blood smears as above for a subset of snakes at 3 h and 3 d. After sample collection, all snakes were released at their point of capture. Fieldwork was conducted under permits from the California Department of Fish and Game and the Iowa

Department of Natural Resources, and was approved by the ISU Institutional Animal Care and

Use Committee (protocol 1-12-7285-J).

Laboratory Methods

We measured CORT concentrations in blood plasma using double-antibody radioimmunoassay kits (Kit #07120102, MP Biomedical, Orangeburg, NY, USA) following the

29 protocol in Robert et al. (2009) with samples diluted 1:80 to account for high concentrations in this study. This assay has been previously validated for use with fecal samples in this species (Halliday et al., 2015), which we confirmed with parallelism of a serially diluted pooled sample (heterogeneity of slopes: F1,10: 1.20, P = 0.30). Measurements were made using two kits, with interassay variability measured using kit-provided controls (CV%: 8.80% and 11.80% for low and high controls, respectively). All samples were run in duplicate and accepted if CV% between the duplicates was less than 10%; otherwise, samples were re-run. We measured glucose from 1.5 μL of the thawed plasma using a FreeStyle Lite Glucometer (Abbott Diabetes

Care, Alameda, CA). We assigned a value of 20 mg/dL for samples which measured below the lower threshold of the glucometer (20 mg/dL; N = 10 samples). We quantified the relative abundances of lymphocytes, monocytes, heterophils, and basophils in blood smears following the procedure described in Sparkman and Palacios (2009). Briefly, we used a compound microscope to classify the first 100 leukocytes encountered under 1000× magnification and then calculated the ratio of heterophils to lymphocytes (H:L ratio).

Statistical Methods

All analyses were conducted with program SAS version 9.4 (SAS Institute, Cary, NC).

Graphics were created using ggplot2 (Wickham, 2009) in the programming language R (v. 3.1.2,

R Core Team, 2014).

Repeated Measures

We assessed the effects of biotic and abiotic factors on the time series of CORT concentration, glucose concentration, and H:L ratios using a mixed-effects generalized linear

model in PROC MIXED. The three dependent variables were log10-transformed to meet assumptions of normality (Sokal and Rohlf, 2011). We began with models including all interactions among our three categorical predictor variables (georegion, sex category, and time point) and removed non-significant terms excepting the interactions of georegion and sex category with time point, which test our hypotheses of interest. The repeated-measures model for the analysis of dependent variables took the form:

Y = μ + Georegion + Sex Category + Time Point + Georegion × Time Point +

Sex Category × Time Point + SVL + Julian Date + Population(Georegion) + ε

where μ represents the grand mean and ε represents the error term. We also included individual as a repeated-measures random effect modeled with first-order ante-dependence

[ante(1)] covariance structure, which accounts for asymmetric intervals between measurements (Littell et al., 2006). We estimated denominator degrees of freedom for random- effects model F-tests using the Kenward-Roger Degrees of Freedom Approximation (Kenward and Roger, 1997). Initial analyses indicated that baseline body temperature was not a significant predictor of any of the three dependent variables, so this effect was removed from subsequent models (CORT: P = 0.27; Glucose: P = 0.51; H:L ratios: P = 0.48). Additionally, baseline dependent variables did not differ significantly between years for the Iowa samples, so we pooled samples from 2013 and 2014 for analysis (CORT: P = 0.090; Glucose: P = 0.086; H:L ratios: P = 0.66).

Cumulative Measures: Total Response, Fold Increase, & Time to Baseline

We assessed the influence of the above factors on three cumulative measures of stress response for both CORT and glucose: total response, fold increase, and time to return to baseline.

Total response represents the cumulative amount of hormone available for interaction with receptors over the time in captivity and was calculated as the area under the curve from baseline to the final time point, using the absolute values of CORT and glucose concentrations. Romero

(2004) advocates the calculation of stress-response measures from zero (rather than normalized to an individual’s baseline) to provide a more biologically relevant estimate of the total actions of these molecules. Furthermore, our data include values over an extended time period during which many individuals dropped below baseline levels in these measures, so including only data on values above the baseline for each individual would result in a significant loss of information.

Since animals were initially caught at different times of day, they spent slightly different amounts of time in captivity until their blood draw on day three. To normalize each of these measures to the same amount of time in calculating the total CORT and glucose responses (the area under the curve for the duration of animals’ time in captivity), we censored these calculations at the lowest amount of time to the 3 d blood draw (3990 minutes from time of capture).

Fold increase, a measure of the relative increase of a physiological variable under stress condition compared to baseline, was calculated as the ratio of the highest value attained over the baseline value for that individual (Patterson et al., 2014). Log10-transformed total and fold- increase measures were analyzed with an ANCOVA with the form:

Y = μ + Georegion + Sex Category + SVL + Julian Date +

Population(Georegion) + ε

where μ represents the grand mean and ε represents the error term.

Time to baseline is the number of minutes it took an individual to reduce its concentration of the hormone/metabolite to within two standard errors (calculated based on the set of individuals in the restraint protocol) of its own baseline value after reaching its maximum value. This measure is thus an indicator of how long the hormone/metabolite concentration remained elevated during the restraint protocol. We calculated this time as the intercept of the line connecting the first measurement below this threshold (baseline + 2 SE) with the preceding measurement. Since this measure was censored at both extremes (some individuals never exceeded the baseline while some never returned), we used a non-parametric Wilcoxon-Mann-

Whitney test to compare the rank-order of values between georegions using PROC NPAR1WAY.

Within-individual Correlations

We tested whether different physiological measures were correlated within individuals and how this relationship changed over time. We estimated the within-individual correlations of

CORT concentration with glucose concentration and H:L ratios at the baseline, 3 h, and 3 d time points, using ordinary least-squares linear regressions conducted in PROC REG. Variables were log10-transformed and normalized to mean of zero and unit variance so that effect sizes (slopes of relationships) could be compared directly among variables.

Results

Repeated Measures

For those samples for which we recorded exact time interval between first capture and first bleed (“bleed time,” N = 66), we confirmed that bleed times of ten minutes or less do not

2 affect baseline CORT concentrations (t64 = 1.51, P = 0.14, R = 0.035). However, we identified a

2 significant effect of bleed time on baseline glucose concentrations (t64 = 2.68, P = 0.0093, R =

0.10). In general, handling stress elevates glucose levels more rapidly than CORT concentrations likely due to the rapid effects of epinephrine or norepinephrine in increasing circulating glucose (Sapolsky et al., 2000). While this trend was significant, glucose levels continued to increase across observations at subsequent time points (Fig. 1B), so this baseline increase exerted only a marginal effect on our analysis and did not change out results qualitatively. Nonetheless, we offer this as a precautionary note of the potential for rapid increase in glucose levels in squamate reptiles under handling stress.

Both plasma CORT and glucose concentrations, but not H:L ratios, were affected by time in restraint and the shapes of these response curves differed between georegions (Table 2, Fig.

1). At the first time point (“baseline”), CA snakes had higher CORT concentrations compared to

IA snakes (difference of least-squares means, Tukey adjusted P = 0.015). While this is the only time point at which the pairwise difference between georegions for CORT concentration differed significantly, the significant georegion × time interaction demonstrates that the shapes of the curves differed between georegions. CORT concentration in CA snakes peaked and was significantly above baseline at 45 min (difference of least-squares means, Tukey-adjusted P =

0.0099). CORT concentration IA snakes, however, elevated more rapidly and was significantly

34 above baseline at 15 min and peaked at 90 min, with every time point from 15 min to 3 d significantly above baseline (all differences of least-squares means, Tukey-adjusted P < 0.021).

When taken across the entire time series, glucose concentrations were higher in snakes from IA (as demonstrated by a significant effect of georegion), with a significant pairwise difference at the 6 h time point (Tukey adjusted P = 0.046). The shapes of the glucose response curves differed between georegions, as demonstrated by the significant georegion × time interaction. Glucose concentration in CA snakes peaked at 45 min and was significantly elevated above baseline between 15 min and 90 min (all differences of least-squares means, Tukey- adjusted P < 0.025). In IA snakes, glucose took longer to reach a maximum and stayed elevated for a longer period, peaking at 3 h and significantly above baseline between 15 min and 6 h (all differences of least-squares means, Tukey-adjusted P < 0.001). Additionally, we found significant heterogeneity among populations from each georegion in measures of glucose concentration and H:L ratios. No other effects significantly affected these physiological measures.

Cumulative Measures: Total, Fold Increase, and Time to Baseline

Total CORT and CORT fold increase differed between georegions, but in opposite directions: CA snakes had higher total CORT response while snakes from IA showed a greater

CORT fold increase (Table 3, Fig. 2). Total CORT and glucose fold increase declined with increasing snake size and there was a similar trend for both CORT fold increase and total glucose. Total glucose response was also reduced for snakes caught later in the year.

Additionally, we found significant heterogeneity among populations within each region for glucose fold increase, largely driven by one CA population with a much reduced response

35 compared to the other populations from this georegion. CA snakes tended to return to baseline

CORT values generally faster than IA snakes (median time for CA snakes was 1708 min, while more than half of the IA snakes never returned to baseline; Wilcoxon-Mann-Whitney exact test, two-sided P = 0.069). This trend corroborates with our finding that the least-squares means in the repeated-measures models of CORT concentration were elevated above baseline in IA snakes for every time point through 3 d while CA were significantly different only at 45 min (see above).

CA Snakes returned to baseline glucose values faster than IA snakes (median times for CA and

IA, respectively: 328 and 1162 min; Wilcoxon-Mann-Whitney exact test, two-sided P = 0.0034), again corroborating our results from the repeated-measures model of glucose concentration.

Within-individual correlations

Within individuals, baseline CORT concentration was correlated with both baseline glucose concentrations and H:L ratios. These measures were not correlated, however, after 3 h of the capture-restraint protocol. After 3 d, CORT concentration was again correlated with glucose concentration, though not with H:L ratios. The total amount of variation in glucose concentration and H:L ratios explained by CORT concentration was small, indicating that even as these measures are related within individuals there are likely other factors determining changes in each biomarker over time in response to a stressor (Table 4, Fig.3).

Discussion

In this widespread reptile species, we found that individuals from CA had higher baseline

CORT levels than individuals from IA (Fig. 1A). A simple interpretation of this result alone might lead to the conclusion that snakes from CA maintain a more active state of the HPI axis

36 relative to snakes from IA, either in response to a greater chronic stress load or differences in food availability. Our measures of additional baseline physiological markers, as well as measures while snakes experienced the short-term stress of captivity and restraint, suggest that the former interpretation is inaccurate. While CA snakes also mounted a greater total CORT stress response, they more rapidly reduced both CORT and glucose levels, generally associated with a healthy stress-response profile (Dickens and Romero, 2013; Tables 2-3, Figs. 1-2). On the other hand, IA snakes began with lower baseline CORT levels, but their stress response was proportionally greater and they maintained these elevated levels for longer periods of time. The higher baseline

CORT in snakes from high-elevation populations in CA is concordant with expectations of elevated baseline glucocorticoids in colder regions with a reduced active season (reviewed in

Jessop et al., 2016). Taken together, these findings indicate that either strategy or capacity to maintain organismal function and energy balance in the face of environmental variation – be it stressful or beneficial – may vary across a species’ range. Thus a comparison of a single biomarker may not provide sufficient information to assess relative physiological status.

Furthermore, stress-induced glucose and H:L ratio profiles were significantly heterogeneous among populations within each georegion, lending support to the idea that geographic variation can exist at multiple scales and that local conditions influence individuals’ perception and/or response to stress (Table 2).

In addition to heterogeneity between and within georegions, we found significant interindividual variation in physiological profiles. Size, which can be used to some extent as proxy for age in snakes (Bronikowski and Arnold, 1999; Sparkman et al., 2007), was negatively correlated with cumulative stress-response measures of CORT and glucose concentrations (Table

3). This is contrary to Moore et al. (2000a), who reported increased total CORT response in

37 longer male T. sirtalis individuals and suggested that this could be due to reduced sensitivity to negative feedback of the HPI axis in these male snakes during the mating season. This pattern is also opposite that generally found in mammals and birds, where hormonal stress response increases with age (Creel et al., 2002; Homberger et al., 2015; Pavitt et al., 2016; Sapolsky et al.,

1986). We see two primary explanations for the pattern of reduced total response in larger individuals in the present study: individual snakes may be altering the activation of this endocrine stress response pathway across their lifespan, with younger snakes exhibiting increased HPI reactivity. Under this scenario, individuals alter their physiology within their lifetime in a predictable manner. This can be an advantageous switch in response to changing food availability or predation threat as an individual grows larger or as a result of senescence.

Alternatively, this observation could be due to the removal of maladapted individuals from the population (van de Pol and Verhulst, 2006) that suffer the negative consequences of an overreactive response. We also found evidence that seasonal cycles influence stress-induced total glucose, but not baseline levels. This could be caused either by individuals changing their energetic response to stress across the season or a result of reduced energetic capacity, likely due to a decrease in food availability. Independent of seasonal variation, reproductive status did not affect stress response in female snakes, which is somewhat surprising given the changes in energetic demands associated with embryonic development (e.g., Bonnet et al., 2002) and elevated CORT concentrations found in non-reproductive female garter snakes found in a previous study (Sparkman et al., 2014). Such reproductive allocation decisions are potentially regulated by other hormonal systems independent of the HPI axis, such as thyroid hormones (e.g., Bona-Gallo et al., 1980).

Understanding variation in the maintenance of organismal homeostasis is critical for establishing links between indicators, such as glucocorticoids, glucose, and leukocyte counts, and physiological status (Bonier et al., 2009; Breuner et al., 2008; Goessling et al., 2015). Within individuals, we found a significant correlation of CORT concentration with glucose concentration and H:L ratios at baseline. This relationship disappeared after 3 h, but partially returned by 3 d (CORT correlated with glucose, but not H:L ratios), indicating the effect of restraint stress on these correlations (Table 4, Fig. 3). This suggests that under baseline conditions, hormonal, energetic, and immune systems are coordinated within the individual to maintain steady-state functionality. Under the stress of restraint, however, these functions can become quickly disassociated or respond on different time scales. Previous work with squamate reptiles indicates that neither snakes nor lizards demonstrate correlated responses of CORT and

H:L ratios to captivity stress (Seddon and Klukowski, 2012; Sparkman et al., 2014). If the correlation among these physiological variables under baseline conditions does represent the integrated operation of various systems in maintaining homeostasis, it is noteworthy how quickly

– within a matter of hours – these functions can become decoupled. Furthermore, these biomarkers appeared to partially re-align after several days, suggesting that these resilient systems can quickly return to pre-stress conditions. Furthermore, the small amount of variation explained by CORT concentration on either glucose concentration or H:L ratios suggests that, while the HPI axis may be driving some changes in these variables, other hormonal systems likely exhibit an effect as well.

Circulating glucocorticoids exert negative feedback on the HPA/HPI axis, which serves to reduce overexposure to these hormones and subsequent detrimental effects (Sapolsky et al.,

2000). With reduced sensitivity to negative control, CORT concentrations could remain elevated

39 and have potentially detrimental effects on physiology and fitness (Korte et al., 2005), though this conclusion is based largely on laboratory studies and not in wild populations (Dantzer et al.,

2014). We found a pattern of both CORT and glucose concentrations peaking within several hours and declining over the next 3 d in captivity, though not always to baseline levels. This is in general agreement with previous work in squamates, which found that CORT concentration in lizards returned to baseline 6 h after a CORT injection (Sceloporus occidentalis: DuRant et al.,

2008) and within 24 h after handling stress (Uta stansburiana: Mills et al., 2008). Interestingly, we found that snakes from CA had a greater cumulative CORT response through 3 d in captivity, though they also returned to baseline CORT more rapidly than snakes from IA, as indicated by both comparisons of least-squares means and the time-to-baseline measures (Tables 2-3, Fig.

1A). This was accompanied by a more sustained increase in stress-induced glucose concentrations in snakes from IA and a longer time to return to baseline glucose levels (Tables 2-

3, Fig. 1B). The rapid reduction in CORT concentration after initial stress response, as seen in snakes from CA, suggests a well-integrated hormonal response. The sustained elevation of both

CORT and glucose concentrations we observed in snakes from IA may be a result of “wear and tear” on an overused hormonal system (sensu McEwen and Wingfield, 2010; Romero et al.,

2009) and may indicate the populations are under chronic stress (Dickens and Romero, 2013).

Given our experimental design, however, we cannot distinguish between the effective action of negative feedback and differences in the perception of a stressor. CA snakes may more quickly acclimate to captive conditions than snakes from IA and therefore reduce the stress response over time, independent of the negative feedback exerted directly by CORT itself. In either case, clearly differences exist between snakes from different georegions in their negative feedback

40 capacity, perception of stressors, and/or life-history allocations, all of which can have important behavioral and long-term energetic consequences (Korte et al., 2005).

We see two important avenues for future research in this area. First, examining the causes and consequences of among-individual variation in stress response profiles on performance and reproduction will be key to linking hormonal responses with fitness (Bonier et al., 2009; Breuner et al., 2008; Cockrem, 2013; Cox et al., 2016). Given the complex mechanisms by which these snakes maintain physiological functionality, data linking hormonal profiles with survival and/or reproductive output, for both males and females, is essential to describing how future environmental variation will affect population dynamics. Second, we need more data explaining the within-individual mechanisms determining variation in response to stressors. For example, the CORT response to stress may be regulated at a number of levels, such as corticosterone binding globulin (Breuner et al., 2013; Edwards and Boonstra, 2015), receptor density (Lutterschmidt and Maine, 2014; Romero, 2002), or by the sensitivity of the adrenals to activation by HPA/HPI axis (Dayger and Lutterschmidt, 2016). By demonstrating both variation across geography and the coordination of stress-response biomarkers within individuals, our results provide key insights into the complex physiological regulators of stress response systems in vertebrates and point to future directions for quantifying the consequences of variation in this stress response.

Acknowledgements

For assistance with fieldwork, we thank N. Carver, K. Kauffman, K. Martin, A.

Pottebaum, C. Rodriguez, & S. Zylstra. Thanks to R. Polich and R. Telemeco for assistance with labwork and C. Vleck for the use of lab space and equipment. G. Burghardt, B. Danielson, L.

Hoekstra, and F. Janzen provided constructive comments on the manuscript and F. Miguez offered statistical advice. Access to field sites was generously granted by W. Clark, B. & T.

Danielson, D. Sheeley with Polk County Conservation, Oakridge Research & Education Prairie,

T. Rickman with the U.S. Forest Service, and the IA Department of Natural Resources. This project received funding support from the Iowa Science Foundation (13-07), IA Department of

Natural Resources Wildlife Diversity Program, Prairie Biotic Research Inc., the Society for

Integrative and Comparative Biology Grants-in-Aid of Research program, and the National

Science Foundation (IOS-0922528 to AMB). EJG received additional support from a fellowship from the ISU Office of Biotechnology.

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Table 1. Individual Thamnophis sirtalis included in this study. Top numbers indicate baseline samples; numbers in parentheses indicate individuals included in restraint protocol.

Number of individuals for baseline and (restraint) procedures Populations Non-gravid Gravid Total Georegion Year Males sampled females females 17 8 10 35 California 4 2013 (6) (2) (2) (10) 13 10 5 28 Iowa 4 2013 (8) (7) (1) (16) 16 7 0 23 Iowa 4 2014 (3) (3) (0) (6) 46 25 15 86 Total (17) (12) (3) (32)

Table 2. Results of repeated-measures mixed model analysis of log10-transformed plasma CORT concentration, glucose concentration, and H:L ratios in wild-caught Thamnophis sirtalis from IA and CA. Individual was treated as a repeated-measure random effect and denominator degrees of freedom for F-tests were calculated using the using the Kenward-Roger Degrees of Freedom Approximation (Kenward and Roger, 1997). Significant effects shown in bold with one (P < 0.05), two (P < 0.01), or three asterisks (P < 0.0001). SVL = snout-vent length (mm).

CORT Glucose H:L Ratio (N = 329 samples)a (N = 334 samples)a (N = 83 samples)b

F (dfn, dfd) 9.93 (8, 57.5) 17.11 (8, 58.2) 0.98 (2, 21.7) Time Pr > F < 0.0001*** < 0.0001*** 0.39

F (dfn, dfd) 0.15 (1, 115) 9.90 (1, 75.8) 0.18 (1, 53.8) Georegion Pr > F 0.70 0.0024** 0.67 F (dfn, dfd) 0.02 (2, 50.4) 0.73 (2, 40.4) 0.53 (2, 36.1) Sex Category Pr > F 0.98 0.49 0.59

F (dfn, dfd) 1.19 (1, 52.3) 2.10 (1, 58.2) 0.97 (1, 53.1) SVL Pr > F 0.28 0.15 0.33

F (dfn, dfd) 0.43 (6, 94.6) 3.34 (6, 86) 2.37 (6, 55.4) Population(Georegion) Pr > F 0.86 0.0053** 0.042*

F (dfn, dfd) 1.13 (1, 108) 2.80 (1, 89.5) 0.10 (1, 44) Julian Date Pr > F 0.29 0.098 0.75

F (dfn, dfd) 2.74 (8, 60) 2.26 (8, 61.5) 1.80 (2, 22.3) Georegion × Time Pr > F 0.012* 0.034* 0.19

F (dfn, dfd) 0.84 (16, 80.6) 2.62 (2, 35.2) 2.28 (4, 22.7) Sex Category × Time Pr > F 0.64 0.42 0.092 aNumber of samples is reduced for CORT and glucose due to plasma limitations. bH:L ratios were measured in a subset of samples from baseline, 3 h, and 3 d timepoints.

Table 3. ANCOVA results of log10-transformed cumulative measures of stress-induced plasma CORT concentration and glucose concentration in wild-caught Thamnophis sirtalis from IA and CA. Significant effects shown in bold with one (P < 0.05), two (P < 0.01), or three asterisks (P < 0.0001). SVL = snout-vent length (mm).

Total CORT fold Total Glucose CORTa increase Glucose fold F (df , df ) 5.21 (1, 22) 5.72 (1, 23) 2.69 (1, 23) 0.48increase (1, 23) Georegion n d Pr > F 0.033* 0.025* 0.11 0.50 F (df , df ) 0.38 (2, 22) 1.63 (2, 23) 1.64 (2, 23) 1.90 (2, 23) Sex Category n d Pr > F 0.69 0.22 0.22 0.17 F (df n, dfd) 6.58 (1, 22) 3.77 (1, 23) 4.27 (1, 23) 7.48 (1, 23) SVL Pr > F 0.018* 0.065 0.050* 0.012* Population F (dfn, dfd) 1.20 (3 ,22) 0.56 (3, 23) 1.17 (3, 23) 3.71 (3, 23) (Georegion) Pr > F 0.33 0.65 0.34 0.026* F (df , df ) 2.70 (1, 22) 0.61 (1, 23) 8.25 (1, 23) 1.97 (1, 23) Julian Date n d Pr > F 0.11 0.44 0.0086** 0.17

aN = 32 individuals for all cumulative measures except Total CORT, for which one individual was excluded due to missing data at the final time point.

Table 4. Within-individual correlations of plasma CORT concentration with glucose concentration and H:L ratios at three time points in wild-caught Thamnophis sirtalis. All variables were log10-transformed and normalized to mean of zero and unit variance before analysis (see text for details). Significant relationships shown in bold with one (P < 0.05) or two (P < 0.01) asterisks.

Time Measure N R2 Pr > t

Glucose 86 0.073 0.012* Concentration Baseline H : L Ratio 53 0.12 0.0093** Glucose 31 0.010 0.59 Concentration 3 h H : L Ratio 14 0.0074 0.76 Glucose 31 0.13 0.045* Concentration 3 d H : L Ratio 16 0.058 0.17

Figure 1. Back-transformed least-squares means of baseline plasma CORT concentration (Panel A), glucose concentration (Panel B), and heterophil:lymphocyte ratios (Panel C) across 3 d in captivity, shown with fitted loess curve. Data are from wild-caught Thamnophis sirtalis from CA and IA. Asterisks denote significant pairwise differences in least-squares means between georegions at a given time point (see text for statistical details). Error bars represent ± SE. Note change in x-axis scale after 360 min.

Figure 2. Back-transformed least-squares means of cumulative CORT (Panels A-B) and glucose (Panels C-D) measures by georegion for wild-caught Thamnophis sirtalis from CA and IA. Significant differences between georegions (P < 0.05) of log10-transformed variables are shown with an asterisk (see text for statistical details). Error bars represent ± SE.

Figure 3. Scatterplots of within-individual relationship between plasma CORT concentration and plasma glucose concentration and H:L ratios at baseline (Panels A-B), 3 h (Panels C-D), and at 3 d (Panels E-F) for wild-caught Thamnophis sirtalis. Regression lines shown for significant relationships of log10-transformed and normalized variables (see text for statistical details).

CHAPTER 3

HORMONAL AND METABOLIC RESPONSES TO UPPER TEMPERATURE

EXTREMES IN DIVERGENT LIFE-HISTORY ECOTYPES OF A GARTER SNAKE

This is a published manuscript in the peer-reviewed journal Journal of Experimental Biology

Eric J. Gangloff, Kaitlyn G. Holden, Rory S. Telemeco, Lance H. Baumgard, and Anne M.

Bronikowski

Author contributions: EJG, AMB, KGH, and RST conducted the experiment. EJG and

KGH performed lab work. EJG performed data analysis and drafted the manuscript. All authors contributed to experimental design, interpretation of results, and manuscript revisions.

Reproduced / adapted with permission of the Journal of Experimental Biology.

Full citation:

Gangloff, EJ, KG Holden, RS Telemeco, LH Baumgard, & AM Bronikowski. 2016. Hormonal

and metabolic responses to upper temperature extremes in divergent life-history ecotypes

of garter snakes. Journal of Experimental Biology 219:2944-2954. doi:

10.1242/jeb.143107

Abstract

Extreme temperatures constrain organismal physiology and impose both acute and chronic effects. Additionally, temperature-induced hormone-mediated stress response pathways

55 and energetic tradeoffs are important drivers of life-history variation. This study employs an integrative approach to quantify acute physiological responses to high temperatures in divergent life-history ecotypes of the western terrestrial garter snake (Thamnophis elegans). Using wild- caught animals, we measured oxygen consumption rate and physiological markers of hormonal stress response, energy availability and anaerobic respiration in blood plasma across five ecologically relevant temperatures (24, 28, 32, 35 and 38°C; 3 h exposure). Corticosterone, insulin and glucose concentrations all increased with temperature, but with different thermal response curves, suggesting that high temperatures differently affect energy-regulation pathways.

Additionally, oxygen consumption rate increased without plateau and lactate concentration did not increase with temperature, challenging the recent hypothesis that oxygen limitation sets upper thermal tolerance limits. Finally, animals had similar physiological thermal responses to high-temperature exposure regardless of genetic background, suggesting that local adaptation has not resulted in fixed differences between ecotypes. Together, these results identify some of the mechanisms by which higher temperatures alter hormonal-mediated energy balance in reptiles and potential limits to the flexibility of this response.

Introduction

Temperature constrains organismal performance in a wide array of traits (Angilletta,

2009; Huey, 1982; Huey et al., 2012). Although ectotherms can tolerate a broad range of body temperatures, many species might soon experience temperatures above those optimal for performance, including growth and reproduction, but below lethal limits as a consequence of ongoing climate change (Diamond et al., 2012; Visser, 2008). Temperatures outside of an organism’s optimal range challenge homeostasis and result in reduced performance and the

56 suspension of normal activity (Huey and Stevenson, 1979). These effects in turn reduce individual fitness and often impact population growth and persistence (Huey et al., 2012;

Lemoine and Burkepile, 2012; Pörtner, 2002; Pörtner and Knust, 2007). Because physiology largely mediates organismal responses to stressful environments, understanding the physiological mechanisms responsible for declining fitness at environmental temperatures above thermal optima is crucial to quantifying organismal responses to environmental change (Angilletta,

2009). Additionally, characterizing the processes that set thermal tolerance limits in ectotherms, in terms of both acute and long-term maintenance of energy balance, is essential for developing predictions of how organisms will respond to projected future environments (Williams et al.,

2016). Our goals in the present study are to quantify the metabolic and hormonal responses to high temperatures, test for local adaptation in the thermal stress response, and test the hypothesis that oxygen limitation is a proximate mechanism setting thermal tolerance limits.

Central to all three of these goals are measures of organismal energetics, such as metabolic rate, which are highly temperature dependent and fundamental to recent models of organismal response to climate change (Bozinovic and Pörtner, 2015; Dillon et al., 2010;

Gillooly et al., 2001; Glazier, 2015; Pörtner and Farrell, 2008). Even so, vertebrate ectotherms such as squamate reptiles (i.e. snakes and lizards) exhibit intraspecific variation in metabolic rate beyond temperature dependence, suggesting that additional physiological mechanisms can modulate metabolic rates. For example, metabolic rate can be temperature insensitive within a range (Aleksiuk, 1971; Davies et al., 1981; Seidel and Lindeborg, 1973), be dependent on ecological role (e.g. foraging strategy; Andrews and Pough, 1985; Angilletta, 2001; Beaupre,

1993; Sears, 2005; Taylor and Davies, 1981; Zaidan, 2003; Zari, 2013) or exhibit temperature- dependent scaling with body size (Buikema and Armitage, 1969; Davies and Bennett, 1981;

Dmi’el, 1972; Gangloff et al., 2015; Vinegar et al., 1970). Both the standard effect of temperature to increase metabolic rate and the capacity of organisms to adjust that relationship

(for example, via the effects of glucocorticoids) will determine the energetic cost of high- temperature exposure.

Characterizing organismal responses to increased temperatures requires examining the endocrine system, which likely mediates metabolic plasticity in squamates. For example, glucocorticoids, such as corticosterone (CORT) in reptiles, can increase metabolic rate independent of temperature (Bradshaw, 2003; DuRant et al., 2008; Preest and Cree, 2008). In general, we expect that ambient temperatures that approach critical limits will induce an acute endocrine-driven stress response as an individual attempts to both survive and avoid permanent damage (McEwen and Wingfield, 2010; Romero et al., 2009). Glucocorticoids are major mediators of energy balance during the stress response in vertebrates, serving to both mobilize energy stores and maintain longer-term energetic homeostasis (Johnstone et al., 2012;Wingfield and Kitaysky, 2002). Even so, relatively few studies have examined the temperature dependence of glucocorticoids in reptiles (but see Dupoué et al., 2013; Sykes and Klukowski, 2009;

Telemeco and Addis, 2014). Moreover, CORT and insulin generally antagonistically regulate circulating glucose concentrations (Dallman et al., 1995; Kaplan, 1996; Strack et al., 1995). In birds, typical CORT and insulin functions are maintained throughout a capture–restraint stress response (Remage-Healey and Romero, 2001). However, heat stress seems to de-couple this counter-regulatory relationship between glucocorticoids and insulin in many mammals. Despite reduced nutrient intake, the glucocorticoid response is accompanied by increased insulin levels, which may aid in the activation and upregulation of heat shock proteins (reviewed in Baumgard and Rhoads, 2013; Li et al., 2006). Few studies have measured insulin in reptiles and none have

58 categorized the insulin response to temperature extremes. By measuring both CORT and insulin concentrations, and their subsequent effects on glucose concentration, we can quantify the impact of heat stress on a specific aspect of energy regulation.

An additional goal of this study is to assess the role of oxygen limitation in setting thermal limits. The recent oxygen and capacitylimited thermal tolerance (OCLTT) hypothesis posits that ectotherms suffer from a mismatch between oxygen demand and supply at high temperatures, leading to reduced energetic capacity and, ultimately, physiological collapse

(Pörtner, 2002). A number of physiological indicators identify this limit, including reduction of whole-organism metabolic rate and an increase in circulating anaerobic metabolites, such as lactate (Frederich and Pörtner, 2000; Pörtner, 2002). Thus, by simultaneously measuring oxygen consumption rate and markers of anaerobic respiration across increasing temperatures, we may elucidate the proximate cause of organismal failure (i.e. death) at critically high temperatures.

The divergent life-history ecotypes in populations of the western terrestrial garter snake

[Thamnophis elegans (Baird & Girard 1853)], around Eagle Lake, CA, USA, provide an excellent system in which to study physiological response to high temperatures and thermal adaptation. Here, replicate fast-living lakeshore populations (hereafter, ‘L-fast’) and slow-living meadow populations (hereafter, ‘M-slow’) share a common ancestral source (Manier and

Arnold, 2005) but have evolved distinct life-history ecotypes on the pace-of-life continuum

(sensu Ricklefs and Wikelski, 2002), diverging in growth, reproduction, lifespan, immune investment and endocrine function (Bronikowski, 2000; Bronikowski and Arnold, 1999; Palacios et al., 2011; Schwartz and Bronikowski, 2011; Sparkman et al., 2007). The selective forces driving these ecotypic differences likely include thermal environment, with air temperature at the lakeshore habitats averaging 5–10°C warmer during the active season than the montane

59 meadows (Bronikowski, 2000; Bronikowski and Arnold, 1999) and corresponding differences in retreat-site temperatures (Huey et al., 1989; Peterson et al., 1993). Additionally, stress physiology pathways likely contribute to the divergent life-history ecotypes (Schwartz and

Bronikowski, 2013). This system of locally adapted populations provides a unique opportunity to test the potential of the thermal environment to shape aerobic performance curves, shifts in the temperature dependence of energetic trade-offs, and divergences in endocrine pathways shaping the evolution of life histories (Andrews and Pough, 1985; Angilletta, 2001; Beaupre, 1995;

Dunham et al., 1989).

We exposed snakes from both ecotypes to ecologically relevant ambient temperatures ranging from moderate to near-lethal, and measured metabolic rate (oxygen consumption rate) and physiological markers of energy regulation and processing ( plasma CORT, insulin, glucose and lactate concentrations). We tested hypotheses focusing on three aspects of the physiological response to high temperatures. First, we hypothesized that temperatures above natural activity temperatures (35°C and above) induce a hormonal stress response (hypothesis 1). Specifically, we predicted that both circulating CORT and glucose concentrations would increase with temperature (glucose: Baumgard and Rhoads, 2013; CORT: Schwartz and Bronikowski, 2013).

Additionally, we predicted that insulin concentration would increase with increasing temperatures independent of hyperglycemia, similar to the pattern observed in mammals. That is, we predicted that insulin and glucose concentrations would respond similarly in overall pattern, but exhibit different within-individual dynamics. Second, we hypothesized that the thermal tolerances of the ecotypes are locally adapted to their divergent thermal environments, as are other aspects of their biology, and that this divergence would be maintained in adult snakes even after an extended period in common-garden conditions (hypothesis 2). Thus, we predicted that L-

60 fast snakes would tolerate higher temperatures before displaying physiological stress and would show an attenuated stress response at high temperatures in comparison with M-slow snakes.

Finally, we tested the OCLTT hypothesis for the mechanism setting thermal limits in our T. elegans populations (hypothesis 3). If thermal tolerance results from oxygen limitation, we predict that oxygen consumption rate will plateau at near-critical high temperatures, plasma glucose concentration will drop, and lactate concentration will increase (Frederich and Pörtner,

2000; Verberk et al., 2016). In quantifying both the hormonal effects on energy mobilization and the whole-organism metabolic response across a range of high temperatures, this study provides insight into the impact of acute exposure to high temperatures on the regulation of homeostasis and energy stores.

Materials and Methods

Experimental animals

Female T. elegans were wild-caught from three fast pace-of-life lakeshore populations

(L-fast; N=13) and four slow pace-of-life meadow populations (M-slow; N=15) around Eagle

Lake, Lassen County, CA, USA in June 2010. All snakes were caught as reproductive-sized adults [snout–vent length (SVL); M-slow range: 416–568 mm; L-fast range: 460–692 mm].

Snakes were transported to Iowa State University and individually housed in 40 gallon (=150 liters) glass aquaria with ground corn-cob substrate and a plastic bowl that served as both water dish and retreat site. They were placed on a thermal gradient for 24 h day−1 (range: 25–34°C) and kept on a 12 h:12 h light:dark schedule. We offered frozen/thawed mice once per week, recording the amount eaten at each feeding and increasing the amount offered weekly according to the amount eaten and in relation to body size. For each of the 4 years in captivity, snakes were

61 hibernated uniformly in the dark at 4°C from January to May. Four days before the experiment began, we measured body mass (in g; M-slow range: 35.6–86.2, s.e.m.=3.3; L-fast range: 63.2–

120.5, s.e.m.=5.3) and SVL (in mm; M-slow range: 454–590, s.e.m.=9.6; L-fast range: 545–736, s.e.m.=13.4). Body condition was calculated as the residual of the log-mass on log-SVL regression (Palacios et al., 2013; Weatherhead and Brown, 1996) and did not differ between the ecotypes at the start of the experiment (t-test, P=0.88). Utilizing animals maintained under identical conditions allowed us to control for any immediate external effects on physiology, which is known to vary seasonally and with environmental conditions (Palacios et al., 2013,

2012). Fieldwork was conducted under permit from the California Department of Fish and

Game, and treatment of all experimental animals was in accordance with Iowa State University

Institutional Animal Care and Use Committee protocol 3-2-5125-J.

Experimental procedure

We conducted the experiment across 15 consecutive days beginning 18 May 2014, with snakes recently emerged from hibernation (5 May 2015) and fasted throughout hibernation and the experiment. Although this time roughly corresponds to the reproductive period and vitellogenesis in wild snakes, which can be energetically costly (e.g. Van Dyke and Beaupre,

2011), females likely did not vary in reproductive status because of similarities in age, body condition and time in captivity. Snakes were divided into five blocks of four to six snakes and each block was subjected to a 3 h treatment at each of five ambient temperatures (24, 28, 32, 35 and 38°C) with temperature order randomized, allowing for a complete, randomized, repeated- measures statistical design. Thus, there were 28 snakes×5 temperatures for N=140 measures of metabolism and physiology. Thamnophis elegans demonstrates a preferred activity range in the

62 wild of 26–32°C (Scott et al., 1982), with a mean field-active body temperature of 29.9°C

(Peterson, 1987). Snakes become inactive at 35°C (Scott et al., 1982), with a maximum recorded field body temperature of 36.0°C (Stevenson et al., 1985). Scott et al. (1982) report a critical thermal maximum (muscle spasms and failure of righting response) of between 43 and 44°C, and

Stevenson et al. (1985) identified a median survival temperature of 42.8°C. It is not known, however, how snakes from these ecotypes differ in preferred or maximal temperatures.

Originally, the intent of our experiment was to measure responses at temperatures up to

40°C; however, two snakes exposed to this temperature during preliminary trials died within several days of treatment. We therefore concluded that an upper extreme of 38°C represents a stressful treatment near the lethal limit for fasted, post-hibernation subjects. Repeated measures were separated by 72 h, with two blocks subjected to temperature treatments from 08:30 to 11:30 h and three blocks from 12:30 to 15:30 h. We chose to randomize order of temperature exposure but not time of day within blocks to avoid confounding the effects of time of day and temperature on individual snakes. The evening preceding each treatment, snakes were moved from their home cages into 1.5 liter plastic boxes fitted with a rubber stopcock and tubing, covered with a mesh lid, and kept at 24°C overnight. At the time of the experiment, each container was sealed with an airtight plastic lid and in- and out-flow tubing was attached to the respirometry equipment. Containers were then placed in an incubator (Econotemp incubator,

Thermo Fisher Scientific, Waltham, MA, USA) at the specified temperature treatment. Snakes were maintained in containers in the incubator, undisturbed and in the dark, for 3 h during oxygen consumption rate measurements. Qualitatively, we observed little movement of snakes immediately before and after the experimental procedure. Incubators were equipped with customized fans to ensure air flow and uniform temperature distribution. Incubator temperature

63 was monitored using thermistor probes (model SENT-TH, Sable Systems, Las Vegas, NV,

USA), corroborated with a calibrated electric thermometer.

Immediately following each temperature treatment, we collected blood samples (∼150

μl) from the caudal vein of each snake using heparin-rinsed syringes. Blood was immediately transferred to cold centrifuge tubes and kept on ice for a short time (<50 min), then centrifuged at

3000 g for 10 min at 4°C. We then aliquoted the plasma into separate tubes for each assay

(CORT, insulin, glucose and lactate), immediately snap-froze them in liquid nitrogen, and stored them at −80°C until analysis. We recorded the time from the end of the experimental treatment to the time each snake was bled and the total handling time for each snake. All handling times were less than 10 min, which is before circulating CORT is elevated as a result of handling stress in T. elegans (Palacios et al., 2012).

Oxygen consumption rate

̇ ̇ Instantaneous oxygen consumption rate (VO2) and carbon dioxide production rate (VCO2) were measured using a push mode flow-through respirometry system (Lighton, 2008; Lighton

̇ and Halsey, 2011). VO2 is a proxy for resting metabolic rate, which is a measure of steady-state maintenance in post-absorptive individuals at rest during normal activity periods (Andrews and

Pough, 1985; Bennett and Dawson, 1976). Standard operating protocols were used for this system: air flow (400 ml min−1) was maintained by an MFS-2 mass flow generator and cycled through eight chambers at 5-min intervals with an RM-8 flow multiplexer. One chamber in each block served as a blank to control baseline values during the experiment. Each chamber was sampled for four 5-min intervals, with the blank chamber sampled for eight intervals. Air outflow was scrubbed of water using Drierite desiccant (Hammond Drierite, Xenio, OH, USA)

64 and then measured for oxygen and carbon dioxide content with CA-10 and FC-10 analyzers, respectively (Sable Systems). Data were recorded using a UI-2 analog–digital interface. Oxygen and carbon dioxide levels were corrected for variation in barometric pressure and then converted

̇ ̇ to VO2 and VCO2 using ExpeData 1.7.30 software (Sable Systems). Measurements from the first intervals (i.e. the first 45 min of the treatment) were excluded from analysis to allow time for

̇ snake body temperature to equilibrate with incubator temperature. For analysis of VO2, we

̇ mathematically corrected for VO2 (following equations in Lighton, 2008) and averaged across the entire 5 min interval, which accounts for any potential periods when the snake was not breathing.

̇ Values from the three 5-min readings were then averaged, resulting in a single value of VO2 for

̇ analysis for each individual at each temperature. Analysis of VCO2 demonstrated the same

̇ qualitative patterns as the analysis of VO2, so we present only the latter here for simplicity.

During analysis, we identified a malfunction whereby the multiplexer machine did not switch between channels as programmed, leaving us with N=82 observations. Because the order of individuals was randomized within each trial, the resulting lost data are equally distributed between ecotypes and among temperature treatments (χ2 test P=0.75 and P=0.54, respectively).

Corticosterone

The concentration of CORT in blood plasma was measured using double-antibody radioimmunoassay kits (kit 07120102, MP Biomedical, Orangeburg, NY, USA). We followed previously described protocols (Robert et al., 2009), except samples were diluted 1:160 (instead of 1:40) to account for high CORT concentrations. All experimental measurements were made with a single kit over 2 days. The kit-provided controls were used to assess overall variability in

65 measurements. Each control was run seven times on each day, yielding coefficients of variation

(CV) of 6.67% and 6.48% for the low and high controls, respectively. All samples (N=140) were run in duplicate with a CV<10%. To compare measures from this experiment with baseline laboratory CORT values, we also included measures made on blood samples from these snakes

(N=26) in normal husbandry conditions from August 2013. Because these samples were measured with a different CORT kit, we transformed the calculated concentrations using the equation from a linear regression of eight samples that were measured with both kits, which were strongly correlated (R2=0.93).

Insulin

The relative change in blood plasma insulin concentration was measured using an enzyme-linked immunosorbent assay (ELISA) with a monoclonal insulin antibody assay kit

(catalog no. MBS269923, MyBioSource, San Diego, CA, USA) following the manufacturer’s protocols with slight modification to sample and reagent volumes. To validate this kit for measures of snake insulin, we tested the parallelism of kit-provided controls with a serially diluted pooled sample from a congeneric garter snake species, Thamnophis marcianus

(heterogeneity of slopes: F1,14=2.04, P=0.17). All samples were run in duplicate on three plates.

A pooled sample was run in duplicate on each plate to assess interassay variability, resulting in a

CV of 11.62% across plates. Because of the large amount of plasma necessary for this assay, insulin concentration could only be measured in a subset of samples, and duplicate samples with a CV>15% (N=8) were excluded, yielding N=85 measurements for analysis. Caution is warranted when concluding absolute insulin concentrations from a heterologous assay; however, in interpreting patterns of relative change, this approach is valid.

Glucose

The concentration of glucose in blood plasma was measured using an ELISA with a glucose assay kit (Autokit Glucose C2, Wako Chemicals, Richmond, VA, USA) following the manufacturer’s protocols with slight modification of reagent volumes. All samples (N=140) were run in duplicate on eight plates and re-run if CV>10%. A pooled sample was run on each plate to assess interplate variability, resulting in a CV of 9.9% across plates.

Lactate

The concentration of lactate in blood plasma was measured using an ELISA L-lactate assay kit (kit A-108L, Biomedical Research Service & Clinical Application, Buffalo, NY, USA) following the manufacturer’s protocols, except that sample volumes and deproteinization reagent

(polyethylene glycol) were reduced to 20 μl. All samples were run in duplicate on four plates and values were accepted if CV<20% ( per the manufacturer’s recommendation). We did not have sufficient blood plasma to rerun 11 samples that exceeded this threshold, leaving N=129 values for analysis. Samples with values outside the standard curve (N=9) were assigned to the extreme ends of the standard curve. Inter-plate variability was assessed with a pooled sample run on each plate, resulting in a CV of 4.4% across plates.

Statistical Methods

̇ Dependent physiological variables (VO2, CORT, insulin, glucose and lactate concentrations) were analyzed using repeated-measures mixed linear models with proc mixed in

SAS version 9.4 (SAS Institute, Cary, NC, USA) with α=0.05. Graphics are presented as back-

67 transformed least-squares means, using ggplot2 (Wickham, 2009) in the programming language

R (v. 3.1.2, R Foundation for Statistical Computing, Vienna, Austria). Variables were log10- transformed to meet assumptions of normality and then standardized to a mean of zero and unit variance to allow for direct comparisons of effect sizes among the different measures (Gelman and Hill, 2006). Temperature was modeled as a categorical factor because this provided a better fit to the data than treating temperature as a continuous variable owing to the non-linear relationship of temperature and some of the dependent variables, as assessed by distribution of residuals. Because we were interested in both linear and possible curvilinear effects of temperature on dependent variables such as metabolic rate (e.g. Iles, 2014), we tested the linear and quadratic effects of temperature by utilizing the ‘estimate’ statement in proc mixed, with coefficients for orthogonal contrasts obtained using our test temperatures (24, 28, 32, 35 and

38°C) in proc iml. The ‘estimate’ statement provides a custom hypothesis test and assesses significance with a t-test. By modeling temperature as a categorical factor and then conducting these post hoc linear contrasts, we are able to both utilize models that best fit the data and subsequently test the linear and quadratic effects of temperature. Block and population of origin nested within ecotype were included as fixed effects to account for block and heterogeneity of populations within each ecotype, respectively. Individual nested within block was treated as a repeated-measures random effect modeled with an autoregressive(1) [ar(1)] covariance structure, which provided the best model fit as assessed by minimizing corrected Akaike information criterion values. Initial models included the fixed effects of temperature, ecotype and body condition, as well as the two- and three-way interactions thereof. With the exception of the temperature × ecotype interaction, which is of biological interest and influences metabolic rate in this species (Gangloff et al., 2015), we removed non-significant interactions from final models.

Because the ecotypes differed in mass (L-fast snakes are larger; t-test, P<0.0001), we accounted for mass by first normalizing the log10-tranformed mass to a mean of zero and unit variance within each ecotype so that we could test for the effect of different sizes without confounding this with ecotype. For analysis of plasma hormone, glucose and lactate concentrations, two covariates were initially included in each model: time (in minutes) from the end of the trial to the time the snake was removed from its chamber and the handling time (in minutes) of the snake needed to complete blood sample collection. Because these covariates represent potential effects of experimental logistics (e.g. we were not able to draw blood from all snakes immediately and simultaneously) rather than biological questions of interest, we removed these covariates from final models if non-significant. After removing non-significant covariates and interactions (all

P≥0.18), the repeated-measures model for the analysis of dependent variables took the form:

Y = μ + Test temperature + Ecotype + Ecotype × Temperature + Condition + Block

+ Population(Ecotype) + [Ecotype-specific mass] + [Handling time] + ε,

̇ where μ represents the grand mean and ε represents the error term. Only the models for VO2 and

CORT concentrations included the term for ecotype-specific mass and only the model for lactate concentration included the term for handling time.

To ensure that our measures represent the effect of temperature treatments and not the effect of handling stress or placement in metabolic chambers, we compared values of CORT concentration from blood samples taken during normal husbandry conditions 9 months prior to the experiment. We compared least-squares means from a repeated-measures ANOVA of log10- transformed CORT concentration with sampling time (baseline or temperature treatment) as a

69 categorical independent variable and individual as a repeated-measures random effect with an ar(1) covariance structure. To further elucidate the relationship between insulin and glucose concentrations within each temperature, we conducted an additional mixed-model analysis of log-transformed and normalized insulin concentration with glucose concentration, temperature and their interaction as independent factors and individual as a repeated-measures random effect with an ar(1) covariance structure. For all mixed models, we estimated denominator degrees of freedom for F-tests using the Kenward-Roger degrees of freedom approximation (Kenward and

Roger, 1997).

Results

Oxygen consumption rate

Oxygen consumption rate increased with temperature, with no indication of plateau

(linear effect of temperature P<0.0001). The main effects of condition and ecotype, and the

̇ interaction of ecotype and test temperature, did not influence VO2 (Table 1, Figs 1A and 2). Mass

̇ did not exhibit a significant effect on VO2, but we retained this as a covariate in the model because of the expected influence of mass on metabolic rates (see Discussion).

Plasma hormone and metabolite concentrations Temperature linearly increased CORT, insulin and glucose concentrations (all P≤0.01) and non-linearly affected CORT, glucose and lactate concentrations (P<0.0001, P<0.0001 and P=0.046, respectively; Table 1, Figs 1B–E and

2). Body condition did not affect hormone or metabolite concentrations (all P>0.53; Fig. 2, Table

1). Ecotype only affected glucose concentration, with M-slow snakes maintaining higher concentrations across all temperatures (P=0.048; Figs 1D and 2, Table 1). Neither the main effect of ecotype nor its interaction with temperature significantly influenced other physiological

70 variables (all P>0.11; Table 1, Figs 1B–E and 2). Ecotype-specific mass negatively influenced

CORT concentrations (P=0.0076; Table 1), while there was significant heterogeneity among populations within each ecotype (P=0.022), largely driven by very low CORT concentrations from one of the M-slow populations. In the analysis of lactate concentration, the handling time to bleed snakes was marginally significant in initial models of (P=0.07), which is not surprising given how quickly activity can induce increases in lactate concentrations in squamate reptiles

(e.g. Bennett and Licht, 1972), and so was retained the final model.

Baseline CORT concentrations from normal husbandry conditions differed significantly only from CORT concentrations at 38°C (back-transformed least-squares mean: 44.1 ng ml−1;

Tukey–Kramer adjusted difference between baseline and 38°C treatment, P<0.0001; all other comparisons with baseline, P>0.13; Fig. 1B). Additionally, glucose concentration significantly influenced insulin concentration (P=0.0040), and this relationship was independent of temperature (interaction of glucose and temperature: P=0.90; Table 2, Fig. 3).

Discussion

In this experiment, we measured whole-organism response to acute exposure to ecologically relevant temperatures and compared norms of reaction between two recently diverged lineages adapted to differing thermal environments (Bronikowski, 2000; Bronikowski and Arnold, 1999). Our results revealed a universal temperature dependence of the physiological systems we measured involved in energy processing and allocation. Oxygen consumption rate, as well as CORT, insulin and glucose concentrations, all increased monotonically with increasing temperature, without evidence of plateauing at the highest temperatures. Body condition, as a metric of present energy stores, did not influence any physiological parameter (Fig. 2). We found

71 support for hypothesis 1: a 3-h exposure to temperatures ≥38°C constitutes a physiological stress.

Insulin concentration increased in response to hyperglycemia (as indicated by the significant effect of glucose on insulin) and in response to temperature in a glucose-independent manner (as indicated by the shape difference in the reaction norms for insulin and glucose; Fig. 1). Even so, insulin and glucose maintained a positive relationship at all temperatures, suggesting that insulin maintains its role in glucose regulation but perhaps plays additional roles in other functional pathways at high temperatures. Hypothesis 2 was not supported: our results did not demonstrate evidence of local adaptation or canalized developmental plasticity in the thermal reaction norms of these two ecotypes (i.e. no ecotype×temperature interaction) in the measured indicators of physiological response to upper temperature extremes. Finally, our results refute the hypothesis that thermal limits in T. elegans result from oxygen limitation at high temperatures (OCLTT hypothesis; hypothesis 3).

In this experiment, individuals maintained physiological functionality at temperatures just below the lethal limit. Such extreme temperatures, however, dramatically affected regulation of important aspects of energy metabolism, specifically, glucose regulation pathways. CORT concentration was thermally dependent, with temperatures near the upper thermal limit inducing elevation above baseline. Somewhat surprisingly, CORT concentrations at all temperatures were well below maximum levels attained by wild-caught Eagle Lake T. elegans under handling stress

(CORT concentration mean±s.e.m.: 338.9±44.7 ng ml−1; Palacios et al., 2012). Though we are unable to make a direct comparison of absolute values across studies, the large difference in maximal values from the two studies as well as the proportional increase from baseline indicate that the capture–restraint protocol of Palacios et al. (2012) induced a much greater CORT response than the temperature treatments of the present experiment. That the CORT response

72 was elevated but not maximal indicates that, even at the highest temperatures, individuals have the capacity to mount a response to additional stressors and that exposure to high temperatures for this time period did not bring snakes across a critical threshold. Even at the highest temperatures, snakes were likely in the ‘reactive homeostasis’ range of Romero et al. (2009) or in ‘type I allostatic overload’ of McEwen and Wingfield (2010). However, prolonged exposure to the highest temperatures that we measured (38°C), exposure to higher temperatures or exposure to additional stressors could potentially continue to elevate CORT concentration beyond the capacity for further response. Furthermore, we found that larger snakes exhibited lower CORT concentrations across the temperature range (Table 1), though post hoc analysis revealed no mass × temperature interaction (F4,91=0.72, P=0.58). Because size can be used as a proxy for age in garter snakes (Sparkman et al., 2007), this result implies that either individual snakes reduce concentrations of circulating CORT as they age or that larger snakes have been able to obtain larger sizes by virtue of reduced hormonal reactivity. This distinction – whether hormonal profiles change with age or whether certain profiles permit greater growth – has important implications for understanding the role of hormonal regulation of life-history strategies

(e.g. Reding et al., 2016).

Stressful stimuli elevate both glucocorticoids, such as CORT, and catecholamines, such as epinephrine and norepinephrine (Akbar et al., 1978; Sapolsky et al., 2000). These hormones trigger pathways to mobilize glucose stores, produce glucose via glycogenolysis and gluconeogenesis (reviewed in Sapolsky et al., 2000), and inhibit glucose uptake into non- essential tissues (Kaplan, 1996). Accordingly, we observed similar patterns in the increases of

CORT and glucose concentrations, with both compounds maintaining baseline levels at moderate temperatures and elevating dramatically in response to near-critical temperatures, as

73 demonstrated by the significant quadratic effect of temperature (Table 1, Fig. 1B,D). Under homeostatic circumstances, insulin modulates the glucose-relevant effects of both catecholamines and glucocorticoids by reducing glucose production and promoting uptake into tissues (Foster and McGarry, 1996; Strack et al., 1995). With increasing heat stress, we observed both elevated insulin and glucose concentrations (Table 1, Fig. 1C,D). We also found evidence that insulin was responding to hyperglycemia and that the general relationship between glucose and insulin concentrations did not change across temperatures (Table 2, Fig. 3). Notably, insulin concentration rose linearly across the range of test temperatures, while glucose concentration increased non-linearly, as indicated by the significant quadratic effect of temperature (Table 1,

Fig. 1C,D). This result suggests that that insulin may play a role under heat stress in addition to glucose regulation, similar to that in mammals under conditions of heat stress (Baumgard and

Rhoads, 2013). For example, insulin may enhance protection from heat stress via its effect on the transcription of heat-shock proteins (Li et al., 2006) and potentially decreased muscle proteolysis

(Foster and McGarry, 1996). The temperature dependence of insulin and its role in energy balance maintenance in ectotherms is an important avenue of future research.

Our data demonstrate that garter snakes from these populations of divergent life-history ecotypes adjust their physiology and deliver the required oxygen to their tissues at sufficient rates to maintain metabolic and physiological competence when acutely exposed to near-lethal temperatures. Neither a metabolic-rate plateau at high temperatures nor a temperature-insensitive range was apparent, in contrast to a temperature-insensitive range of oxygen consumption rate from 28 to 34°C reported in a previous study of T. elegans (Seidel and Lindeborg, 1973).

Notably, the steep increase in oxygen consumption rate by L-fast snakes at the highest temperature (38°C), as well as the large variance associated with the least-squares mean estimate

(Fig. 1A), was driven by two snakes with an extreme response in oxygen consumption rate to high temperatures. The importance of individual variation in temperature sensitivity of these physiological traits in shaping the evolution of the stress response to high temperatures represents another important direction for future research (Angelier and Wingfield, 2013;

Cockrem, 2013). Surprisingly, the dependence of oxygen consumption rate on ecotype-specific body mass was not statistically significant. In an analysis of data across all temperatures, oxygen consumption rate scales positively with body mass (in grams) to an exponent of 0.37±0.28

(estimate±s.e.). Though low, this estimate does not differ significantly from estimates for scaling exponents in males of this species (0.59; Bronikowski and Vleck, 2010) or from squamates generally (0.7– 0.8; Andrews and Pough, 1985; McNab, 2002). We attribute the large variance around this estimate, rendering it insignificant in our models, to a combination of small sample

̇ sizes, a limited range of body masses sampled from each ecotype, and the incomplete VO2 data.

In contrast to the temperature dependence of oxygen consumption rate, we found no evidence that the concentration of lactate, a by-product of anaerobic metabolism, increased with increasing temperature, as might be expected if high temperatures disrupt oxygen supply and induce anaerobic metabolism (Frederich and Pörtner, 2000). We did find a significant quadratic effect of temperature on lactate, however, because of the high lactate concentrations at the low and high experimental temperatures (24 and 38°C). The increases we observed in lactate concentrations at these temperatures are likely not driven by energetic demand outpacing aerobic capacity, given that the relative increases are far below lactate increases resulting from exercise in squamates (e.g. Bennett and Licht, 1972). Rather, this pattern might result from the temperature dependence of lactate dehydrogenase (e.g. Johansson, 1969; Mendiola and De

Costa, 1990), though the activity of this enzyme this has not been quantified in T. elegans. Given

̇ that neither VO2 nor glucose concentration plateaued at high temperatures and that lactate concentration did not escalate with temperature, these snakes are not limited by oxygen acquisition or transport at temperatures near their thermal maximum. This result is in accordance with recent studies that refute the OCLTT hypothesis in free-living stages of terrestrial ectotherms (Fobian et al., 2014; He et al., 2013; McCue and Santos, 2013; Overgaard et al.,

2012; but see Shea et al., 2016). Our data do not address other possible mechanisms that may contribute to thermal tolerance limits, including protein denaturation, membrane fluidity, enzyme-substrate interactions, mitochondrial failure and failure of neural processes (reviewed in

Angilletta, 2009; Schulte, 2015). Regardless of the mechanism setting thermal tolerance limits, our results demonstrate that high temperatures increase energetic expenditure, increase resting oxidative metabolism, and affect energy-regulating pathways, which in turn could influence individual activity patterns, response to additional stressors and, ultimately, fitness.

The present study enhances our understanding of the complex interplay of genetic background and developmental environments in shaping physiology in T. elegans from Eagle

Lake. First, a previous study found ecotypic differences in metabolic rate in wild-caught adult males after 4 months in captivity (contrasting with the 4 years in captivity in the present study).

Male snakes from the L-fast ecotype had higher mass-independent oxygen consumption rates across a range of non-stressful temperatures (15–32°C; Bronikowski and Vleck, 2010). Second, studies in laboratory-born offspring from these two ecotypes indicate no difference in oxygen consumption rates between neonates at 28°C (Robert and Bronikowski, 2010), but identified ecotype-specific thermal response curves in older snakes (Gangloff et al., 2015), suggesting that genetic background exerts developmental effects on resting metabolic rates. Captive-born snakes from both ecotypes have similar CORT thermal response curves, but differ in their responses to

high ambient temperatures for both liver mitochondrial H2O2 production and erythrocyte superoxide concentration, two measures related to mitochondrial respiration (Schwartz and

Bronikowski, 2013). The present study detected no ecotypic differences in the responses of metabolic rate and hormone concentration to increasing temperature in wild-caught, laboratory- acclimated adult female snakes under heat stress, though we did identify an ecotypic difference in glucose concentrations across the range of measured temperatures. Given that the glucose concentration differences were parallel across temperature treatments (i.e. there is no ecotype × temperature interaction), the data suggest that there is variation in the maintenance of available energy between the ecotypes and that this difference is maintained under both normative and stressful conditions. After being caught as adults, these snakes were housed under identical laboratory conditions for several years, which can affect endocrine profiles (Sparkman et al.,

2014). Thus, either adult females from these ecotypes do not differ in resting metabolic rates or environmental effects on thermal reaction norms remain plastic throughout adulthood

(Angilletta, 2009). Although previous studies in garter snakes have showed that stress response and endocrine function are affected by current energetic stores (Dayger et al., 2013; Palacios et al., 2013), we found that body condition, which was largely homogeneous among individuals in our study, did not impact physiological variables. These animals, maintained in favorable conditions with ample food stores for 4 years, may not represent enough variation in body condition to reveal trade-offs as expected under resource-limited conditions (Glazier, 1999; van

Noordwijk and de Jong, 1986).

Snakes from these locally adapted ecotypes did not differ in response to stressfully high ambient temperatures, despite being adapted to differing thermal environments in life-history traits (Bronikowski, 2000). This fits observations that upper thermal tolerance limits in terrestrial

77 ectotherms display limited variance, and thus may have little capacity to adapt to changing local conditions (Huey et al., 2012; Sunday et al., 2011). That upper limits are constrained, however, does not mean that all thermal tolerances are fixed: in studies of other snakes in this genus, the critical thermal minima vary among and within species and are tied to temperatures experienced by populations in the field (Burghardt and Schwartz, 1999; Doughty, 1994). Garter snakes behaviorally avoid thermal extremes (Scott et al., 1982), which reduces selection on thermal physiology at high temperatures (Buckley et al., 2015; Huey et al., 2003). Although behavioral thermoregulation may provide relief from extreme high temperatures, increasing environmental temperatures could still reduce the number of suitable retreat sites (Huey et al., 1989), increase the distance between retreat sites and increase the resting metabolic rates of inactive snakes.

Nonetheless, it is possible that snakes might benefit from the increased foraging capacity and energy input of warmer temperatures (Huang et al., 2013; Weatherhead et al., 2012), especially in habitats that are less restricted by high temperatures (i.e. meadow populations). In general, studies that quantify long-term energy budgets at increased ambient temperatures and test for temperature-induced mismatches between metabolic rate and consumption (e.g. Iles, 2014;

Lemoine and Burkepile, 2012) are needed to assess the possible effects on individual fitness and population persistence. By utilizing an integrative, mechanistic approach in a broader energetic and ecological context, we can further develop our understanding of the processes that set thermal limits and the consequences of exposure to extreme but sub-lethal temperatures.

Acknowledgements

We thank S. Lei,W. Richardson at MP Biomedical, and B. Joos at Sable Systems for advice on laboratory techniques; G. Palacios, M. Manes and T. Schwartz for fieldwork; C.

Corwin and A. Pottebaum for experimental assistance; L. Follett for statistical advice; and G.

Burghardt, B. Danielson and F. Janzen for manuscript feedback.

FUNDING

Funding was provided by the Chicago Herpetological Society, the Iowa Science

Foundation (13-07) and the National Science Foundation (IOS-0922528). We also recognize the support of fellowships from the Iowa State University Office of Biotechnology to EJG., the

Environmental Protection Agency (FP-91723101) to R.S.T., and additional support to R.S.T. during manuscript preparation from the National Science Foundation (EF-1065638 to L. B.

Buckley).

DATA AVAILABILITY

All data are available in the Dryad Digital Repository http://dx.doi.org/10.5061/dryad. cs5th

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Table 1. Parameter estimates (partial regression coefficients) and repeated-measures mixed linear model analysis across five ̇ -1 temperatures of: log10-transformed and standardized oxygen consumption rate (VO2 in ml O2 h ) and log10-transformed and standardized plasma CORT concentration (ng/mL), insulin concentration (IU/L), glucose concentration (mg/dL), and lactate concentration (mg/dL) measured after a 3-hr temperature exposure in adult female T. elegans. Temperature was treated as a categorical effect in the model, so parameter estimates and t-test results for temperature are from orthogonal linear contrasts (see text for details). Denominator degrees of freedom for F-tests were estimated using the Kenward-Roger Degrees of Freedom Approximation. Significant effects designated in bold with a single (P < 0.05) or double (P < 0.001) asterisk.

Source of Variation ̇ CORT Insulin Glucose Lactate 퐕O2 Temperature: Linear Partial regression coefficient 0.97 1.04 0.63 1.02 0.0047 t (d.f.) 4.28 (48.3) 5.97 (73.7) 2.65 (48.6) 5.80 (77.6) 0.02 (63.5) Pr > t < 0.0001**.3) < 0.0001** 0.011* < 0.0001** 0.98 Temperature: Quadratic Partial regression coefficient 0.37 0.67 0.034 0.58 0.38 t (d.f.) 1.64 (57) 4.65 (116) 0.15 (64.9) 4.03 (117) 2.02 (108) Pr > t 0.11 < 0.0001** 0.88 < 0.0001** 0.046*

Ecotype 8

Partial regression coefficient 1.26 -0.89 0.12 0.37 0.066 F (dfn, dfd) 2.55 (1, 28.2) 0.52 (1, 24.2) 1.21 (1, 26.6) 4.32 (1, 24.7) 0.09 (1, 26.7) Pr > F 0.12 0.48 0.28 0.048* 0.77 Temperature × Ecotype Partial regression coefficient ------F (dfn, dfd) 2.02 (4, 47.5) 0.57 (4, 92.8) 0.30 (4, 51.7) 0.33 (4, 96.1) 0.93 (4, 80.4) Pr > F 0.11 0.69 0.88 0.85 0.45 Condition Partial regression coefficient -0.053 -0.037 0.072 0.039 -0.070 F (dfn, dfd) 0.20 (1, 21.2) 0.10 (1, 25.5) 0.34 (1, 23) 0.11 (1, 25.9) 0.41 (1, 30.6) Pr > F 0.66 0.76 0.56 0.74 0.53 Ecotype-specific mass Partial regression coefficient 0.011 -0.42 ------F (dfn, dfd) 0.01 (1, 19) 8.40 (1, 25.4) ------Pr > F 0.94 0.0076* ------Block Partial regression coefficient ------F (dfn, dfd) 1.62 (4, 23.4) 1.35 (4, 25.8) 1.82 (4, 24.4) 0.38 (4, 25.9) 1.78 (4, 29.4) Pr > F 0.20 0.28 0.16 0.82 0.16

Table 1. (Continued) Population(Ecotype) Partial regression coefficient ------F (dfn, dfd) 0.29 (5, 21.5) 3.17 (5, 26.7) 2.35 (5, 25.6) 1.88 (5, 25.9) 0.76 (5, 28.9) Pr > F 0.91 0.022* 0.070 0.13 0.59 Handling time Partial regression coefficient ------0.065 F (dfn, dfd) ------3.54 (1, 106) Pr > F ------0.063

Table 2. Results of repeated-measures mixed linear model of log10-transformed and normalized insulin concentration in adult female T. elegans, after a 3-hour treatment at each of five temperatures. Temperature was treated as a categorical effect in the model and denominator degrees of freedom for F-tests were estimated using the Kenward-Roger Degrees of Freedom Approximation. Significant effects (P < 0.05) designated in bold with a single asterisk.

Source of variation F (dfn, dfd) Pr > F

Temperature 0.26 (4, 64.3) 0.90 Glucose 8.88 (1,.3) 71.6) 0.0039* Temperature × Glucose 0.27 (4, 61.8) 0.90

̇ Fig. 1. Temperature had a linear effect on VO2, CORT, insulin, and glucose concentrations (all P ≤ 0.01) and a quadratic effect on CORT, glucose, and lactate concentrations (P < 0.0001, P < 0.0001, and P = 0.046, respectively) in adult female T. elegans, but ecotypes only differed in glucose concentration (P = 0.048). Thermal reaction norms are for (A) oxygen consumption rate (N = 82 data points), (B) plasma CORT concentration (N = 140 data points), (C) plasma insulin concentration (N = 85 data points), (D) plasma glucose concentration (N = 140 data points), and (E) plasma lactate concentration (N = 129 data points) measured at five temperatures. Plasma concentrations were measured after a 3-hr exposure to each temperature. In panel (B), the dotted line and corresponding grey shading represent mean ± S.E.M. of baseline CORT concentration values of these same individuals from common garden conditions nine months prior to the experiment. Data are back-transformed least-squares means from repeated- measures mixed linear models (see text for details). Error bars represent ± S.E.

̇ Fig. 2. Temperature exhibits strong linear effects on physiological variables (VO2, CORT, insulin, and glucose concentrations) while body condition does not. Path diagram shows partial regression coefficients for linear influences of temperature and body condition on measured physiological variables in wild-caught adult female T. elegans after a 3-hr exposure to five temperatures. Significant paths (all P ≤ 0.01) shown in black, non-significant paths (all P > 0.41) shown in gray; positive paths shown with solid line, negative with dotted line. Line size is proportionate to effect size on standardized variables. Significant estimates designated with one (P < 0.05) or two (P < 0.001) asterisks. See text and Table 1 for model details and results.

Fig. 3. Relationship between insulin and glucose concentrations within adult female T. elegans does not change with temperature. Scatterplot shows relationship between log- transformed and normalized glucose and insulin concentrations after a 3-hr exposure at five test temperatures. Overall effect of glucose concentration on insulin concentration is significant from repeated-measures linear model (P = 0.0040; see text for details). Regression lines are shown for each temperature. Data are from individuals for which we measured both plasma glucose and insulin concentrations at a given temperature (N = 85).

CHAPTER 4

AMONG-INDIVIDUAL HETEROGENEITY IN BEHAVIOUR AND PHYSIOLOGY

INTERACT TO INFLUENCE FITNESS IN A MODEL ECTOTHERMIC AMNIOTE,

THE GARTER SNAKE THAMNOPHIS ELEGANS

This manuscript is submitted as an invited manuscript for a themed issue of the peer-reviewed

journal Oikos

Eric J. Gangloff, Amanda M. Sparkman, and Anne M. Bronikowski

Author contributions: EJG designed the experiment, conducted behavior assays, performed lab work, analyzed the data, and wrote the manuscript. EJG and AMS conducted experiment. AMB provided guidance in experimental design, data analysis, and interpretation.

All authors contributed to manuscript revisions.

Abstract

Accumulating evidence suggests that within-individual plasticity of behavioural and physiological traits is limited, resulting in stable among-individual differences in these aspects of the phenotype. Furthermore, these traits often covary within individuals, resulting in a continuum of correlated phenotypic variation among individuals within populations and species. This heterogeneity, in turn, affects fitness and can have cross-generational effects. Patterns of covariation, among-individual differences, and subsequent fitness consequences have long been

recognized in reptiles, but are often overlooked in current literature. Here we briefly review the fitness consequences of among-individual variation in behaviour-physiology-life history relationships in reptiles. We then provide a test of fitness consequences of among-individual heterogeneity in the garter snake Thamnophis elegans by analyzing relationships among measures of physiological parameters, behavioural activity levels, and reproductive success. In garter snakes, we find that physiological and behavioural traits interact to form ‘energetic phenotypes’ and that maternal traits predict offspring body condition and mass at birth. This differential allocation of energy to offspring, in turn, strongly influences subsequent offspring growth and survival. In the context of fluctuating resource availability, this pattern suggests the potential for strong selection on phenotypes defined by behaviour-physiology interactions. By offering both a synopsis of previous literature and new empirical results, this study contributes to the growing understanding of how individual differences in suites of phenotypic traits affect life- histories, are maintained within populations, and bear cross-generational impacts.

Introduction

Among-individual heterogeneity and fitness

Individuals often maintain consistency in both behaviour and physiology over time, in contrast to the historical assumption of generally unconstrained flexibility in these traits.

Furthermore, there is great among-individual heterogeneity in these traits within populations and species. Recently, this emphasis on among-individual variation and its adaptive significance has become the focus of studies in numerous areas of organismal biology, including physiology (Bennett 1987, Williams 2008, Cockrem 2013, Taff and Vitousek 2016), behaviour (Sih et al. 2004, Réale et al. 2010), life-history (Wilson and Nussey 2010, Vindenes

and Langangen 2015, Hamel et al. 2016), and population ecology (Bolnick et al. 2011,

Dochtermann and Gienger 2012). Patterns of trait covariance within individuals suggest that combinations of traits are not random, but that within-individual consistency in traits can results from natural selection operating to maintain trait combinations that optimize fitness outcomes (Dall et al. 2004, Careau et al. 2008, Réale et al. 2010). Such phenotypic patterns can come about through several genetic mechanisms, including linkage disequilibrium, pleiotropy, and the non-random adaptive association of alleles maintained by selection (Falconer and

Mackay 1996, Lynch and Walsh 1998). Recent work has sought to explicitly integrate expectations of how these ‘personalities’ or ‘temperaments’ might be characterized within the broader framework of a pace-of-life continuum describing ‘slow’ to ‘fast’ life histories (Koolhaas et al. 1999, Réale et al. 2007, Réale et al. 2010). Empirical studies have revealed within-individual consistency in potentially labile traits (e.g., behaviour and physiology) and that suites of such traits are correlated in a wide range of taxa (e.g., Niemelä et al. 2012, Careau et al. 2014, Kern et al. 2016, Metcalfe et al. 2016, Monestier et al. 2016).

The goals of the present study are twofold: (1) to briefly review studies examining the evolutionary and ecological significance of among-individual heterogeneity in reptiles in order to bridge our understanding of the relationship between among-individual heterogeneity in physiological and behavioural traits and life-history trajectories, and (2) to present empirical evidence that physiological and behavioural phenotypes interact to influence fitness and bear cross-generational consequences for life-history trajectories in a model reptile system, the western terrestrial garter snake, Thamnophis elegans.

Among-individual heterogeneity in reptiles

Early reviews of the animal personality literature provided important insights into the fitness consequences of among-individual heterogeneity, but omitted studies on reptiles (e.g.,

Réale et al. 2007, Smith and Blumstein 2008, Biro and Stamps 2010, Carere et al. 2010). This shortcoming was addressed by Careau and Garland (2012), who describe the extensive literature examining among-individual variation of physiological and behavioural traits in squamate reptiles (lizards and snakes) and examined these studies explicitly in a framework bridging energetics and personality. Nonetheless, there is a need for studies in reptiles that link personality traits to life-history variation and fitness outcomes. Given the flexibility of reptilian physiologies

(e.g., infrequent feeding in snakes, extreme hypoxia tolerance in turtles) and unique life-histories

(e.g., indeterminate growth, increasing fecundity with age), studies testing the impact of among- individual heterogeneity on life-history trajectories, as well as covariation in life-history traits with physiological and behavioural phenotypes, will fill an important knowledge gap.

Under the pace-of-life syndrome hypothesis, we expect that behavioural, physiological, and life-history traits will covary in predictable directions within individuals (Careau et al. 2008,

Réale et al. 2010, Careau and Garland 2012). However, the mechanistic relationships between these traits and components of fitness, and how this covariation may change across environmental or social conditions, are not well understood. For example, we might expect the emergence of a continuum of ‘energetic phenotypes’ in which physiological traits related to energy allocation and mobilization are coupled with behavioural traits related to food acquisition and energy expenditure to maintain energy balance (Careau et al. 2008, Biro and Stamps 2010).

For example, within-individual measures of energy intake, energy expenditure, and growth were strongly correlated in a field study of the common garter snake (Thamnophis sirtalis), supporting

the expectation that behaviour and physiology covary within individuals and that variation in foraging effectiveness can influence individual life-histories (Peterson et al. 1998). Additional evidence of such performance-linked phenotypic integration was identified in mountain spiny lizards (Sceloporus jarrovi) in which testosterone levels were artificially elevated to increase energy expenditure on territorial defensive behaviours. This hormonal treatment disrupted energy budgets and resulted in reduced survivorship (Marler and Moore 1988, Marler et al.

1995). Recent studies in the common lizard (Zootoca vivipara) have also addressed this question and found limited support for coadaptation of physiological and behavioural traits and the pace- of-life syndrome hypothesis. Within individuals, there was no evidence for correlation among behavioural, metabolic, and locomotor traits. However, captive-born offspring experienced correlational selection such that individuals with reduced exploration behaviour and high metabolic rates, as well as the opposite combination, survived better in outdoor mesocosms (Le

Galliard et al. 2013). A study in adult Z. vivipara found no evidence of correlated selection among measures of performance (sprint speed), thermoregulatory behaviour, and energy metabolism (Artacho et al. 2015). Interestingly, however, large lizards with high metabolic rates enjoyed higher survival rates in captive mesocosms but had reduced reproductive output. This suggests a classic survival-reproduction trade-off mediated by energetic phenotype. Given the limited support and the few studies conducted to test links between among-individual variation and fitness across taxa, both the relationship among personality traits, physiology, and fitness, as well as the conditions under which different trait combinations are advantageous or disadvantageous, are still largely unknown (Réale et al. 2007).

In squamate reptiles, it is established that there is often large within-cohort variation in size, which in turn affects subsequent life-history traits, including growth and survival (e.g.,

Bronikowski 2000, Kissner and Weatherhead 2005, Blouin-Demers and Weatherhead 2007). Yet there is a lack of an adaptive explanation for this within-cohort variation, which is in turn essential for understanding the evolution of reproductive trade-offs in squamate reptiles generally (Niewiarowski and Dunham 1994). Empirical studies of growth trajectories in reptiles, while not explicitly modeling among-individual heterogeneity, demonstrate two important themes: (1) environmental conditions, especially variation in food availability, largely determine growth rates and (2) environmental conditions of birth and early development bear lifetime consequences for individuals. Such patterns of variation were reviewed in Andrews and Pough

(1985) and evidenced in more recent work in squamates (Madsen and Shine 2000, Baron et al.

2010), crocodilians (Tucker et al. 2006), and turtles (Lindeman 1997, Spencer and Janzen 2010,

Eguchi et al. 2012). Variation in growth trajectories, in turn, is important because size is closely tied to important fitness traits, including reproduction and survival. For example, a series of studies on the xeric lizard Ctenophorus (=Amphibolurus) ornatus, demonstrates that fast- growing lizards suffer higher mortality in drought conditions, whereas slow-growing lizards are more cold sensitive (Bradshaw 1970, Bradshaw 1971, Baverstock 1975). Taken together, these studies show that reptiles exhibit within-individual plasticity in growth rate and maximum size and that among-individual heterogeneity in growth and size is largely determined by early-life environments. The adaptive significance of this among-individual variation, however, remains an important open question.

Recently, King et al. (2016) examined potential causes and consequences of among- individual heterogeneity in growth rates and asymptotic size in the Lake Erie Watersnake,

Nerodia sipedon insularum. Data collected over thirteen years of field study demonstrate that snakes from these populations exhibit significant among-individual heterogeneity in asymptotic

size. Using data on the size-dependency of reproductive output from prior work in this system,

King et al. (2016), modeled the potential effects of differences in asymptotic size on female lifetime reproductive success and found that the benefit of larger size was dependent on annual rates of survival: if attaining larger sizes resulted in delayed reproduction, small females had greater lifetime success under scenarios of low annual survival. Assuming that a life-history trade-off exists between growth and age of first reproduction, differences in survival across years would provide a mechanism to maintain growth polymorphisms within the population. With the present study, we connect individual heterogeneity in maternal phenotypes with variation in offspring mass and body condition at birth, providing a putative mechanism by which stable individual phenotypes influence life-history trajectories across generations.

Empirical test of fitness consequences of among-individual variation

Extreme within-species diversity in life-history traits, as well as physiological correlates thereof, is well-exemplified in the metapopulation system of western terrestrial garter snakes around Eagle Lake, CA. Though connected by gene flow (Manier and Arnold 2005), replicate populations along the lakeshore and in the mountain meadows exhibit divergence in life-history strategies on the pace-of-life continuum (sensu Ricklefs and Wikelski 2002). Lakeshore populations exhibit a fast-paced strategy, characterized by more rapid growth rates to larger adult body sizes, higher annual reproductive output, and relatively higher annual mortality and therefore shorter lifespans in comparison with the slow-living meadow populations (Sparkman et al. 2007, Schwartz et al. 2015). Divergences in life-history strategies are accompanied by differences in physiology, including endocrine pathways, immune function, and metabolism (Sparkman et al. 2009, Bronikowski and Vleck 2010, Robert and Bronikowski 2010,

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Palacios et al. 2011, Gangloff et al. 2015). Importantly, the mountain meadow habitats are characterized by dramatic stochastic variation in seasonal rainfall and thus prey (anuran) availability (Bronikowski and Arnold 1999, Miller et al. 2011). Given that environmental variation can maintain trait polymorphisms and that we might expect standing genetic variation for such traits in a recently-diverged system such as this, snakes from these meadow populations present an ideal system in which to test for a relationship between maternal phenotypes, reproductive output, and fitness consequences. We expect that maternal behavioural and physiological traits will interact to affect potential energetic allocation to offspring, mediated through environmental conditions. In turn, this initial allocation will interact with developmental enviornments to shape offspring growth, survivorship, and life-history trajectories. We test these hypotheses in two components: first, we utilize empirical data on maternal phenotypes to test the relative influence of these traits on energetic allocation to offspring at birth; and second, we test the importance of offspring mass and body condition at birth on early-life growth and survival

(Fig 1.).

To address our first question, we quantify indices of among-individual heterogeneity in behavioural and physiological traits measured both before and after parturition in wild-caught female garter snakes from a single slow pace-of-life meadow population. The hypothalamus- pituitary-adrenal/interrenal (HPA/HPI) axis is likely to be implicated in the maintenance and coordination of energetic phenotypes because of its central role in modulating energetic allocation decisions, especially in response to perceived stressors (Sapolsky et al. 2000, Careau et al. 2008, Angelier and Wingfield 2013, Dantzer et al. 2016). In reptiles, the primary glucocorticoid is corticosterone (CORT; Norris and Jones 1987), which acts to increase glucose availability to tissues by initiating processes of glycogenolysis and gluconeogenesis (Sapolsky et

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al. 2000). Thus, measures of CORT and glucose concentrations provide data on both the activation of the HPI axis (CORT) and its downstream effect on energy mobilization (glucose) across ecological contexts of varying metabolic and energetic demands (field baseline, during embryonic development, post-parturition).

Under the pace-of-life syndrome hypothesis, we predicted that traits are correlated within individuals such that snakes exhibiting more active behaviours (more tongue-flicks and faster escape times) also have higher indicators of energy availability (plasma glucose concentration) and mobilization (plasma corticosterone concentration). We then test whether the phenotypes of these reproductive females (hereafter, ‘moms’) interact to shape reproductive investment and success by quantifying the effect of these traits on the mass and body condition of offspring at birth, metrics of energetic allocation with established effects on subsequent growth and survival in this system (Bronikowski 2000). To address our second question, we quantify the influence of birth body condition and mass on growth and survival at ages four months and one year, as well as survivorship through the age of maturity, in offspring raised under differing thermal regimes designed to mimic field variation (Bronikowski 2000, Gangloff et al. 2015). Taken together, this research provides (i) an experimental test linking individual heterogeneity in behavioural and physiological traits to trans-generational effects and fitness in an ectothermic amniote and (ii) a potential mechanism by which such phenotypic polymorphisms may be maintained in stochastic environments by fluctuating correlated selection.

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Methods

Animal Collection and Care

On 15 June 2012, we hand-captured 21 pregnant western terrestrial garter snakes from a single meadow population (elevation 1645 m) near Eagle Lake, California, USA (population

“M1” from Sparkman et al. 2007, and population “PAP” from Manier and Arnold 2005). Snakes were immediately bled in the field (approximately 150 μL) from the caudal vein using heparin- rinsed syringes within 10 minutes of capture, before plasma CORT concentration is elevated in this species (Palacios et al. 2012). We kept whole blood on ice for a short time (< 3 h), aliquoted and snap-froze plasma in liquid nitrogen, and stored samples at -80°C until measurements. All snakes were weighed (mass in g; range: 33–90, SD = 15.8), measured (snout-vent length [SVL] in mm; range: 407–549, SD = 41.6), gently palpated for the presence of embryos, and transferred to Iowa State University. There, we kept snakes under common-garden conditions (see husbandry details in Gangloff et al. 2015). We collected blood samples and weights/measures at two subsequent time points: after snakes had been in captivity for about one month (hereafter

‘pre-parturition’; range 27-28 days after capture) and one month after giving birth (hereafter

‘post-parturition’; range 31-35 days after birth, 80-106 days post-capture).

Offspring were live-born between 6–28 August (total N = 116; median litter size = 5, range: 2–10) and were weighed, measured, sexed, and moved to individual housing within 24 hours of birth (mass range: 1.31–3.94 g, SD = 0.45; SVL range: 145–212 mm, SD = 11.7). We excluded from analysis four stillborn snakes and one snake born with a severely kinked back that died at age four days. Offspring were randomly assigned, evenly divided among litters and between sexes, to either a “warm” or “cool” rearing environment designed to mimic the environmental conditions experienced by the fast-paced and slow-paced life history

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ecotypes (details in Gangloff et al. 2015). We recorded the amount of food consumed with each weekly feeding and measured offspring mass and SVL again at approximately 4 and 12 months of age. We calculated body condition at each time point as the residual of the ln-mass on ln-SVL regression including all live animals (Weatherhead and Brown 1996). We calculated specific growth rate (SGR), a measure of growth scaled to body size, as SGR = 100 × (ln(SVL2) – ln(SVL1))/days (Reid et al. 2011, Killen 2014).

Corticosterone and Glucose Assays

We measured corticosterone (CORT) and glucose concentrations in mom blood plasma collected at three time points: time of capture, pre-parturition, and post-parturition. Because of logistical constraints, we were unable to obtain blood samples for ten individuals immediately upon capture. CORT concentration was measured with double-antibody radioimmunoassay kits

(Kit #07120102, MP Biomedical, Orangeburg, NY, USA) following the protocol described in

Robert et al. (2009) with samples diluted 1:80. We measured CORT concentration using two kits and assessed inter-assay variability with kit-provided controls, yielding coefficients of variation

(CV%) of 8.2% and 3.8% for high and low controls, respectively. All samples were run in duplicate and re-run if CV% between the duplications was > 10%. We measured glucose from

1.5 μL of the thawed plasma using a FreeStyle Lite Glucometer (Abbott Diabetes Care,

Alameda, CA). Samples measuring below the lower threshold of the glucometer (20 mg/dL; N =

3 samples) were assigned a value of 20 mg/dL. Plasma limitations precluded measures in five and four samples for captive measures of CORT and glucose, respectively. We analyzed a total of N = 48 measures for CORT and N = 49 measures for glucose.

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Behaviour Assays

We quantified behaviour of mothers in repeated open-field tests to measure two axes of personality: exploratory activity and boldness toward a predator (Réale et al. 2007, Carter et al.

2013). We conducted two assays, 72 hours apart, both pre- and post-parturition, for a total of four assays for each individual. The order of the trials was randomized within each testing day.

Following identical protocols for each individual, we placed moms in a neutral test arena

(modified plastic bucket with blocked escape portals) where we left the snake undisturbed for 15 s. Snakes were then subjected to a simulated predator attack consisting of five movements of a foam-tipped rod directed toward the snake’s head but not contacting the snake. Such simulated predator attacks have been demonstrated to elicit an antipredator response in snakes similar to behaviours observed in nature (Herzog et al. 1989). Attacks were repeated three times at 20 s intervals. Following the final set of simulated attacks (after 60 s), we lifted the gate to reveal the escape portals. We continued to subject the snakes to simulated attacks at 20 s intervals until they escaped the arena or until the total trial time reached 180 s. As a metric of exploratory behaviour, we quantified the number of tongue flicks exhibited in the first 15 s in the test arena, which indicates information-gathering via olfaction and is an established index of exploratory behaviour in squamate reptiles (Chiszar et al. 1976, Gove and Burghardt 1983). We quantified boldness as the latency to escape from the test arena after the gate was opened (Debecker et al.

2016, Mell et al. 2016). Trials were video recorded and behaviours scored using

JWatcher (Blumstein et al. 2006). Behaviours were quantified by one author and we validated intra-rater reliability by rescoring 10% of the trials and calculated Kendall’s coefficient of concordance, a non-parametric measure of repeatability (Tongue flicks: W = 0.91, P = 0.0037;

Escape Latency: W = 0.94, P = 0.0008; Burghardt et al. 2012).

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Statistical Methods

To estimate the repeatability of the physiological traits (CORT and glucose concentrations) we calculated consistency repeatability, which provides a measure of the relative amount of variation explained by differences among individuals after accounting for changes in measures across time (Biro and Stamps 2015). We created models with the dependent variables

CORT concentration and glucose concentration with the fixed effect of time point and a random effect of individual, modeled with a compound symmetric covariance structure. CORT and glucose concentrations were log10-transformed before analysis to meet assumptions of normality (Sokal and Rohlf 2011). Our repeatability estimates are the ratio of between-individual variance to total variance, with significance assessed by a likelihood ratio test using χ2

2 distribution with one and zero degrees of freedom (χ0:1 in results; Snijders and Bosker 2012,

Wolak et al. 2012).

To estimate the repeatability of behavioural traits (tongue flicks and latency to escape), we estimated Kendall's coefficient of concordance (W) using the MAGREE macro in SAS (Gwet

2002). The distribution of the escape latency times were largely bimodal, with snakes either escaping within the first few seconds or not leaving the test arena at all. Furthermore, the count data of tongue-flicks did not meet assumptions of normality. As a non-parametric measure of repeatability, Kendall’s W compares changes the rank-order of individuals across trials and is therefore insensitive to changes in mean trait values over time, providing a statistic of rank agreement on the same scale (0-1) as repeatability (Sokal and Rohlf 2011).

While repeatability statistics as described above provide a measure of the relative stability of individual traits over time, we also sought to identify the major axes of variation in

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both behavioural and physiological traits. A principal components analysis (PCA) permits us to identify the primary axes of among-individual variation in all measures to illustrate an individual’s behavioural or physiological phenotype while simultaneously retaining variation in the original data. For physiological traits, we log10-transformed measures of plasma CORT and glucose concentrations from the three time points (see above) and used a correlation matrix to identify the major axes of variation and the corresponding loadings. We utilized a rank-order correlation matrix for the PCA of behavioural traits (Helsel 2012). We also inverted the escape latency times so that the behavioural traits would be on the same scale (higher values corresponded both to more tongue flicks and a quicker escape time). PCA analyses were conducted with the PRCOMP function and missing values imputed with the IMPUTEPCA function from the ‘missMDA’ package (Josse and Husson 2016) for R (R Core Team 2014). To test for within-individual correlations, we regressed the individual scores of the first PC axes for behavioural and physiological traits using an ordinary least-squares regression with PROC REG in

SAS.

To test for the influence of behavioural and physiological phenotypes on body condition and mass of offspring at birth, we utilized mixed-effects models with mom size (SVL from first measure in captivity), Behaviour PC1, Physiology PC1, and their interaction as fixed effects and the random effect of litter, modeled with a compound symmetric covariance structure. We normalized SVL to a mean of zero and standard variance so that parameters for other fixed effects would be estimated at the mean mom size.

Finally, we tested the impact of body condition and mass on subsequent specific growth rate across two time intervals (birth to four months and birth to one year) with mixed-effects linear models and probability of survival (to ages four months and one year) with mixed-effects

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logistic regression models. Because offspring birth body condition and mass can be considered distinct measures of maternal allocation to offspring, we conducted the analyses of condition and mass separately. Initial models included the fixed effect of offspring sex and all possible two- and three-way interactions with treatment and body condition/mass, but these effects were removed as they did not significantly explain variance in these traits (models of growth: all P >

0.29; models of survival: all P > 0.20). Initial models of survival probability included the main effects of sex, treatment, and body condition/mass, as well as the two-way interaction of rearing treatment and body condition/mass (logistic regression models did not converge when all possible two-way interactions were included). We then removed the non-significant effects of sex to simplify models. For all models, we included litter as a random effect modeled with a compound symmetric covariance structure to account for non-random covariance among littermates.

Additionally, we estimated survival of offspring through four years of age (approximate time of sexual maturity in wild populations) with a semi-parametric proportional hazards model.

This frailty model allows us to assess the effects of covariates on survivorship while utilizing data on each individual’s lifespan (Cox 1972). As above, we analyzed the effects of birth condition and birth body mass independently, with models also including rearing treatment, sex, and all two-way interactions thereof, as well as the random effect of litter.

We utilized PROC MIXED, PROC GLIMMIX, and PROC PHREG in SAS version 9.4 (SAS

Institute, Cary, NC) for mixed linear models, logistic regression models and hazard models, respectively. We estimated the denominator degrees of freedom for F-tests tests for all mixed models using the Kenward-Roger Degrees of Freedom Approximation (Kenward and Roger

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1997). All graphics were created with the ‘ggplot2’ and ‘plotly’ packages and in R (Wickham

2009, Plotly Technologies 2015).

Field and laboratory work with live animals was approved by the Iowa State University

Institutional Animal Care and Use Committee (protocols #3-2-5125-J and #1-12-7285-J) and the

California Department of Fish and Game (SC-11973). Wild-caught animals were released at point of capture after data collection and captive-born snakes were maintained in the lab colony.

Results

Repeatability of Physiological and Behavioural Traits

To estimate repeatability of physiological biomarkers (plasma CORT and glucose concentrations), we utilized consistency repeatable (Rc), which accounts for changes in group means across measures made in the field, before parturition, and after parturition. Our estimates of Rc were at the high end of the range generally reported for hormonal measures in other taxa (e.g., birds Holtmann et al. 2016). Estimates of Rc for glucose measures approached significance while measures of CORT were not significantly repeatable (Table 1).

Both measures of behaviour (tongue-flicks and escape latency) were significantly repeatable and in accord with repeatability estimates from other behaviour studies, including those with snakes (Brodie and Russell 1999, Bell et al. 2009, Mayer et al. 2016). This indicates that individuals retained their rank-order position in these measures across the four behavioural trials (two pre-parturition; two post-parturition).

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Principal Component Analysis

Maternal Physiology. The first two axes (eigenvalues > 1) explained a total of 67.2% of the variation in measures of maternal physiological biomarkers (plasma CORT and glucose concentrations) made in the field, before parturition, and after parturition (Table 2). PC1 loadings distinguished between individuals with high concentrations of plasma CORT and glucose across all measurements and individuals with low concentrations across all measures. This demonstrates that the primary axis of physiological variation is among individuals displaying consistently high levels of these markers and those exhibiting consistently low levels of these markers. PC2 describes variation among individuals which decreased glucose concentration between field and lab measures while increasing CORT concentration between these measures, or the opposite pattern (increasing glucose while decreasing CORT between field and lab measures).

Maternal Behaviour. For measures of maternal behaviour, the first three axes of the PCA

(eigenvalues > 1) explained a total of 66.5% of variation in the rank-transformed data (Table 2).

PC1 loadings distinguished ‘high reactive’ individuals – with high numbers of tongue flicks and a rapid escape in all trials – from ‘low reactive’ individuals with low numbers of tongue flicks and slow escape. PC2 describes differences between the two behavioural measures among individuals: either exhibiting high numbers of tongue flicks and slow escape behaviours or vice- versa. PC3 describes changes in reactivity in both measures before and after parturition (Table

2). Despite the fact that the first axes of both PCA analyses described variation among individuals with ‘high reactive’ and ‘low reactive’ behavioural and physiological phenotypes, there was no significant within-individual correlation between scores on Behaviour PC1 and

Physiology PC1 (slope estimate: 0.29 ± 0.23 SE, t19 = 1.25, P = 0.23). In practice, this means that individuals do not fall along a single axis of variation in these physiological and behavioural

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traits. That is, individuals with high reactive physiology could be either high or low reactive behaviourally and vice versa.

Effects of maternal phenotype on reproductive allocation

The first principal component axes of behaviour and physiology interacted to influence offspring body condition and mass at birth. Additionally, both the main effects of the first axes of behaviour and physiology exhibited non-linear effects on offspring mass at birth. The effect of the interaction on both body condition and mass at birth describes a saddle-shaped function with highest values of both condition and body mass at birth at the extremes of maternal trait combinations (high-high or low-low; Table 3, Fig. 2).

Effects of body condition at birth, mass at birth, and rearing environment on

growth and survival

Body condition at birth (a measure of size-corrected mass) and birth mass were collinear

(ordinary least-squares regression, p < 0.001), with heavier snakes also being in better condition.

Given that these measures provide different interpretations of potential energetic stores (relative vs. absolute), we present analyses of both the effects of body condition and mass at birth to subsequent growth and survival. Growth rate for the first four months was determined by an interaction of birth condition and rearing treatment, such that condition had a more pronounced and positive effect on offspring in the warm rearing treatment. Neither body condition nor mass at birth influenced growth rates through one year. Snakes born heavier and snakes in the cool rearing treatment had a higher probability of surviving to four months of age. Snakes born in

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better body condition and snakes born heavier were more likely to survive to one year of age

(Table 4, Fig. 3).

In our analysis of survivorship including all offspring through four years in captivity, heavier offspring had a higher probability of survival while the effect of birth condition was not significant. Additionally, in our model testing the effects of birth condition, we found a significant interaction of sex and rearing treatment, whereby males lived longer when reared in a warm environment, whereas females lived longer when reared in the cool environment (Table 5,

Fig. 4).

Discussion

These results demonstrate that major axes of variation in maternal behavioural and physiological traits describe a continuum of reactivity across all measurements, though the pattern is more pronounced for behavioural traits. These among-individual differences in maternal phenotypes then interact to determine the body condition and mass of offspring at birth, suggesting a trade-off between maternal energy use and energetic allocation to offspring. Moms with matched phenotypes (‘high-high’ or ‘low-low’ for physiological and behavioural traits) gave birth to offspring with greater energetic stores. In turn, these higher-quality offspring grew faster in the first months of life and were more likely to survive to one year of age. These results demonstrate that among-individual differences in physiological and behavioural traits have trans- generational effects, influencing important early life-history traits of offspring. Additionally, growth and survival of neonates was also influenced by immediate environmental conditions, indicating that environmental variation may impact the relative influence of maternal traits on fitness. Taken together, these results demonstrate that important early-life demographic and life-

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history parameters, including growth rate and survivorship, vary among individuals within a cohort and that this variation is likely driven by energetic trade-offs determined in part by stable maternal phenotypes.

We found a high consistency of exploratory and antipredator behaviours within individuals (Kendall’s W = 0.49 and 0.50, respectively) and that the primary axis of behavioural variation represents a continuum of activity levels in both behavioural traits across all measures.

Given this pattern, these two behaviour metrics likely represent different aspects of a single behavioural axis, that of general activity (Réale et al. 2007) with higher values describing a proactive coping style and lower values a reactive coping style (Koolhaas et al. 1999, Coppens et al. 2010). This finding correlates with a study of behaviours in field-caught animals from this garter snake system, in which both tongue-flick rate and activity level were correlated and repeatable within individuals (Chow et al., submitted). Together these two studies show that multiple behavioural traits are stable under both field and captive conditions, lending strong support to the existence of animal personalities in this system.

The physiological indicators we measured are reliable biomarkers of potential trade-offs in energetic allocation decisions within individuals: increases in circulating CORT promote the mobilization of energy stores, while glucose concentration serves as a direct measure of energy availability (Sapolsky et al. 2000). After accounting for differences in group means across measured time points, consistency repeatability of plasma glucose concentration was in the high range for physiological traits generally (Rc = 0.27) and approached significance. Repeatability of plasma CORT concentration was lower (Rc = 0.19) and not statistically significant, consistent with hormonal repeatabilities in this and other taxa (e.g., Sparkman et al. 2014, Holtmann et al.

2016, Pavitt et al. 2016). Such a pattern of stable glucose concentrations within individuals but

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less consistency in CORT may be expected if the HPI axis operates to maintain glucose concentrations within a narrow homeostatic range through the modulating effects of CORT. That is, we expect that circulating CORT concentrations respond to both external and internal conditions and act to maintain stable energetic availability in the form of glucose. Thus, it is likely that measures of plasma glucose concentration describe differences in energetic phenotypes more accurately than measures of CORT. For example, previous work in this system demonstrates that adult females from the fast-living and slow-living ecotypes differ in glucose concentrations, but not CORT (Gangloff et al. 2016).

As with our measures of behaviour, the first axis of variation from our physiological principal component analysis reveals that snakes primarily vary on a reactivity continuum. Given that baseline CORT concentrations mediate metabolic trade-offs and influence general activity levels (Landys et al. 2006), we predicted that CORT and glucose concentrations would be correlated with the behavioural activity levels of individual animals. Higher levels of the physiological biomarkers promote higher activity levels, thus requiring more energy but also increasing the possibility of food acquisition. While we did not identify such a correlation between proactive behaviours and proactive physiologies (by comparing the first PCA axes in both traits), the trend is in the direction expected under our hypothesis that these behavioural and physiological traits can be described along a continuum of ‘energetic phenotypes’. We note that this prediction is contrary to the prediction of bolder individuals demonstrating lower stress reactivity in the HPA/HPI axis in endothermic vertebrates (reviewed in Koolhaas et al. 1999,

Carere et al. 2010), but is concordant with results in fish (e.g., Boulton et al. 2015). Though we found maternal phenotypes across the range of possible combinations, individuals with matched behavioural and physiological phenotypes (either ‘high-high’ or ‘low-low’) had heavier offspring

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in better condition at birth. This suggests that an energetic trade-off between maternal energetic phenotype and reproductive allocation acts to promote variation in birth mass and condition within populations.

The first year represents a critical period in snake life-history with neonates experiencing low survivorship in these and other natural snake populations (Brodie 1992, Bronikowski and

Arnold 1999, Baron et al. 2010, Miller et al. 2014). Furthermore, studies in both natural populations and experimental mesocosms demonstrate that snakes born large and/or in better condition grow more and experience higher survivorship, especially while overwintering (Brown and Shine 2005, Kissner and Weatherhead 2005, Manjarrez and San-Roman-Apolonio 2015).

Our data corroborate these findings by demonstrating that maternal energetic allocation to offspring, as measured by the body condition and mass at birth, affected the early-life growth and survival of neonates in captivity. As expected from previous work in this and other snake systems, larger moms produced larger offspring (Bronikowski and Arnold 1999, Brown and

Shine 2005), though not necessarily offspring in better condition (Table 4, Fig. 3). The importance of body mass and condition at birth on subsequent growth and survival is well- established in snakes (e.g., Bronikowski 2000) and in animals generally (reviewed in Roff 1992).

However, we cannot assume that low rates of growth are necessarily unfavorable in this system.

For example, reduced growth rates may be optimal under specific environmental conditions (Bronikowski 2000) and/or may represent a trade-off between storage or immediate use of energetic resources, such as between metabolic rate and growth (Gangloff et al. 2015).

The effect of mass and condition at birth on growth seems to disappear after one year, suggesting that this amount of time is sufficient time for snakes in poor condition to “catch up” to snakes born into better condition or that individuals born in poor body condition have

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selectively disappeared from the cohort over the first year (sensu van de Pol and Verhulst 2006).

Our analysis of survival supports the latter interpretation: survival to one year of age is highly dependent on both body mass and body condition at birth (Table 4, Fig. 3) while survivorship to maturity (4 years of age) is dependent on mass at birth (Table 5, Fig. 4). After one year, only those snakes born in good condition remain, thus removing our ability to detect an effect of birth condition on growth in individuals surviving this time period. Interestingly, we also observed that neonates raised in the ‘cool’ rearing treatment experience higher survivorship through their first four months of life. Rearing environment did not impact survivorship through one year

(Table 4, Fig. 3), though after accounting for variation in body condition females lived longer in the cool treatment and males in the warm treatment through the juvenile stage (Table 5). For individuals that survived to four months of age, the highest subsequent mortality occurred during hibernation and this probability of surviving hibernation was strongly influenced by both body condition and mass. Taken together, this implies that early-life environments in the first months of life are important in determining not only short-term survivorship but survival to sexual maturity. Living through the first hibernation period is largely dependent on maternal energetic allocation at birth.

The fluctuating resource availability in garter snake populations in the mountain meadows may exert variable selective pressures on energetic phenotypes – ‘low reactive’ vs.

‘high reactive’ – in their respective energetic allocation to offspring. Elsewhere we have argued that unpredictable food availability in meadow populations has been a strong selective force over many generations for low growth rates, even in the presence of high food (Robert and

Bronikowski 2010). The present study broadens our hypothesis of the importance of predictability of food availability as a strong agent of selection for life-history traits in these

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populations. In the Eagle Lake system, precipitation the previous year serves as a proxy for food

(anuran prey) availability the following spring, with ‘good’ years following a year of > 500 mm total precipitation and ‘bad’ years following a year of < 500 mm (Bronikowski and Arnold 1999,

Miller et al. 2011, Miller et al. 2014). The total precipitation from the three weather stations nearest to Eagle Lake averaged 354 mm for 2011, classifying 2012 (the year of our experiment) as a ‘bad’ year (California Climate Data Archive, available at http://www.calclim.dri.edu/).

In temperate regions, garter snakes mate immediately upon emergence from hibernation and utilize energy stores from the previous year during vitellogensis and therefore allocation to developing offspring (Gregory and Skebo 1998, Gregory 2006, Tuttle and Gregory 2014, Feriche et al. 2016). The energetic consequences of variable prey availability thus manifest as a trade-off between maternal use and allocation to offspring. We found that this relationship follows an expected life-history trade-off when resources are limited in a ‘bad’ year such as this (sensu van

Noordwijk and de Jong 1986): moms of the matched ‘high reactive’ phenotypes are presumably able to acquire more of the limited resources, while moms of the matched ‘low reactive’ phenotypes use less energy. During a poor year, these trade-offs lead to a fitness advantage for matched extremes of behavioural and physiological phenotypes (‘high-high’ and ‘low-low’). On the other hand, when food is plentiful in good years, we may not expect such a relationship since trade-offs may not be evident under conditions in which resources are not limited (van

Noordwijk and de Jong 1986). Fluctuating resource availability maintains personality differences in birds (Dingemanse et al. 2004) while spatial heterogeneity in habitat complexity affects growth and maintains behavioural polymorphisms in fish (Höjesjö et al. 2004). To test the hypothesis that unpredictable environments drive the maintenance of energetic polymorphisms in this garter snake system, we should conduct these same measures across a number of ‘good’ and

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‘bad’ years to quantify fluctuations in resource allocation to offspring in relation to maternal energetic phenotypes.

The results of our present study suggest several potentially fruitful avenues to test the relationships among environmental conditions, integrated maternal phenotypes, and offspring success. In the stable habitat of the lakeshore populations around Eagle Lake, characterized by more constant environments and continuous food supply (in the form of minnows), there is no evidence of a ‘cohort effect’ impacting long-term survival (Miller et al. 2014). In these populations, where resources are abundant, we do not expect a trade-off between maternal energetic phenotype and allocation to offspring. Analysis of growth data from an earlier cohort supports this view: babies born larger enjoy a higher probability of survival to one year in snakes from the meadow ecotype, but there is no influence of mass at birth in snakes from the stable lakeshore environments (A.M. Bronikowski, unpublished data). Future studies would also benefit from additional measures of energy processing, including standard metabolic rates, maximal metabolic rates, energy assimilation rates, and/or field measures of daily energy expenditure, which are known to vary among individuals within garter snake populations (e.g.,

Peterson et al. 1998, Gangloff et al. 2015) and are all important in understanding allocation vs. acquisition trade-offs (Careau et al. 2008, Mathot and Dingemanse 2015). Additionally, to quantify how selection might act on phenotypic trait combinations, measuring both the heritability and the underlying genetic correlations of these characters is necessary (Lande and

Arnold 1983, Arnold 1987). Numerous studies have demonstrated that both behavioural and physiological traits in garter snakes are both repeatable and heritable (e.g., Arnold and Bennett

1984, Garland 1988, Brodie 1993, King et al. 2004), though generally physiological markers seem to be less consistent within individuals (Careau and Garland 2012, Sparkman et al. 2014).

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Furthermore, common garden experiments reveal that growth, metabolic rates, and endocrine function are partially determined by genetic background (Bronikowski 2000, Gangloff et al.

2015, Reding et al. 2016). More precise estimates of heritability will be needed to assess the potential for correlated selection to shape integrated phenotypes.

These results demonstrate that the interaction of different axes of maternal phenotypes shapes maternal-offspring energetic trade-offs, which in turn differentially affect the life-history trajectories of individual offspring. This trade-off provides a mechanism describing why, in squamate reptiles, there is no putatively adaptive explanation for observations of within-cohort variation in offspring size and condition. In this long-lived species, individual heterogeneity in maternal ‘energetic phenotypes’ will interact with environmental conditions over several years to determine the reproductive success of an individual snake over her lifespan, the life-history trajectories of her offspring, and ultimately population demography and dynamics.

Acknowledgements

We thank K. Martin, T. Rickman, and S. Zylstra for support in field work; P. Dixon and

K. Goode for statistical advice; and M. Barazowski, C. Corwin , C. Hinsley, and A. Wendt for assistance with behaviour assays and animal care. G. Burghardt, B. Danielson, F. Janzen, and members of the Bronikowski and Janzen labs provided valuable manuscript feedback. Funding was provided by the Society for Integrative and Comparative Biology Grants-in-Aid-of-

Research, the Iowa Department of Natural Resources, the Iowa Science Foundation (15-11), and the U.S. National Science Foundation (IOS-0922528 to AMB). We also recognize the support of a fellowship from the ISU Office of Biotechnology to EJG. We are grateful to S. Hamel, N.

Yoccoz, and J.M. Gaillard for insightful discussions and organization of the themed issue.

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Table 1. Repeatability estimates of physiological measures and behaviours in adult female T. elegans (N = 21). CORT and glucose concentrations were measured at time of capture, after one month in captivity, and one month after parturition. Behaviours were measured in two trials 72 h apart after one month in captivity and two trials one month after giving birth (4 trials total). Significant results shown in bold with two (P < 0.001) asterisks. Rc = consistency repeatability. See text for statistical details.

Measure Statistic Estimate Test statistic Significance 2 CORT Concentration Rc 0.19 χ0:1 = 1.1 P = 0.15 2 Glucose Concentration Rc 0.27 χ0:1 = 2.36 P = 0.062 Tongue-flicks Kendall’s W 0.48** F17.5,52.5 = 2.86 P = 0.0017** Escape Latency Kendall’s W 0.50** F18.5,55.5 = 3.00 P = 0.0008**

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Table 2. Results of principal component analyses of behaviour and physiology measures in reproductive female Thamnophis elegans (N = 21). Higher loading values for escape latency describe a more rapid escape from the test arena. See text for statistical details.

Behaviour Physiology PC1 PC2 PC3 PC1 PC2 Proportion of variance 35.8% 17.3% 13.4% Proportion of variance 42.3% 24.9%

Loadings Loadings Tongue Flicks Trial 1 0.277 -0.502 0.347 Field glucose 0.569 -0.300 Tongue Flicks Trial 2 0.224 -0.542 0.171 Pre-parturition glucose 0.282 0.545 Tongue Flicks Trial 3 0.389 -0.027 -0.255 Post-parturition glucose 0.372 0.192 Tongue Flicks Trial 4 0.436 -0.237 -0.082 Field CORT 0.342 0.415 Escape Latency Trial 1 0.125 0.400 0.788 Pre-parturition CORT 0.536 -0.096 Escape Latency Trial 2 0.477 0.285 0.187 Post-parturition CORT 0.231 -0.628

Escape Latency Trial 3 0.278 0.336 -0.207 28

Escape Latency Trial 4 0.460 0.207 -0.281

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Table 3. Results of mixed linear model analysis of the effects of maternal phenotypes on offspring body condition and mass at birth (log10-transformed in g) in Thamnophis elegans (N = 111 offspring from N = 21 mothers). Models also include litter as a random effect (see text for statistical details). Significant effects designated with a single (P < 0.05) or double (P < 0.01) asterisk.

Birth Body Condition Birth Mass

Source of variation F (dfn, dfd) Pr > F F Pr > F Mom SVL 0.79 (1, 12.7) 0.39 17.51(dfn, 0.0013** dfd) Behaviour PC1 (linear) 0.13 (1, 14.3) 0.72 1.25(1, 0.28 Physiology PC1 (linear) 0.50 (1,.3) 14.7) 0.49 11.8)3.21(1, 0.096 Behaviour PC1 (quadratic) 0.58 (1, 13.2) 0.46 14.5613.2(1, ) 0.0024** .3) Physiology PC1 (quadratic) 0.15 (1, 14.5) 0.71 13.55.07(1, ) 0.042* Behaviour PC1 × Physiology PC1 4.79 (1, 14.2) 0.046* 12.3)8.19(1, 0.013* 13.3)(1, 13.1) 130

Table 4. Results of analysis of effects of the effects of body condition at birth and mass at birth (log10-transformed in g) on growth (mixed linear model) and on probability of survival (mixed logistic regression model) in Thamnophis elegans (N = 111 offspring from N = 21 mothers). Models include litter as a random effect (see text for statistical details). Significant effects designated with a single (P < 0.05) or double (P < 0.01) asterisk.

Growth through Growth through Probability of Probability of Source of Variation 4 Months First Year Survival to First Survival to 4 Months (N = 94) (N = 24) Year Birth Condition F (dfn, dfd) 42.67 (1, 63.2) 0.72 (1, 105.5) 2.59 (1, 16.4) 6.13 (1, 75.7) Pr > F < 0.0001**.3) 0.40 0.13 0.016* Rearing Treatment F (dfn, dfd) 4.79 (1, 80.5) 5.22 (1, 99.7) 1.03 (1, 18.4) 0.32 (1, 95.4) Pr > F 0.019* 0.024* 0.32 0.57 Birth Condition × Rearing Treatment F (dfn, dfd) 5.19 (1, 82.8) -- 0.29 (1, 19.7) -- Pr > F 0.025* 0.60

1 Birth Mass 30

F (dfn, dfd) 2.90 (1, 44.2) 11.27 (1, 31.4) 1.13 (1, 17.9) 8.35 (1, 63.2) Pr > F 0.096.3) 0.0021** 0.30 0.0053** Rearing Treatment F (dfn, dfd) 2.23 (1, 85.1) 9.06 (1, 107.9) 1.98 (1, 19.7) 0.01 (1, 83.4) Pr > F 0.14 0.0033** 0.18 0.94 Birth Condition × Rearing Treatment F (dfn, dfd) 1.80 (1, 84.4) -- 1.80 (1, 19.6) -- Pr > F 0.18 0.19

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Fig. 1. Theoretical schematic of how variation in maternal phenotypes is filtered through environmental conditions to affect early life growth and survivorship of offspring. Aspects of organism phenotypes in solid boxes; environmental factors in dashed boxes.

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Fig. 2. Three-dimensional plots showing the effects of maternal phenotypes on offspring body condition and mass (log10-transformed g) at birth in Thamnophis elegans (N = 111 offspring from N = 21 mothers). First principal component axis for maternal behavioural and physiological traits are shown on the x-axis and y-axis, respectively, for both plots. Body condition at birth is on the z-axis (panel A) and mass at birth (Panel B). Surface is a fitted plane to function described by parameter estimates from mixed linear model (see text for statistical details).

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Fig 3. Scatterplots of the effect of rearing treatment and body condition at birth (Panels A-D) and mass at birth (Panels E-H) on growth through 4 months of age (N = 94), probability of survival to 4 months of age (N = 111), growth rate through one year of age (N = 24), and probably of survival to one year of age (N = 111) for captive-born Thamnophis elegans (from N = 21 litters). Regression lines shown for significant effect of body condition at birth on growth through 4 months of age (see text for statistical details). Cubic splines shown for probability of survival. Separate curves are shown for offspring raised in warm rearing treatment (red line) and cool rearing treatment (blue).

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Fig 4. Survival plot for captive-born Thamnophis elegans (N = 111 offspring from N = 21 mothers) for four years in captivity. Separate curves are shown for offspring raised in warm rearing treatment (red line) and cool rearing treatment (blue). Notice high mortality rates during first hibernation.

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CHAPTER 5

SUMMARY AND CONCLUSIONS

The goal of these studies is to elucidate the proximal physiological mechanisms determining how environmental stressors can drive higher-order population effects and the potential for natural selection to shape these traits. This includes measures across levels of biological organization: measures of multiple traits within individuals and repeated measures over time to assess within-individual correlations and repeatability; measures of among- individual differences in physiological and behavioral traits and the consequences to reproductive output; and comparisons of populations adapted to different environments to assess adaptive potential of traits within species (Fig. 1). This work highlights some important limitations on behavioral and physiological plasticity that are essential in considering how these animals may be able to respond to changing environments. First, it appears that both behavioral and physiological traits are consistent within individuals, implying that these traits are partially genetically determined and/or canalized in response to developmental environments. This means that individuals may not always respond optimally to a given scenario if the optimal response (either behavioral or physiological) is outside of their repertoire. Second, upper thermal limits do not appear to be adapted to local thermal conditions, implying that either snakes are able to behaviorally Fig. 1. Schematic representation of measures collected across levels of biological organization.

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avoid extreme temperatures and thus reduce selection on thermal tolerance or that the there is no heritable variation in these traits on which selection can act. Third, microevolutionary processes are important in maintaining trait variation in environments that vary over space (e.g., lakeshore vs. mountain meadows) or time (e.g., fluctuating environments of meadow populations).

These results also point to some enticing potential future directions. For example, it would be useful to integrate data on short-term biomarkers of stress (e.g., CORT and glucose) with data on long-term stress consequences (e.g., telomere length) to quantify the relationship between acute stress response and long-term energy balance. Combined with a measure of reproductive output or success, such a study would provide much-needed data on the fitness consequences of variation in stress response. Additional biomarkers of physiological status, such as those to assess immune or reproductive functions, would provide additional insight into how physiological systems are coordinated within individuals. Such studies would also benefit from the collection of data across years in variable environments. For example, stronger evidence for temporal heterogeneity maintaining polymorphisms within populations would be provided by combining measures of behavior, physiology, and reproduction over a number of years under different food availabilities in replicate populations. And finally, I see much benefit to be gained in studies of energetics during overwintering, an important period for energy balance maintenance in temperate ectotherms. While much attention has been given to the effects of upper thermal extremes under climate change scenarios, the energetic consequences of increased hibernation temperatures will likely bear enormous consequences on ectotherm populations.

These questions can be addressed by using garter snakes as model organisms, especially in the well-established evolutionary and ecological background of the Eagle Lake system. I hope it is my good fortune to continue working to address such important and fundamental questions.

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APPENDIX A

DEVELOPMENTAL AND IMMEDIATE THERMAL ENVIRONMENTS SHAPE

ENERGETIC TRADE-OFFS, GROWTH EFFICIENCY, AND METABOLIC RATE IN

DIVERGENT LIFE-HISTORY ECOTYPES OF THE GARTER SNAKE THAMNOPHIS

ELEGANS

This is a published manuscript in the peer-reviewed journal Physiological and Biochemical

Zoology

Eric J. Gangloff, David Vleck, and Anne M. Bronikowski

Author contributions: AMB designed the experiment. EJG and AMB conducted the experiment. EJG analyzed data and drafted manuscript with guidance from DV and AMB. All authors contributed to interpretation of results and manuscript revisions.

Reproduced / adapted with permission of Physiological and Biochemical Zoology.

Full citation:

Gangloff, EJ, D Vleck, & AM Bronikowski. 2015. Developmental and immediate thermal

environments shape energetic trade-offs, growth efficiency, and metabolic rate in

divergent life-history ecotypes of the garter snake Thamnophis elegans. Physiological

and Biochemical Zoology 88:550-563. doi: 10.1086/682239

550

Developmental and Immediate Thermal Environments Shape Energetic Trade-Offs, Growth Efﬁciency, and Metabolic Rate in Divergent Life-History Ecotypes of the Garter Snake Thamnophis elegans

Eric J. Gangloff* White 2012; Iles 2014; Maino et al. 2014). In turn, trade-offs in David Vleck energetic allocation among growth, reproduction, and main- Anne M. Bronikowski tenancecomposethebasisforlife-historytheory(Roff1992; Department of Ecology, Evolution, and Organismal Biology, Stearns 1992). Despite decades of empirical and theoretical Iowa State University, Ames, Iowa 50011 work, we still lack comprehensive models of the intrinsic and extrinsic drivers of variation in metabolic rate, including the Accepted 5/8/2015; Electronically Published 6/12/2015 influences of biophysical constraints, ecological heterogeneity, and natural selection (Burton et al. 2011; Reid et al. 2011; White and Kearney 2013). Even the effects of the three pri- ABSTRACT mary determinants of metabolic rate—body size, temperature, and ecological factors—remain unclear and at times debated Interactions at all levels of ecology are influenced by the rate (Gillooly et al. 2001; Agutter and Wheatley 2004; White et al. at which energy is obtained, converted, and allocated. Trade- 2006). Here, we test the influences of genetic background, phys- offs in energy allocation within individuals in turn form the iological plasticity, and temperature on growth and metabolic basis for life-history theory. Here we describe tests of the in- rate in western terrestrial garter snakes (Thamnophis elegans) fluences of temperature, developmental environment, and ge- from natural populations. Our results provide empirical clarity netic background on measures of growth efficiency and rest- on the complex interactions that drive variation in how organ- ing metabolic rate in an ectothermic vertebrate, the western isms process energy in their environment. terrestrial garter snake (Thamnophis elegans). After raising Populations of the western terrestrial garter snake (T. captive-born snakes from divergent life-history ecotypes on elegans) near Eagle Lake, California, have provided a natural thermal regimes mimicking natural habitat differences (2 # 2 laboratory for examining the physiological mechanisms and experimental design of ecotype and thermal environment), we ecological causes of divergent life-history evolution. Here, dis- measured oxygen consumption rate at temperatures spanning tinct ecotypes at differing positions along the pace-of-life con- the activity range of this species. We found ecotypic differ- tinuum (Ricklefs and Wikelski 2002) have emerged in replicate ences in the reaction norms of snakes across the measured populations along the lakeshore and in the surrounding moun- range of temperatures and a temperature-dependent allome- tain meadows (Bronikowski and Arnold 1999; Bronikowski 2000). tric relationship between mass and metabolic rate predicted by The lakeshore populations (hereafter, L-fast) grow fast, are short- the metabolic-level boundaries hypothesis. Additionally, we lived, reproduce yearly or biennially, and demonstrate greater present evidence of within-individual trade-offs between growth fecundity per reproductive bout; the meadow populations (here- efficiency and resting metabolic rate, as predicted by classic life- after, M-slow) grow slowly, are long-lived, reproduce intermit- history theory. These observations help illuminate the ultimate tently, and produce fewer offspring per reproductive bout (see and proximate factors that underlie variation in these interrelated detailed descriptions in Sparkman et al. 2007; Bronikowski and physiological and life-history traits. Vleck 2010; Schwartz and Bronikowski 2011). This divergence in Keywords: metabolic scaling, oxygen consumption rate, de- life-history strategies, despite significant gene flow between pop- velopmental plasticity, western terrestrial garter snake. ulations (Manier and Arnold 2005, 2006), is putatively driven by differing thermal regimes (Bronikowski 2000), resource availability (Bronikowski and Arnold 1999; Miller et al. 2011), and predation rates (Sparkman et al. 2013). Studies in this system Introduction have demonstrated ecotypic differences in immune investment Metabolic rate, as a measure of how quickly individual or- (Sparkman and Palacios 2009; Palacios et al. 2011), endocrine ganisms process energy, is central to interactions at all levels function (Sparkman et al. 2009; Palacios et al. 2012), cellular of ecology (Kooijman 2000; Brown et al. 2004; Kearney and repair efficiency (Robert and Bronikowski 2010), and stress response pathways (Schwartz and Bronikowski 2012). Adult L-fast *Corresponding author; e-mail: [email protected]. snakes have higher resting metabolic rates than M-slow snakes 7– 7 Physiological and Biochemical Zoology 88(5):550–563. 2015. q 2015 by The across 15 32 C (Bronikowski and Vleck 2010), but this ecotypic University of Chicago. All rights reserved. 1522-2152/2015/8805-4133$15.00. difference was not present in month-old snakes reared under DOI: 10.1086/682239 common laboratory conditions (Robert and Bronikowski 2010),

This content downloaded from 129.186.252.27 on Mon, 10 Aug 2015 09:42:56 AM All use subject to JSTOR Terms and Conditions Growth and Metabolic Rate in Garter Snakes 551 which suggests that developmental habitats have permanent ef- 1987) and positive (e.g., Hoogenboom et al. 2012) relation- fects of metabolic rate. Here, we test the metabolic consequences ships between growth and metabolic rate (reviewed in Glazier of variation in early life thermal environment, using a 2 # 2 2015). Taken together, these studies indicate that a single op- experimental design of captive-born neonatal garter snakes of timal metabolic rate may not exist and that fluctuations in both ecotypes reared under thermal conditions designed to resource availability may maintain metabolic polymorphisms mimic field ecological differences. We report measures of the within populations (Burton et al. 2011; Reid et al. 2011). resting metabolic rate of individual snakes at four temperatures Additionally, we explore ecological and thermal effects on spanning the natural activity range of this species, first-year the relationship between metabolic rate and body mass. This growth, and growth efficiency (defined as increase in size per relationship is commonly described as the slope of the log-log _ unit of food consumed; Ivlev 1945; Czarnołęski et al. 2008). plot of oxygen consumption rate (VO2) on body mass (the Because temperature affects metabolic rate and, concomitantly, scaling exponent b in the power function R p aMb, where R is energy intake and growth, we are able to test for plasticity and metabolic rate, M is body mass, and a is a scaling coefficient). A subsequent developmental canalization of metabolic pathways negative allometric relationship (b ! 1), as is predicted by theory resulting from early life-history experiences. and most commonly found in empirical studies, describes in- Many ectotherms exhibit some modulation of the tem- creased energetic efficiency with increasing body size and there- perature dependency of metabolism (in snakes: Aleksiuk 1971; fore has direct implications in trade-offs between growth and Seidel and Lindeborg 1973; Davies et al. 1981; reviewed in other energetic demands (Carey et al. 2013). For decades, re- Glazier 2005, 2010, 2015). Nonavian reptiles in particular dem- searchers have debated the universality of the scaling exponent onstrate great variation in thermal physiology across latitudinal (b), arguing that this value is determined by the physical prop- clines, which has been interpreted as an adaptation to climate erties of biological systems and thus is constant across the (reviewed in Kingsolver and Huey 1998; Angilletta et al. 2003). diversity of life with a value of 2/3 (Rubner 1883) or 3/4 (Kleiber In snakes, this is generally manifest as populations from higher 1932). Empirical studies in snakes have found evidence that latitudes (or otherwise cooler climates) having higher meta- extrinsic factors affect metabolic scaling (Buikema and Armitage bolic rates than conspecifics and congeners from lower latitudes 1969; Vinegar et al. 1970; Dmi’el 1972; Davies and Bennett 1981), across a range of active temperatures (Aleksiuk 1971; Davies and but these studies have been relatively unappreciated. Recently, Bennett 1981; Zaidan 2003). Temperature-driven differences tests in a wide range of taxa have found support for variation in among populations in how energy is allocated at different life scaling exponents, leading to a refutation of such universal scaling stages can in turn drive geographic variation in life-history laws (reviewed in Glazier 2005, 2010). strategies (e.g., Angilletta 2001). Maintenance metabolism, the This experiment tested three primary hypotheses of po- energy required for an organism to maintain biological steady tential drivers of variation in metabolic rate within a frame- state in the absence of growth, reproduction, or locomotion, is a work of divergent life histories. First, we tested the relative significant component of the energy budget of animals. Even in influences of genetic background (maternal ecotype) and envi- ectothermic reptiles, where this cost is greatly reduced compared ronmental conditions (rearing treatment) and their interaction with endotherms, maintenance metabolism may account for on metabolic rate and growth efficiency, the growth rate ac- more than a third of an individual’s yearly energy budget counting for the amount of food consumed. As found previously (Congdon and Tinkle 1982; Secor and Nagy 1994; Beaupre (Bronikowski 2000), we expect snakes from the L-fast popu- 1995). Only after the obligatory costs of maintenance metabo- lations to exhibit higher growth rates than M-slow snakes re- lism are met can an individual allocate energy to activity, growth, gardless of rearing conditions. A significant effect of rearing or reproduction (Steyermark and Spotila 2000; Angilletta 2001). treatment on metabolic rate or a significant interaction between The components of an individual’senergybudget,including ecotype and rearing treatment implies that metabolic rate is maintenance, growth, and reproduction, compete with each developmentally plastic (and possibly adaptive, depending on other and are thus subject to trade-offs (Gadgil and Bossert the direction of the interaction). Second, we tested for evidence 1970). We might expect to observe such trade-offs, however, of within-individual trade-offs between maintenance metabo- only when the variance in resource allocation exceeds that of lism and first-year growth efficiency. While we might expect a acquisition (van Noordwijk and de Jong 1986; Glazier 1999). positive relationship between growth and metabolic rate in actively Assuming constant acquisition rates, high maintenance me- growing snakes (Peterson et al. 1999), at the time we measured tabolism should be associated with reduced energy allocated to metabolic rate snakes were fasting and not actively growing. With other functions (Kooijman 2000), while lowered maintenance this in mind, we hypothesize that there will be a trade-off between metabolic costs should be associated with higher production maintenance metabolism and first-year growth efficiency: those rates and the attainment of higher growth rates (Danzmann individuals that expend more energy on metabolic rate (after et al. 1987; Killen 2014). In captive-born juveniles, where energy correcting for body size) will have grown less in their first year, is not yet being allocated to reproduction while acquisition accounting for the amount of food consumed. Third, we tested the rates are controlled or statistically accounted for, the trade-off effects of ecotype and rearing treatment on the metabolic scaling between maintenance and growth should be most prominent exponent as well as the temperature dependence of the relationship (Glazier 1999). Laboratory studies controlling for resource between body mass and metabolic rate. These empirical tests of the availability have found both negative (e.g., Danzmann et al. complex proximate and ultimate factors determining growth rate,

This content downloaded from 129.186.252.27 on Mon, 10 Aug 2015 09:42:56 AM All use subject to JSTOR Terms and Conditions 552 E.J.Gangloff,D.Vleck,andA.M.Bronikowski metabolic rate, and metabolic scaling in the context of differing life- the requirements of standard metabolic rate met except that history strategies provides a solid platform for understanding the snakes were measured during their normal activity period adaptive significance and potential of these traits. (Bennett and Dawson 1976; McNab 2002; Careau et al. 2008). _ We quantified VO2 in offspring from both ecotypes (L-fast: n p Material and Methods 64, representing 20 litters from four populations; M-slow: n p 42, representing 17 litters from three populations) in each of Study Organisms four temperature-treatment groups (207,247,287, and 327C) In June 2010, gravid females were collected from eight popu- using closed-system respirometry (Vleck 1987). Snakes were lations of Thamnophis elegans around Eagle Lake, California— postabsorptive after fasting for 2 wk before the whole-body four replicate populations each of the two ecotypes, L-fast (n p metabolic rate measurements, to eliminate the metabolic in- 22) and M-slow (n p 22). These eight populations are repre- fluence of digestion (i.e., specific dynamic action) and ensure sentative of the approximately 35 populations in the vicinity that our measures were of maintenance metabolism (Stevenson (1–25 km) of Eagle Lake. Within this metapopulation, gene et al. 1985; Álvarez and Nicieza 2005). At the time of metabolic flow is such that neighboring populations within each ecotype measurement, we weighed each snake with an electronic balance show evidence of plentiful gene flow, whereas populations of the (range: 3.76–17.30 g; SD: 2.8 g). In the 4 wk before the ex- contrasting ecotypes have significantly diverged at neutral loci periment, snakes did not gain weight (average change in mass (Manier and Arnold 2005; Manier et al. 2007). Gravid females from November 1 through December 1, 2011: 20.13 g; range: were brought back to the laboratory colony at Iowa State Uni- 22.66 to 1.36 g; SD: 0.71 g), thus reducing the risk of con- versity and individually maintained in 10-gal glass aquariums founding our measures of resting metabolic rate with delayed with ground corn cob substrate and a plastic bowl that served as overhead costs of growth (Rosenfeld et al. 2015). Metabolic both water dish and retreat site. They were placed on a thermal chambers consisted of darkened metal cans sealed with modified gradient for 24 h per day (range: 257–347C), kept on a 12∶12 L∶D lids to include a tube with a stopcock on the end. Two can sizes schedule, and offered 1–2 mice once a week until parturition. were used in the measurements (245 and 995 mL) for smaller Offspring were born between August 12 and September 19, and larger snakes, respectively. Before the start of each trial, 2010. Within 24 h of birth, offspring were sexed, weighed, and 5 mL of water was injected into each can to ensure that water measured, and they were then housed individually in plastic content would remain constant (i.e., saturated) throughout the boxes with paper substrate and a water bowl. Litters were experiment. Snakes were placed within metabolic chambers in randomly divided, with sex split evenly, into two temperature- temperature-controlled incubators (Econotemp; Thermo Fisher treatment groups designed to mimic the differing thermal Scientific, Waltham, MA) 30 min before trials to become ac- regimes of the lakeshore (L-fast) and meadow (M-slow) hab- customed to test conditions (Hare et al. 2004), with stopcocks in itats (Bronikowski 2000). Ambient room temperature was 207C, open position. Additionally, 50 cm3 of room air was drawn with with the warmer treatment receiving 16 h of supplemental heat- a 60-cm3 syringe and placed within incubators at the trial tem- ing per day and the cooler treatment receiving supplemental perature at the same time. At the start of trials, this volume of air heating for 8 h per day. This supplemental heating, supplied by was injected into the cans and mixed by plunging and with- under-tank heat tape, provided a gradient of 227–327C, allowing drawing the syringe twice; 30 cm3 of air was then removed from the animals to behaviorally thermoregulate. All juveniles were the can, and the stopcocks were sealed. Air pressure was recorded kept on a 12∶12 L∶D schedule and offered a fraction of a frozen/ at the start of each trial to correct calculations to standard thawed pinky mouse once a week. Individuals that repeatedly temperature and pressure. Each incubator was fit with a thermo- consumed all food were offered a greater amount in subsequent couple thermometer, and temperature was measured at time 0, feedings. All snakes were weighed (range: 1.70–5.79 g; SD: 0.76 g) 15 min, 30 min, and every 30 min thereafter to ensure consis- and measured (snout-vent length; range: 157–247 mm; SD: tency throughout trials. At the end of 2 h, 40 cm3 of air was re-

14.3 mm) on November 29, 2010. Snakes were moved to a cold moved from each can. We measured the O2 concentration in room and hibernated uniformly in the dark at 47C from January initial and final samples using an Ametek N-37M oxygen sen- through May 2011. Following hibernation, animals were placed sor and an Ametek S-3A/11 oxygen analyzer (Pittsburgh, PA). back into their thermal treatment groups and were offered whole Water and CO2 were removed from air samples by injection pinky mice once per week. We recorded the amount eaten at each through Drierite (Hammond Drierite, Xenio, OH) and Ascarite II feeding. We again weighed (range: 3.27–15.94 g; SD: 2.8 g) and (Thomas Scientific, Swedesboro, NJ), respectively, before entering measured (snout-vent length; range: 190–357 mm; SD: 36.3 mm) the oxygen sensor. Following Vleck (1987), rates of oxygen con- _ snakes on November 1, 2011. All experimental animals were sumption (VO2) were calculated as treated in accordance with Iowa State University Institutional Animal Care and Use Committee protocol 3-2-5125-J. # 2 _ can volume (initial O2 final O2) VO2 = , (1) duration#(1 2 final O2) Metabolic Rate Measurement _ Oxygen consumption rate (VO2) is widely used as a measure where initial O2 is the initial O2 concentration and final O2 is — — fi of metabolic rate in this case, resting metabolic rate with all the nal O2 concentration.

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Snakes were randomly grouped into four batches of 26–27 the log-transformed range to estimate intercepts of the mass- individuals and randomly assigned to a metabolic chamber oxygen consumption regression at a biologically meaningful within each batch. The sequence of test temperatures was then mass as well as to remove statistical covariation between our randomized within each block such that individuals experi- estimates of slope and intercept (Glazier 2010; Killen et al. enced a randomized permutation of test temperature order. 2010). The effect of temperature was treated as a continuous Snakes showed no signs of decreased activity or sluggishness variable and was therefore modeled in kelvins so that differ- associated with hibernation in the period before the oxygen ences in temperature between treatments would be correctly consumption rate measurements were made. Each snake was proportional, although we present results in degrees Celsius for tested once at each of the four temperatures, and measurements ease of understanding. To account for a possible curvilinear re- were conducted on consecutive days, December 1–5, 2011. lationship between metabolism and increasing test temperature, we included a squared test temperature term in the model Statistics (Sokal and Rohlf 2011; Iles 2014). After inspecting data for normality and homogeneity of variances, we used repeated- Growth Efficiency measures mixed linear models to test for the effects of body First-year growth efficiency was measured as change in snout- mass, test temperature, ecotype, rearing treatment (warm/cool), vent length (mm) during the first year of growth, accounting for sex, and interactions thereof. As above, population nested within fi variation in the amount of food consumed. We analyzed growth ecotype was included as a xed effect, litter nested within pop- from November 29, 2010, to November 1, 2011, to ensure that ulation and ecotype was included as a random effect, and de- the growth period was uniform for all snakes, because L-fast nominator degrees of freedom for F-tests were estimated using snakes were born before M-slow snakes. As such, the effects of the Kenward-Roger degrees of freedom approximation. The ecotype and the number of growth days are confounded in an time of day at which the metabolic measurements were made analysis using growth measures from birth. After inspecting data was randomized within batch order for each day; time of day and fi fi for normality and homogeneity of variances, we used mixed all interactions with xed effects were not signi cant and were 1 linear models to test for the effects of ecotype, rearing treatment removed from the model (all P 0.09). We removed additional fi 1 (warm/cool), sex, and interactions thereof on growth. We in- nonsigni cant interaction terms from the model (all P 0.14) cluded the amount of food consumed (g) as a covariate, as well as and used the following mixed linear model in our analysis of log _ O interactions thereof. To account for variation in growth due to (V 2): size, we used initial size as a covariate in the analysis. Population p m 1 1 1 nested within ecotype was included in the model, treated as a Y log(mass) test temperature log(mass) # 1 1 # fixed effect, to account for among-population habitat hetero- temperature ecotype ecotype temperature 1 1 1 2 geneity within ecotypes (Palacios et al. 2013). We also included rearing treatment sex temperature 1 1 the random effect of litter nested within population and ecotype, population(ecotype) litter(population ecotype) 1 1 ε which accounts for among-litter variation within populations individual ID , (Robert and Bronikowski 2010). The interaction between eco- (3) type and rearing treatment was left in the model, as this in- m ε teraction is of biological interest; the remaining nonsignificant where represents the grand mean and represents the error interaction terms were removed from the model (all P 1 0.35). term. The final mixed linear model used for our analysis of first-year growth efficiency was Correlation between Growth Efficiency and Metabolic Rate Y p m 1 initial size 1 food consumed 1 ecotype To quantify the relationship between growth efficiency and _ 1 rearing treatment 1 sex 1 ecotype#rearing treatment resting metabolic rate, we modeled the residuals of the VO2 1 population(ecotype) 1 litter(population ecotype) 1 ε, model (eq. [3]) as function of the growth efficiency residuals (2) (eq. [2]) in a repeated-measures mixed linear model. We used _ marginal residuals from the repeated-measures VO2 model be- where m represents the grand mean and ε represents the error cause these represent the distance between data points and term. Denominator degrees of freedom for F-tests were esti- overall means, not means within individuals. Additionally, we mated using the Kenward-Roger degrees of freedom approxi- created simple regression models for each of the four test tem- mation, which weights the denominator degrees of freedom ac- peratures and calculated the Pearson correlation coefficients of _ cording to the variance of the effect (Kenward and Roger 1997). residuals from the VO2 and growth efficiency models at each temperature. Oxygen Consumption Rate fi fi _ Allometric Scaling Coef cients and Exponents We rst log10-transformed VO2 and body mass to linearize the relationship between these variables. After log transformation, We estimated scaling exponents and confidence intervals us- mass measurements were standardized around the midpoint of ing restricted maximum likelihood from a log-log model of

This content downloaded from 129.186.252.27 on Mon, 10 Aug 2015 09:42:56 AM All use subject to JSTOR Terms and Conditions 554 E.J.Gangloff,D.Vleck,andA.M.Bronikowski oxygen consumption rate on mass at each test temperature across this temperature range (fig. 3). This interaction is ana- using ordinary least squares linear regression, which gives the lyzed further below (“Allometric Scaling Coefficients and Ex- most accurate parameter estimates across a small range of ponents”). body sizes (White 2011). Estimates of the intercept of the log- log regression correspond to the log of the scaling coefficient Correlation between Growth Efficiency and Metabolic Rate (a), and estimates of the slope of the log-log regression correspond to the scaling exponent (b). We used Proc Mixed and Across individuals, there is a significant negative correlation Proc Reg in SAS (ver. 9.4; SAS Institute, Cary, NC) for all sta- between residuals from the models of growth efficiency and tistical analyses, with a level of significance of a p 0.05. oxygen consumption rate (repeated-measures mixed linear

model: F1, 104 p 4.06, P p 0.047). Within each test temperature, Pearson correlation coefficients are all negative, although Results only the correlation at the highest test temperature (327C) is Growth Efficiency marginally significant (P p 0.054; table 3; fig. 4). This means that snakes exhibiting lower resting metabolic rates converted a In the model of growth efficiency (i.e., change in body length greater proportion of food consumed to an increase in body size accounting for food consumed), the amount of food conover the first year of growth. sumed, initial size, and rearing treatment were significant factors. Not surprisingly, snakes consuming more food grew more, while larger snakes exhibited higher growth efficiency Allometric Scaling Coefficients and Exponents than smaller snakes. Additionally, snakes receiving the warm Because neither the interaction between ecotype and mass nor rearing treatment had higher growth efficiency than those the interaction between rearing treatment and mass was sig- receiving the cool rearing treatment. Population nested within nificant in the initial mixed model of oxygen consumption rate, ecotype was significant in this model, largely driven by low scaling coefficients and exponents were calculated using ob- growth efficiency in snakes from one of the M-slow popu- servations from all individuals. As evidenced by the significant lations (table 1; fig. 1). interaction between mass and temperature described above, metabolic rate scaled differently at each temperature (table 4; fig. 5). Oxygen Consumption Rate The scaling coefficient (a) showed a significant increasing trend from 207 to 287C, which corresponds with our expectations based Temperature interactions with both body mass and ecotype as on the thermal reaction norms (fig. 2). The scaling exponent (b) well as the main effects of these three factors were significant _ showed a significant decreasing trend from 207 to 327C. At the in determining VO2. Additionally, the squared temperature term lowest temperature, the relationship between body mass and was significant, indicating a nonlinear relationship between metabolic rate was positively allometric (b 1 1), indicating that oxygen consumption rate and temperature (table 2). The sig- smaller snakes consumed less oxygen per unit of body mass than nificant interaction between test temperature and ecotype in- larger snakes. As temperature increased, estimates of the scaling dicates differences in the shapes of thermal reaction norms of _ exponent decreased, indicating increasing efficiency of larger snakes from each ecotype (fig. 2): L-fast snakes had highest VO2 _ snakes at higher temperatures. at 247C, while M-slow snakes had highest VO2 at 287C. A post hoc comparison of least squares means, adjusted for multiple Discussion comparisons (Tukey’s method), shows that M-slow snakes had _ lower VO2 than L-fast snakes at 207C(P p 0.046). The signif- In this experiment, we measured oxygen consumption rate _ icant interaction between body mass and test temperature in- (VO2) and growth efficiency of captive-born juvenile garter _ dicates that the allometric relationship of mass to VO2 changed snakes (Thamnophis elegans) from mothers of divergent life-

Table 1: Mixed linear model analysis of Thamnophis elegans growth efﬁciency from November 2010 to November 2011, includingfoodconsumedasacovariate

Source of variation Estimate dfn,dfd F Pr ≤ F Direction of signiﬁcant factors Food consumed 3.04 1, 92 109.03 !.0001** More consumed 1 less consumed Initial size 2.33 1, 65.3 4.62 .04* Larger 1 smaller Ecotype 26.47 1, 30.4 .65 .43 . . . Rearing treatment 14.53 1, 84.1 10.03 .0022** Warm 1 cool Ecotype # rearing treatment 25.70 1, 78.8 .77 .38 . . . Sex 2.31 1, 89 .50 .48 . . . Population(ecotype) . . . 5, 24.7 3.57 .014* . . . Note. Litter nested within population and ecotype was included as a random effect. Denominator degrees of freedom for F-tests were estimated using the Kenward-Roger degrees of freedom approximation. Signiﬁcant effects are designated with a single asterisk (P ! 0.05) or a double asterisk (P ! 0.01).

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growth efficiency, which is growth rate accounting for food consumption, we found that both initial size and rearing treatment had significant effects on growth efficiency, while there was no difference between the ecotypes. Furthermore, we found that residuals from the models of growth efficiency and oxygen consumption rate were slightly but significantly negatively correlated, demonstrating support for trade-offs or covariation between these important life-history traits. Finally, we found a temperature-dependent allometric relationship between mass and metabolic rate across the temperature range, supporting our emerging understanding of the drivers of variation in this relationship (Glazier 2005, 2010). Together, these results demonstrate the complex interactions among extrinsic and intrinsic forces shaping growth, metabolism, and metabolic scaling. Snakes from both ecotypes showed greater growth efficiency in warmer conditions, implying that the thermally dependent plasticity of growth rate may be independent of ecotypic back- fi Figure 1. Growth ef ciency of Thamnophis elegans juveniles, mea- ground or can be mediated by behavioral thermoregulation. In sured by change in snout-vent length (SVL) from November 2010 to contrast to Bronikowski (2000), who found evidence of adaptive November 2011, accounting for the amount of food consumed (see eq. [2]). Data are least squares means from a model also accounting divergence in growth rates in snakes from different ecotypes for for initial size. Snakes receiving the warm rearing treatment grew snakes held at constant temperatures, we did not find that eco- more efficiently than snakes receiving the cool treatment for both type was a significant factor in determining growth efficiency. fi p ecotypes. Error bars represent 95% con dence intervals. L-fast fast- In the present study, we allowed animals to choose their tem- growing lakeshore ecotype; M-slow p slow-growing meadow ecotype. perature along a gradient for either 8 or 16 h per day, to more closely resemble natural conditions. In general, food intake, which greatly influences the rate of increasing body size, was history ecotypes, reared in controlled conditions to mimic dif- largely determined by ecotypic background. However, as we ferences in the natural thermal environments. The reaction norms show here, the ability of snakes to efficiently convert food source for resting metabolic rate, as measured by oxygen consumption to body mass was largely determined by thermal rearing en- _ rate (VO2), differed in snakes from the two ecotypes across the vironment. Snakes given more opportunity to stay warm both range of test temperatures, with L-fast snakes having a signif- attained larger sizes and grew more efficiently. This is congruent icantly higher metabolic rate at the lowest temperature. This with the findings of Tsai et al. (2009), who used bioenergetic finding, coupled with a lack of evidence that rearing treatment modeling to show that Chinese green tree vipers (Trimeresurus affected resting metabolic rate, suggests genetic divergence be- stejnegeri) select postprandial thermal environments to max- tween ecotypes in traits determining resting metabolic rate. For imize both rate of energy gain and efficiency of energy gain.

_ Table 2: Repeated-measures mixed linear model analysis of log-transformed oxygen consumption rate (VO2) in juvenile Thamnophis elegans, measured at four test temperatures ≤ ﬁ Source of variation Estimate dfn,dfd F Pr F Direction of signi cant factors Between subjects: Log(body mass) 12.29 1, 315 9.44 .0023** Larger 1 smaller Ecotype 3.29 1, 315 7.64 .0060** L-fast 1 M-slow Rearing treatment 2.033 1, 84.5 1.64 .20 . . . Ecotype # rearing treatment 2.015 1, 78.7 .07 .79 . . . Sex 2.020 1, 87.5 .48 .49 . . . Population(ecotype) ... 5,29.4 .37 .86 ... Within subjects: Temperature .99 1, 314 10.78 .0011** Higher 1 cooler Temperature # log(body mass) .037 1, 314 7.81 .0055** . . . Temperature # ecotype .011 1, 314 7.49 .0066** . . . Temperature # temperature 2.0016 1, 314 9.97 .0017** . . . Note. Litter nested within population and ecotype was included as a random effect. Denominator degrees of freedom for F-tests were estimated using the Kenward-Roger degrees of freedom approximation. Signiﬁcant effects are designated with a double asterisk (P ! 0.01). L-fast p fast-growing lakeshore ecotype; M-slow p slow-growing meadow ecotype.

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(i.e., juveniles) but that are not actively growing at the time of the metabolic measurements because of food limitation. While snakes are growing, we would expect to see a positive relationship between metabolic rate and growth, as has been demonstrated in field studies of garter snakes (Peterson et al. 1999). Even during short-term fasting, metabolic measurements can be upwardly influenced by delayed overhead costs of growth resulting from tissue synthesis (Rosenfeld et al. 2015). That our animals were on a maintenance ration and did not gain weight during the month before metabolic measurements allows us to test for trade-offs in metabolic rate and first-year growth efficiency without the confounding effect of biosynthesis associated with growth. While we did not systematically vary food availability between treatment groups, snakes ate with different frequencies and therefore consumed different amounts of food. Our analysis, however, statistically accounts for this variation in acquisition and thereby exposes a trade-off between traits driven by variation in allocation (van Noordwijk and de Jong 1986). Evidence of such a trade-off between these traits in a labo- _ Figure 2. Thermal reaction norms for resting metabolic rate (VO2) for ratory setting corroborates the majority of such studies re- Thamnophis elegans juveniles by ecotype and rearing treatment group. ported in the literature (reviewed in Glazier 1999). Snakes, fi Higher means correspond to higher mass-speci c metabolic rates be- ectotherms with low metabolic and energetic demands, were cause calculation of least squares means removes variation associated “ ” fi most likely not resource limited in their ability to allocate with body mass (see Statistics ). A signi cant ecotype by test tem- fi perature interaction demonstrates differences in the shapes of the re- suf cient energy to both metabolic rate and growth. Why then action norms (P p 0.007; see table 2). Additionally, least squares mean does such a trade-off exist? These traits may be regulated, or differences between ecotypes are significant at 207C(P p 0.046), in- possibly coregulated, by underlying mechanisms that set bound- fi dicated with an asterisk. Error bars represent 95% con dence intervals. aries on plasticity when resources are bountiful. Even under p p L-fast fast-growing lakeshore ecotype; M-slow slow-growing favorable ecological conditions, it may not be optimal to in- meadow ecotype. crease allocation to these life-history traits simultaneously. For example, Cox and Secor (2007) found evidence of a trade- In the present study, snakes receiving the warm rearing treat- off between standard metabolic rate and the amount of food ment most likely were able to attain preferred postprandial body energy converted to growth in juvenile Burmese pythons (Py- temperatures more often and were thus able to optimize both thon molurus), even while maintained on a generous ration, and their growth rate and their growth efficiency. This combination that this trade-off had a genetic component. In our experiment, of results implies that total growth is influenced by a number of we found evidence of such a trade-off between production and interacting factors, including both thermal environment and ge- metabolic rate as well. That thermal rearing environment, but netic background. not ecotype, affects growth efficiency and that ecotype, but not Additionally, initial size influenced growth efficiency, with thermal rearing environment, affects resting metabolic rate larger snakes growing more efficiently than smaller snakes. supports the idea that there is a limit to resource-driven plas- This, in combination with evidence that larger snakes also dem- ticity in shaping metabolic pathways. A context dependency of onstrate increased survival over their first year (Bronikowski a trade-off between maintenance metabolism and growth has 2000), points to clear fitness advantages of being born large. If been found in studies of salmon (Salmo salar; Reid et al. 2011) larger snakes are also metabolically more efficient, as would be and cotton rats (Sigmodon hispidus; Derting 1989) and helps expected when b ! 1, then this is consistent with the principal explain polymorphisms in populations where conditions vary of allocation (Gadgil and Bossert 1970; Careau et al. 2008) in temporally and spatially. Given the resource fluctuations found demonstrating trade-offs between growth and metabolic rate. in this garter snake system, both within and between ecotypes Indeed, we found evidence of such trade-offs within individuals (Bronikowski and Arnold 1999; Miller et al. 2011), it is not in our analysis of the relationship between residuals of the surprising that we found such high levels of variation in both growth efficiency model and residuals of the oxygen consump- metabolic rate and growth among individuals. tion rate model. The small but statistically significant negative Weseetwopossibleadaptiveexplanationsforthediver- relationship between these residuals indicates that snakes that gence in thermal reactions norms of oxygen consumption rates had higher growth efficiencies had lower resting metabolic rates, between ecotypes. The low metabolic rate of M-slow snakes at providing evidence of a trade-off in energetic allocation between 207C may represent a mechanism to conserve energy when these two important life-history traits. We note that this trade- temperatures are low and resources are unavailable (Aleksiuk off is evident in snakes that are in a life-history phase of growth 1971, 1976; Burton et al. 2011). A low metabolism would allow

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_ Figure 3. Regression of log-transformed oxygen consumption rate (VO2) on log-transformed and centered body mass across the range of test temperatures in juvenile Thamnophis elegans. Body mass is centered on 0 so that estimates are made at a biologically relevant mass, the midpoint of the log-transformed range.

these snakes to retreat underground for longer periods of time thermal dependence of metabolic rate. For example, the ox- without depleting energy stores. Alternatively, the higher met- ygen and capacity limitation of thermal tolerance hypothesis abolic rate of L-fast snakes may be a mechanism to maintain a (Pörtner 2002; Pörtner and Knust 2007) predicts that thermal higher aerobic scope as they forage for fish, their primary food limits in ectotherms are set as a result of a mismatch between source, in the cold lake water (Bronikowski and Arnold 1999). oxygen demand and supply at high temperatures. This hypoth- In contrast to adult snakes from these and nearby populations esis has not found support in terrestrial ectotherms (Stevens (Stevenson et al. 1985; Bronikowski and Vleck 2010), metabolism in juvenile snakes did not increase monotonically with temperature. Oxygen consumption rate decreased at the high- Table 3: Pearson correlation coefficients of growth efficiency est temperature in all ecotype and rearing treatment groups, _ with the metabolic rate of L-fast snakes peaking at 247Cand residuals and VO2 residuals measured at four test temperatures that of M-slow snakes at 287C. Interestingly, this pattern is in juvenile Thamnophis elegans similar to the “lag” in oxygen consumption rates across a sim- Pearson ilar high range of temperatures (287–347C) reported by Seidel Test temperature correlation coefficient Pr ≠ 0 and Lindeborg (1973) for wild-caught T. elegans. Unfortunately, 207C 2.13 .17 no size range of the tested individuals is reported in that study. 247C 2.12 .24 Such a leveling or decrease in oxygen consumption rate 287C 2.12 .21 could be an adaptive mechanism to conserve energy at higher 327C 2.19 .054 temperatures (Glazier 2015) or a hint at some limit to the

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_ Figure 4. Regression of oxygen consumption rate residuals (VO2; see eq. [3]) and growth efficiency residuals (change in snout-vent length; see eq. [2]) for juvenile Thamnophis elegans. Data shown are from measures of oxygen consumption rate at 327C, where the negative correlation was most significant (Pearson correlation coefficient p 20.19, P p 0.054). The repeated-measures mixed linear model demonstrates the significance p p of the negative correlation when data from all test temperatures are analyzed (F1, 104 4.06, P 0.047). The shaded area represents the 95% confidence envelope for the regression line. et al. 2010; Overgaard et al. 2012; Fobian et al. 2014), but these garter snakes from Eagle Lake populations exhibited this pat- tests have not included juveniles, which may experience lim- tern while adults did not could be due to ontogenetic changes itations of oxygen delivery at early stages of development. For in physiology, such as more effective oxygen delivery and in- example, Steyermark and Spotila (2000) report such a leveling crease in blood oxygen capacity (Pough 1977) or shifts in the off of oxygen consumption rate at high temperatures in ju- relative mass of organs with high oxygen demand (Oikawa and venile snapping turtles (Chelydra serpentina). That juvenile Itazawa 2003; Landgraf et al. 2006). We are planning future

Table 4: Allometric equation parameter estimates relating standard metabolic rate to body mass in juvenile Thamnophis elegans Test temperature Scaling coefficient (a) Scaling exponent (b) R2 207C 1.53 (1.38–1.70) 1.33 (1.02–1.63) .416** 247C 1.76 (1.61–1.93) 1.23 (.95–1.50) .426** 287C 1.85 (1.68–2.05) .87 (.57–1.17) .242** 327C 1.69 (1.56–1.82) .78 (.55–1.01) .299** Note. The scaling coefficient (a) is a measure of metabolic level at the range midpoint of log-transformed sample mass. The scaling exponent (b) describes the relationship between body mass and metabolic rate at the indicated test temperature. Parentheses indicate 95% confidence intervals for estimates. All correlations were negative and highly significant (P ! 0.001), indicated with a double asterisk.

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et al. 2012; Carey et al. 2013), but not with universal support (Gifford et al. 2013). Our estimates of scaling exponents at 207C (b p 1.33) and 247C(b p 1.23) were significantly higher than those of both adult and month-old snakes from these populations (b p 0.59 [Bronikowski and Vleck 2010]; b p 0.38–0.58 [Robert and Bronikowski 2010]). At 287C(b p 0.87) and 327C (b p 0.78), our estimates are not statistically different from the estimates from these previous studies or from the average for squamate reptiles (b p 0.80; Andrews and Pough 1985). The scaling exponent estimate at 207C is significantly greater than 1. This is surprising because it indicates that smaller snakes are more metabolically efficient than larger snakes at cool temperatures. Such high scaling exponents (b 1 1) have been reported in rapidly growing juvenile ectotherms (reviewed in Glazier 2015) and may be attributable to ontogenetic shifts in the relative mass of organs with differing oxygen demands or a size dependency of underlying mechanisms determining metabolic rate, such as T4 thyroxine or mitochondrial density (Steyermark et al. 2005). The scaling exponent estimates did not differ between ecotypes or rearing treatment groups, implying that variation in the allometric scaling of metabolic rate did not drive the observed divergences in growth rate or metabolic rate. In ectotherms, colder temperatures are generally associated with lower whole-organism metabolism, activity level, and maintenance costs. In T. elegans,207C (the lowest temperature at which we measured metabolic rate) represents a threshold between active and inactive snakes, with crawling speed, swimming speed, tongue-flicking rate, and digestive rate all decreasing rapidly below this temperature (Stevenson et al. 1985). Within

Figure 5. A, Estimates of metabolic scaling coefficients (a)across temperature range in juvenile Thamnophis elegans. B,Estimatesof scaling exponents (b) across temperature range. Error bars represent 95% confidence intervals. Scaling estimates did not differ between ecotypes or treatments, so data are from all individuals (n p 106). experiments to test the capacity of oxygen delivery systems at thermal extremes and the subsequent consequences for metabolic rate. We found that both metabolic level (a from the allometric equation of metabolic rate) and scaling exponent (b) are temperature dependent within our range of test temperatures. This is consistent with the metabolic-level boundaries hypothesis (MLBH; Glazier 2005, 2010), which acknowledges variation in this scaling exponent and adopts a focus on physical attributes Figure 6. Schematic relationship between metabolic level (a,theback- that set boundary conditions on scaling exponent values. The transformed intercept of the log-log plot) and metabolic scaling MLBH predicts a negative relationship between the scaling ex- exponent (b, the slope of the log-log plot), as predicted by the ponent (b) and the scaling coefficient (a) in inactive ectotherms metabolic-level boundaries hypothesis (Glazier 2005, 2010). While at cool temperatures (see fig. 6 for details). Empirical studies inactive at cool temperatures, ectotherms will have a low metabolic testing the MLBH hypothesis with ectotherms across a range of level (a) and a metabolic rate that scales approximately isometrically (b ≈ 1), limited by the energetic requirements to sustain body tissues. temperatures have demonstrated the ecological dependence and At higher temperatures, metabolic level will increase (increasing a) adaptive significance of the relationship between mass and met- while the metabolic scaling exponent will decrease, now bounded by abolic rate (Killen et al. 2010; Vaca and White 2010; Ohlberger fluxes of resources and heat across surfaces (b ≈ 2/3).

This content downloaded from 129.186.252.27 on Mon, 10 Aug 2015 09:42:56 AM All use subject to JSTOR Terms and Conditions 560 E.J.Gangloff,D.Vleck,andA.M.Bronikowski the activity range of this species, the scaling exponent decreased Literature Cited with increasing temperature but was never significantly less than 1, even at the highest temperature measured (table 4). At Agutter P.S. and D.N. Wheatley. 2004. Metabolic scaling: con- the two highest temperatures, this estimate also did not differ sensus or controversy? Theo Biol Med Model 1:1. significantly from 2/3, as predicted by the MLBH (Glazier 2005). Aleksiuk M. 1971. Temperature-dependent shifts in metab- We may expect the value of b to be greater than 2/3 if body shape olism of a cool temperate reptile, Thamnophis sirtalis pa- allometry—and therefore surface area to volume ratio—changes rietalis. Comp Biochem Physiol 39:495–503. across ontogeny (Glazier 2014; Hirst et al. 2014). However, ———. 1976. Metabolic and behavioral adjustments to tem- previous work with garter snakes showed no evidence that body perature change in red-sided garter snake (Thamnophis sirtalis shape is affected by increased growth rates associated with higher parietalis): an integrated approach. J Therm Biol 1:153–156. temperatures (Arnold and Peterson 1989). In future experi- Álvarez D. and A.G. Nicieza. 2005. Is metabolic rate a reliable ments, we will test whether the trend of decreasing b values predictor of growth and survival of brown trout (Salmo continues at higher temperatures and the scaling exponent trutta) in the wild? Can J Fish Aquat Sci 62:643–649. becomes less than 1, meaning that larger snakes have reduced Andrews R.M. and F.H. Pough. 1985. Metabolism of squamate maintenance metabolic demands. Such increased efficiency of reptiles: allometric and ecological relationships. Physiol Zool larger snakes could potentially facilitate divergences in growth 58:214–231. rates and thus potential life-history differences (Carey et al. Angilletta M.J. 2001. Variation in metabolic rate between 2013). Furthermore, we will test the dependency of metabolic populations of a geographically widespread lizard. Physiol scaling on activity and energetic demands, as found in other Biochem Zool 74:11–21. snake systems (e.g., Taylor and Davies 1981; Beaupre 1993). ———. 2009. Thermal adaptation: a theoretical and empirical Understanding the relative contributions of developmen- synthesis. Oxford University Press, New York. tal plasticity and local adaptation in producing divergence of AngillettaM.J.,R.S.Wilson,C.A.Navas,andR.S.James.2003. traits between populations is a central question in an era of Tradeoffs and the evolution of thermal reaction norms. rapid environmental changes (Carroll et al. 2014). The ther- Trends Ecol Evol 18:234–240. mal dependence of growth efficiency, energy allocation, and Arnold S.J. and C.R. Peterson. 1989. A test for temperature metabolic rate in ectotherms has immense implications not effects on the ontogeny of shape in the garter snake Tham- only for the fitness of individual organisms but also for pop- nophis sirtalis. Physiol Zool 62:1316–1333. ulation dynamics (Burton et al. 2011; Ohlberger et al. 2012; Beaupre S.J. 1993. An ecological study of oxygen consumption Carey et al. 2013), yet mechanistic models of the physiologi- in the mottled rock rattlesnake, Crotalus lepidus lepidus, cal impacts of temperature are still needed (Angilletta 2009; and the black-tailed rattlesnake, Crotalus molossus molossus, Somero 2011; da Silva et al. 2013). The empirical data pro- from two populations. Physiol Zool 66:437–454. vided here offer evidence that important aspects of the phe- ———. 1995. Comparative ecology of the mottled rock rat- notype—in this case, metabolic rate and growth—have com- tlesnake, Crotalus lepidus,inBigBendNationalPark.Her- plex interdependent relationships with both developmental petol J 51:45–56. and immediate environments. By making explicit the mech- Bennett A.F. and W.R. Dawson. 1976. Metabolism. Pp. 127– anisms linking physiology with life history, we can understand 223 in C. Gans and W. R. Dawson, eds. Biology of the Rep- better how variation in physiological traits affects higher levels tilia. Academic Press, New York. of biological organization. Bronikowski A. and D. Vleck. 2010. Metabolism, body size and life span: a case study in evolutionarily divergent populations of the garter snake (Thamnophis elegans). Integr Acknowledgments Comp Biol 50:880–887. Bronikowski A.M. 2000. Experimental evidence for the adap- This research was supported by a grant from the National Sci- tive evolution of growth rate in the garter snake Thamnophis ence Foundation to A.M.B. (IOS-0922528). We thank G. Palacios elegans. Evolution 54:1760–1767. for assistance with fieldwork and experimental setup; M. Manes, Bronikowski A.M. and S.J. Arnold. 1999. The evolutionary W. Manhart, R. Potter, and A. Wendt for assistance in captive ecology of life history variation in the garter snake Tham- snake care; H. Messersmith and D. Reding for assistance with nophis elegans. Ecology 80:2314–2325. metabolic measurements; L. Baumgard, W. Clark, Bronikowski Brown J.H., J.F. Gillooly, A.P. Allen, V.M. Savage, and G.B. Laboratory members, and two anonymous reviewers for con- West. 2004. Toward a metabolic theory of ecology. Ecology structive comments on manuscript drafts; and P. Dixon for sta- 85:1771–1789. tistical advice. E.J.G. was supported by fellowships from the In- Buikema A.L. and K.B. 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APPENDIX B

FAST- AND SLOW-LIVING SNAKES DIFFER IN INFORMATION-GATHERING AND

ACTIVITY IN OPEN-FIELD TESTS

This manuscript is currently in review with the peer-reviewed journal Behavioral Ecology and

Sociobiology

Melinda Chow*, Eric J. Gangloff*, Vianey Leos-Barajas, Stephanie Hynes, Brooke Hobbs, and

Amanda M. Sparkman

*First two authors share first authorship.

Author contributions: EJG and AMS designed the experiment. MC, SH, BH, and AMS conducted experiment. EJG and MC analyzed videos. EJG, VLB, and AMS analyzed data. AMS drafted manuscript. MC, EJG, VLB, and AMS contributed to interpretation of results and manuscript revisions.

Abstract

An animal’s life history, physiology, and behavior can be shaped by selection in a manner that favors strong associations among these aspects of an integrated phenotype. Recent work combining animal personality and life-history theory proposes that animals with faster life- history strategies (i.e., fast growth, high reproduction, short lifespan) should exhibit higher general activity levels relative to those with slower life-history strategies. We tested for differences in behavior in garter snakes from well-characterized ecotypes which are known to 153

differ in ecology, life-history strategy, and physiology. We quantified two aspects of behavior in a modified open field test: tongue-flick rate as a measure of information gathering and movement patterns as a measure of general activity. We examined both baseline behavior as well as habituation to behavioral trials over time, as a simple form of learning. Tongue flicks and movement were strongly positively correlated and both behaviors were repeatable across trials.

Snakes from the fast-living ecotype were more active and showed evidence of habituation with regard to both variables. The slow-living ecotype maintained low levels of activity throughout the trials. We propose that environmental factors, such as high predation, experienced by the fast-living ecotype select for both increased information-gathering and activity levels to facilitate efficient responses to repeated challenges. In contrast, selection may favor a more energetically- conservative strategy in the slow-living ecotype.

Introduction

An animal’s life history, physiology, and behavior can each be finely adapted to specific ecological circumstances. How these different levels of organization are integrated and interact as they shape organismal fitness in a given context is far less clear (Ricklefs and Wikelski 2002;

Dall et al. 2004; Careau et al. 2008). Recent scholarship has implicated interactions between physiological and behavioral traits in shaping life-history trade-offs (Stamps 2007; Wolf et al.

2007; Biro and Stamps 2008; Réale et al. 2010). Integration of research on animal personality, which highlights consistent individual differences in suites of behavior across situations (Sih et al. 2004), with differences in physiology and/or life history strategy (also known as pace-of-life) has achieved some success in both intraspecific and interspecific contexts (e.g., Arnott et al.

154

2006; Careau et al. 2009; Réale et al. 2010; Le Galliard et al. 2012; Kiørboe et al. 2015;

Nakayama et al. 2016).

Such patterns may emerge from the effects of pleiotropy, correlated selection resulting in linkage between genes, or common physiological pathways underpinning suites of traits. For example, genetic correlations among traits may serve to provide optimal combinations of traits or may constrain adaptive evolution of certain aspects of the phenotype (Lande and Arnold 1983;

Arnold 1987). In support of the pace-of-life hypothesis, an interspecific study of mice showed that both age at first reproduction and exploratory behavior were negatively correlated with basal metabolic rate (Careau et al. 2009). Long-term research on wild populations of guppies with diverging life-history strategies resulting differing predatory regimes (Reznick et al. 1996) also show differences in boldness (Gilliam and Fraser 1987) and learning ability (Burns et al. 2008).

However, other work has failed to find strong phenotypic or genetic correlations across these levels of organization (e.g., Réale et al. 2010; Niemelä et al. 2013). Even where associations do occur, they are not always in the manner predicted by pace-of-life theory (Réale et al. 2010; Le

Galliard et al. 2012). Additionally, ecological context can determine the strength and direction of the correlation among traits as well as the fitness consequences of trait combinations. For instance, interpopulation comparisons in fish have shown that associations between aggression and boldness, which are widespread among vertebrates (Sih et al. 2004; Réale et al. 2007), occur in high predation, but not in low predation environments (Dingemanse et al 2007; Martins and

Bhat 2014). The inherent complexity within these different spheres of organization and the accumulation of apparently contradictory findings suggest that in-depth knowledge of specific natural systems and their ecological/evolutionary contexts is essential both to demonstrate and

155

appropriately interpret correlations within and among levels of organization (Adriaenssens and

Johnsson 2010; Dall and Griffith 2014; Dantzer et al. 2016).

Populations of the western terrestrial garter snake, Thamnophis elegans, residing in

Lassen County, California, present an excellent opportunity to test for associations among life history, physiology, and behavior. Forty years of study have resulted in extensive characterization of the ecology, life-history strategies, and physiology of two distinct pace-of-life ecotypes. Populations along the lakeshore of Eagle Lake (hereafter ‘L-fast’ ecotype) exhibit fast growth, early maturation, high annual reproduction, and short lifespans relative to populations in nearby montane meadow (‘M-slow’ ecotype; Bronikowski and Arnold 1999; Bronikowski 2000,

Sparkman et al. 2007). Evidence suggests that these ecotypes also differ on multiple physiological levels, with L-fast populations exhibiting higher metabolic rates in adult males

(Bronikowski and Vleck 2010), higher levels of indices of both innate and acquired immunity

(Sparkman and Palacios 2009, Palacios et al. 2011; Palacios et al. 2013), and lower levels of baseline plasma corticosterone concentration (Palacios et al. 2012) compared to M-slow populations. Physiology in part mediates important life-history trade-offs as well, for example between maintenance metabolism and growth in juvenile snakes (Gangloff et al. 2015) and between maternal energy use and allocation of energetic stores to offspring (Gangloff et al. in review). Furthermore, L-fast populations experience more reliable prey availability

(Bronikowski and Arnold 1999; Miller et al. 2011) and are subjected to higher predation pressure

(Manier et al. 2007, Sparkman et al. 2013), major ecological differences which have likely been critical selective factors in the evolutionary divergence in life history and physiology.

To assess repeatability of behaviors within individuals and differences in behavior between wild-caught L-fast and M-slow snakes, we employed a simple open field test (Archer

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1973; Carter et al. 2013) involving forced entry (i.e., snake deposited by hand) of snakes into a restricted behavioral arena, repeated three times over a period of three days. Two measurable behaviors are evident in snakes in this context: tongue-flick rate and movement. Snakes rely heavily on olfaction, sampling particles from the environment with the tongue (Lillywhite,

2014). As such, tongue-flicking behavior is well-known index of information-gathering in squamates, including garter snakes (Gove and Burghardt 1983; Chiszar et al. 1976). We analyze movement using hidden Markov models (HMMs), a statistical tool to infer behavioral states from continuous and serially dependent movement data behavior (e.g., Gurarie et al. 2015;

Patterson et al. 2009; Towner et al. 2015). HMMs work on the assumption that our observations of animal movement (in this case distance moved in each time step) result from an unobserved underlying process, often termed behavioral “states” or “modes” (Patterson et al. 2008). HMMs offer a number of advantages over subjective behavioral classification. For example, HMMs provide a model-based approach to segment observed movements into behavioral states, which may not always be readily identifiable to a subjective observer. Furthermore, these models allow quantification of latency to remain in behavioral states and transitions between behaviors, which provide data on individual flexibility not apparent from summary statistics. From our HMM models, we extract two important metrics of behavior: proportion of time spent in a behavioral state and the amount of time staying in a behavioral state before transitioning.

Within this framework, we test for differences in tongue flicks and movement patterns across trial, ecotype, and sex. First, we expect that individuals will be consistent in behaviors across trials, as has been well-established in studies of garter snake behavior (e.g., Chiszar and Carter

1975; Arnold and Bennett 1984; Herzog and Burghardt 1988). We also predict that tongue-flick rates will be positively correlated with activity levels within individuals, as has been identified in

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previous work with garter snakes (Chiszar and Carter 1975). Finally, we predict that snakes from L-fast populations should exhibit higher activity levels and tongue-flick rates in line with the pace-of-life syndrome hypothesis (Réale et al. 2010; Careau et al. 2008). This could be due to direct relationships between behavior and life history, whereby more active behavior facilitates resource acquisition and consequently increases growth/reproductive rate, but potentially incurs risks that reduce survival. With this test of how behavioral traits correlate with divergence in life-history and physiological variation in well-characterized ecotypes, we provide insight on how environmental variation can drive selection on suites of traits along a predictable pace-of-life continuum.

Methods

Experimental design

We collected adult western terrestrial garter snakes from one L-fast and two M-slow populations in the area surrounding Eagle Lake in Lassen County, CA (1550 m above sea level;

40.65N, 120.74W). Forty-two adult free-ranging snakes were collected by hand in June 2015, during the main active season for these populations but after the breeding season. Fourteen adult

L-fast snakes (7 females, 7 males) and 28 adult M-slow snakes (19 females, 9 males) were kept in captivity in the field for behavioral trials (snout-vent length range: 362-557 mm). Snakes were housed in the field at ambient temperatures in the shade, with 3-4 individuals of the same population in plastic containers with bedding and water ad libitum. Behavioral trials began on the first full day of captivity (after a 24-hour acclimation period) and repeated at 24-hour intervals on days two and three (3 trials total). In this species, physiological indicators of stress response (plasma corticosterone and glucose concentrations) are reduced from maximal levels

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and approach baseline after snakes are kept overnight in similar conditions (Sparkman and

Gangloff unpublished data). All snakes were released at capture site after four days.

Behavioral trials consisted of a modified open field test (Archer 1973; Carter et al. 2013).

A white, 20-gallon, circular (39 cm diameter) plastic bucket served as the behavioral arena. A level was used to ascertain that the bucket was positioned on level ground. Snakes were released into the arena for a period of one minute and trials were video-recorded using a high-definition camera (Vixia HFG10, Canon, Tokyo, Japan). The arena was cleaned with 70% ethanol before each trial to eliminate chemical signals from the previous subject. Body temperature was measured immediately following each trial using a cloacal thermometer (DeltaTrak model

11063, Pleasanton, CA; range: 20.0–29.9ºC, SD = 1.7).

Tongue Flicks

We used behavioral analysis software Solomon Coder (Péter, 2015) to code and quantify tongue-flick rate from the video recordings. For coding, trial time began when the snake was released from hand into the bottom of the arena and ended after approximately one minute.

Tongue-flick rate was calculated as the number of tongue flicks per second. The single observer scored 10% of the trials a second time and we estimated Kendall's coefficient of concordance as

0.99, indicating extremely high intrarater reliability (Burghardt et al. 2012). We used mixed linear models of tongue-flick rate to test for effects of ecotype (L-fast/M-slow), population nested within ecotype (to control for potential differences across the two meadow populations), trial (1, 2 or 3), sex (male/female), body temperature, and snout-vent-length (SVL). Snake ID was included as a random effect, modeled with a compound symmetric covariance structure, which provided the best model fit as assessed by AICc. We compared models containing all

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combinations of these variables, as well as all two- and three-way interactions among sex, ecotype, and trial, using Akaike’s Information Criterion corrected for small samples (AICc).

Degrees of freedom for F-tests were calculated using the Kenward-Roger Degrees of Freedom

Approximation (Kenward and Roger 1997).

Movement

HMMs are not yet commonly applied to behavioral studies in a captive, experimental- design context. Data in these contexts are collected at a fine-scale resolution over multiple trials, producing multiple time series per individual. Summary statistics from each trial (e.g., total or average distance travelled), when analysed in a generalized linear model framework, can mask important information about the animal’s behavior. For instance, understanding how an animal switches between multiple movement modes throughout a trial provides more insight into its behavior than if we were to only look at total distance travelled.

We analyzed snake movements during the time interval from when the snake is first placed in the test arena until approached by the experimenter, approximately the first 60 s of each trial. Movement data was extracted from video recordings using Ethovision software

(Noldus Information Technology, Leesburg, VA). A small adhesive dot was placed in the center of the snake’s head before each trial, providing a distinct colour cue for the software to track. For analysis, we thinned the data from 30 to 3 observations per second as this provides a more biologically meaningful interval of movement. We analysed step lengths, the distance the snake's head moved during each 1/3 s interval, which we transformed by the natural logarithm to facilitate selection of parametric state-dependent distributions.

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We developed a 2-state HMM to analyze the time series collected from 43 individuals across three trials (N = 120 tracks; for two individuals we conducted only two trials) after visual inspection of the data (on the log-scale) showed a clear bi-modal distribution of step lengths (Fig.

1). We were unable to extract movement data from video recordings of six trials because of software inability to track the colored marker. For analysis, each track is assumed to behave according to a random walk with step lengths generated by a state-dependent mixture of two normal distributions. "Stationary state" behavior consists of very short step lengths in which the snake is motionless, or nearly so, and "moving state" consists of large step lengths. In order to accommodate for zero step lengths (~0.55% of the data), we included a point mass for zero in the stationary state (cf. Towner et al. 2015).

For each track 푘, 푘 = 1, … ,120, the transition probability matrix (t.p.m.) of the underlying

Markov chain generating the state sequence is given by

푘 푘 푘 훾11 훾12 횪 = ( 푘 푘 ) 훾21 훾22

푘 where 훾푖푗 is the conditional probability of the snake being in state j in the time interval (t, t+1), given it is in state i during the interval (t-1, t).

We allowed the state transition probabilities to be functions of up to three categorical variables: trial, ecotype, and sex, including all main effects and possible interaction terms. The full model is as follows:

푘 푘 logit (훾푖푖(푡)) = 푿푖 휷푖

푘 for 푖 =1,2 and 푘 =1,2,…,120, where 푿푖 is a row vector of zeros and ones that denotes the trial number, ecotype, and sex of track 푘 and 휷푖 is the column vector of coefficients for state 푖. The

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intercept terms in the model, {훽0,1, 훽0,2}, correspond to a L-fast female snake during trial 1 either in the stationary state or the moving state. In order to assess differences in the state-switching dynamics by trial, ecotype, and sex, we computed the stationary distribution across all combinations of covariates in the selected model, as described by Patterson et al., (2009). As a result, we can compare the marginal probability of each state, the expected proportion of time that the snakes spent in each state, across trials by ecotype and sex.

We also computed the time (in seconds) that a snake would spend, on average, in a given state (dwell time) in the following manner,

퐸(퐷푖) = 1/(1 − 훾푖푖) with 퐸(퐷푖) corresponding to the expected duration in state 푖 and 훾푖푖 the diagonal entry of the t.p.m., for 푖 = 1,2. In this case, as we only have two states, the diagonal entries will reflect how much time, on average, a snake will move around the arena before it stops (or nearly so), and vice versa. In this sense, two snakes can spend an equal amount of time in each state, yet one snake alternates quickly between states and the other switches infrequently, and these dynamics are reflected in the t.p.m.

The model formulation (full details provided in Zucchini et al. 2016) allows for a numerical optimization of the likelihood, i.e., a simultaneous estimation of all model parameters via maximum likelihood, which we conducted using R (R Core Team 2014). For each model, we considered 50 sets of random initial values in the numerical maximization, with at least 32% converging to the same maximum value. As a result, we are confident we found the global maxima of the respective likelihoods. A forward selection approach was implemented and covariates included according to the AICc of the corresponding models (Table 2). Model goodness-of-fit was assessed via the pseudo-residuals and the Viterbi algorithm implemented to

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decode each time series into the optimal sequence of underlying states, so that each observation is connected to either the stationary or moving state (Zucchini et al. 2016). We used a parametric bootstrap with 1,000 samples to construct 95% confidence intervals.

Repeatability & Correlation between Measures

To assess individual consistency in behaviors, we estimated the consistency repeatability of tongue-flick rate and proportion of time moving. To calculate the consistency repeatability, which accounts for changes in behavior over time (Biro and Stamps 2015), we created a model for each of the two behaviors with the fixed categorical effect of trial and a random effect of individual, modeled as above with a compound symmetric covariance structure. We calculated the repeatability as the ratio of between-individual variance to total variance (Wolak et al. 2012;

Snijders and Bosker 2012). We assessed the significance of this estimate using a likelihood ratio

2 2 test, using a χ distribution with one and zero degrees of freedom (χ0:1 in results below).We assessed the correlation of behavioral measures with a mixed linear model of tongue flicks as the dependent variable, the proportion of time moving as a continuous predictor variable, and individual as a random effect as above. For the repeatability and correlation analyses, we arcsine- transformed the proportion of time moving to meet assumptions of normality (Sokal and Rohlf

2011).

So that we could examine the relationship between tongue-flicks and movement patterns, we compared a narrow interval of time (approximately 1 s) in which a tongue flick occurred to the decoded HMM states. As each interval corresponded to multiple movement observations, we created a new categorical variable that takes on three values: “Moving” if during the interval the snake was only in the moving state, “Resting” if the snake was only in the resting state, and

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“Switching” if there was a combination of moving and resting behavior exhibited. All analyses except for HMMs were conducted using PROC MIXED in program SAS version 9.4 (SAS Institute,

Cary, NC) and plots were generated using the ggplot2 package in R (Wickham 2009).

Results

Tongue Flicks

The model with the lowest AICc contained the fixed effects of ecotype, population nested within ecotype, trial, and a trial by ecotype interaction (Table 1). Each of the terms in this top model significantly influenced tongue-flick rate. However, in Table 2 we provide the results of a more complex model (ΔAICc = 5.2) that also contained sex, sex × ecotype, and trial × sex interactions, so as to provide estimates of effect sizes for these non-significant variables that can be directly compared to our final model for movement (see below). Our interpretations regarding the analysis of tongue-flick behavior are the same for both the top model and the model presented in Table 2. L-fast snakes showed higher tongue-flick rates than M-slow snakes and tongue-flick rate decreased overall from trial 1 to 3. The trial by ecotype interaction revealed that L-fast snakes exhibited higher rates of tongue flicking for trial 1 and 2 than for trial 3, whereas M-slow snakes did not differ in rates among trials (Fig. 1).

Movement

Based on the AICc, the selected HMM model includes differences in movement patterns across trials, by ecotype, and by sex, including all two-way interactions thereof (Table 3). Across sex in trials 1 and 2, L-fast snakes spent more time on average in the active state than the M-slow snakes. The differences were more pronounced for females than males (Fig. 3). By trial 3,

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however, there was a negligible difference in the amount of time that either sex, across ecotype, spent in each state. Specifically in trial 1, female L-fast snakes spent, on average, very little time in the stationary state and most time in the active state.

According to the state-dwell times, L-fast female snakes spent an average of 13.7 s (trial

1) and 23.6 s (trial 2) moving around the arena before remaining stationary for approximately 5 s

(trial 1 and 2). L-fast males spent about 26 s (trial 1 and 2) moving and only about 2.3 s (trial 1) and 8.8 s (trial 2) stationary at a time. By trial 3, L-fast snakes of both sexes had similar state- dwell times (roughly 22 s moving and 10-12 s stationary; Fig. 4). Overall, the state-dwell time for M-slow snakes did not differ between states. That is, the M-slow snakes spent approximately equivalent amounts of time moving before becoming stationary, and stationary before deciding to move around the arena. The exception is that in first trial, M-slow males, on average, moved for 21.2 s at a time while remaining stationary for 5.7 s (Fig. 4).

Using the Viterbi algorithm, we decoded the optimal state sequence of each track and assigned the distances to either the stationary state or the moving state. With this assignment, we can visualize the movement patterns of each individual across trial and match the decoded states to what we observed during the experiment. We include example tracks for four snakes, one for each combination of sex and ecotype, across the three trials (Fig. 5; Supplemental Video).

Overall, snakes generally maintained a path along the outer limits of the test arena, producing the circular paths demonstrated here.

Repeatability & Correlation Between Measures

Both the tongue-flick rate and the proportion of time spent moving were repeatable

2 2 (tongue-flick rate: Rc = 0.33; χ0:1 = 11.9, P = 0.0003; time moving: Rc = 0.25; χ0:1 = 6.4, P =

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0.0057). Within individuals, there was a significant and positive correlation between the tongue- flick rate and the proportion of time spent moving (Estimate ± SE: 0.83 ± 0.054, F1,114 = 232.6, P

< 0.0001) and snakes overwhelmingly flicked their tongue more often while in the moving state rather than the stationary state. This relationship holds for both M-slow snakes (Estimate ± SE:

0.74 ± 0.057, F1,79 = 166.6, P < 0.0001) and L-fast snakes (Estimate ± SE: 0.96 ± 0.12, F1,28.2 =

60.4, P < 0.0001; Fig. 6).

Discussion

These results support the integration of behavior into the pace-of-life syndrome framework, but also offer some surprises. Both behavioral measures were significantly repeatable, indicating that individuals were consistent in their behaviors over time and that individuals differed from each other in these behaviors. As predicted, activity level and tongue- flicking were highly correlated within individuals (Fig. 6), and L-fast snakes showed higher activity levels and tongue-flick rates upon being placed within a novel arena, particularly within the first two trials (Tables 1-3; Figs. 1 and 3). This is consistent with the general prediction that fast-living animals will exhibit more active behaviors and complements the few studies in fish to show this association between life history and behavior across populations with differing predation regimes (reviewed in Réale et al., 2010). However, patterns in activity and tongue- flicking varied within ecotypes over the three trials, providing some evidence for ecotype- specific patterns of habituation.

We interpret the correlation between movement and tongue flicking as an indication that these two behavioral measures are different aspects of the same personality axis (sensu Réale et al. 2010). This correlation provides interesting insight into the sensory modalities of snakes:

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because of their general reliance on olfactory and tactile cues, snakes may be sampling their environment through both tongue-flicking and searching the periphery of the test arena for an escape route. Additionally, snakes must move around their environment to appropriately sample both the substrate and air. Thus, we interpret both tongue-flicks and movement states as metrics of an exploratory escape behavior.

The more pronounced exploratory escape behavior of L-fast snakes may reflect an ability to respond adaptively to an acute stimulus, such as a predatory attack. This may be especially important in lakeshore habitats, where predation pressure is high and rapid escape to cover may be a more effective means of evading predation than camouflage. In contrast, M-slow snakes may rely more on camouflage in the context of similarly-colored meadow grasses and sparse rocky cover, and selection may favor immobility in response to an acute stimulus. While it is clear that L-fast individuals do exhibit the more active response within this experimental context, this does not necessarily mean that M-slow snakes will have a more subdued response to novelty in every context. For instance, when held in short-term captivity in the field, M-slow snakes feed readily on amphibian prey when placed in a feeding chamber, whereas L-fast snakes may take weeks to desist from frenzied attempts to escape and begin to feed (A.M. Sparkman, unpublished data). In this case, M-slow snakes appear quickly adaptable to a novel environment, perhaps due to increased selection for opportunistic feeding behavior resulting from variable prey availability in meadows. Future research will be directed towards exploring ecotype-specific responses to different ecological contexts to further elucidate ecological drivers of behavioral variation in different personality axes.

We found striking evidence for ecotype differences in changes in response behavior over the three trials, most clearly with regard to tongue flicking. L-fast tongue-flick rates decreased in

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the third trial to rates and times comparable to M-slow, whereas M-slow tongue-flick rates did not change across the three trials (Fig. 1). Similarly, while L-fast females spent more time in the active state for the first two trials than M-slow females (and the same for L-fast males vs. M- slow males in trial 2), by the third trial there were no differences among sex or ecotype (Fig. 3).

Our measure of dwell time can be interpreted as a measure of behavioral flexibility, or how often an individual changes behavior once it has made a decision. While both male and female L-fast snakes spent longer bouts being stationary before switching in the third relative to the first trial,

M-slow snakes generally spent the same amount of time before switching throughout the three trials (Fig. 4). Interestingly, this implies that L-fast snakes are relatively inflexible in the first two trials, then appear to revise their behavior in a manner that causes them to behave more like M- slow snakes, who remain largely unchanged from trial to trial.

Behavioral alterations in L-fast snakes over repeated trials can be seen as evidence for habituation, a simple form of learning (Burghardt 1977; Rankin et al. 2009). Though cognition has historically been understudied and underestimated in squamate reptiles (snakes and lizards), some empirical studies have provided evidence of simple learning such as response decrement, or habituation, to photic, vibratory, and predatory cues (e.g., Crawford and Holmes 1966;

Luttges and Gamow 1970; Andry and Luttges 1972; Fuenzalida 1975; Rodríguez-Prieto et al.

2011). The most straightforward interpretation of our findings is that L-fast snakes form memories of their previous experiences and thus alter their behavior (Stamps 2016). By the third trial, L-fast snakes are less motivated to explore it rapidly—perhaps because they have learned either that no escape is possible, or that the arena does not pose an acute threat to their survival.

It may also be that L-fast snakes have habituated to captivity itself, and the behavioral trial is simply measuring an adjustment in behavior that L-fast snakes are slower to make over time in

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captivity than M-slow snakes. In the future, trials should be conducted solely the third full day of captivity, without having prior exposure to the behavioral arena, to determine whether their behavior at this time mimics that of individuals first exposed to the arena on first full day of captivity or whether it is already attenuated without prior exposure. Since after one night in captivity, stress indicators (corticosterone and glucose) are greatly reduced from stress-induced levels and decline to baseline in both ecotypes, it seems more likely that snakes are habituating to the behavioral arena (Sparkman and Gangloff, unpublished data).

Whatever the case, there are clear ecotypic differences in behavior over subsequent trials, which may reflect differences in selection on how these animals process information, form memories, and enact responses in the context of an acute stimulus. These cognitive capacities are evolutionarily significant because they can impact fitness via adaptive revisions of behaviors such as foraging, social interactions, and escape from predators (Dukas 1998; Pritchard et al.

2016). A variety of hypotheses relate cognition to life-history evolution. It has been proposed that fast-living animals that also score high for tests of bold/aggressive/active behavior would also have a faster “cognitive style” (i.e., a manner of acquiring, processing, or storing of information) that emphasizes speed versus accuracy (Sih and Del Giudice 2012; but see

Mamuneus et al. 2015 and Lucon-Xiccato et al. 2016). Alternatively, enhanced learning ability may be correlated with a slow-living strategy because the energetic investment necessary for learning will be beneficial over a longer lifespan and could incur early-life costs that might delay reproduction (Dukas 1998; Snell-Rood 2013). As these two contrasting hypotheses reveal, it is not easy to define and use general characterizations of cognition but, given a deep understanding of a particular ecological context, reasonable inferences might be made with regard to specific cognitive abilities (Griffin et al 2015).

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Our findings are most consistent with the hypothesis that fast-living snakes should show enhanced cognitive ability, in that they appeared to habituate faster. However there is some sense in which they are changing their behaviors to match the consistent behaviors of M-slow snakes. This pattern of consistent behavior in M-slow snakes may indicate a lack of need on the part of M-slow snakes to adaptively change their behavior over time rather than a lack of cognitive capacity to do so. Note that there is also a trend for sensitization with respect to time spent in the active state for M-slow snakes by the third trial, bringing their activity levels up even as L-fast snakes have declined (Fig. 3). This suggests that M-slow snakes may become slightly more active over time with respect to at least one behavioral metric, even as L-fast snakes are becoming less so. Habituation has been reported in various experimental contexts in organisms as diverse as chipmunks, sharks, and lizards (e.g., Martin et al. 2008; Le Rodríguez-Prieto et al.

2011; Finger et al. 2016). In snakes, variation in habituation is linked to differences in both predation pressure (Herzog et al. 1989) and habitat complexity (Almli and Burghardt 2006), two factors that are divergent between L-fast and M-slow populations in this system.

To our knowledge this is the first study showing evidence for differences in habituation between natural populations exhibiting distinct life-history strategies in response to divergent ecological pressures. M-slow snakes may be enacting a more energetically conservative strategy, with low movement and tongue flicks. This could be a canalized strategy for avoiding detection, even when detection is unavoidable as it is in these trials. As for L-fast snakes, in natural circumstances behavior that facilitates rapid escape may be adaptive, but the potential cost of such a response to a repeated stimulus may be attenuated by cognitive mechanisms directed towards assessing whether this energetic expenditure is worthwhile. Thus, in this artificial experimental set-up, we may be seeing evidence of cognitively-driven behavioral flexibility that

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allows L-fast snakes to fine-tune their responses to environmental stimuli, depending on the level of threat or the efficacy of their previous responses to a specific stimulus.

Our results suggest that the same environmental factors that select for distinct life-history strategies may also select for distinct behavioral and perhaps even cognitive strategies.

Furthermore, there is potential that these levels of organization may interact with one another in affecting fitness, facilitating correlated evolution or constraining adaptation in individual traits

(Lande and Arnold 1983; Arnold 1988; Careau et al. 2008). For example, in snakes from one M- slow population in this system, behavioral and physiological traits interact to shape energetic allocation to offspring, which provide differential fitness outcomes (Gangloff et al. in review).

Our findings complement recent studies in dogs and lizards, where increased activity, boldness, and aggression have been associated with enhanced growth and lower survival (Careau et al.

2010; Le Galliard et al. 2013), and suggest that this pattern may be taxonomically widespread.

The ecotypic variation in behavior and habituation we observed may be due to either genetically canalized differences between ecotypes or due to developmental plasticity, based on environmental cues or individual experience. Future work on this system will test for behavioral differences in captive-born offspring and provide more insight into how genetic background and developmental environments interact to drive behavioral variation among individuals and between ecotypes.

Acknowledgements

We are grateful to our funders for supporting this research, and to Karen Martin for support in the field. We also acknowledge constructive manuscript feedback from G. Burghardt,

B. Danielson, and P. Dixon.

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Funding This project received funding support from Westmont College Undergraduate Research

Fellowships to MC, SH, and BH and the Iowa Science Foundation (13-07). EJG received additional support from a fellowship from the ISU Office of Biotechnology.

Conflict of interest The authors declare that they have no conflict of interest.

Ethical approval Snakes suffered no evident ill-effects from these relatively non-invasive trials and were released in good health. All animals were captured with the permission of the

California Department of Fish and Wildlife (SCP 8727). The Institutional Animal Care and Use

Committee (IACUC) at Iowa State University (protocol # 1-12-7285-J) and the Institutional

Review Board (IRB) at Westmont College (protocol # 855) approved these methods.

Informed consent Not applicable.

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Table 1. Model effects and AICc values of top 10 models for tongue-flick rate in Thamnophis elegans measured across three trials. All models also included snake ID as a random effect.

Selected model is shown in bold with underline. See text for discussion.

Main Effect Interactions AICc ΔAICc

Ecotype + Population(Ecotype) + Trial Trial × Ecotype 162.9 0

Ecotype + Population(Ecotype) + Trial Trial × Ecotype 164.7 1.8 + Sex

Ecotype + Trial Trial × Ecotype 165.6 2.7

Ecotype + Population(Ecotype) + Trial None 166.3 3.4 + Sex

Ecotype + Population(Ecotype) + Trial None 166.6 3.7

Ecotype + Population(Ecotype) + Trial × Ecotype + Trial × 168.3 5.2 Trial + Sex Sex + Ecotype × Sex

Ecotype + Population(Ecotype) + Trial Trial × Ecotype 168.4 5.5 + Temperature

Ecotype + Population(Ecotype) + Trial None 168.8 5.9 + Sex

Ecotype + Trial None 169.6 6.7

Ecotype + Population(Ecotype) + Trial Trial × Ecotype + Sex × Trial 170.4 7.5 + Sex

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Table 2. Results of mixed linear model of tongue-flick rate in Thamnophis elegans measured across three trials. Individual was included in the model as a random effect (see Table 1 for model selection criteria and text for statistical details).Significant effects shown in bold with one

(P < 0.05) or two (P < 0.01) asterisks.

Effect Estimate SE dfn, dfd F P

ecotype 0.24 0.21 1,38.4 13.78 0.007**

population(ecotype) 0.25 0.12 1,39.7 4.30 0.04*

sex -0.08 0.17 1,38.2 0.87 0.36

trial 0.21 0.16 2,78.3 8.03 0.007**

trial × ecotype 0.41 0.18 2,78.3 3.52 0.03*

ecotype × sex -0.02 0.21 1,38.2 0.01 0.92

trial × sex -0.12 0.18 2,78.7 0.69 0.51

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Table 3. Selection criteria for hidden Markov model of movement data in Thamnophis elegans measured across three trials. Selected model with minimum AICc value is shown in bold with underline.

Main Effects Interactions AICc ΔAICc

Ecotype + Sex + Trial Ecotype × Trial + Trial × Sex + Ecotype × Sex 57738.25 0

Ecotype + Sex + Trial Trial × Sex + Ecotype × Sex 57739.78 1.53

Ecotype × Trial + Trial × Sex + Ecotype × Sex + Ecotype × 57740.47 2.22 Ecotype + Sex + Trial Trial × Sex

Ecotype + Sex + Trial Trial × Ecotype + Trial × Sex 57741.27 3.02 80

Ecotype + Sex + Trial Trial × Sex 57745.01 6.76

Ecotype + Sex + Trial Trial × Ecotype + Ecotype × Sex 57748.57 10.32

Ecotype + Sex + Trial Ecotype × Sex 57748.85 10.6 88

Ecotype + Sex + Trial Trial × Ecotype 57750.92 12.67

Ecotype + Sex + Trial None 57755.58 17.33

Ecotype + Sex Ecotype × Sex 57757.24 18.99

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Figure 1. Ecotype differences in tongue-flick rate over three behavioral trials in Thamnophis elegans. Different letters denote means that are significantly different. Least square means and standard errors are shown.

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Figure 2. Density plot showing distribution of step lengths moved by Thamnophis elegans across three behavior trials and state-dependent densities. Light grey bars represent the histogram of observed data and solid line the marginal distribution. Dotted and dashed lines represent the stationary state and moving state densities, respectively, each composed of a mixture of two normal distributions. The state-dependent distributions were weighted according to the percentage of observations in each state, as calculated by the Viterbi algorithm (see text for statistical details).

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Figure 3. Results of hidden Markov model describing movement in novel arena in Thamnophis elegans snakes from divergent life-history ecotypes: expected proportion of time in moving state by trial, ecotype, and sex. Error bars represent 95% bootstrap confidence interval (see text for statistical details).

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Figure 4. Results of hidden Markov model describing movement in novel arena for Thamnophis elegans snakes from divergent life-history ecotypes: state dwell time by trial, ecotype, and sex.

Dwell time represents the expected amount of time an individual remains in one state before switching states. Error bars represent 95% bootstrap confidence interval (see text for statistical details).

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Figure 5. Plots of decoded paths for four representative Thamnophis elegans individuals from each sex/ecotype combination. Grey lines show path travelled when snake was in moving state; black dots show position when snake was in stationary state. State decoding was determined by hidden Markov model incorporating trial, ecotype, and sex (see text for details).

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Figure 6. Within-individual relationship of movement and tongue flicking behaviors. (A)

Within-individual correlations of tongue-flick rate and proportion of time moving for

Thamnophis elegans across three trials. The relationship between behaviors is positively and significantly correlated (F1,114 = 232.6, P < 0.0001; see text for statistical details). (B) Bar chart of the movement states during which a tongue-flick occurred for individual Thamnophis elegans across all trials (see text for statistical details).