Ecological and Evolutionary Responses of a Polymorphic Amphibian, Plethodon Cinereus, to Climate Change

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Ecological and Evolutionary Responses of a Polymorphic Amphibian, Plethodon cinereus, to Climate Change

Annette Evans University of Connecticut - Storrs, [email protected]

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Recommended Citation Evans, Annette, "Ecological and Evolutionary Responses of a Polymorphic Amphibian, Plethodon cinereus, to Climate Change" (2020). Doctoral Dissertations. 2481. https://opencommons.uconn.edu/dissertations/2481 Ecological and Evolutionary Responses of a Polymorphic Amphibian, Plethodon

cinereus, to Climate Change

Annette Elizabeth Evans, PhD

University of Connecticut, 2020

Global climate is changing at an alarming rate and how species will respond to the associated environmental changes remains largely uncertain. The multidimensional selective pressures created by climate change often make it difficult to determine both if and how species are likely to respond. Spatial and temporal changes in environmental conditions, particularly temperature, often have a disproportionate impact on amphibians, which typically require cool, moist conditions to survive. My dissertation research examines how climate change, specifically temperature, influences the ecological and evolutionary responses of a color polymorphic salamander, Plethodon cinereus. This salamander has two main color morphs, striped and unstriped, which vary spatially in their relative abundances and show a variety of physiological, dietary, and behavioral differences. These differences suggest that these color morphs may have different ecological or evolutionary strategies and hence may respond differently to stressors associated with climate change. Through a combination of field observations, controlled lab experiments, and ecological modelling my dissertation extends our knowledge into how temperature impacts the ecology and evolution of P. cinereus. In

Chapter 1, I resurveyed P. cinereus populations originally surveyed in the 1970s to determine how climate change has impacted morph proportions. My results suggest

Annette Elizabeth Evans

University of Connecticut, 2020

climate is important across space, although we observed no significant changes in relative morph proportions in response to climatic changes over time. Chapter 2 expands on this work by determining whether forest cover interacts with climate to buffer salamander populations from recent climate changes. My results show that forests are buffering salamander populations, but also suggests that forest cover alone cannot explain spatial and temporal variations in morph frequencies across New

England. In Chapter 3, I tested whether temperature affected the coloration and development of P. cinereus. I found evidence that developmental temperature influences hatchling coloration– either via plasticity or differential mortality. Finally, in

Chapter 4 I tested the thermal sensitivity of various physiological traits to determine whether color morphs differed physiologically. All the physiological traits I measured were sensitive to temperature and I found that morphs differed in some, but not all physiological traits.

Ecological and Evolutionary Responses of a Polymorphic Amphibian, Plethodon

cinereus, to Climate Change

Annette Elizabeth Evans

B.S., University of Auckland, 2010

M.S., University of Auckland, 2013

A Dissertation

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Doctor of Philosophy

at the

University of Connecticut

2020

Annette Elizabeth Evans

2020

APPROVAL PAGE

Doctor of Philosophy Dissertation

Ecological and Evolutionary Responses of a Polymorphic Amphibian,

Plethodon cinereus, to Climate Change

Presented by

Annette Elizabeth Evans, B.S., M.S.

Co-Major Advisor ______Elizabeth L. Jockusch

Co-Major Advisor ______Mark C. Urban

Associate Advisor ______Kentwood D. Wells

Associate Advisor ______Morgan W. Tingley

University of Connecticut 2020

iii

Acknowledgements

This dissertation was made possible thanks to the support and encouragement of many wonderful people from around the world. People often say that you are lucky if you get a good PhD advisor and I feel incredibly fortunate to have had not one, but two amazing advisors, Elizabeth Jockusch and Mark Urban. Over the past six years you both have been my harshest critics and my biggest cheerleaders, and I couldn’t have done this without your unwavering support. To my committee members, Morgan Tingley and

Kentwood Wells, your knowledge of statistics and herpetology have strengthened my research and given me confidence in my own research abilities. I feel so privileged to have had the opportunity to work with and learn from you, thank you for all the support and advice you have provided me throughout my dissertation. Special thanks to Kurt

Schwenk, Eric Schultz, and Carlos Garcia-Robledo who all served as faculty committee members on general exams and dissertation defenses. I literally could not have finished my dissertation without you. Thanks also to Dave Wagner without whom I would never have applied to pursue my PhD at UConn. Thanks to the Jockusch and Urban labs for reading and listening to a ridiculous number of research drafts, presentations, endless struggles, and general science musing. To the many research assistants who have volunteered their time and energy to helping me collect my dissertation data – you have my eternal gratitude! Thanks also to the many incredible collaborators and herpetology peers from whom I’ve learnt so much from, I am a better scientist thanks to your help!

Also thank you to the numerous people who provided food, accommodation, and site access during my field surveys, whose generosity made much of my research possible.

Thank you to the EEB Department, particularly all the office staff who have encouraged, copied, printed, signed, and chased paperwork. Without your help, myself along with countless other EEB grads would have been lost. Thank you to all the wonderful EEB graduate students, your support and camaraderie has been a highlight of my time at UConn and I cannot think of a better group of people with which I could have shared this crazy journey with. Extra special thanks to my amazing cohort for going through the trials and tribulations of this PhD journey alongside me, for helping me with statistics (thanks Chris Nadeau!), writing with me (senior grad writing group!), and sharing in study distractions (Pottery – thanks Katie Taylor!). Thanks also to my non-UConn friends and family throughout America and back home in New Zealand. Your support, skype calls, and games nights have kept me sane, even during the difficult times.

To my family back in New Zealand, thank you for your continued love and support over the 10+ years that I pursued multiple university degrees. Your encouragement to follow my dreams, no matter where in the world they take me, has been incredible and thank you for putting up with my stress and anxieties throughout my academic journey thus far. To my pooch Ngaio, who continuously reminds me there is more to life than just research (and that I should exercise more). Finally, this dissertation would not have been possible without the unwavering support of my husband, Mark Smith. You have picked me up off the floor (sometimes quite literally) countless times and my early field work adventures would not have been a success without your help and encouragement. You believe in me, even when I do not. I am so grateful for your love and patience, and I feel so lucky to have someone who shares my passion for biology and science.

Dedication

I dedicate this work to my family, who have always encouraged and nurtured my love of biology, and never doubted my place in the scientific community.

TABLE OF CONTENTS

Overall Introduction……………………………………………………………………..……... 1

References……………………………………………………………..………………..……... 6

Chapter One: Salamander Morph Frequencies Do Not Evolve as Predicted In

Response to 40 Years of Climate Change.

Abstract……………………………………………………………………………………..…. 12

Introduction……………………………………………………………………………….…… 12

Methods……………………………………………………………….…………….………… 14

Results…………………………………………………………………………….…………... 16

Discussion…………………………………………………………………………….………. 17

References…………………………………………………………………………….……… 20

Appendices…………………………………………………………………………….……… 23

Chapter Two: Can Forest Cover Mediate the Evolutionary Responses of a

Terrestrial Salamander to Climate Change?

Abstract……………………………………………………………………………………..…. 38

Introduction……………………………………………………………………………….…… 39

Methods……………………………………………………………….…………….………… 42

Results…………………………………………………………………………….…………... 49

Discussion…………………………………………………………………………….………. 52

References…………………………………………………………………………….……… 59

vii

Appendices…………………………………………………………………………….……… 65

Chapter Three: Developmental Temperature Influences Color Polymorphism but not Hatchling Size in a Woodland Salamander.

Abstract……………………………………………………………………………………..…. 78

Introduction……………………………………………………………………………….…… 78

Methods……………………………………………………………….…………….………… 79

Results…………………………………………………………………………….…………... 81

Discussion…………………………………………………………………………….………. 82

References…………………………………………………………………………….……… 85

Appendices…………………………………………………………………………….……… 88

Chapter Four: Thermal Sensitivity of Physiological Performances in the

Polymorphic Salamander, Plethodon cinereus.

Abstract……………………………………………………………………………………... 102

Introduction……………………………………………………………………………….… 103

Methods……………………………………………………………….…………….……… 106

Results…………………………………………………………………………….………... 113

Discussion………………………………………………………………………………….. 119

References…………………………………………………………………………………. 131

Appendices…………………………………………………………………………………. 136

viii

Introduction

Human-induced climate change is affecting species distributions, species abundances, and ecosystem functions (Walther et al. 2002; Parmesan 2006; IPCC 2014). The geographic location and ecology of some organisms makes them particularly vulnerable to changes in environmental conditions, and populations that are unable to respond to these changing conditions will likely face extirpation. In response to changing conditions, species can track suitable climatic conditions through dispersal (Chen et al. 2011), they can persist in microclimate refugia (Ashcroft et al. 2012; Scheffers et al. 2014), or they can respond to changing conditions in place (e.g. via plasticity and phenological shifts)

(Hoffmann and Sgro 2011). The relative importance of these different responses and their effectiveness at ensuring population persistence remain unclear for most species (Shaw and Etterson 2012), and this makes it difficult to evaluate which species are most at risk of extinction from climate change.

Climate change offers a unique natural experiment to test hypotheses about how climate affects evolutionary dynamics. My dissertation explores the impacts of climate change both within and between different populations of a widespread, terrestrial amphibian, the eastern red-backed salamander Plethodon cinereus. This species is abundant in forest ecosystems of North America ranging from Canada to North Carolina, and from the

Atlantic coast west to Mississippi, with populations reaching densities of over 2,118 individuals per hectare (Klein 1960; Burton and Likens 1975). The species plays an

ecologically important role in eastern North American forest ecosystems, linking upper and lower tropic levels in its role as both prey and predator (Burton and Likens 1975).

Plethodon cinereus is a polymorphic species, which, coupled with its widespread abundance, offers several advantages for studying evolutionary responses to climate change. First, genetic variation in the form of alternative color morphs is present and the long term persistence of the two main color morphs suggests that they confer differential fitness advantages in time and/or space (Moreno 1989; Forsman et al. 2008; Fisher-Reid and Wiens 2015). Second, spatial variation in morph frequencies across climatic gradients has led to suggestions of a climate-morph adaptive link (Galeotti et al. 2003;

Lepetz et al. 2009; Antoniazza et al. 2010). Thirdly, the ability to easily score these color morphs provides greater opportunities to document variation across space and time.

The color polymorphism observed in Plethodon has persisted for millions of years (Fisher-

Reid and Wiens 2015) and is also observed in at least seven other plethodontid species

(Petranka 1998). The two main color morphs are characterized by the presence (‘striped’) or absence (‘unstriped’) of a dorsal stripe that ranges in color from red to yellow and white

(Moore and Ouellet 2014). A third, rarer ‘erythristic’ (fully red) morph also occurs sporadically. This striped/unstriped polymorphism is genetically determined though the exact mechanism is unknown (Highton 1975; Moreno 1989). The relative proportions of the color morphs varies across the species range (Gibbs and Karraker 2006; Moore and

Ouellet 2014) and though the color morphs regularly interbreed (Moreno 1989; Acord et al. 2013), morphs vary in their abundance and distribution and show a variety of

physiological, dietary, and behavioral differences (Lotter and Scott 1977; Moreno 1989;

Anthony et al. 2008; Stuczka et al. 2016). Most previous research has indicated that the unstriped morph is at a fitness disadvantage relative to the striped morph. For example, the unstriped morph is associated with prey items of lower nutritional value (Anthony et al. 2008), higher levels of predation (Lotter and Scott 1977; Moreno 1989), and higher levels of circulating stress hormones (Davis and Milanovich 2010). However, the unstriped morph has also been associated with warmer, drier conditions compared to the striped morph, with a lower standard metabolic rate (Lotter and Scott 1977; Moreno 1989;

Gibbs and Karraker 2006). This may allow unstriped individuals to remain on the surface and forage under warmer and drier conditions with less energetic cost relative to striped morphs (though this response is not universal across the range; see Petruzzi et al. 2006).

These many ecological differences have resulted in multiple hypotheses regarding the maintenance of this color polymorphism in P. cinereus, ranging from assortative mating

(Anthony et al. 2008), and differential predation pressure (Moreno 1989; Venesky and

Anthony 2007) to temperature-related selection (Gibbs and Karraker 2006), or some combination of these factors. As lungless, terrestrial salamanders, P. cinereus rely entirely on cutaneous respiration; thus, the need to maintain cool, moist skin places a major constraint on activity, including foraging, growth, and reproduction (Feder 1983).

As a result, P. cinereus could be particularly vulnerable to projected climatic changes.

Because adaptive (polymorphic) variation may allow species such as P. cinereus to be more resilient and persist in their current location (Forsman et al. 2008; Urban et al. 2014),

my dissertation examines the ecological and evolutionary responses of P. cinereus populations to climate change – specifically changes in temperature. The four chapters of my dissertation address questions relating to how the alternative color morphs of P. cinereus are responding to environmental changes and how environmental conditions may act to shape the distribution and relative abundance of polymorphic P. cinereus populations across northeastern America. In Chapter 1 of my dissertation I tested whether climate changes over the past 40 years have produced a predictable shift in morph frequencies across New England. I used a combination of field surveys (resurveying 50 populations originally sampled during the 1970s) and Bayesian species-climate models to test the hypothesis that the unstriped morph of P. cinereus is better adapted to warmer, drier conditions that the striped morph. I found that despite correlations between morph frequency and climate within each survey period (1970 and 2015) and significant changes in climate, the models failed to accurately predict changes in morph frequency across

New England. These results show that, even for well-studied species, we have yet to fully understand the link between phenotypic traits and climate change and still struggle to accurately predict species responses over even relatively short time frames.

Chapter 2 of my dissertation expands on my work from Chapter 1 by testing whether land use changes, specifically forest cover, may buffer salamander populations from the effects of climate change. Using Landsat images, combined with salamander and climate data from Chapter 1, I found that the interaction between forest cover and climate variables significantly affect the spatial distribution of salamander morph frequencies in the 1970s but this interaction does not significantly impact current morph

frequency distribution (or the change in morph frequencies over the past 40 years). This suggests that while forest cover provides an important buffering effect to salamander populations, changes in forest cover alone cannot explain spatial and temporal variations in morphs frequencies across New England. Consequently, organisms with limited dispersal capabilities will likely need to use a combination of behavioral plasticity or evolution to persist under future climate changes.

In Chapter 3 I tested whether color expression in P. cinereus is influenced by developmental temperature and if color morphs differ in reproductive output or hatching size. I found that eggs incubated at warmer temperatures produced a higher proportion of unstriped hatchlings than those at cooler temperatures. I also found that color morphs did not differ in reproductive output and that neither coloration, temperature, nor initial egg size affected initial hatchling sizes. This work provides the first evidence that coloration in P. cinereus may be influenced by developmental temperature via plasticity.

This result adds a new dimension to the mechanisms producing and maintaining phenotypic diversity in this species.

In the final chapter of my dissertation I investigated the effects of temperature on the energy assimilation efficiency and standard metabolic rate within and between color morphs of P. cinereus. My study is the first to test for temperature-induced plasticity in these two physiological traits simultaneously in the same individuals and across multiple polymorphic salamander populations. The results of this chapter confirm that physiological processes in P. cinereus are sensitive to temperature conditions and I show that color morphs and populations differ in some, but not all measure of physiology. This 5

work expands our current knowledge and understanding of how alternative color morphs may differ along a physiology axis and highlights the importance to testing physiological traits across multiple populations.

Literature cited

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Anthony CD, Venesky MD, Hickerson CAM (2008) Ecological separation in a polymorphic terrestrial salamander. J Anim Ecol 77:646–653

Antoniazza S, Burri R, Fumagalli L, et al (2010) Local adaptation maintains clinal variation in melanin-based coloration of european barn owls (Tyto alba). Evolution 64:1944–1954. https://doi.org/10.1111/j.1558-5646.2010.00969.x

Ashcroft MB, Gollan JR, Warton DI, Ramp D (2012) A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob Change Biol 18:1866–1879. https://doi.org/10.1111/j.1365- 2486.2012.02661.x

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Chen IC, Hill JK, Ohlemüller R, et al (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026

Davis AK, Milanovich JR (2010) Lead-phase and red-stripe color morphs of red-backed salamanders Plethodon cinereus differ in hematological stress indices: A consequence of differential predation pressure? Curr Zool 55:238–243

Feder ME (1983) Integrating the ecology and physiology of plethodontid salamanders. Herpetologica 39:291–310

Fisher-Reid MC, Wiens JJ (2015) Is geographic variation within species related to macroevolutionary patterns between species? J Evol Biol 28:1502–1515. https://doi.org/10.1111/jeb.12670

Forsman A, Ahnesjö J, Caesar S, Karlsson M (2008) A model of ecological and evolutionary consequences of color polymorphism. Ecology 89:34–40. https://doi.org/10.1890/07-0572.1

Galeotti P, Rubolini D, Dunn PO, Fasola M (2003) Colour polymorphism in birds: causes and functions. J Evol Biol 16:635–646. https://doi.org/10.1046/j.1420- 9101.2003.00569.x

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Chapter 1:

Salamander morph frequencies do not evolve as predicted in

response to 40 years of climate change

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ECOGRAPHY Research Salamander morph frequencies do not evolve as predicted in response to 40 years of climate change

Annette E. Evans*, Brenna R. Forester*, Elizabeth L. Jockusch and Mark C. Urban

A. E. Evans (http://orcid.org/0000-0001-6439-4908), E. L. Jockusch and M. C. Urban (http://orcid.org/0000-0003-3962-4091), Univ. of Connecticut, Dept of Ecology and Evolutionary Biology, Storrs, CT, USA. MCU also at: Univ. of Connecticut Center of Biological Risk, Storrs, CT, USA. – B. R. Forester (http://orcid.org/0000-0002-1608-1904) ([email protected]), Duke Univ., University Program in Ecology, Nicholas School of the Environment, Durham, NC, USA. Present address: Colorado State Univ., Dept of Biology, Fort Collins, CO, USA.

Ecography !e global climate is changing rapidly, yet biotic responses remain uncertain. Most 41: 1–11, 2018 studies focus on changes in species ranges or plastic responses like phenology, but doi: 10.1111/ecog.03588 adaptive evolution could be equally important. Studying evolutionary responses is challenging given limited historical data and a poor understanding of genetically Subject Editor: Ken Kozak variable traits under selection. We take advantage of a historical dataset to test for Editor-in-Chief: an adaptive response to climate change in a widespread, polymorphic amphibian, Jens-Christian Svenning the eastern red-backed salamander Plethodon cinereus. We resurveyed color morph Accepted 9 January 2018 frequencies across New England to test for an adaptive shift in response to climate change. We modeled historical and present-day morph proportions as a function of climate and tested the accuracy of predictions both within and across different time periods. Our models showed moderate accuracy when predicting morph frequencies within time periods, but poor accuracy across time periods. Despite substantial changes in climate and significant relationships between morph frequency and climate variables within periods, we found no evidence for the predicted shift in morph frequencies across New England. !e relationship between climate and color morph frequencies is likely more complex than originally suggested, potentially involving the interplay of additional factors such as microclimate variation, land use changes, and frequency-dependent selection. Model extrapolation and changes in the correlation structure of climate variables also likely contributed to poor predictive ability. Evolu- tion could provide a means to moderate the effects of climate change on many species. However, we often do not understand the direct links between climate variation, traits, and fitness. !erefore, forecasting climate-mediated evolution remains an ongoing and important challenge for understanding climate change threats to species.

Keywords: adaptation, Plethodon cinereus, polymorphism

Introduction

Human-induced climate change is aﬀecting species distributions, species abundances, and ecosystem functions worldwide (Walther et al. 2002, Parmesan 2006, IPCC 2014). For decades, biologists have been trying to predict the substantial, and –––––––––––––––––––––––––––––––––––––––– www.ecography.org © 2018 !e Authors. Ecography © 2018 Nordic Society Oikos * !ese authors contributed equally 1 12

often idiosyncratic, responses of species to climate change polymorphism provides an exceptional opportunity to assess (Barry et al. 1995, Parmesan and Yohe 2003, Lenoir et al. adaptive evolution in response to climate change. 2010, Chen et al. 2011, Crimmins et al. 2011). In response to Our study explores the relationship between climate changing conditions, species can track suitable climatic con- and evolutionary responses in a widespread, polymorphic ditions by dispersing (Chen et al. 2011), shifting phenology amphibian, the eastern red-backed salamander Plethodon (Parmesan and Yohe 2003, Visser and Both 2005), persisting cinereus. Plethodon cinereus has two common color morphs in microclimate refugia (Ashcroft et al. 2012, Scheffers et al. (Fig. 1 inset) that are characterized by the presence (‘striped’) 2014), or adapting to changing conditions in place (Hoff- or absence (‘unstriped’) of a dorsal stripe. "e relative propor- mann and Sgro 2011). Given the complexity of ecological tions of these morphs vary across the species range in eastern systems, the frequent lack of proximal environmental data North America (Gibbs and Karraker 2006, Moore and Ouel- at species-relevant scales, and the scarcity of detailed histori- let 2015). "e color polymorphism is genetically determined, cal data, the relative importance of these different responses although the exact genetic architecture remains unknown remains unclear for most species (Lavergne et al. 2010, Shaw and may vary across localities (Highton 1959, 1975). As a and Etterson 2012, Urban et al. 2016). "ese factors make it lungless, terrestrial salamander, P. cinereus relies entirely on difficult to evaluate which species are most at risk of extinc- cutaneous respiration, and the need to maintain moist skin tion caused by climate change. constrains its activity, including foraging, growth, and repro- "e extent to which we currently understand the relation- duction (Feder 1983, Peterman and Semlitsch 2013, 2014). ship between climate change and species responses is largely Given this sensitivity to environmental conditions, climate centered on changes in species distributions or phenology. has long been hypothesized to be a primary driver maintain- However, for some species these responses are unfeasible ing the color polymorphism in P. cinereus. given the magnitude and rate of climate change in combi- Multiple lines of evidence suggest that this polymorphism nation with habitat loss and fragmentation. Consequently, is linked to climate. Geographic patterns of morph frequen- adapting to changing conditions in place may be a poten- cies suggest unstriped morphs are cold-intolerant (Lotter and tially critical response, especially in dispersal-limited species Scott 1977, Gibbs and Karraker 2006). Likewise, temporal (Norberg et al. 2012, Urban et al. 2012). Rapid adaptive variation in morph frequencies at some sites indicates that responses require sufficient standing genetic variation among unstriped morphs are less likely to be active on the surface individuals in order to match the rate of environmen- and will retreat to underground refugia in cold temperatures tal change (Skelly et al. 2007, Barrett and Schluter 2008). (Lotter and Scott 1977, Moreno 1989, Anthony et al. 2008). Convincing examples of adaptive responses to climate Finally, population-specific physiological and mechanistic change can be found across a diversity of taxa (Bearhop et al. data show metabolic differences between the color morphs 2005, Franks et al. 2007, Buckley et al. 2012, Chirg- corresponding to differential heat tolerance (Moreno 1989, win et al. 2015), yet in some species, constraints such as lack Petruzzi et al. 2006). "e lower metabolic rates of unstriped of genetic variation have prevented adaptation to climate morphs may allow them to occupy drier habitats (Fisher- change (Hoffmann et al. 2003). Due to the multidimen- Reid et al. 2013). As a result, warmer and drier conditions sional selective pressures created by climate change, we sel- are generally expected to select for higher unstriped morphs dom know which traits are under selection or whether those frequencies. However, the relationship between morph fre- traits are heritable and are underlain by sufficient genetic quencies and climate has also been questioned based on variation (Hoffmann and Sgro 2011). Additionally, we often inconsistent patterns between populations and insignificant lack the historical data needed to validate our predictions, differences in the response of color morphs to temperature or which makes adaptive evolution the least understood of moisture (Petruzzi et al. 2006, Fisher-Reid et al. 2013, Moore the responses to climate change (Merilä and Hendry 2014, and Ouellet 2015). Urban et al. 2014). Here, we test for an evolutionary response to climate Given the above challenges, polymorphic species offer sev- change in P. cinereus by resurveying sites originally sampled eral advantages for studying potentially adaptive evolutionary by Lotter and Scott (1977) in the early 1970s. Lotter and responses to climate change. First, genetic variation in the Scott (1977) surveyed morph frequencies at 50 locations form of alternative morphs is already present, and the long- across New England and concluded that the regional clines term persistence of alternative morphs suggests that they in color morph frequencies indicated adaptation to local tem- confer differential fitness advantages in time or space (Fors- peratures. Our study addresses four research questions: 1) is man et al. 2008, Forsman and Wennersten 2016). Second, there a relationship between climate and unstriped morph spatial variation in morph frequencies across climatic gradi- frequency in the New England region? If so, 2) has regional ents often suggests a climate-morph link (Galeotti et al. 2003, climate shown sufficient change since the 1970s that we Lepetz et al. 2009, Antoniazza et al. 2010). "ird, the ability would expect to see changes in morph frequencies? If yes, to score morphs easily in the field provides greater opportuni- then 3) what evolutionary change is predicted by the climate- ties to document variation across space and time. As a result, morph relationship and the magnitude of observed climate the availability of a large-scale, detailed historical dataset for a change? Finally, 4) do we observe this predicted evolutionary dispersal-limited species with a hypothesized climate-related response? Given the previously proposed climate-morph link

Figure 1. Map of survey locations showing change in proportion of unstriped morphs between 1970 and 2015. Circles are scaled to the magnitude of change in unstriped morph proportions (percentage, speciﬁed by the number within each circle), with increasing grey tints indicating increasing frequency of unstriped morphs and increasing red tints indicating increasing frequency of striped morphs. Inset: photo of Plethodon cinereus morphs, unstriped (left) and striped (right). Photo copyright: Binbin Li (used with permission). and an average generation time of 2–3 yr (Sayler 1966), we Methods hypothesized that changes in selection resulting from climate change have caused changes in morph frequencies across Salamander surveys New England since the 1970s. We expected that models based on correlations between climate and morph frequen- In 2015, we resampled the 50 localities that Lotter and cies would accurately predict morph frequencies both within Scott (1977) surveyed in 1971–1973. Like Lotter and Scott and between time periods (i.e. 1970s and 2015). We spe- (1977), we searched for salamanders under cover objects ciﬁcally predicted an increase in the proportion of unstriped including fallen logs, bark fragments, and rocks, in wet leaf individuals at sites that have become warmer and drier during litter, and inside decaying logs, and at each site recorded the last 40 yr. the morph (striped, unstriped, erythristic (fully red), or

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amelanistic) of a minimum of 100 P. cinereus individuals one case where there is a significant difference in the morph (Supplementary material Appendix 1 Table A1). !e sites frequencies across subsites (Supplementary material Appen- selected for the resurvey are detailed in Supplementary mate- dix 1 Table A3) and exploratory analysis of the 2015 data rial Appendix 1 Fig. A1 and Table A2. Because the original showed no evidence that weighting subsites based on sample sampling localities were specified only at the level of towns numbers improved model accuracy or precision (R2 = 0.70, and counties, we selected resampling locations using a com- RMSE = 37.93). bination of town center coordinates (Lotter and Scott 1977) To account for spatial autocorrelation in sampling, we and vegetation and topographic descriptions provided in selected a subset of spatial eigenfunctions that significantly Lotter’s (1975) dissertation appendix. We matched specific accounted for spatial structure in morph data for each time localities (e.g. state parks) in seven of the eight cases in which period. First, we calculated spatial eigenfunctions from they were provided (the eighth is now a residential block). the current sampling site coordinates using distance-based !e total area searched during each survey varied among sites Moran’s eigenvector maps (dbMEMs, Legendre and Leg- but was consistent with areas covered by the original surveys endre 2012). When sites were represented by more than (1–3 km in diameter; Lotter and Scott 1977). one sampling location (Supplementary material Appendix We divided the resurvey effort into five time periods, 1 Table A2), we used the geometric median of the subsite spanning 14 May to 11 October 2015, and conducted each coordinates. Second, we built a full multiple linear regression resurvey in the month that most closely matched the original model for each time period where the response was either survey time (see Supplementary material Appendix 1 Table the proportion of unstriped morphs at each site during each A2 for original and resurvey dates). Intra-annual temporal time period or the change in unstriped morph proportions surveys by Lotter (1975) and AEE (unpubl.) indicated no across time periods and the predictors were the full set of significant differences in morph proportions across repeated dbMEMs. Finally, we used forward selection (permutation survey efforts during the months when sampling occurred, p-value of 0.05 for adding a dbMEM variable to the model) suggesting that morph frequencies observed following the to retain significant dbMEM predictors, using the full same procedures are comparable across years. Additionally, model-adjusted R2 as the stopping criterion (Blanchet et al. we sampled two sites across multiple months and found no 2008). We checked correlations and variance inflation factors significant differences in morph proportions (Supplementary (VIFs) between significant dbMEM predictors and climate material Appendix 1 Table A3). variables to ensure correlations and VIFs were less than |0.7| and 10, respectively. All predictor variables were calculated Predictor variables using R ver. 3.2.3 (R Development Core Team) and the packages ncdf4 ver. 1.16 (Pierce 2015), raster ver. 2.6-7 (Hijmans We developed a set of nine climatic predictor variables rel- 2015), PCNM ver. 2.1-4 (Legendre et al. 2012), pracma ver. evant to salamander biology and ecology (Supplementary 1.6.4 (Borchers 2015), and usdm ver. 1.1-18 (Naimi et al. material Appendix 1 Table A4). Five of these variables (water 2014). balance and temperature variables) were further refined for the seasonal activity period of terrestrial salamanders across Models of color morph frequency and climate the survey area (April–November, determined by literature searches; Cochran 1911, Lotter and Scott 1977, Nagel 1977, To test for a relationship between climate variables and Jaeger 1979, Gergits and Jaeger 1990, Dawley and Crowder unstriped morph frequency for each time period (‘1970’ 1995, Leclair et al. 2008). Temperature variables were derived and ‘2015’ hereafter) we used a Markov chain Monte Carlo from TopoWx daily data (Oyler et al. 2015). Monthly water (MCMC) approach to fit Bayesian generalized linear mixed balance data recently recalculated using TopoWx tempera- models in the MCMCglmm package, ver. 2.22 (Hadfield ture data were provided by Dobrowski et al. (2013). !e 2010). We first created a set of candidate models for each time spatial resolution of both data sets is 30 arc-seconds. We period, consisting of all combinations of predictor variables used the average of each variable for the 10 yr prior to the that had correlations and VIFs less than |0.7| and 10, respec- historic and current salamander surveys as the predictor. We tively (see Supplementary material Appendix 1 Table A5 for used a decadal average to more accurately capture average cli- candidate model lists). To allow for comparison between the mates experienced by surveyed populations throughout the two survey periods, we also ran any additional factor combi- lifetime of adults (up to 8–9 yr, Leclair et al. 2006). Lotter nations that were run in the other time period, regardless of (1975) conducted his surveys from 1971–1973, so we used variable correlation and VIF. For each candidate model, we a decadal average of 1962–1971; for current surveys we used modeled the proportion of unstriped individuals where our 2005–2014. For water balance metrics, we used data from response variable was a two-vector count of unstriped indi- 2005–2010 because more recent data were not available. For viduals and individuals of all other color morphs (striped, sites that had more than one sampling location (subsites, erythristic and amelanistic morphs pooled) at each site (see Supplementary material Appendix 1 Table A2), we used Supplementary material Appendix 1 Table A1 for morph the mean of the variable values at each location. We did not counts). We used multinomial2 (binomial) as the model weight subsites based on sample size since that information family, and included three significant dbMEMs as random was not available for Lotter’s (1975) data. We found only slopes (see Results). We fitted all random effects and residuals

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using an uninformative inverse Wishart prior with V = 1 and Supplementary material Appendix 1 Table A6 for candidate nu = 0.002, where V is an estimate of variance and nu is a model list). We fitted Bayesian generalized linear mixed mod- parameter for the degree of belief in V (Hadfield 2010). We els to these data, where the response variable was the change ran all models for 2 500 000 iterations, with a burn-in of in unstriped morph proportion, assuming a Gaussian distri- 500 000 iterations, and a thinning interval of 250 to reduce bution. We used one random effect, the variance of the dif- autocorrelation. !ese parameters provided 8000 samples ference between the proportion of unstriped morphs in the from the posterior distribution for each model parameter. 1970s and 2015 to account for measurement binomial error For a subset of these models and for all final models, we variance (J. Hadfield pers. comm.). We found no significant ensured suitable convergence and mixing using Heidelberger spatial eigenfunctions for the change models (dbMEMs, see and Welch (1983), Geweke (1991), and Gelman and Rubin results). We fitted these models and checked model conver- (1992) diagnostics, as well as visual inspection of trace plots gence as described above. Because we were not using the and posterior distributions plots to check that distributions change model for prediction, we ranked models using DIC were symmetrical. in the package MuMIn ver. 1.15.6 (Barton 2012) instead of To select our best climatic model for each time period, we using cross-validation. Additionally, we tested for significant used leave-one-out cross-validation to build each model in site-specific changes in color morph proportions between the the candidate set with data from 49 out of 50 sites and then two surveys using Fisher’s exact tests, followed by Holm– predicted morph counts for the withheld site. We repeated Bonferroni sequential correction for multiple tests. this process 50 times, sequentially leaving out and predicting each site. We then calculated log-likelihood of the observed unstriped morph count based on morph predictions for each Results site, and summed the log-likelihoods across all sites to rank the candidate models based on overall log-likelihood (see Relationship between color morph frequency and Supplementary material Appendix 1 Table A5 for full list). climate To evaluate the fit and transferability of our best 1970 and 2015 models, we compared the observed and predicted We found significant relationships between climate variables proportion of unstriped individuals under four scenarios: 1) and unstriped morph proportion in the study area during the 1970 model predicting unstriped morph proportions each of the survey periods (Table 1). For each time period, observed in the 1970s, 2) the 2015 model predicting 2015 our best morph-climate model showed moderate fit and unstriped morph proportions, 3) the 1970 model predict- accuracy (Table 2, Fig. 2a, c). However, the most important ing 2015 unstriped morph proportions based on 2015 cli- predictors differed across time periods (Supplementary mate- mate predictors (forecast model), and 4) the 2015 model rial Appendix 1 Table A5). !e best (highest cross-validation predicting 1970s unstriped morph proportions based on score) 1970 model included average freezing degree days 1970 climate predictors (hindcast model). Sites were over- (FDD), average actual evapotranspiration from April– or underpredicted if observed values fell outside the 95% November (AET), and average number of days above the highest density posterior intervals (HDPI) of the model critical thermal maximum (CTmax). In this model, increases prediction. We rounded HDPI values to the nearest whole in FDD and AET both had a significant positive effect on number since the MCMC predict function cannot generate the proportion of unstriped morphs (pMCMC = 0.0001 and HDPI values that equal exactly zero (e.g. if the lower HDPI pMCMC = 0.011 respectively, Table 1), whereas increases is 0.005, it is rounded down to 0). We estimated model pre- in CTmax had a marginally significant positive effect on cision using Pearson’s r correlation and relative model accu- unstriped morph proportion (pMCMC = 0.084, Table 1). racy using root mean squared error (RMSE) based on point By contrast, the best 2015 model included only average estimates of unstriped morph proportions. We also estimate maximum temperature from April–November (Tmax0411), the pMCMC statistic, which tests whether the parameter is which had a significant positive effect on unstriped morph significantly different from zero and is calculated as two times proportion (pMCMC = 0.0001, Table 1). For both 1970 the smallest MCMC estimate of the probability that a param- and 2015, three spatial eigenfunctions (MEM1, MEM2, and eter value is either 1) Ͻ 0 or 2) Ͼ 0. MEM6) accounted for a significant spatial signal in the salamander morph data. None of these dbMEMs were correlated Models of changes in morph frequency and climate with our climate predictors above |0.7| and all VIFs were less than four. All candidate models had VIFs less than ten, and Because our predictive models performed poorly across time only two were greater than five. periods, a posteriori, we asked whether change in morph proportions was correlated with changes in any of our climate Regional climate change variables. To calculate change in climate variables and color morph proportions, we subtracted the 1970s data from the !e four important climate variables included in our best 2015 data. Because our ‘change’ predictors showed reduced models (AET, FDD, CTmax and Tmax0411) changed correlation structure, we created a new candidate model significantly between the 1970s and 2015 (all paired t-tests, set using a more conservative correlation cut-off of 0.4 (see p Ͻ 0.0001, Fig. 3). Mean FDD decreased by 229 degree days,

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Table 1. MCMCglmm model parameters for the best (highest log- Evolutionary change in morph frequency based on likelihood value) morph frequency-climate models for each survey change in climate period (1970 and 2015). Climate variables included in best models are FDD (freezing degree days), AET (actual evapotranspiration from April to November), CTmax (number of days above critical thermal We observed no change in the overall (pooled) proportion of maximum, base 32°C), and Tmax0411 (mean monthly maximum unstriped morphs between the two survey periods (z = 0.70, temperature from April to November). HDPI represents the 95% p = 0.48, Supplementary material Appendix 1 Table A1). Morph highest density posterior interval. The pMCMC statistic tests whether frequency changed significantly at only one site, after correcting the parameter is significantly different from zero and is calculated as for multiple tests (site 9; p = 0.0004, see Supplementary mate- two times the smallest MCMC estimate of either 1) the probability that a parameter value is Ͻ 0 or 2) the probability that a parameter rial Appendix 1 Fig. A1 for site locations), where the proportion value is Ͼ 0. of unstriped individuals decreased by 16%. !e proportions of a third, rarer erythristic color morph also appeared stable Model Variable Posterior mean (95% HDPI) pMCMC between the two surveys (Supplementary material Appendix 1 1970 FDD 0.0047 (0.0031, 0.0063) 0.0001 Table A1) with only one site experiencing a significant change AET 0.1483 (0.0388, 0.2576) 0.0110 at the 5% level after applying the Holm–Bonferroni correction CTmax 0.2467 (–0.0333, 0.5437) 0.0842 (site 50, p = 0.001; see Supplementary material Appendix 1 2015 Tmax0411 1.3150 (0.9119, 1.7443) 0.0001 Fig. A1 for site locations). Unsurprisingly given the lack of change in morph proportions, none of the climate predictors that we examined explained a significant amount of the vari- mean AET increased by 8.9 mm, mean CTmax increased ance in the change in unstriped morph proportion over time. by 1.5 d, and mean Tmax0411 increased by 0.8°C. !ese !e best change model contained only an intercept, with all changes correspond to overall warmer temperatures with an nine univariate models within 5 DIC points of the null model increase in days above 32°C, a reduction in days below freez- (Supplementary material Appendix 1 Table A6). !e change ing, and overall drier conditions. We also found a significant model had no significant spatial eigenfunctions. difference in the overall correlation structure between the 2015 and 1970s correlation matrices (Mantel test, z = 29.04, p = 0.001), with variables becoming increasingly correlated Discussion over time (Table 3). Despite a significant relationship between climate and Evolutionary predictions based on morph frequency- Plethodon cinereus morph frequencies within each time climate models period and an overall warming of the study region, we found no evidence for the predicted evolutionary change in morph Both the forecast and hindcast models predicted changes frequencies in response to 40 yr of climate change. Morph in morph frequency. However in both cases, the models frequencies appeared stationary across virtually all sites, even predicted larger changes in unstriped morph proportions though the significant changes in climate we detected have than were actually observed (Fig. 2b, d). For example, the occurred over a time span encompassing approximately 13 best 1970s model predicted an overall increase in unstriped generations of salamanders, which has been enough time for morph frequency of 27.4% across the 50 sites but we evolution to occur in other species (Kinnison and Hendry observed a non-significant increase of only 0.4%, (Pearson’s 2001). !is outcome could be explained if P. cinereus color chi-squared = 0.371, p = 0.543). In the forecast model, pre- morph proportions are not directly related to climate, selec- dictive accuracy was poor and 82% of sites were incorrectly tion from climate change is weak, or the morph-climate inter- predicted with a strong bias for overprediction of unstriped action is more complex than a simple linear response over morph proportions (Table 2). By comparison, the hindcast time. Other factors that may complicate the color morph model tended to underpredict unstriped morph proportions response include 1) interacting selective processes, 2) the (Table 2). Full observed and predicted data with credible decoupling of macro- and microscale climate and behavioral intervals are provided in Supplementary material Appendix buffering, and 3) variability in the strength and direction of 1 Table A7 and Table A8. selection over time.

Table 2. Model performance for each morph–frequency–climate model under four prediction scenarios; 1970 model predicting 1970 morph proportion, 2015 model predicting 2015 morph proportion, 1970 model predicting 2015 morph proportion (forecast), and 2015 model predicting 1970 morph proportion (hindcast).

Overprediction Underprediction Model Precision (Pearson’s r) Accuracy (RMSE) no. sites (%) no. sites (%) 1970→1970 0.74 7.67 13 (26%) 9 (18%) 2015→2015 0.74 7.70 15 (30%) 9 (18%) 1970→2015 (forecast) 0.72 35.09 39 (78%) 2 (4%) 2015→1970 (hindcast) 0.69 9.50 10 (20%) 19 (38%)

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Figure 2. Proportion of Plethodon cinereus unstriped morphs observed during field surveys against the number predicted by models under four scenarios: (a) 1970 model predicting 1970 morph proportion, (b) 1970 model predicting 2015 morph proportion (forecast), (c) 2015 model predicting 2015 morph proportion, and (d) 2015 model predicting 1970 morph proportion (hindcast). Error bars show 95% highest density posterior intervals. Diagonal line is the 1:1 line that signifies perfect model prediction. Note different y-axis scale in (b).

!e presence of a morph–climate relationship but lack of maintaining this color polymorphism (Fitzpatrick et al. a predictable temporal response points to the basic challenge 2009). However, the distribution of known visual predators of determining the ultimate cause of selection over space of P. c i n e r e u s (e.g. blue jays) does not coincide with morph and time (Merilä and Hendry 2014). In other polymorphic variation in New England, and color morphs also experi- species, morphs often differentiate along multiple ecologi- ence selective pressure from other predators, which may per- cal axes other than climate (Jones et al. 1977, Hoffman and ceive and discriminate between the color morphs differently Blouin 2000, Roulin 2004). Previous work on the P. c i n e r e u s (Venesky and Anthony 2007, Kraemer and Adams 2014). polymorphism has largely identified ways in which unstriped More research is needed to evaluate the degree to which individuals are at a competitive disadvantage compared to differences in predation contribute to regional variation in striped individuals (Reiter et al. 2014): they have a poorer morph frequencies. quality diet (Anthony et al. 2008, Stuczka et al. 2016), higher !e lack of temporal response in morph frequencies levels of stress hormones (Davis and Milanovich 2010), and over the past 40 yr may also be related to the decoupling of higher levels of predation (Lotter and Scott 1977, Moreno coarse climate predictors (such as ours) from microclimates 1989, Venesky and Anthony 2007). Despite these apparent that can vary profoundly over centimeters for small, dis- disadvantages, the unstriped morph has persisted over evolu- persal-limited species like terrestrial salamanders (Heatwole tionary time (Fisher-Reid and Wiens 2015), indicating that 1962, Feder 1983, Peterman and Semlitsch 2013, 2014, additional factors, such as the proposed ability to exploit Storlie et al. 2014). For example, aspect and elevation can warmer and drier microclimates (Lotter and Scott 1977, have a substantial impact on microclimate conditions, with Fisher-Reid et al. 2013) possibly due to a lower metabolic low elevation, south-facing slopes typically experiencing rate (Moreno 1989, Petruzzi et al. 2006), likely convey fit- warmer temperatures and drier habitats (Fu and Rich 2002), ness benefits to unstriped individuals. Frequency-dependent which may favor unstriped individuals. Macroscale climate selection mediated by visual predators, such as blue jays also interacts with site-specific changes in land use, such Cyanocitta cristata, has also been implicated as a mechanism as logging and urbanization, which can alter regional and

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climate on morph frequencies (Gibbs and Karraker 2006). For example, Cosentino et al. (2017) demonstrated a relationship between P. cinereus striped morph frequencies and forest cover in warmer regions of the species’ range, but this relationship weakens in colder regions. Because up to 75% of plethodontid salamander populations shelter underground at any one time (Connette and Semlitsch 2015), a combination of behavioral plasticity in response to local weather conditions (Muñoz et al. 2016) and microclimate heterogeneity mediated by mesoscale changes in land use could be sufficient to buffer populations from changes in macroscale climate observed over the past four decades. Examining if and how site-level variation in multiple environmental stressors such as land use and climate affect our ability to model and predict the evolutionary responses of P. cinereus populations is an important follow up to this research. Lastly, surveys spaced over four decades might miss potentially important temporal variability both in climate and morph frequency changes. "e temporal and spatial variability in climate and an increasing frequency of extreme events (Buckley et al. 2012, Vasseur et al. 2014) might explain some site-specific responses better than simple hypotheses equat- ing morph frequencies to mean climate variables (Gibbs and Karraker 2006, Moore and Ouellet 2015, Nadeau et al. 2017). "is result has been demonstrated in repeated surveys of polymorphic land snail species, which show correlations between morph frequency changes and climate variation across multiple time periods (Cameron and Pokryszko 2008, Johnson 2011). "ese studies suggest that long periods Figure 3. Box plots of climate variables: FDD (freezing degree days), AET (actual evapotranspiration from April to November), CTmax between resurvey efforts may conceal temporal variation in (number of days above critical thermal maximum, base 32°C), selective pressures, which act in the opposite direction to the Tmax0411 (mean monthly maximum temperature from April to overall predicted trend, thus mediating expected climate- November) from 50 survey locations for 1970 and 2015 survey driven responses. "e 40-yr temporal resolution of our sur- periods. Horizontal black line represents the median, while the bot- veys and the coarse spatial and temporal resolution of the tom and top of the box represent the 25th and 75th percentiles; climate data mean that we are unable to account for changes whiskers extend to the minimum and maximum values. in the strength and direction of selection on morph frequencies over time (Siepielski et al. 2009). microscale forest composition and climate (Jemison 1934). Even over the relatively short time span of our study, In recent decades, anthropogenic disturbances, particularly we found substantial changes in the correlations among suburbanization, have increased across the northeastern climate factors. "ese changing correlations likely contrib- United States (Jeon et al. 2014), inducing changes in meso- uted to the poor predictions and asymmetries in the tem- scale climate, e.g. urban heat-island effects. "us, site-level poral transferability of climate models in our study and variation in forest type, land-use change, and land-use history others (Dobrowski et al. 2011). Changing correlations among may play important roles in mediating regional effects of predictors make it difficult to understand the actual mechanistic driver of distribution patterns and thereby extrapolate Table 3. Correlation structure within time periods (1970 and 2015) for climate variables included in the best models; AET (actual evapo- to the future (Braunisch et al. 2013). Moreover, shifting cor- transpiration from April to November), CTmax (number of days related predictors could buffer or exacerbate climate effects above critical thermal maximum, base 32°C), FDD (freezing degree such as we might see for integrated and more biologically days), Tmax0411 (mean monthly maximum temperature from April informed measures like actual evapotranspiration. "ese fac- to November). tors should be considered in future studies given that many 1970 future climates are predicted to have no modern analog (Williams et al. 2007), and current modeling approaches AET CTmax FDD Tmax0411 often cannot accurately predict distributions when extrapo- 2015 AET – 0.42 0.55 0.79 lated into unknown climatic regimes (Araújo et al. 2005, CTmax 0.65 – 0.42 0.69 Fitzpatrick and Hargrove 2009, Veloz et al. 2012). FDD 0.71 0.58 – 0.84 Tmax0411 0.87 0.81 0.86 – In our study we encountered a number of limitations including 1) variation in weather conditions before and

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during survey periods and 2) an inability to resurvey identical to BRF), and a UConn EEB Zoology Grant (to AEE). BRF was locations for the majority of sites. Weather conditions prior supported by the Duke Univ. Graduate School (Summer Research to surveys can affect salamander surface activity and detection Fellowship and Katherine Goodman Stern Fellowship). MCU probability and thus may affect population-level inferences was supported by NSF awards DEB-1555876 and PLR-1417754, (Connette and Semlitsch 2015, Muñoz et al. 2016). However, UConn’s Center of Biological Risk, and the James S. McDonnell Foundation. two large-scale studies found no significant intrapopula- Author contributions – "is paper represents equal effort by co-first tion differences in surface activity between P. cinereus color authors AEE and BRF, who conducted all field work and statistical morphs under different weather conditions, suggesting both analyses and drafted the manuscript. All authors jointly designed color morphs respond similarly to changes in temperature and the study and contributed to manuscript writing. moisture-related conditions across most of the activity period Permits – "is research was conducted under approvals from the (Anthony et al. 2008, Muñoz et al. 2016). "e lack of dif- Univ. of Connecticut IACUC (protocol number A15-023) and Duke ferences between morphs in surface activity is also supported Univ. IACUC (protocol numbers A281-12-11 and A215-15-08). by repeated temporal surveys at site 7 (Fenton, Mansfield, CT) (Lotter and Scott 1977; AEE unpubl.). Additionally, we controlled for the effects of earlier retreat of unstriped References individuals in the fall (Lotter and Scott 1977, Anthony et al. 2008) by matching survey and resurvey months as closely as Anthony, C. D. et al. 2008. Ecological separation in a polymorphic possible. "us, we do not expect that weather and seasonal terrestrial salamander. – J. Anim. Ecol. 77: 646–653. differences in survey effort should have a substantial impact Antoniazza, S. et al. 2010. Local adaptation maintains clinal variation in melanin-based coloration of European barn owls on our results. Finally, while we lacked specific information (Tyto alba). – Evolution 64: 1944–1954. regarding the location of the original survey sites, in addi- Araújo, M. B. et al. 2005. Validation of species–climate impact tion to our careful matching of site descriptions from Lotter models under climate change. – Global Change Biol. 11: (1975), we did not select sites based on elevation or aspect. 1504–1513. "erefore, we did not systematically bias sites towards specific Ashcroft, M. B. et al. 2012. A novel approach to quantify and locate microclimates. Moreover, a significant difference in morph potential microrefugia using topoclimate, climate stability, and proportions between subsites within a site was detected at isolation from the matrix. – Global Change Biol. 18: 1866–1879. only one of the sites where subsites were sampled in 2015, Barrett, R. D. H. and Schluter, D. 2008. Adaptation from standing and both morphs were still detected (Supplementary material genetic variation. – Trends Ecol. Evol. 23: 38–44. Appendix 1 Table A3). "ese data indicate that small-scale Barry, J. P. et al. 1995. Climate-related, long-term faunal changes in a California rocky intertidal community. – Science 267: 672. spatial and temporal variation is unlikely to substantially alter Barton, K. 2012. MuMIn: multi-model inference. – R package ver. our regional-scale findings. 1.15.6, Ͻ https://CRAN.R-project.org/package=MuMIn Ͼ. Climate has changed substantially in the New England Bearhop, S. et al. 2005. Assortative mating as a mechanism USA region over the last 40 yr, yet we did not observe the for rapid evolution of a migratory divide. – Science 310: expected evolution of morph frequencies. "is result calls 502–504. into question the simple climate–morph link thought to exist Blanchet, F. G. et al. 2008. Forward selection of explanatory for this species. In our study, we were fortunate in having variables. – Ecology 89: 2623–2632. a detailed historical dataset to test for a predicted adaptive Borchers, H. W. 2015. pracma: practical numerical math functions. response in a species with known genetic and ecological diver- – R package ver. 1.6.4, Ͻ https://CRAN.R-project.org/ Ͼ sity. We demonstrate that, even for well-studied species like package=pracma . P. cinereus, we have yet to fully understand the link between Braunisch, V. et al. 2013. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions traits and climate change (Merilä and Hendry 2014, Urban under climate change. – Ecography 36: 971–983. 2015). As climate continues to change rapidly in the coming Buckley, J. et al. 2012. Evidence for evolutionary change associated decades, additional field and experimental research efforts are with the recent range expansion of the British butterfly, needed to understand these links more fully and predict the Aricia agestis, in response to climate change. – Mol. Ecol. 21: degree to which evolution can mitigate future impacts. 267–280. Cameron, R. A. D. and Pokryszko, B. M. 2008. Variation in Cepaea populations over 42 years: climate fluctuations destroy a Acknowledgements – We thank Kent Holsinger, Morgan Tingley, topographical relationship of morph-frequencies. – Biol. J. Jarrod Hadfield, and Dean Urban for statistical advice and the Linn. Soc. 95: 53–61. many people who provided field assistance, accommodation, and Chen, I. C. et al. 2011. Rapid range shifts of species associated with site access, particularly Kay and Glenn Dunn at Pond Mountain high levels of climate warming. – Science 333: 1024–1026. Inn and John and Kim Frigault at Mt Philo State Park. We thank Chirgwin, E. et al. 2015. Revealing hidden evolutionary capacity Solomon Dobrowski for providing water balance data, and the to cope with global change. – Global Change Biol. 21: Jockusch and Urban labs, and four anonymous reviewers for 3356–3366. comments on earlier versions of this manuscript. Cochran, M. E. 1911. "e biology of the red-backed salamander Funding – Field work was supported by the American Museum (Plethodon cinereus erythronotus Green). – Biol. Bull. 20: of Natural History ("eodore Roosevelt Memorial Fund grant 332–349.

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Supplementary material (Appendix ECOG-03588 at Ͻ www. ecography.org/appendix/ecog-03588 Ͼ). Appendix 1.

Appendix from A. Evans et al., 2018 “Salamander morph frequencies do not evolve as predicted in response to 40 years of climate change”

ECOG-03588

Supplementary material Appendix 1

Evans, A. E., Forester, B. R., Jockusch, E. J., and Urban, M, C. 2018. Salamander morph frequencies do not evolve as predicted in response to 40 years of climate change. – Ecography doi:10.1111/ecog.03588

Fig. A1: Map of survey locations showing site numbers.

Table A1: Plethodon cinereus color morph count data from the 1970s (Lotter and Scott 1977) and 2015 surveys.

1970s Surveys 2015 Surveys Site # Striped Unstriped Erythristic Amelanistic Striped Unstriped Erythristic Amelanistic 1 81 19 0 0 72 29 0 0 2 69 31 0 0 81 20 0 0 3 67 33 0 0 64 37 0 0 4 67 33 0 0 74 26 0 0 5 84 16 0 0 65 35 0 0 6 87 13 0 0 87 13 0 0 7 76 33 0 0 81 19 0 0 8 71 29 0 0 81 20 0 0 9 82 18 0 0 98 2 0 0 10 89 11 0 0 85 15 0 0 11 76 24 0 0 72 29 0 0 12 88 12 0 0 101 2 0 0 13 86 14 0 0 92 9 0 0 14 87 13 0 0 81 19 0 0 15 88 9 2 1 91 7 2 0 16 80 18 2 0 86 15 0 0 17 82 18 0 0 75 25 0 0 18 67 33 0 0 58 42 0 0 19 93 7 0 0 87 12 0 1 20 94 6 0 0 99 3 0 0 21 91 9 0 0 94 5 1 0 22 95 5 0 0 97 3 0 0 23 99 1 0 0 100 0 0 0 24 99 1 0 0 98 2 0 0 25 100 0 0 0 100 0 0 0 26 100 0 0 0 127 3 0 0 27 97 0 3 0 100 0 0 0 28 100 0 0 0 100 0 0 0 29 98 0 2 0 101 0 0 0 30 100 0 0 0 100 0 0 0 31 100 0 0 0 101 0 0 0 32 100 0 0 0 101 0 0 0 33 98 2 0 0 99 3 0 0 34 100 0 0 0 100 0 0 0 35 100 0 0 0 99 1 0 0 36 99 1 0 0 106 0 0 0 37 100 0 0 0 100 0 0 0 38 99 0 1 0 98 2 0 0 39 100 0 0 0 102 0 0 0 40 100 0 0 0 98 3 0 0 41 100 0 0 0 95 3 2 0 25

1970s Surveys 2015 Surveys Site # Striped Unstriped Erythristic Amelanistic Striped Unstriped Erythristic Amelanistic 42 99 1 0 0 106 0 0 0 43 100 0 0 0 100 0 0 0 44 100 0 0 0 100 0 0 0 45 96 4 0 0 102 7 0 0 46 87 0 13 0 94 0 10 0 47 112 0 10 0 101 0 0 0 48 111 1 25 0 98 0 7 0 49 92 0 8 0 89 0 11 0 50* 362 2 52 0 102 0 0 0 Totals 4848 417 118 1 4638 411 33 1 % 90.0% 7.7% 2.2% 0.02% 91.2% 8.1% 0.6% 0.02%

* In the 1970s survey, site 50 counts were derived from museum specimens rather than a field survey as in 2015.

Table A2: Plethodon cinereus survey locations (2015) and survey dates for 1970s and 2015. 2015 Site 1970 Survey 2015 Site Name Township State Latitude Longitude Survey # Date Date Leonard Bradley 1 Wilton CT 41.1827 -73.4311 9 Oct 2015 Oct 1971 Park 2 Mine Hill Preserve Roxbury CT 41.5596 -73.3375 9 Oct 2015 Oct 1971 Edith Scoville 3 Salisbury CT 42.0006 -73.3980 8 Oct 2015 Oct 1971 Memorial Park 14 May 4 North Farms Park North Branford CT 41.3152 -72.7656 Apr 1973 2015 10 Oct 5 Sunset Rock Plainville CT 41.6470 -72.8424 Oct 1971 2015 Devil's Hopyard 14-15 May 6 East Haddam CT 41.4743 -72.3400 Apr 1973 State Park 2015 12-13 June Univ. Conn. Forest; 7 Mansfield CT 41.8238 -72.2356 2015; 30 Jun 1973 Fenton River June 2015 Shenipsit State 10 Oct 8 Somers CT 41.9616 -72.4085 Oct 1971 Forest 2015 15 May 9 Browning Mill Pond Exeter RI 41.5608 -71.6832 May 1973 2015 Black Hut State 16 May 10 Burrillville RI 41.9820 -71.6454 May 1973 Management Area 2015 Mt Holyoke State 16 Sep 11 Amherst MA 42.3075 -72.4709 Sep 1971 Park 2015 Northfield State Northfield MA 42.6586 -72.3930 Forest 18 May 12 May 1973 Northfield Town 2015 Northfield MA 42.6596 -72.4190 Forest Nickerson State 9-10 June 13 Brewster MA 41.7690 -70.0311 Jun 1972 Park 2015 Myles Standish - South Carver MA 41.8897 -70.6321 9-10 June Middle 2015; 6 14 Myles Standish - Aug 2015; Jun 1972 South Carver MA 41.8395 -70.6907 Cranberry Rd 20 Sept Rocky Gutter WMA South Carver MA 41.8527 -70.8428 2015 Norton - Great Norton MA 42.0102 -71.2192 6 Aug Woods 15 2015; 19 Jul 1973 Norton - Gilbert Norton MA 42.0491 -71.2667 Sept 2015 Hills Whitney & Thayer Cohasset MA 42.2340 -70.8240 Woods Wompatuck State 16-17 May 16 Hingham MA 42.2169 -70.8619 May 1973 Park 2015 Scituate Town Scituate MA 42.2077 -70.7814 Forest

2015 Site 1970 Survey 2015 Site Name Township State Latitude Longitude Survey # Date Date 11 Oct 17 Ashland State Park Ashland MA 42.2455 -71.4690 Oct 1971 2015 Boxford Wildlife Boxford MA 42.6402 -70.9911 Sanctuary 18 5 Aug 2015 Jul 1971 Wildcat Boxford MA 42.6895 -71.0166 Conservation Area Silver Lake State Hollis NH 42.7600 -71.5996 Park 4-5 Aug 19 Aug 1971 Big Dickerman 2015 Hollis NH 42.7829 -71.6061 Town Forest Bear Brook State 20 Allenstown NH 43.1149 -71.3255 2 Aug 2015 Aug 1971 Park Stratham Hill Park Stratham NH 43.0408 -70.8919 21 Stratham Hill - 3 Aug 2015 Aug 1973 Stratham NH 43.0286 -70.8815 Elementary 28-29 Jul 22 Mt Philo Charlotte VT 44.2779 -73.2138 Jul 1971 2015 Lake Carmi State 29-30 Jul 23 Franklin VT 44.9528 -72.8682 Jul 1971 Park 2015 24 Brighton State Park Brighton VT 44.7941 -71.8504 30 Jul 2015 Jul 1971 White Mt. National Milan NH 44.5075 -71.3364 Forest 25 31 Jul 2015 Sep 1971 Mill Brook Trail - Milan NH 44.4979 -71.3429 York Pond Rd 11 Jun 26 Smugglers’ Notch Cambridge VT 44.5382 -72.7909 Jun 1973 2015 Groton State Forest, Groton VT 44.2857 -72.2646 Nature Trail 27 28 Jul 2015 Jul 1971 Groton State Forest, Peacham VT 44.2980 -72.2952 Owls Head 11 Jun 28 Franconia Notch Lincoln NH 44.1421 -71.6812 Jun 1973 2015 Jefferson Notch 1 Randolph NH 44.2579 -71.3824 31 Jul (Jeff. Notch Rd) 29 2015; 1 Aug 1971 Jefferson Notch 2 Jefferson NH 44.2300 -71.4065 Aug 2015 (Mt. Clinton Rd) Green Mt. National 30 Rutland VT 43.8483 -72.9003 27 Jul 2015 Aug 1971 Forest Podunk Wildlife 11 Jun 31 Strafford VT 43.8886 -72.3313 Jun 1973 Mngt Area 2015 White Mt. National 32 Gorham NH 43.9060 -71.5890 1 Aug 2015 Jul 1971 Forest Pond Mountain 25-26 Jul 33 Wells VT 43.4369 -73.1788 Aug 1971 B&B, backyard 2015 34 Okemo State Park Ludlow VT 43.4314 -72.7611 27 Jul 2015 Aug 1971

2015 Site 1970 Survey 2015 Site Name Township State Latitude Longitude Survey # Date Date 17 Sep 35 Moody Park Claremont NH 43.3577 -72.3662 Sep 1971 2015 17 Sep 36 Winslow State Park Wilmot NH 43.3897 -71.8660 Sep 1971 2015 Thompson Town 37 Thompson NH 43.4549 -71.3439 1 Aug 2015 Aug 1971 Forest Arlington State Arlington VT 43.0209 -73.1851 Forest 38 25 Jul 2015 Aug 1971 Fisher-Scott Pines Arlington VT 43.1027 -73.1367 Park Townshend State 39 Townshend VT 43.0416 -72.6925 26 Jul 2015 Aug 1971 Park 40 Vincent State Forest Deering NH 43.1157 -71.8082 2 Aug 2015 Aug 1971 41 Kennedy Park Lenox MA 42.3830 -73.2787 24 Jul 2015 Jul 1971 Tob Hill Rd Town 16-17 Sept 42 Westhampton MA 42.3064 -72.7802 Sep 1971 parcel 2015 Lawrence Brook 18 Sep 43 Royalston MA 42.7118 -72.1865 Sep 1971 WMA 2015 19 Sep 44 Rutland State Park Rutland MA 42.3690 -71.9880 Sep 1971 2015 13 Jun 45 Black Pond YMCA Woodstock CT 41.9719 -72.0737 Jun 1972 2015 Bear Den 18 May 46 Gilsum NH 43.0253 -72.2680 May 1971 Geological Park 2015 Mt. Greylock - Adams MA 42.6734 -73.1391 Bellows Pipe Mt. Greylock - 19 May 47 Adams MA 42.6121 -73.2001 Jul 1971 Slopes 2015 Mt. Greylock - Adams MA 42.5536 -73.2122 Visitor Center Catamount State 19 May 48 Colrain MA 42.6348 -72.7406 Sep 1971 Forest 2015 12 Jun 49 Tunxis State Forest Hartland CT 42.0162 -72.9201 Jun 1971 2015 NA 18 Sep 50 Wachuset Mountain Princeton MA 42.5079 -71.8926 (Museum 2015 collection)

Table A3: Polymorphic 2015 sites that had more than one survey location. Striped+Other column includes counts of striped, erythristic, and amelanistic morphs. Includes a test of subsite morph differences (Chi-squared with simulated p-value). Two sites include a Chi-squared test at the same subsite, but in different sampling months. Four other sites with more than one survey location were monomorphic for striped morphs so not included in the analysis (Sites 25, 27, 29 and 47).

Site Chi- Simulated Site Name Unstriped Striped+Other # squared p-value Northfield State Forest 0 53 12 2.162 0.247 Northfield Town Forest 2 48 Myles Standish - Cranberry Rd 19 71 14 Myles Standish - Middle 0 6 2.606 0.352 Rocky Gutter WMA 0 4 Norton - Great Woods 2 84 15 20.618 0.001 Norton - Gilbert Hills 5 9 Whitney & Thayer Woods 8 46 16 Wompatuck State Park 2 19 0.866 0.657 Scituate Town Forest 5 21 Silver Lake State Park 10 54 19 2.212 0.206 Big Dickerman Town Forest 2 34 Arlington 1 1 42 38 0.041 1.000 Arlington 2 1 56

SITES SAMPLED ACROSS TWO MONTHS: Myles Standish - Cranberry Rd, June 1 17 14 3.269 0.112 Myles Standish - Cranberry Rd, September 18 54 Norton - Great Woods, August 1 9 15 2.934 0.217 Norton - Great Woods, September 1 75

Table A4: Predictor variables averaged over 1962–1971 for Lotter and Scotts 1970s survey and 2005–2014 for our 2015 resurvey. Months Predictor Description Included Actual evapotranspiration The supply component of the climatic water April- (AET) balance; accounts for the concurrent November* availability of water and energy. Water deficit (DEF) The unmet demand component of the climatic April- water balance; accounts for the concurrent November* availability of water and energy.

Mean monthly mean Average monthly mean temperature; April- temperature (Tmean0411) calculated from daily mean temperatures. November

Mean monthly maximum Average monthly maximum temperature; April- temperature (Tmax0411) calculated from daily maximum temperatures. November

Mean monthly minimum Average monthly minimum temperature; April- temperature (Tmin0411) calculated from daily minimum temperatures. November

Growing degree days, 0°C Average annual sum of degrees above 0°C; January- base (GDD) calculated from daily mean temperatures. December

Freezing degree days Average annual sum of degrees below 0°C; January- (FDD) calculated from daily minimum temperatures. December

Frost free days (FrFD) Number of days between the last 0°C day in January- the spring and the first 0°C day in the fall; December calculated from daily minimum temperatures. Number of days above Number of days where maximum temperature January- critical thermal max is above Plethodon cinereus critical thermal December (CTmax) max (> 32°C)†. * Current water balance data are a six-year average of 2005–2010. † Spotila JR (1972) Role of temperature and water in the ecology of lungless salamanders. Ecological Monographs, 42, 95-125.

Table A5: Candidate model sets ordered by log-likelihood values (lnL, best models at top) for 1970 and 2015. Grey shading indicates candidate models that included variables correlated above |0.7|. These were included to allow all models to be compared between time periods. See Supplementary materials Appendix 1, Table A4 for the full name of each abbreviated climate variable.

Model 1970 lnL Model 2015 lnL AET + CTmax + FDD -240.25 Tmax0411 -236.69 AET + FDD -252.07 CTmax + Tmean0411 -237.40 AET + Tmean0411 -253.29 Tmean0411 -238.62 AET + GDD -257.45 CTmax + Tmax0411 -246.36 AET + CTmax + Tmean0411 -261.71 CTmax + GDD -246.44 AET + CTmax + GDD -263.15 AET + CTmax + Tmean0411 -253.58 Tmean0411 -266.92 AET + Tmean0411 -254.32 Tmax0411 -268.47 GDD -254.70 CTmax + Tmean0411 -272.49 CTmax + FDD -261.83 CTmax + Tmax0411 -272.69 AET + CTmax + GDD -262.05 AET + DEF -278.05 AET + CTmax + FDD -265.99 GDD -279.49 AET + CTmax + DEF -266.53 CTmax + GDD -282.57 AET + GDD -267.59 AET + CTmax + Tmin0411 -284.25 CTmax + Tmin0411 -271.77 AET + CTmax + DEF -285.97 AET + DEF -276.18 AET + CTmax + FrFD -292.77 CTmax + DEF -276.64 CTmax + FDD -303.61 AET + CTmax + Tmin0411 -279.09 AET + Tmin0411 -305.72 AET + CTmax + FrFD -285.36 AET + FrFD -320.16 AET + FDD -288.03 FDD -325.99 CTmax + FrFD -301.88 CTmax + Tmin0411 -363.72 AET + Tmin0411 -308.48 AET -389.87 FDD -308.68 AET + CTmax -391.75 AET + FrFD -316.54 DEF -395.62 DEF -320.47 CTmax + FrFD -404.23 AET + CTmax -332.65 Tmin0411 -407.08 Tmin0411 -333.50 CTmax + DEF -413.25 AET -363.00 FrFD -462.68 CTmax -366.83 CTmax -485.72 FrFD -415.48

Table A6: Candidate change models ordered by change in DIC values. See Supplementary materials Appendix 1, Table A4 for the full name of each abbreviated climate variable.

Model DIC delta DIC weight null model -288.2 0.0 0.3 CTmax -285.4 2.8 0.1 Tmean0411 -285.2 3.0 0.1 GDD -285.2 3.0 0.1 AET -285.1 3.2 0.1 Tmax0411 -284.9 3.3 0.1 Tmin0411 -284.9 3.3 0.1 DEF -284.7 3.5 0.1 FDD -284.5 3.7 0.0 FrFD -283.7 4.5 0.0 DEF + CTmax -282.3 6.0 0.0 CTmax + Tmax0411 -282.0 6.2 0.0 AET +Tmean0411 -281.9 6.3 0.0 AET + Tmax0411 -281.8 6.4 0.0 DEF + GDD -281.7 6.5 0.0 AET + Tmin0411 -281.7 6.6 0.0 DEF + Tmean0411 -281.6 6.6 0.0 Tmax0411 + Tmin0411 -281.6 6.6 0.0 FDD + Tmean0411 -281.6 6.7 0.0 CTmax + FDD -281.4 6.8 0.0 DEF + Tmin0411 -281.4 6.9 0.0 DEF + FDD -281.3 6.9 0.0 FDD + Tmax0411 -281.2 7.0 0.0 FDD + Tmin0411 -281.1 7.1 0.0 FrFD + GDD -280.7 7.6 0.0 CTmax + FrFD -280.6 7.6 0.0 FrFD + Tmean0411 -280.5 7.7 0.0 AET + FrFD -280.4 7.9 0.0 FrFD + Tmin0411 -280.3 8.0 0.0 FDD + FrFD -279.9 8.3 0.0 DEF + CTmax + FDD -278.6 9.6 0.0 AET + Tmax0411 + Tmin0411 -278.3 9.9 0.0 DEF + FDD + Tmin0411 -278.1 10.1 0.0 DEF + FDD + Tmean0411 -278.0 10.3 0.0 CTmax + FDD + Tmax0411 -277.9 10.3 0.0 AET + FrFD + Tmean0411 -277.1 11.1 0.0 AET + FrFD + Tmin0411 -276.9 11.3 0.0 FDD + FrFD + Tmean0411 -276.9 11.4 0.0 CTmax + FDD + FrFD -276.5 11.7 0.0 FDD + FrFD + Tmin0411 -276.3 11.9 0.0 global model -109.2 179.1 0.0

Table A7: Observed and predicted unstriped morph frequencies (unstriped morph percent with 95% credible intervals) for the 1970s survey.

Predicted: 1970 model Predicted: 2015 model Site Observed: 1970 predicting 1970 data predicting 1970 data 1 19 36 (22-–48) 17 (10–25) 2 31 28 (17–40) 13 (8–19) 3 33 15 (6–25) 6 (4–10) 4 33 27 (16–40) 12 (7–17) 5 16 23 (13–34) 9 (6–14) 6 13 26 (14–39) 7 (4–10) 7 30 15 (9–22) 8 (5–12) 8 29 8 (4–13) 4 (2–6) 9 18 26 (13–41) 4 (2–7) 10 11 16 (11–23) 8 (5–12) 11 24 10 (5–16) 8 (5–12) 12 12 3 (1–5) 2 (1–3) 13 14 22 (4–44) 1 (0–2) 14 13 11 (3–22) 4 (2–7) 15 9 21 (13–29) 10 (6–14) 16 18 30 (17–44) 7 (5–11) 17 18 27 (14–41) 12 (7–17) 18 33 16 (6–29) 7 (4–10) 19 7 11 (3–21) 9 (5–13) 20 6 7 (4–11) 3 (2–6) 21 9 12 (4–22) 5 (3–7) 22 5 2 (0–3) 1 (0–3) 23 1 0 (0–0) 0 (0–1) 24 1 0 (0–0) 0 (0–0) 25 0 0 (0–0) 0 (0–0) 26 0 0 (0–0) 0 (0–0) 27 0 0 (0–0) 0 (0–0) 28 0 0 (0–0) 0 (0–0) 29 0 0 (0–0) 0 (0–0) 30 0 0 (0–1) 0 (0–1) 31 0 0 (0–0) 0 (0–0) 32 0 0 (0–1) 0 (0–1) 33 2 2 (0–3) 1 (0–2) 34 0 0 (0–1) 0 (0–1) 35 0 2 (1–3) 3 (2–5) 36 1 1 (0–2) 0 (0–0) 37 0 2 (1–4) 2 (1–3) 38 0 4 (2–8) 4 (2–7) 39 0 9 (2–17) 7 (4–10) 40 0 1 (0–3) 1 (0–2)

Predicted: 1970 model Predicted: 2015 model Site Observed: 1970 predicting 1970 data predicting 1970 data 41 0 2 (1–4) 2 (1–3) 42 1 4 (2–7) 3 (1–5) 43 0 2 (1–3) 2 (1–4) 44 0 6 (3–8) 3 (2–5) 45 4 11 (6–18) 3 (2–6) 46 0 1 (0–2) 1 (0–2) 47 0 0 (0–1) 0 (0–1) 48 1 5 (2–8) 5 (3–8) 49 0 6 (2–10) 2 (1–3) 50 0 3 (1–5) 3 (1–5)

Table A8: Observed and predicted unstriped morph frequencies (unstriped morph percent with 95% credible intervals) for the 2015 survey.

Predicted: 2015 model Predicted: 1970 model Site Observed: 2015 predicting 2015 data predicting 2015 data 1 29 34 (20–48) 88 (70–99) 2 20 29 (17–41) 82 (58–99) 3 37 14 (9–21) 62 (32–88) 4 26 26 (15–36) 78 (59–95) 5 35 21 (12–30) 66 (43–88) 6 13 15 (9–22) 70 (45–92) 7 19 16 (10–23) 54 (33–76) 8 20 8 (5–12) 41 (20–62) 9 2 12 (7–17) 70 (45–92) 10 15 17 (10–25) 65 (42–88) 11 29 16 (10–23) 56 (29–83) 12 2 3 (2–5) 24 (8–42) 13 9 4 (2–6) 58 (34–82) 14 19 12 (7–17) 57 (37–75) 15 7 23 (14–33) 78 (55–98) 16 15 17 (10–24) 85 (66–99) 17 25 24 (14–34) 82 (55–100) 18 42 14 (9–20) 71 (46–94) 19 12 16 (10–22) 56 (24–87) 20 3 7 (4–10) 41 (19–62) 21 5 9 (5–13) 61 (33–87) 22 3 4 (2–7) 19 (5–36) 23 0 2 (1–3) 3 (1–6) 24 2 0 (0–1) 0 (0–1) 25 0 0 (0–1) 0 (0–0) 26 2 0 (0–0) 0 (0–1) 27 0 0 (0–1) 1 (0–1) 28 0 0 (0–0) 1 (0–1) 29 0 0 (0–0) 0 (0–0) 30 0 1 (0–2) 3 (1–5) 31 0 0 (0–1) 1 (0–2) 32 0 1 (0–2) 3 (1–6) 33 3 2 (1–3) 18 (2–39) 34 0 1 (0–2) 3 (1–7) 35 1 7 (4–10) 21 (7–38) 36 0 0 (0–1) 3 (1–6) 37 0 3 (1–5) 18 (8–30) 38 2 9 (5–13) 25 (10–42) 39 0 12 (7–17) 43 (16–73) 40 3 2 (1–4) 8 (4–11)

Predicted: 2015 model Predicted: 1970 model Site Observed: 2015 predicting 2015 data predicting 2015 data 41 3 4 (2–6) 16 (7–27) 42 0 7 (4–11) 38 (16–59) 43 0 4 (2–7) 19 (8–29) 44 0 6 (4–10) 36 (19–56) 45 6 7 (4–11) 43 (22–65) 46 0 2 (1–4) 7 (3–11) 47 0 1 (0–2) 3 (1–4) 48 0 11 (7–15) 44 (19–68) 49 0 5 (3–7) 37 (14–60) 50 0 5 (3–8) 25 (14–37)

Chapter 2:

Can forest cover mediate the evolutionary responses of a

terrestrial salamander to climate change?

Abstract

A myriad of biotic and abiotic factors can act simultaneously to amplify or dampen the selective pressures driving the eco-evolutionary responses of species. Yet despite this multifaceted nature of selection, the joint operation of multiple major disturbances, such as land use changes and climate, is often overlooked when evaluating evolutionary responses of populations. Terrestrial amphibians are particularly sensitive to environmental stressors given their limited dispersal capabilities and need to maintain cool, moist skin for respiration. Previous research on the response to environmental stressors by the polymorphic salamander Plethodon cinereus indicates both cool climates and forest cover are associated with a higher ratio of striped to unstriped individuals at broad spatial scales. However, at the regional New England (USA) scale, recent population resurveys revealed no changes in morph frequencies over the past 40 years despite model prediction of substantial changes in response to changes in climate.

Here, we used historical and contemporary data to test whether forest cover interacts with climate to affect the distribution of P. cinereus color morphs throughout New England.

Our results suggest that the interaction between forest cover and climate is an important

predictor associated with morph frequencies in both historical and current salamander populations, although forest cover alone is a poor predictor of striped-unstriped ratios throughout New England. These results indicate that persistence and increase of forest cover throughout New England may explain the lack of relative morph frequency changes since the 1970s, although forest cover alone may be insufficient to buffer future salamander populations from continuing climate change. Our study highlights the importance of simultaneously examining multiple environmental stressors when evaluating and predicting species responses to climate change.

Introduction

Climate change is among the most serious threats to species persistence, eliciting ecological and evolutionary responses in species ranging from shifts in geographical distribution or phenology to adaptive evolution facilitating persistence in novel environments (Parmesan 2006; Rosenzweig et al. 2008; Hoffmann and Sgro 2011; Urban et al. 2012). Numerous studies have examined the evolutionary responses of species to individual components of environmental change, such as changes in temperature or land use (Gibbs 1998a; Parmesan 2006; Homyack and Haas 2009; Li et al. 2013). Yet, in most systems it remains unclear how multiple simultaneously acting environmental stressors will affect population evolutionary responses (Cosentino et al. 2017; Brennan et al. 2017; Hodgson and Halpern 2019). Environmental change can produce complex interactions among biotic and abiotic factors, which may amplify or dampen the selective

pressures imposed by climate change and thus impact the evolutionary and ecological responses of species (Brook et al. 2008; Mantyka-pringle et al. 2012).

Land use change and climate change are two disturbances that frequently occur simultaneously and are known to interact in complex ways (Oliver and Morecroft 2014), which can shape the environmental selective pressures acting on forest-dwelling organisms. Fragmented and deforested areas typically experience elevated soil temperatures (Chen et al. 1995; Tuff et al. 2016), larger fluctuations in daily air temperatures (Chen et al. 1995, 1999) and higher evapotranspiration rates, which in combination with a greater vegetation breeze (air movement through the forest) lead to a desiccating environment (Chen et al. 1999; Cochrane and Laurance 2008). As a result, reductions in forest cover can interact with climate change to amplify selection imposed by abiotic factors like temperature and moisture (Dale 1997; Hansen et al. 2001;

Cosentino et al. 2017). Conversely, the persistence or recovery of forested areas can create or protect microhabitat refugia where populations are buffered against climate change, thus enabling species to persist (Scheffers et al. 2014; De Frenne et al. 2019).

Terrestrial amphibians are expected to be particularly sensitive to changes in both land use and climate given their limited dispersal capabilities and the need to maintain cool, moist skin for respiration (Feder 1983). In particular, the vulnerability of terrestrial plethodontid salamanders to desiccation constrains their distribution and surface activity period (Muñoz et al. 2016). Consequently, most terrestrial plethodontid species are

associated with late successional, mature forests, which are characterized by relatively high humidity and cooler temperatures (Heatwole 1962). By altering microclimate conditions experienced by salamanders at the forest floor, climate change and land use change may cause selection on traits associated with thermal performance. Over the past four decades, numerous researchers have investigated the individual impacts of either climate or landscape changes on the presence and evolutionary responses of eastern red-backed salamanders, Plethodon cinereus (Lotter and Scott 1977; Pfingsten and

Walker 1978; Gibbs 1998a; Gibbs and Karraker 2006; Evans et al. 2018). This species shows a color polymorphism, in which the two main morphs are characterized by the presence (striped morph) or absence (unstriped morph) of a colored dorsal stripe.

Previous work on the ecological differences between these morphs suggest that both climatic conditions and land use may influence the broad-scale spatial distribution of the respective color morphs, with higher unstriped proportions associated with warmer, more disturbed or fragmented habitats (Lotter and Scott 1977; Pfingsten and Walker 1978;

Gibbs 1998a; Gibbs and Karraker 2006; Evans et al. 2018). In an earlier study, Evans et al. (2018) showed that while climate was important for predicting morph frequencies within temporal periods, climate alone was a poor predictor of morph frequency changes across time. Given the evidence of macro-scale impacts of climate and land use change

(Cosentino et al. 2017), we investigated whether the interaction between climate and land use can explain the proportions of P. cinereus color morphs observed at a finer spatial scale across New England. Since forest cover may mediate the selective forces driven by climate, we predict that models including both land use and climate variables will best

explain not only historical and present-day morph frequencies but also changes in morph frequencies observed across New England over the past 40 years. More specifically, given previous evidence that unstriped individuals have a higher tolerance of warmer and/or more disturbed regions than striped individuals (Pfingsten and Walker 1978;

Cosentino et al. 2017; Evans et al. 2018), we predict that the proportions of striped individuals at a site will be positively associated with forest cover but negatively associated with warmer climate and forest fragmentation across New England.

Methods

Salamander morph frequencies and climate data

In 2015 we resurveyed the frequency of P. cinereus color morphs at 50 sites across New

England (Appendix 1) (Evans et al. 2018) originally surveyed in the 1970s (Lotter and

Scott 1977). To evaluate if relative morph frequencies have changed over the past 40 years, we compared salamander morph frequency data from these resurveys to the historical salamander morph frequency data collected by Lotter and Scott (1977). Morph proportions at each site were based on a minimum of 100 hand-caught salamanders in both the original 1970s survey and our 2015 resurvey. To model the effects of climate we selected three climate variables, correlated below 0.7, that were shown in this earlier study to explain variation in relative color morph frequencies in New England (Evans et al. 2018). These climate variables were average freezing degree days (FDD, average annual sum of degrees below 0°C), average annual number of days above 32oC (the critical thermal maximum of P. cinereus, CTmax), and actual evapotranspiration during

the surface active period for this species in this region (April to November, AET). We derived temperature variables from the TopoWx daily dataset; a 800 m resolution gridded dataset that uses a unique algorithm to ensure better temporal consistency than comparable open source datasets for data dating back to 1948 (Oyler et al. 2015).

Dobrowski (2013) provided us with monthly water balance data (recalculated using the

TopoWx temperature dataset). All climate variables were averaged over a 10-year period prior to each salamander survey (1962 – 1971 and 2005 – 2014) except current water balance metrics, which were averaged over a 5-year period from 2005 – 2010 (as later data were not available). Further information on resurvey efforts and how climate variables were calculated is provided in Evans et al. (2018).

Land use data acquisition and processing

We used Landsat data to build a model of annual forest cover at our study sites covering the time period between the historical and resurvey efforts. We acquired Landsat images from the USGS Earth Explorer website (http://earthexplorer.usgs.gov/) for the peak growing season (June, July, August) between 1972 and 2015 for the seven WRS-1 scenes and seven WRS-2 scenes that covered our study area (Appendix 2). This yielded a total of 1542 images, which included images from Thematic Mapper (TM), the

Enhanced Thematic Mapper Plus (ETM+) and the Multispectral Scanner (MSS) sensors.

Forest cover estimates

We used the package LandsatLinkr (LLR) (version 0.5.0) (Braaten et al. 2017) in R

(version 3.5.1) (R Core Team 2013) to calibrate the spectral data from MSS, TM and

ETM+ sensors and create annual, cloud-free, tasseled cap indices that are spectrally consistent across our temporal surveys and study area. We followed the workflow outlined in Vogeler et al. (2018) for creating a time series of forest cover from Landsat data using LLR. LandsatLinkr was used to create annual composites of brightness (TCB), greenness (TCG), and wetness (TCW) tassel cap indices, using the mean to summarize overlapping pixel values. The angle index exhibited poor fit (i.e., non-linear relationship) between MSS and TM sensor imagery during the MSScal stage and was excluded from analysis. We used LLR-LandTrendr (version 0.2.2) to apply a temporal segmentation algorithm to the annual composite images from LLR, which smooths the trend in each tassel cap index during the time series, reducing year-to-year noise (Kennedy et al. 2010).

To generate a predictive model of forest cover based on the spectral data, we first randomly sampled 998 locations across our study area to estimate forest cover directly from aerial imagery. We created a 90 x 90 m plot around each location and overlaid a

100 – point dot grid within each plot on aerial images from the National Agriculture

Imagery Program taken in 2014. We estimated canopy cover for each plot as the proportion of dots that intersected tree crowns. To minimize potential processing

(observer) bias, all canopy cover estimations were done by a single observer.

Forest cover models

We used a random forest regression tree approach (Breiman 2001) to create a statistical model of forest cover that links canopy cover data estimated from aerial imagery to the spectral data calibrated with LLR. Following Vogeler et al. (2018), we calculated the mean and standard deviation of TCB, TCG, and TCW in each of our 998 plots. We also calculated mean elevation and mean slope (generated from a 1 arc-second digital elevation model from USGS) for each plot. The mean and standard deviation of each tassel cap index along with mean elevation and slope were used as predictors of canopy cover in our random forest model. We ran each model for 1000 individual regression trees and used the rfUtilities package (version 2.1.3) in R (Evans and Murphy 2017) to select the best predictors of canopy cover and ensure that the model did not violate multi- collinearity assumptions (Evans and Murphy 2017). The model selection approach identified six variables important for predicting forest cover: mean TCW, mean TCB, mean

TCG, mean elevation, mean slope, and standard deviation of TCW. We used these six predictors identified in the model selection approach to generate a final regression tree model with the randomForest package (version 4.6.14) (Liaw and Wiener 2002). We ran

1000 trees and randomly sampled two variables at each split. The predictive accuracy of the final model was assessed with a cross validation approach, withholding 10% of the data from each model run over 1000 cross validations. We calculated median pseudo R2 values and Root Mean Squared Error (RMSE) for the final model and assessed the relative importance of model predictors (Appendix 3).

We used this final model to predict canopy cover in 1974 and 2014 for each of our 50 salamander survey sites, encompassing the time period between the historical and resurvey efforts. The predicted canopy cover data were on a continuous scale for each

90 m pixel (0-1) and we chose a 25% threshold to categorize each cell as forested (≥

25% canopy cover) or unforested (< 25% canopy cover) (Figure 1). We chose this approach to enable us to also calculate fragmentation indices (which require binary pixel values), although we ultimately excluded other fragmentation indices from our final analyses due to high correlations among variables. We quantified forest cover at each site as the proportion of forested grid cells within a 3 km buffer around each site. These buffers were centered around gps coordinates taken during 2015 resurveys as the original

1970s sampling localities were only specified to town/county. We selected resurvey locations using a combination of town center coordinates (Lotter and Scott 1977) and vegetation and topographic descriptions provided in Lotter’s (1975) dissertation appendix.

More information about sampling procedures is detailed in Evans et al. (2018). In order to determine how sensitive our subsequent analyses were to the forest classification threshold we repeated our statistical analyses using forest cover estimates with 20% and

30% thresholds.

Statistical analysis

To assess the effects of forest cover and climate change on color morph proportions across New England we used a Markov chain Monte Carlo approach to fit Bayesian hierarchical models using JAGS (version 4.3.0) (Plummer 2004) in R (R Core Team

2013). We modeled the probability of observing a striped individual at a site within a given time period (1970s or 2015) using a binomial distribution. We calculated the changes in striped proportions by subtracting our 1970s proportions from the 2015 proportions and modeled the changes in striped proportions between the two time periods (delta model) using a Gaussian distribution.

1974 2014

1.0 1.0

0.5

0.0 0.0

Figure 1 Proportion of forest cover, shown in green, (using 25% threshold) throughout

the New England region of eastern North America in 1974 (left) and 2014 (right). Circles represent our 50 study sites. Change in forest cover within the 3km buffer around each study site shown in Appendix 8.

For all three data sets (1970s, 2015, and delta), we assessed the relative importance of forest cover and climate using five candidate models: 1) a model including both the additive effects and interaction effects between climate and forest cover (see Appendix 4 for JAGS model code), 2) an additive model including both climate and forest cover, 3) a climate-only model, 4) a forest cover only model, and 5) an intercept-only model. We coded climate as a normally distributed variable, with the mean (climate.mui) modeled as a function of our three climate variables; actual evapotranspiration from April to

November, average freezing degree days, and average number of days above critical thermal maximum. In all cases, we used vague priors (e.g. normal prior with mean (u) =

0, and precision (tau) = 0.001; gamma prior with shape = 0.001 and rate = 0.001,

Appendix 4). We ran our 1970s and 2015 models using three chains of 6,000,000 with a burn-in of 5,000,000 and a thinning interval of 250. We ran our Delta models using three chains of 5,000,000 with a burn-in of 2,400,000 and a thinning interval of 650. These model parameters were selected to ensure the three chains converged and posterior distributions were symmetrical. We checked for model convergence and mixing using visual inspections of traceplots and posterior distribution plots as well as Geweke

(Geweke 1991) and Gelman and Rubin diagnostics (Gelman and Rubin 1992).

We used DIC values generated in the JAGS model output and WAIC values calculated using the loo package (version 2.1.0, (Vehtari et al. 2017)) to select a best fit model for each dataset (1970s, 2015 and delta). We also calculated Bayesian p-values (defined as the probability that a mean data value (mean posterior estimate) predicted by the model

is more extreme than the observed data (Gelman 2005), values closer to 0.5 indicate better model predictive ability) generated from posterior predictive checks to assess the fit of our five candidate models for each time period and to determine the sensitivity of the analyses to the choice of threshold for defining whether each pixel was forested or not.

Finally, we used normalized root mean squared error values (NRMSE), calculated by dividing the RMSE value by the standard deviation of observed values, to assess the predictive accuracy of the best model for each of our three datasets. For all three of our datasets (1970s, 2015 and delta), there was little difference in the predictive accuracy among candidate models run using the three different canopy cover thresholds (Appendix

5). This suggests our analyses were insensitive to the canopy cover threshold used; therefore, we just report the results from the 25% threshold models.

Results

Over the past 40 years, climate changed significantly across our New England study area

(Evans et al. 2018), with a 229 degree day reduction in mean freezing degree days, a mean increase of 1.5 days above the critical thermal maximum of 32oC, and an increase in mean actual evapotranspiration of 8.9 mm across our sites between 1970 and 2015

(Evans et al., 2018, Appendix 6 and 7). Between 1974 and 2014 the proportion of forest cover (25% threshold) at our 50 study sites increased by an average of 7%. Overall, 38/50 of our sites showed an increase in forest cover proportion (average increase of 10.3%) while 12/50 showed a decrease in forest cover proportion (average decrease of 3.7%) from 1974 to 2014 (Figure 2, Appendix 8).

1.0

0.0 1.0

1.0 0.0

1.0

Figure 2 Map of New England showing the changes in proportion of forest cover at the 25% threshold between 1974 and 2014. Points indicate the location of the 50 study

sites, with blue dots showing sites where forest cover decreases and green dots showing sites where forest cover increased. Change in forest cover within the 3 km buffer around each study site shown in Appendix 8.

The forest cover and climate interaction model best explained morph frequencies observed in the 1970s (DIC = 246.8, Bayesian p-value = 0.558, WAIC = 226.1, Appendix

5). In this model, striped proportions in the 1970s were positively associated with the

interaction between proportion of forest cover and climate (posterior mean = 0.327, 95% credible intervals (CI) = 0.096, 0.485) and negatively associated with the number of freezing degree days (posterior mean = -1.538, 95% CI = -2.313 -0.872, Figure 3a). The forest cover – climate interaction model was also the best model for our 2015 resurvey data (DIC = 226.4, Bayesian p-value = 0.606, WAIC = 206.1, Appendix 5). However, in this model, only the effect of freezing degree days had credible intervals that did not overlap zero, suggesting a negative association between freezing degree days and striped morph proportions (posterior mean = -1.327, 95% CI = -2.234, -0.494), with the interaction between forest and climate no longer overlapping zero (posterior mean =

0.260, 95% CI = -0.016, 0.345) (Figure 3b). Both our 1970s and 2015 models fit the data well, with the historical model explaining more of the variance in striped proportions than the 2015 model (1970s model NRMSE = 0.011, 2015 model NRMSE = 0.035). Finally, the change in striped morph proportions between the 1970s and 2015 was best explained by our forest-cover change only model (DIC = -127.2, Bayesian p-value = 0.5953, WAIC

= -127.8, Appendix 5). In this model, the credible intervals for the change in proportion of forest cover overlapped zero, (posterior mean = -0.004, 95% CI = -0.022 0.014, Figure

3c), suggesting forest cover changes alone explained little of the variance observed in relative morph proportions across New England (NRMSE = 6.978).

(a) (b)

Forest Forest Forest−Climate Interaction Forest−Climate Interaction

AET AET

CTmax CTmax

FDD FDD

−3 −2 −1 0 1 2 −3 −2 −1 0 1 Mean posterior estimates Mean posterior estimates

Figure 3 Plots showing the mean posterior (c) estimates and 95% credible intervals for parameters included in the best supported model for each of the three datasets (a) 1970s, (b) 2015, and (c) delta. AET stands for

actual evapotranspiration, CTmax is number Forest of days above critical thermal maximum (base 32oC), and FDD is cumulative number of freezing degree days. Thick grey lines represent 50% credible intervals. Parameters in grey have 95% credible −0.03 −0.01 0.01 intervals (thin grey lines) overlapping zero. Black parameters do not have 95% credible Mean posterior estimates intervals overlapping zero.

Discussion

Our study found that the proportion of striped P. cinereus individuals throughout New

England during the 1970s was positively associated with the interaction between climate

and forest cover, suggesting that forest cover may have potentially buffered historical

salamander populations from climatic conditions. The forest cover – climate interaction model was also the best model for explaining current salamander morph frequencies.

While we found no credible evidence that the interaction between forest cover and climate was associated with striped morph frequencies in our 2015 model, the credible intervals for the forest-climate interaction in the 2015 model barely overlap zero and show large overlap with the historical climate-forest interaction term. We therefore have no credible evidence for a shift in the strength of the interaction between forest cover and climate over the past 40 years in our New England study region.

The climate-buffering effects of forests are widely documented, with tree canopies sheltering understory ecosystems from both high and low temperature extremes, creating less thermally varying environments (Mantyka-pringle et al. 2012; Woods et al. 2015). As a result, the presence or absence of forest cover can play an important role in both mediating and constraining the responses of forest-dwelling organisms to climate change

(De Frenne et al. 2019). Land use changes are known to act synergistically with climate to drive species range shifts. For example, in a review of 2798 species range shifts, Guo et al. (2018) showed that species, particularly from lower elevation or latitude areas, typically display greater range shifts when warmer temperatures also coincided with habitat loss. Yet for dispersal-limited species, the synergistic effects of forest cover and climate are more likely to alter in situ ecological or evolutionary responses (Chevin et al.

2010). Our results show that forests interact with climate variables and hence may be important for buffering terrestrial plethodontid salamanders from changes in temperature.

The interaction between forest cover and climate may therefore contribute to the persistence of striped P. cinereus morphs observed in the northeastern United States despite substantial climate change. Most of our study sites have showed some degree of warming over the past 40 years (Appendix 6 and 7) but have also experienced increases in forest cover. Very few sites in our study region have lost forest cover (Appendix 8). This may explain why the change in morph frequencies over the past 40 years are small on average - the regional warming and increasing forest oppose each other, assuming a thermal mechanism of selection on color morphs. Given our evidence of an interactive effect between forest cover and climate in our study region, the opposite impact is likely true for other areas across the world that are experiencing the joint impacts of deforestation and climate change. Such areas are likely to experience increasing drying and warming, exacerbating climate-related species responses (Mantyka-Pringle et al.

2012; Mantyka-Pringle et al. 2015; Guo et al. 2018).

For terrestrial ectotherms, many aspects of climate change are known to influence the distribution and behavior of individuals (Deutsch et al. 2008; Woods et al. 2015; Sunday et al. 2019). In our study, we selected three climate variables that were correlated below

0.7 among themselves but were highly correlated with other measures of climate (such as maximum temperature) previously suggested to influence morph proportions (Evans et al. 2018). To test whether our models were sensitive to the specific climate variables we included, we conducted post-hoc tests to substitute mean maximum temperature from

April to November (Tmax) at our sites – an additional measure of heat stress, instead of

mean freezing degree days – a cumulative measure of coldness. These post-hoc analyses showed no overall change in model selection or fit for the 1970s, 2015 or delta datasets (Appendix 9) but did reveal support for the effect of maximum temperature on striped proportions in place of the number of freezing degree days. This suggests that the association between climate and salamander populations in our study likely represents a general temperature effect, as the high correlations among temperature variables makes it difficult to assess the individual contribution of different measures of climate to the variance in morph proportions across New England.

The mechanisms underlying the capacities for forests to provide buffering effects are likely multifaceted (Dobrowski 2011; Bertrand et al. 2011; Ewers and Banks-Leite 2013;

Davis et al. 2019). For forests to protect populations from extreme environmental conditions, buffering effects must be substantial enough to be biologically relevant for the focal species or population, but should also be stable enough to facilitate their long-term persistence (e.g., via adaptation) (Hylander et al. 2015). One factor contributing to limitations in forest buffering capacity is the influence of biophysical variables such as temperature, water balance (Ashcroft and Gollan 2013; Davis et al. 2019; Northrup et al.

2019), and topography (Åström et al. 2007; Lenoir et al. 2017; Muscarella et al. 2020).

Average regional temperature can play an important role in governing species responses to multiple stressors (Northrup et al. 2019). For example, Cosentino et al. (2017) found that the synergistic effects of land use and climate at broad spatial scales had a significant positive effect on striped frequencies in P. cinereus only in the warmer areas of the

species’ range, with forest cover less important in colder regions. Similarly, moisture conditions can also greatly impact the buffering capacity of forests, with drier conditions reducing the capacities of forest to buffer against climate extremes (Ashcroft and Gollan

2013; Davis et al. 2019). Moist soils have higher heat capacities (Ashcroft and Gollan

2013), and this can result in a lag effect between changes in macroclimate (used in climate models such as ours) and corresponding changes in microclimates near the forest floor.

Given that forests are sometimes limited in their buffering capacities, organisms may utilize other responses such as behavioral shifts in habitat use and activity patterns (Taub

1961; Spotila 1972; Jaeger 1980; Muñoz et al. 2016) to buffer against current and future climatic changes. For example, Jaeger (1980) found that P. cinereus salamanders closer to forest edges occupied larger cover objects than those in forest interiors, possibly due to increased moisture retention. Likewise, topographical features such as hills and ravines can also affect the strength of the interactions between forest and climate by either amplifying or dampening the microclimate conditions created by the thermal buffering effects of forest structure (Lenoir et al. 2017). Microtopographical features such as slope and aspect can create fine-scale heterogeneity in microclimate conditions via localized changes in moisture, wind, and temperature (Heatwole 1962; Peterman and Semlitsch

2014). In North America, salamander densities are typically highest on cooler, north- facing aspects and in depressions or gullies (areas which receive less solar radiation) compared to warmer and drier south facing slopes and ridges (Heatwole 1962; Moseley

et al. 2009). Consequently, the absence of significant morph frequency changes over the past 40 years may also reflect changes in the behavioral buffering or microhabitat selection of individuals (that we were unable to quantify in this study) if salamanders are also shifting their cover object use in response to climate changes throughout New

England. In our study, we used proportion of forest cover as a proxy for the land use changes caused by habitat fragmentation as forest cover on its own should be ecologically meaningful to terrestrial salamanders. We selected this variable of land use change because the power of our analyses was limited by our small number of sites (n =

50) from the historical survey. The proportion of forest cover was highly correlated (>0.6) with other commonly used fragmentation indices (cohesion, edge density, landscape shape index (LSI), and aggregation (Wang et al. 2014; McGarigal 2015)) that we extracted from the Landsat data. Given these high correlations, it is difficult to determine what additional aspects of land use change, represented in other fragmentation indices, might also be driving or limiting the forest-buffering signal in our data.

Overall, our study found that striped proportions were positively associated with the interaction between forest and climate, suggesting that broader-scale relationship suggested between the frequency of striped individuals and the interaction between climate and forest cover (Cosentino et al. 2017) is also maintained at finer spatial scales in the New England region. However, poor predictive accuracy of the forest-only delta model suggests that forest cover alone is a poor predictor of variations in morph proportions across New England. Our study highlights the importance of empirically

testing the interactive effects of multiple selective pressures across time. Our results indicate that both climate and forest cover are important for influencing the distribution and population dynamics of forest species. This finding has important implications for the accuracy of species response predictions made using models that assume the synergistic effects of multiple selective pressures remain constant over time. Given projections of future climatic change, it is likely that forest cover also may not be sufficient to buffer the ecological and evolutionary responses of forest-dwelling species. In this situation, the importance of other microclimate-altering variables, such as microtopographical features or behavioral buffering, are likely to become more important for dispersal-limited, environmentally sensitive species like salamanders for responding to the changes in selective pressures caused by climate change.

Acknowledgements

We thank Solomon Dobrowski for providing water balance data, and Britta Goncarovs for processing all canopy cover estimates. We also thank James Gibbs for encouragement and comments on earlier study design and Chris Nadeau for statistical advice. The field work associated with this research was conducted under approvals from the University of

Connecticut IACUC (protocol number A15-023) and Duke University IACUC (protocol numbers A281-12-11 and A215-15-08). Field work was supported by the American

Museum of Natural History (Theodore Roosevelt Memorial Fund grant to BRF), and a

UConn EEB Zoology Grant (to AEE). BRF was supported by the Duke University

Graduate School (Summer Research Fellowship and Katherine Goodman Stern

Fellowship).

Statement of intended authorship

This research represents a collaborative effort between a number of researchers. The following people are intended and approved as co-authors on this research when it is submitted for peer-reviewed publication: Annette Evans, Brenna Forester, Mark Urban,

Elizabeth Jockusch, M. Caitlin Fisher-Reid, and Bradley Cosentino. All authors contributed to project conception and revisions of this work. AE and BF collected salamander and climate data, MCFR and BC collected and processed forest cover data,

AE, MU, EJ, and BC contributed to statistical analyses. AE ran the statistical analyses and wrote the manuscript.

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Appendices: Can forest cover mediate the evolutionary responses of a terrestrial salamander to climate change?

Appendix 1 Map of New England study region showing 50 study sites. Additional locality details are available in Evans et al. (2018).

Appendix 2 Landsat WRS-1 (blue polygons: 012031, 013030, 013031, 014029, 014030, 014031, 015029) and WRS-2 scenes (black polygons: 011031, 012030, 012031, 013029, 013030, 013031, 014029) encompassing the sampling locations (circles).

! ! ! ! ! ! ! !

! ! !

! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

0 50 100 200 Km ¯

Appendix 3 Relative importance values of variables in the final random forest regression tree model of canopy cover. Median pseudo R2 was 74.6% and median RMSE was 0.16 for the final model.

Variable Relative importance value Mean TCW 30.98 Mean TCG 25.46 Mean slope 12.64 SD TCW 12.35 Mean TCB 11.74 Mean elevation 7.88

Appendix 4 JAGS code for hierarchical Bayesian model including both additive and interaction effects of climate variables and forest cover proportion for explaining the proportion of Plethodon cinereus striped individuals in the 1970s and 2015 and change in proportion of striped individuals between these periods. Model abbreviations are as follows: AET represents Actual Evapotranspiration, FDD stands for Freezing Degree Days, CTmax stands for Critical Thermal Maximum, prop.forest stands for Proportion of forest cover. sink("landuse_hier_code.3var.txt") cat("model{ #process model for (i in 1:n.site){ y[i] ~ dbin(p[i], n[i]) logit(p[i])<- b0 + Climate[i] + b.forest*prop.forest[i] + b.interaction*(Climate[i]*prop.forest[i]) Climate[i] ~ dnorm(mu.climate[i], tau.climate) mu.climate[i] <- c.aet*AET[i] + c.fdd*fdd[i] + c.ctmax*CTmax[i] y.pred[i]~ dbin(p[i], n[i]) log.likeli[i] <- logdensity.bin(y[i], p[i], n[i])

} #define priors b0~ dnorm(0, 0.001) b.forest~dnorm(0, 0.001) b.interaction~ dnorm(0, 0.001) c.aet~ dnorm(0, 0.001) c.fdd~ dnorm(0, 0.001) c.ctmax~ dnorm(0, 0.001) tau~ dgamma(0.001, 0.001) sigma.climate ~ dunif(0, 100) tau.climate <- pow(sigma.climate, -2)}", fill=T); sink()

Appendix 5 Summary showing the output for the five different Bayesian hierarchical models (Climate * Forest interaction, Climate + Forest additive, Climate only, Forest only, and Intercept only) run using either 25%, 20%, or 30% thresholds for quantifying forest cover for the two sampling periods and the difference between them. Models were run using the following three climate variables: mean actual evapotranspiration from April to November, mean Freezing Degree Days, and Mean Critical Thermal Maximum (base 32oC). Best supported model for each time period shown in bold. Time Model Threshold DIC pD Bayesian WAIC Period p-value Interaction 25% 246.8 53.4 0.5583 226.1 20% 248.8 54.2 0.5692 227.7 30% 244.8 52.0 0.5599 225.0 Additive 25% 255.0 58.2 0.5759 229.7 20% 254.6 57.8 0.5793 230.0 Historical 30% 253.1 56.3 0.5835 230.2 Climate only NA 252.4 55.4 0.5788 229.8 Forest only 25% 1399.1 649.9 0.9905 719.2 20% 1444.1 657.9 0.9717 747.8 30% 1435.7 709.7 0.9849 697.5 Intercept only NA 838.4 1.0 0.7881 838.4 Interaction 25% 226.4 50.8 0.6059 206.1 20% 228.2 51.1 0.6230 208.2 30% 224.3 48.6 0.6003 206.1 Additive 25% 232.7 53.3 0.6348 211.0 20% 233.4 53.5 0.6343 211.4 Current 30% 232.8 53.2 0.6278 211.1 Climate only NA 234.3 53.4 0.6176 212.8 Forest only 25% 736.6 1422.6 0 655.2 20% 738.0 1503.0 0 728.2 30% 758.3 1392.1 0 609.9 Intercept only NA 889.1 1.0 0 904.2 Interaction 25% 336.0 497.3 0.6577 -135.5 20% 492.0 330.8 0.6513 -135.4 30% 351.4 512.9 0.6581 -135.7 Additive 25% 349.9 511.3 0.6917 -134.2 20% 350.9 512.9 0.6859 -134.7 Delta 30% 349.8 510.7 0.6876 -133.7 Climate only NA 326.6 488.0 0.6593 -135.4 Forest only 25% -127.2 4.9 0.5693 -127.8 20% -127.8 4.7 0.5606 -128.6 30% -126.0 5.8 0.5703 -127.3 Intercept only NA -129.8 3.1 0.5125 -130.2

Appendix 6 Decadal average of climate variables across New England for each survey period. Circles show magnitude of morph frequency changes (%) over the past 40 years. (a) and (b) show mean Actual Evapotranspiration from April to November (AET, mm), (c) and (d) show mean number of days above Critical Thermal Maximum, base of 32oC (CTmax, oC), (e) and (f) show mean cumulative number of Freezing Degree Days (FDD, days), and (g) and (h) show mean Maximum temperature from April to November (Tmax, oC). AET - April-November (a) (b) Morph frequency change 1962-1971 2005-2010 0% change 5% change 10% change

High : 110

High : 25

± Low : 0 0 45 90 180 270 360 Km 70

Appendix 6 continued… Degree Days Below Zero

(f) Morph frequency change (e) 1962-1971 2005-2010 0% change 5% change 10% change

High : 0

Maximum tempera±tures - April-November Low : -3015

0 45 90 180 270 360 Km (g) 1962-1971 (h) 2005-2010

High : 23

± Low : 7 0 45 90 180 270 360 Km

Appendix 7 Change in decadal average of climate variables across New England between 1970 and 2014. Circles show location of the 50 study sites. (a) shows change in mean Actual Evapotranspiration from April to November (AET, mm), (b) show change in mean number of days above Critical Thermal Maximum, base of 32oC (CTmax, oC), (c) shows change in 45 mean cumulative number 45 of Freezing Degree Days (FDD, days), and (d) shows change in mean Maximum temperature from April to November (Tmax, oC).

(a) (b) 44 44 15 8

10 6 15 4 8 43 43 5 10 2 6 0 4 5 0

42 2 42

0 0 41 41 −74 −73 −72 −71 −70 −69−74 −73 −72 −71 −70 −69

1.0 1.4 200 300 0.8 150 1.2 250 0.6 1.0 100 200 0.4 0.8 50 150 0.6 0 100 0.4 50 0

Appendix 8 Change in forest cover within a 3 km buffer around each of our 50 study sites (ordered left to right, top down from 1 to 50) between 1974 and 2014. Green represents areas where forest cover proportion has increased, blue shows decreases in forest cover while white is no change. * denotes sites with average decreases in forest cover. See Appendix 1 for map showing geographic location of sites.

* * * * *

* * * *

* *

Appendix 9 Summary showing the output for the post-hoc tests of five different Bayesian hierarchical models (Climate * Forest interaction, Climate + Forest additive, Climate .only, Forest only, and Intercept only) run using either 25%, 20%, or 30% thresholds for quantifying forest cover for our three sampling periods. Models included all three climate variables: mean actual evapotranspiration from April to November, mean maximum temperature from April to November, and mean critical thermal maximum (base 32oC). Best supported model for each time period shown in bold Time Model Threshold DIC pD Bayesian WAIC Period p-value Interaction 25% 249.6 54.9 0.5468 228.2 20% 252.4 56.9 0.5498 229.4 30% 249.5 54.6 0.5424 228.7 Additive 25% 255.5 58.2 0.5551 231.1 20% 255.0 57.8 0.5438 231.1 Historical 30% 256.5 59.0 0.5580 231.3 Climate only NA 256.6 58.5 0.5613 231.8 Forest only 25% 1399.1 649.9 0.9905 719.2 20% 1444.1 657.9 0.9717 747.8 30% 1435.7 709.7 0.9849 697.5 Intercept only NA 838.4 1.0 0.7881 838.4 Interaction 25% 229.8 53.2 0.6185 208.1 20% 233.1 54.5 0.6313 212.1 30% 228.5 52.3 0.6207 207.5 Additive 25% 237.2 56.5 0.6425 213.5 20% 237.6 57.0 0.6373 212.9 Current 30% 238.1 57.5 0.6347 213.3 Climate only NA 240.5 58.6 0.6394 215.0 Forest only 25% 736.6 1422.6 0 655.2 20% 738.0 1503.0 0 728.2 30% 758.3 1392.1 0 609.9 Intercept only NA 889.1 1.0 0 904.2 Interaction 25% 65.9 212.6 0.7109 -125.6 20% 149.0 297.0 0.7236 -127.4 30% 303.1 464.9 0.6896 -130.5 Additive 25% 350.3 510.9 0.6938 -133.3 20% 345.7 506.6 0.6924 -134.0 Delta 30% 345.8 505.4 0.6908 -132.9 Climate only NA 358.1 519.5 0.6616 -135.0 Forest only 25% -127.2 4.9 0.5693 -127.8 20% -127.8 4.7 0.5606 -128.6 30% -126.0 5.8 0.5703 -127.3 Intercept only NA -129.8 3.1 0.5125 -130.2

Chapter 3:

Developmental temperature influences color polymorphism

but not hatchling size in a woodland salamander

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Developmental temperature inﬂuences color polymorphism but not Licensed Content Title hatchling size in a woodland salamander

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Evolutionary responses of a polymorphic amphibian, Plethodon Title cinereus, to climate change

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Oecologia https://doi.org/10.1007/s00442-020-04630-y

PHYSIOLOGICAL ECOLOGY ! ORIGINAL RESEARCH

Developmental temperature in!uences color polymorphism but not hatchling size in a woodland salamander

Annette E. Evans1 · Mark C. Urban1 · Elizabeth L. Jockusch1

Received: 22 August 2019 / Accepted: 3 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020

Abstract Phenotypic plasticity can be an important adaptive response to climate change, particularly for dispersal-limited species. Tem- perature frequently alters developmental and phenotypic traits including morphology, behavior, and reproductive cycles. We often lack crucial information about if and how thermal conditions during development will interact with genetic responses and facilitate persistence or adaptation under climate change. Polymorphic species offer an ideal test for this, as alternative morphs often confer differential adaptive advantages. However, few studies have examined the effects of incubation temperature on color expression or development in polymorphic taxa. Here we test if developmental temperature mediates morph frequency in the polymorphic salamander Plethodon cinereus. Although previous research suggests geographic variation in morph proportions results from differential climate adaptation, it remains unknown if plasticity also contributes to this variation. We used a split-clutch common garden experiment to determine the effects of developmental temperature on the color and development of P. cinereus. Our results indicate developmental temperature affects coloration in P. cinereus, either via plasticity or differential mortality, with eggs incubated at warmer temperatures yielding a higher proportion of unstriped individuals than those from cooler temperatures. This temperature response may contribute to the spatial variation in morph frequencies in natural populations. Surprisingly, we found neither temperature nor egg size affected hatchling size. Our study provides important insights into the potential for climate-induced responses to preserve diversity in dispersal-limited species, like P. cinereus, and enable time for adaptive evolution.

Keywords Plasticity · Adaptation · Plethodontidae · Amphibians · Spatial variation

Introduction in traits such as phenology, physiology, and behavior has been demonstrated to alter species responses to climate Climate is projected to change rapidly in the coming decades change (Merilä and Hendry 2014). Yet plasticity might be and will likely alter the structure and functioning of biologi- as important as genetic variation for responding to climate cal communities (Petchey et al. 1999; Gilman et al. 2010). change by facilitating rapid shifts in ecologically important Populations may vary in their resilience to climate change traits (Charmantier et al. 2008; Merilä and Hendry 2014). either due to underlying evolutionary potential or pheno- Changes in environmental factors, such as temperature, typic plasticity (Charmantier et al. 2008). Genetic variation induce variation in many phenotypic and developmental traits, including morphology (Gomez-Mestre et al. 2010; Tejedo et al. 2010), coloration (Uhlenhuth 1919; Harkey and Communicated by Jean-François Le Galliard. Semlitsch 1988; Garcia et al. 2003), sex (Valenzuela and Electronic supplementary material The online version of this Lance 2004), behavior (Brodie III and Russell 1999; Ballen article (https ://doi.org/10.1007/s0044 2-020-04630 -y) contains et al. 2015), and reproductive cycles (reviewed in Gotthard supplementary material, which is available to authorized users. and Nylin 1995; Urban et al. 2014). Polymorphisms are useful for understanding responses * Annette E. Evans [email protected] to climate change because the long-term persistence of alternative morphs suggests diﬀerential adaptive advan- 1 Department of Ecology and Evolutionary Biology, tages (Moran 1992; Roulin 2004; Gray and McKinnon University of Connecticut, 75 North Eagleville Rd. U-3043, 2007). Color polymorphisms are by definition under Storrs, CT 06269-3043, USA 78 Vol.:(0123456789)1 3

Oecologia genetic control (Fogleman et al. 1980; O’Neill and Beard prediction aligns with the temperature-size rule predicted 2010), although alternative color forms can also occur, for ectotherms (Atkinson 1994; Zuo et al. 2012). at least in part, by phenotypic plasticity (Davison 1964; Given that life history characteristics can also deter- Harkey and Semlitsch 1988; Garcia et al. 2003; Leimar mine evolutionary responses of populations to climate 2009). Here we use a classic climate-linked polymorphic change, we also compared the reproductive output of the species, the eastern red-backed salamander (Plethodon two color morphs. In P. cinereus, it has been suggested cinereus), to evaluate the importance of developmental that unstriped individuals delay reproductive maturity to temperature in mediating morph proportions. This species achieve larger body sizes, whereas striped individuals may has two main color morphs, striped and unstriped, which reach reproductive maturity earlier and then produce more also differ in physiology (Moreno 1989; Petruzzi et al. egg clutches with fewer eggs per clutch across their lifetime 2006), diet (Anthony et al. 2008; Stuczka et al. 2016), (Lotter 1978). Consequently, we predicted that, regardless of behavioral responses to predators (Moreno 1989; Venesky source population, color morphs of P. cinereus would differ and Anthony 2007; Reiter et al. 2014), and disease suscep- in reproductive output, with unstriped females producing tibility (Venesky et al. 2015). larger clutch sizes, thereby matching the lifetime reproduc- The striped and unstriped color morphs of P. cinereus tive output of striped individuals (Lotter 1978). vary in proportion across the species’ geographic range (Gibbs and Karraker 2006; Cosentino et al. 2017), and multiple hypotheses have been proposed to explain the observed Methods distribution patterns. In particular, the temperature-depend- ence hypothesis suggests unstriped individuals tolerate We used a split-clutch common garden experiment to exam- warmer and drier conditions than striped individuals. This ine the effects of incubation temperature on embryo develop- hypothesis is supported by observations of greater propor- ment and growth. We collected 369 wild gravid female sala- tions of unstriped individuals in warmer regions (Lotter and manders from six polymorphic populations, three in Ohio Scott 1977; Gibbs and Karraker 2006; Anthony et al. 2008; and three in Connecticut (Online Resource 1). Females were Evans et al. 2018; but see Moore and Ouellet 2014) and collected between May and June over 3 years (2016–2018), lower energetic costs for unstriped individuals during warm shortly before natural oviposition occurs (Fraser 1980). For periods due to lower metabolic rates (Moreno 1989; but see all gravid females collected, we recorded morph (striped or Petruzzi et al. 2006). Early studies suggested that one or a unstriped). We also photographed each gravid female col- few loci of major effect likely underlie this polymorphism lected and measured snout–vent length (SVL, mm) and tail (Highton 1959, 1975), but these studies measured genetic length (TL, mm) from photographs using ImageJ, version effects in a single environment. Highton (1959) indicated the 1.48. For practical reasons, we measured SVL from the tip need for controlled experiments to rule out possible effects of the snout to the posterior edge of the rear legs, rather than of environmental factors. However, no one has tested this the posterior edge of the vent. This measure is highly corre- possibility, and yet studies continue to assume that the color lated with the more typical SVL measure (Mott et al. 2010). forms are completely genetically determined (Test 1952; Tail length was measured from the posterior edge of the Williams et al. 1968; Lotter and Scott 1977; Pfingsten and hind legs to the tail tip. To maximize egg sample sizes, we Walker 1978; Venesky and Anthony 2007; Fisher-Reid et al. only collected gravid females carrying a minimum of four 2013). If this assumption is wrong, then predictions about eggs (eggs are visible through the female’s body wall), and the genetic and plastic responses of populations to future where possible, we attempted to collect equal numbers of temperatures also might be inaccurate. striped and unstriped individuals even if this meant not col- We used a split-clutch common garden experiment to lecting females of a certain morph with more than four eggs. determine how temperature affects the coloration, growth, During our 2018 sampling, we also recorded the number of and development rate of P. cinereus embryos. We predicted eggs yolked by all gravid females (both those collected and that eggs incubated at warmer temperatures would a yield a not collected from the field), and we photographed gravid higher proportion of unstriped individuals than those incu- females we did not collect before release. The collected bated at cooler temperatures, due to the temperature-depend- gravid females were brought into the laboratory where they ence hypothesis. We also tested the effects of temperature were housed in individual plastic containers lined with damp on the development and growth rates of P. cinereus from paper towels and fed fruit flies (Drosophila melanogaster) multiple populations. We predicted that regardless of color ad libitum. The animals were kept at 20 °C and under 24-h morph or source population, eggs incubated at warmer tem- darkness to mimic their behavior of retreating inside cover peratures (20 °C) would yield smaller hatchlings than those objects or beneath the ground prior to oviposition. Salaman- at colder temperatures (15 °C) due to higher metabolic costs ders were given a minimum of 24 h to acclimate to labora- experienced by ectotherms at warmer temperatures. This tory conditions before oviposition was induced. 79 1 3

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To induce oviposition, we injected each salamander all coloration plasticity models, we included incubation tem- intra-abdominally with 0.1 ml of 0.05 mg/ml of mamma- perature (15 °C or 20 °C) and mother’s phenotype (striped or lian luteinizing hormone-releasing hormone (LHRH, Sigma- unstriped) as fixed factors, and year, population, and family Aldrich #L4513), a standard procedure for obtaining eggs in as random factors. We used two data sets: (1) a paired data- plethodontid salamanders (Jockusch 1996). Eggs obtained set that only included salamander clutches with hatchlings via hormonal injections develop normally (Collazo and surviving at both incubation temperatures (paired sibling Marks 1994) and, therefore, this procedure is unlikely to families), and (2) data from all surviving hatchlings. Due affect the study results. For females that did not oviposit to high egg mortality, we did not include either Dunham- or deposited an incomplete clutch, this injection procedure town Forest (hatchlings = 0) or Hoffman Forest Metropark was repeated up to two more times, with at least 1 week (hatchlings = 6, only 2 related) in plasticity analyses. We between subsequent injections. Salamanders were chilled on used a χ2 distribution with one degree of freedom as our ice for 20–30 min before being injected to reduce stress. We prior distribution on the family variance components (de also applied a topical anesthetic Bactine spray to the injec- Villemereuil et al. 2013) and fixed the residual variance at tion site immediately prior to injections to minimize pain one (Hadfield 2010). Model parameters (iterations, burn-in and any potential for infection. For each resulting clutch, length and thinning interval) are listed in Online Resource we recorded the date(s) of oviposition and number of eggs. 3. For these, and all subsequent models, we checked for suit- We photographed each egg clutch and measured initial egg able model convergence and mixing using Geweke (1991) width and length from photographs using ImageJ, version and Gelman and Rubin (1992) diagnostics as well as visual 1.48. These measurements were used to determine initial inspection of trace plots and posterior distribution plots. egg size, which we calculated as the volume of an ellipsoid To test the effects of incubation temperature on devel- V = 4 !(r × r2) ( 3 1 2 ), where r1 is the longer radius and r2 is the opment and growth of P. cinereus embryos, we fit Bayes- shorter radius of the two initial egg measurements (Furtula ian linear mixed models with a Gaussian family where the et al. 2008). Half of each clutch was assigned to the 15 °C response variable was either development time (number of treatment and half to the 20 °C treatment (Homyack et al. days between oviposition and hatching) or offspring size at 2010). We selected these two experimental temperatures as hatching (total length, mm). Hatchling size was transformed they reflect temperatures of the forest floor during summer using a square root transformation to improve normality. months (Taub 1961; Heatwole 1962) when wild P. cinereus We included temperature, color morph of mother, and color are incubating egg clutches. morph of hatchling as fixed factors, and family, population Within each treatment, each egg was randomly assigned and experimental block as random factors (Gomez-Mestre to a well of a six-well perforated histology cassette, and each and Tejedo 2003). We fitted the family variance components cassette was placed within a plastic container lined with using a prior with a χ2 distribution and the residual variance damp filter paper (Houck et al. 1985). To control for a poten- using an inverse Wishart prior with V = 1 and nu = 0.002, tial block effect, no eggs within a single cassette (‘block’) where V is an estimate of variance and nu is the parameter originated from the same clutch, and block position within for the degree of belief in V (Hadfield 2010). To improve the incubator was rotated daily. To reduce mortality due to model fit, we removed one hatchling outlier with a develop- fungal pathogens, we washed eggs daily in 0.5% hydrogen mental deformity that failed to hatch on its own, shown in peroxide solution followed by a rinse in distilled water and diagnostic tests to have a significant influence on the models. 0.1 × Marc’s Modified Ringer solution (Online Resource 2). To test for differences in reproductive output between P. Eggs with fungal infection or decay were removed. Upon cinereus color morphs, we fitted Bayesian generalized lin- hatching, we recorded the date, total size (snout–tail length, ear mixed models using a Poisson distribution, where the from photographs measured using ImageJ) and color morph response variable was either clutch size (number of eggs (striped or unstriped) of each hatchling. laid), or number of yolked eggs (number of eggs visible through the female body wall prior to laying). The analysis Statistical analyses of number of eggs yolked was restricted to females recorded during the 2018 surveys (including females both collected To determine the effects of incubation conditions on colora- and not collected in the wild), as data on wild gravid tion and development and growth of P. cinereus embryos, females encountered but not collected were not recorded for we used a Markov Chain Monte Carlo (MCMC) approach 2016–2017 samples. For all reproductive output models, we to fit Bayesian generalized linear mixed models in the included female morph (striped or unstriped), SVL and TL MCMCglmm package ver. 2.2.2 (Hadfield 2010). To test as fixed effects, and source population as a random factor. for temperature effects on hatchling coloration, we used the We used Bayesian linear regression to test for differences in phenotype (striped or unstriped) of hatchling salamanders mean initial egg volume (egg size) and mean clutch volume as our response variable and assumed binomial errors. For (product of mean egg volume and clutch size) (Milanovich 80 1 3

Oecologia et al. 2006). Mean egg volume and mean clutch volume were mortality from egg to hatchling stage. We raised fewer both square root transformed to improve normality. We also hatchlings (n = 38) at 15 °C than at 20 °C (n = 62). included clutch size as a predictor variable when modeling Phenotypic plasticity Temperature had a marginally sig- mean initial egg volume. Model parameters (iterations, nificant effect [posterior mean: 0.360, 95% highest poste- burn-in length, and thinning interval) varied between our rior density interval (HPDI): − 0.023, 0.768, Fig. 1a, Online general development models and reproductive models (see Resource 5] on the morph of hatchling salamanders when Online Resource 3). We determined which parameters were using our full hatchling dataset (100 individuals), with 13% significant by evaluating whether the 95% highest posterior more unstriped individuals observed at warmer tempera- density intervals (HPDI) around the posterior mean over- tures compared to cooler temperatures, as predicted (Fig. 2a, lapped zero. We also estimated the pMCMC statistic, which Table 1). Likewise, female phenotype in the full hatchling is calculated as two times the smallest MCMC estimate of dataset also significantly affected hatchling phenotype the probability that a parameter value is either greater than (posterior mean: − 2.697, 95% HPDI: − 5.125, − 0.243, or less than zero. For each random variable, we assessed Fig. 1a, Online Resource 5) as expected by genetics, with heterogeneity, which we calculated as the proportion of the striped females producing 31% more striped hatchlings than total model variance (i.e., the sum of all variance compo- unstriped females. However, neither temperature (posterior nents in the model) associated with that factor (Prokop et al. mean: 0.296, 95% HPDI: − 0.215, 0.785, Fig. 1a, Online 2012). Finally, we used Spearman’s rank correlation test to Resource 5) nor female coloration (posterior mean: -3.145, determine whether a relationship existed between initial egg 95% HPDI: − 7.292, 0.977, Fig. 1a, Online Resource 5) volume and hatchling snout–tail length. All analyses were was significant for the smaller (51 individuals, 16 families) run in R (version 3.2.3, R Core Team 2013). Data gener- paired sibling dataset (Table 1, Fig. 2b). Including Hoff- ated or analysed during this study are available in the Online man Forest Metropark, which had low sample size, in the Resources associated with this article. full plasticity analysis did not change conclusions (Online Resource 6). We found no differences in the development time between Results hatchlings of different color morphs (posterior mean: 1.617, 95% HPDI: − 1.925, 5.238, Fig. 1b, Online Resources 5 and Over 3 years, we captured 369 gravid females, of which 270 7), and we also found that hatchling size did not significantly (73%) successfully laid eggs following LHRH injections differ between morphs (posterior mean: − 0.305, 95% HPDI: (Online Resources 1 and 4). Most females began oviposition − 0.161, 1.027) or developmental temperatures (posterior 3–4 days following LHRH injections, and very few females mean: 0.010, 95% HPDI: − 0.180, 0.199, Fig. 1c, Online (1.6%) laid eggs following a third LHRH injection. We suc- Resources 5 and 8). As expected, individuals developed sig- cessfully raised 100 hatchlings from 51 families across four nificantly faster at 20 °C with a mean development time of out of six original populations (Table 1) despite substan- 58 days, compared to 82 days in the 15 °C treatment (pos- tial, but normal (Highton 1960; Jockusch 1996), laboratory terior mean: − 5.197, 95% HPDI: − 5.727, − 4.675, Online

Table 1 The number of Population Temperature Full hatchling dataset Paired hatchling dataset salamander families and hatchlings of each color morph Families Hatchlings Families Hatchlings that successfully hatched in (striped, (striped, each developmental temperature unstriped) unstriped) treatment for each population included in our plasticity Edison Woods (OH) 15 7 7 (6, 1) 5 6 (5, 1) models 20 13 17 (11, 6) 6 (4, 2) Hinckley Reservation (OH) 15 8 11 (11, 0) 3 3 (3, 0) 20 9 16 (15, 1) 5 (4, 1) Shenipsit State Forest (CT) 15 9 13 (11, 2) 5 8 (7, 1) 20 10 15 (11, 4) 10 (8, 2) Moss Tract (CT) 15 4 7 (7, 0) 3 6 (6, 0) 20 7 14 (12, 2) 7 (7, 0) Global 15 28 38 (35, 3) 16 23 (21, 2) 20 39 62 (49, 13) 28 (23, 5)

The full hatchling dataset includes all hatchling salamanders, regardless of whether related hatchlings were present in both of the temperature treatments and the paired dataset only includes salamander families with related hatchlings in both temperature treatments 81 1 3

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(a) Phenotypic plasticity Resources 5 and 7). Source population, salamander family, and experimental block explained 14.7%, 14.3%, and 1.1% Temperature (respectively) of the total variance (heterogeneity) in development time and 16.5%, 10.4%, and 5.5% (respectively) of Female color the total variance in hatchling size (Online Resource 9). Reproductive output Data on clutch size, egg volume, −8 −6 −4 −2 0 (b) Development time and clutch volume were collected from 267 egg clutches (3 clutches laid were eaten by the female before data Temperature were collected). Color morphs showed no significant dif- Hatchling ferences in any of the four reproductive output measures color we recorded after accounting for female size (Fig. 1d–g, −6 −3 036 Online Resources 10 and 11). For both striped and unstriped (c) Initial hatchling size females, all four reproductive output measures were significantly positively related to female tail length (Fig. 1d–g, Temperature Online Resource 10), meaning that females with longer Hatchling tails not only yolked and laid more eggs but that egg and color clutch volumes were also greater. On average, for every addi- −1 0 (d) Eggs laid tional 1 mm of tail length, the number of eggs yolked and Female color laid by females increased by 10.1% while average egg and clutch volumes increased by 10.6% and 12.0% respectively. Female SVL We found no significant differences in tail length between Female TL color morphs (t = 0.360, p = 0.719) but morphs differed −0.10 −0.05 0.00 0.05 0.10 significantly in SVL (t = 2.023, p = 0.045), with unstriped (e) Eggs yolked individuals being on average 0.80 mm longer than striped Female color individuals. Only two reproductive output measures were Female SVL significantly influenced by female SVL—the number of Female TL eggs yolked and mean initial egg volume (Fig. 1e, f, Online Resource 10)—where females with larger bodies yolked a −0.1 0.0 (f) Egg volume larger number of smaller eggs. On average, for every 1 mm Female color of body length, females yolked an additional 1.6 eggs but 3 Female SVL the average volume of each egg laid decreased by 0.04 mm . Female TL Finally, we found that population differences explained 11% Eggs laid of the variance in mean egg volume and 12% of the variance −0.4 −0.2 0.0 in mean clutch volume. In contrast, population differences (g) Clutch volume explained 92% of the variance in both our models of clutch Female color size and number of eggs yolked (Online Resource 9). Female SVL Female TL Discussion −1.0 −0.5 0.00.5 Mean effect size Coloration plasticity

Fig. 1 Mean effect sizes for a full hatchling coloration plasticity model (in black, top) and the paired hatchling coloration plastic- Our study suggests that we cannot assume, as many earlier ity model (in blue, bottom), b hatchling development time, c initial studies have done, that alternative phenotypes in polymor- hatchling size, and four reproductive output models for the sala- phic species are completely genetically determined. Our Plethodon cinereus mander , d number of eggs laid, e number of work provides the first evidence that color morph may be eggs yolked, f egg volume, and g clutch volume. Circles represent the posterior means for each parameter and the horizontal lines rep- influenced by temperature during development in plethod- resent the 50% (thick lines) and 95% (thin lines) credible intervals. ontid salamanders. Thus, thermal developmental conditions SVL indicates ‘snout–vent length’ and TL indicates ‘tail length’, both likely explain some of the variations in relative frequency measured in mm. Additional information is available from Online of color morphs observed in natural populations that has, Resource 5 (for panels a–d) and Online Resource 10 (for panels e–g) to this point, remained enigmatic (Harkey and Semlitsch 1988). Recent studies have shown that incorporating measures of genetic variation and evolution into species–climate 82 1 3

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(a) (b)

0.3 0.3 d

pe Population ri EW 0.2 0.2 HR MT SSF ion unst t Global or

0.1 0.1 Prop

0.0 0.0

15 20 15 20 Temperature Temperature

Fig. 2 Population reaction norms of the proportion of unstriped indi- families with related hatchlings in both temperature treatments). The viduals of Plethodon cinereus hatching at 15 °C and 20 °C devel- black line shows the global plasticity pattern across all populations, opmental temperatures. Plot a shows the reaction norms for the full obtained by pooling all hatchling data within each of the developmen- hatchling dataset regardless of whether related hatchlings were pre- tal temperatures. See Table 1 for sample sizes. Population abbrevia- sent in both of the temperature treatments; plot b shows the reac- tions are EW (Edison Woods, OH), HR (Hinckley Reservation, OH), tion norms using the paired dataset (only including salamander MT (Moss Tract, CT), and SSF (Shenipsit State Forest, CT) predictive models can substantially alter model predictions and Henry 2010), particularly those that are dispersal-lim- (Kearney et al. 2009; Pearman et al. 2010; Chevin et al. ited such as P. cinereus, from rapid climate change. 2010; Benito Garzón et al. 2019). Plasticity is taxonomi- The expression of the striped phenotype at warmer tem- cally widespread and can vary within and between popula- peratures as well as cold is likely related to the genetic tions (Scheiner 1993). Therefore, incorporating measures mechanism(s) that also influences coloration in this species of plasticity into mechanistic models may resolve some of (Highton 1959). Although the results from our full hatchling the ongoing challenges associated with accurately predict- dataset suggested that developmental temperature may influ- ing species responses to climate change (Urban et al. 2016; ence coloration in P. cinereus, this effect was not significant Riddell et al. 2018). in the paired sibling dataset, which had a wider confidence In most polymorphic species, once established, color intervals, likely the result of small sample sizes in the paired morph is fixed throughout the life of an individual, yet dataset. The temperature effect shown in our results could surprisingly little is known about the role of temperature- also be driven by differential mortality during the incuba- induced coloration, particularly in amphibians. To date, pre- tion stage rather than phenotypic plasticity (Burger and Zap- vious work is restricted to just four studies on two anurans palorti 1988; Viets et al. 1994; Reichling and Gutzke 1996). [ornate chorus frog Pseudacris ornata (Harkey and Sem- It is difficult to assess the effects of differential mortality litsch 1988), leopard frog Rana pipiens (Davison 1964)] and as death often occurs before phenotypic traits of interest three salamander species in the genus Ambystoma [Amby- are detectable (Burger and Zappalorti 1988). Because the stoma barbouri, A. texanum (Garcia et al. 2003), and A. genetic architecture underlying the color polymorphism in P. tigrinum (Uhlenhuth 1919)]. Our results combined with cinereus remains uncertain (Highton 1959, 1975), and most these previous studies suggest that developmental plasticity egg mortality events occurred prior to the formation of the could contribute to the maintenance and spatial variation neural crest (where chromatophores are produced), we could of color polymorphisms in amphibians. Alternative color not directly test whether differential mortality altered the morphs often differ in ecologically important traits (Fors- morph proportions of surviving hatchlings in our study. We man et al. 2008), and heterogeneous environments typically sought to test if selective mortality contributed to our finding impose differential selection on organism traits (DeWitt and that developmental temperature affected the frequency of Scheiner 2004). Therefore, climate-induced plasticity in eco- color phenotypes by testing if greater embryonic mortality logically relevant traits may help buffer populations (Canale occurred in families expected to produce the morph selected 83 1 3

Oecologia against in each thermal environment. However, this was not offspring to forage longer and grow larger before having to the case. Post hoc analyses showed no evidence for differ- retreat to winter refuges (Fraser 1980). ences in egg mortality as a function of female morph or Our work suggests that female body condition (particu- population at either developmental temperature tested in our larly tail length) is responsible for much of the variation study (Online Resource 12). Despite this evidence, we can- observed in reproductive success of this species. Tail size in not completely reject the hypothesis of differential mortality plethodontids affects many ecological traits ranging from the given the limitations of our system. An additional constraint ability to defend a high-quality territory (Wise et al. 2004), we faced is that sperm storage (Sever 1997) and multiple to escape from predators via autotomy (Jamison and Har- paternity within clutches are common in this species (Lieb- ris 1992), and time to maturity (Maiorana 1977). In most gold et al. 2006). It is, therefore, possible that some of our plethodontids, clutch size for the upcoming breeding sea- temperature–coloration results could be genetic if eggs from son is determined during the year preceding oviposition one father made it into one treatment and another father into by factors such as female size (Nagel 1977; Fraser 1980; another treatment. However, because eggs were randomly Herbeck and Semlitsch 2000) or environmental conditions assigned to temperatures, this possibility should not sig- like precipitation (Milanovich et al. 2006). Once established, nificantly affect the overall results other than to increase the clutch size cannot be altered during the year of oviposition variance. (Fraser 1980). In contrast, final ovum volumes (at maturation) are determined after clutch size has been fixed and, Variation in development and reproductive output therefore, are more easily altered based on the addition or loss of energy reserves in the tail (Fraser 1980). Life history characteristics and reproductive output are Most studies on clutch size relationships in salamanders also expected to be important for determining the ecologi- examine the number of eggs yolked (ovarian eggs) rather cal and evolutionary responses of populations to climate than number of eggs laid as the measure of reproductive change (Bradshaw and Holzapfel 2008; Reed et al. 2011). success (Blanchard 1928; Nagel 1977; Lotter 1978; Fraser Phenological shifts are among the most common climate 1980; Hom 1987; Herbeck and Semlitsch 2000). However, change responses (Root et al. 2003; Thackeray et al. 2016), detectable differences between these two clutch size meas- and are often the result of plasticity (Urban et al. 2014). In ures and post hoc tests suggest that the number of eggs ectotherms, temperature-induced plasticity in developmental yolked (rather than eggs laid) significantly affected average traits such as growth rate, morphology, and time to hatch- egg volume (posterior mean: − 0.037, 95% HPDI: − 0.075, ing are frequently observed (Atkinson 1994; Du and Shine 0.000, Online Resource 12). This discrepancy suggests the 2015), with cooler developmental temperatures typically importance of distinguishing the different effects of these inducing slower growth and development rates, larger initial two measures of reproductive potential when considering hatchling sizes, and an increase in developmental abnormali- the viability and life history traits of wild populations. For ties (Voss 1993; Kaplan et al. 2006; Ringia and Lips 2007). example, while we did not detect significant differences Our study suggests that P. cinereus might benefit from faster in reproductive success between P. cinereus color morphs developmental rates under projected climate changes with- after correcting for female size, our results do suggest that out having to compromise on initial hatching size. This spe- unstriped individuals have greater reproductive potential cies is, therefore, an exception to the ectotherm developmen- due to their overall larger SVL and the positive correlation tal temperature–size rule (Atkinson 1994; Zuo et al. 2012) as between SVL and number of eggs yolked (but not number of temperature did not significantly affect hatchling size. The eggs laid). The frequency and benefits of egg reabsorption in reasons for this pattern remain unknown, although they may the wild are uncertain (Ng and Wilbur 1995), but if natural relate to the physiology and efficiency of yolk absorption egg reabsorption occurs less frequently than in the labora- (Montague 1987). tory, this higher reproductive potential in unstriped individu- Our finding that hatchling size is not correlated with als may ultimately translate into higher reproductive output initial egg size contrasts with another general reproductive (increased clutch size), although at a cost to initial egg size. trend often observed in ectotherms (Salthe 1969; Salthe and Mecham 1974; Kaplan 1980; Duellman and Trueb 1994; Wells 2007). For the trade-off between clutch size and egg Conclusion size to persist, larger eggs must experience some meaningful benefit over smaller eggs, and in our experiment, post hoc For dispersal-limited, environmentally sensitive species, tests revealed that larger eggs developed more rapidly than plasticity can provide a crucial and potentially favorable smaller eggs (posterior mean: − 0.199, 95% HPDI: − 0.318, response to climate change. Despite decades of research, − 0.073, Online Resource 13), which might reduce the risk little work has directly tested if and how phenotypic of fungal infections (Warkentin et al. 2001) and enable 84 1 3

Oecologia plasticity affects the development and reproductive success References of many polymorphic species. Here, we provide the first evidence that developmental temperature influences col- Anthony CD, Venesky MD, Hickerson CAM (2008) Ecological sepa- oration in the salamander P. cinereus, suggesting that tem- ration in a polymorphic terrestrial salamander. J Anim Ecol 77:646–653 perature likely influences the morph frequency distribution Atkinson D (1994) Temperature and organism size—a biological in this species, although the relative effects of plasticity law for ectotherms? In: Begon M, Fitter AH (eds) Advances in compared to other mechanisms like differential mortality ecological research. Academic Press, New York, pp 1–58 remain uncertain. This result suggests the need to test for Ballen CJ, Shine R, Olsson MM (2015) Developmental plasticity in an unusual animal: the effects of incubation temperature on plasticity in coloration in other polymorphic species. We behavior in chameleons. Behaviour 152:1307–1324 also unexpectedly found that temperature did not affect the Benito Garzón M, Robson TM, Hampe A (2019) ΔTrait SDMs : spe- initial size of individuals at hatching, which contrasts with cies distribution models that account for local adaptation and observations in other taxa. Given that P. cinereus embryos phenotypic plasticity. New Phytol 222:1757–1765. https ://doi. org/10.1111/nph.15716 can benefit from faster developmental rates without having Bernardo J, Arnold SJ (1999) Mass-rearing of plethodontid to compromise on initial hatching size, this may allow P. salamander eggs. Amphib Reptil 20:219–224. https ://doi. cinereus populations to derive reproductive benefits from org/10.1163/15685 3899X 00231 projected climatic changes. Finally, our work highlights Blanchard FN (1928) Topics from the life history and habits of the red-backed salamander in southern Michigan. 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Clim Acknowledgements We thank Sandy Flittner from Erie MetroParks Res 43:135–147. https ://doi.org/10.3354/cr008 97 (Ohio), Bridget Derrick and Rocky Carpenter from Findley State Park Charmantier A, McCleery RH, Cole LR et al (2008) Adaptive phe- (Ohio), Ann Dunnack and Careen Jennings of Joshua’s Trust (Con- notypic plasticity in response to climate change in a wild bird necticut), and Patrick Lorch from Hinckley Reservation (Ohio) for their population. Science 320:800–803. https://doi.org/10.1126/scien assistance with site permission and access. We thank the numerous ce.11571 74 people who helped with fieldwork and animal care, particularly Mark Chevin L-M, Lande R, Mace GM (2010) Adaptation, plasticity, and R. Smith, Amanda Pastore, Dana Drake, Meera Joshi, Jack Phillips, and extinction in a changing environment: Towards a predictive Julia Peay. Thanks to Barry and Nancy Forester as well as Carlton and theory. PLoS Biol 8:e1000357. https ://doi.org/10.1371/journ Lisa Park-Boush for kindly providing assistance and accommodation al.pbio.10003 57 in Ohio. We also thank Carl Schlichting, Morgan Tingley, Kentwood Collazo A, Marks SB (1994) Development of Gyrinophilus porphy- Wells, and two anonymous reviewers for comments on earlier versions riticus: identification of the ancestral developmental pattern in of this manuscript. This project was supported by funding from Sigma the salamander family Plethodontidae. J Exp Zool 268:239–258 Xi GIAR, the Society for Integrative and Comparative Biology (SICB) Cosentino BJ, Moore J-D, Karraker NE et al (2017) Evolutionary GIAR, the Society for the Study of Evolution (SSE) Rosemary Grant response to global change: climate and land use interact to shape Award, and UConn EEB Zoology awards to AE. Order of the last two color polymorphism in a woodland salamander. Ecol Evol. https authors was determined by Plethodontid identification contest. ://doi.org/10.1002/ece3.3118 Davison J (1964) A study of the spotting patterns in the Leopard frog Author contribution statement AE designed the project, collected and III. Environmental control of genic expression. J Hered 55:47– analyzed the data, and wrote and revised the manuscript; MU and EJ 56. https ://doi.org/10.1093/oxfor djour nals.jhere d.a1072 89 contributed to project design and manuscript revisions. de Villemereuil P, Gimenez O, Doligez B (2013) Comparing par- ent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simula- Compliance with ethical standards tion study for Gaussian and binary traits. Methods Ecol Evol 4:260–275. https ://doi.org/10.1111/2041-210X.12011 Research involving human participants and/or animals All applicable DeWitt TJ, Scheiner SM (2004) Phenotypic plasticity: Functional institutional and/or national guidelines for the care and use of animals and conceptual approaches. Oxford University Press, Oxford were followed. This research was conducted under approvals from the Du W-G, Shine R (2015) The behavioural and physiological strategies University of Connecticut IACUC (protocol numbers: A15-023, A18- of bird and reptile embryos in response to unpredictable varia- 025), Federal Fish and Wildlife permit (MA93731B-0), Ohio permits tion in nest temperature: Thermal response of reptile and bird (numbers: 17-271, 18-184, and 19-151), and Connecticut permits embryos. Biol Rev 90:19–30. https ://doi.org/10.1111/brv.12089 (numbers: 0915008 and 0920008). Duellman WE, Trueb L (1994) Biology of Amphibians. JHU Press, Baltimore

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Electronic Supplementary materials

Developmental temperature influences color polymorphism but not hatchling size in a woodland salamander

Authors: Annette E. Evans1*, Mark C. Urban1, Elizabeth L. Jockusch1

1University of Connecticut, Department of Ecology and Evolutionary Biology, 75 North Eagleville

Rd. U-3043, Storrs, CT 06269-3043, USA

88 1

Online Resource 1 Number of gravid female eastern red-backed salamanders (Plethodon cinereus) that were collected and successfully laid eggs, and average underlying unstriped frequency for each of our study sites in Ohio (OH) and Connecticut (CT) over the three-year duration of our study. Average underlying unstriped frequency represents the frequency of all unstriped individuals in the population (n= combined number of males, females and juveniles) encountered during across field surveys. Population Average underlying Gravid females Females (Latitude, Longitude) Year unstriped collected that laid frequency Edison Woods (OH) 2016 17 6 (41.35127, -82.46744) 2017 33 28 41.7% 2018 33 32 (n = 598) Hinckley Reservation (OH) 2016 0 -- (41.21534, -81.71474) 2017 33 26 8.3% 2018 33 21 (n = 479) Hoffman Forest Metropark (OH) 2016 0 -- (41.31995, -82.52622) 2017 17 11 59.1% 2018 21 21 (n = 227) Shenipsit State Forest (CT) 2016 28 15 (41. 96238, -72.41429) 2017 11 9 18.9% 2018 33 28 (n = 890) Moss Forest Tract (CT) 2016 22 13 (41.84579, -72.24535) 2017 23 14 29.3% 2018 33 23 (n = 598) Dunhamtown Forest (CT) 2016 0 -- (41.78406, -72.25856) 2017 0 -- 24.9% 2018 30 23 (n = 325)

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Online Resource 2

Recipe used to produce 10x Marc’s Modified Ringer (MMR) solution, derived from: https://ase.tufts.edu/biology/labs/levin/resources/documents/GettingEggsInjection.pdf

58 g NaCl 1.5 g KCl

2.5 g MgSO4 2.9 g CaCl 11.9 g Hepes 0.29 g EDTA Water to 1 L Buffer pH to 7.8 and autoclaved.

3 Online Resource 3 Number of iterations, burn-in length, thinning interval, and samples from the posterior for each of our MCMCglmm models for our analyses of general salamander reproduction and developmental responses.

Model response variable Number of Burnin length Thinning Samples from iterations interval posterior Hatchling color 6,000,000 600,000 220 24,546 (Full hatchling dataset) Hatchling color 6,000,000 600,000 220 24,546 (Paired hatchling dataset) Development time 5,000,000 500,000 220 20,455 Hatchling size 6,000,000 500,000 250 22,000 Eggs laid 6,000,000 500,000 250 22,000 Eggs yolked 6,000,000 600,000 250 21,600 Egg volume 6,000,000 600,000 265 20,378 Clutch volume 1,000,000 50,000 270 3,519 Post-hoc tests Egg volume ~ eggs yolked 5,000,000 500,000 250 18,000 Development time ~ egg volume 5,000,000 500,000 250 18,000

Online Resource 4 Summary table showing average values of population and reproductive characteristics for each of the six sites included in our study. SVL (snout-vent length) and TL (tail length) are measured in mm

Hoffman Shenipsit Edison Hinckley Moss Population Forest State Dunhamtown Woods Reservation Forest characteristics Metropark Forest Forest (CT) (OH) (OH) Tract (CT) (OH) (CT) Population unstriped 41.7% 8.3% 59.1% 18.9% 29.3% 24.9% ratio Ratio of unstriped 47.0% 9.1% 57.9% 27.8% 24.7% 23.3% females collected Number of females 82 66 38 72 81 30 collected Number of females that 66 47 32 52 50 23 laid eggs Average SVL of females 40.3 38.7 42.4 39.3 39.3 41.8 that laid Average TL of females 42.8 40.6 50.0 40.0 42.2 45.4 that laid Average clutch size 3.9 4.5 5.8 5.4 6.1 8.1 Average number of eggs 7.8 6.6 10.7 6.9 8.7 8.7 yolked (all wild females) Average number of yolk eggs in females 8.2 6.9 10.7 7.9 8.3 8.7 collected Average egg size 25.4 25.3 24.1 20.3 20.1 22.1 (volume) Average clutch volume 208.28 174.57 257.87 160.37 166.83 192.27 Average development 60.4 59.6 62.3 55.5 54.4 NA time in days (20oC) Average development 88.4 84.4 92 81.8 79.1 NA time in days (15oC) Unstriped hatchling 0.29 0.04 0.43 0.21 0.10 NA ratio

Online Resource 5

1.00

0.6

0.75

p ed i r

p Population i r 0.4 EW HR HP

ility unst 0.50 b ility unst MT a b b a SSF b ro Global P ro

P 0.2 0.25

0.0 0.00

15 Temperature 20 15 Temperature 20

Reaction norm plots for (a) full hatchling and (b) paired hatchling datasets including Hoffman Forest Metropark (HP) population. The black line shows the global plasticity pattern across all populations. Posterior means (and 95% highest posterior density interval) for developmental temperature in the full hatchling model including the HP population is 0.316 (-0.025, 0.681) for the full hatchling model. The paired plasticity model would not converge when the HP population was included due to the very small sample size (n=1) for this population. Population abbreviations are EW (Edison Woods, OH), HR (Hinckley Reservation, OH), HP (Hoffman Forest Metropark, OH), MT (Moss Tract, CT), and SSF (Shenipsit State Forest, CT).

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Online Resource 6 Posterior means of MCMC models examining the effects of developmental temperature on the phenotype and developmental rate of Plethodon cinereus hatchlings. HDPI represents the 95% highest density posterior interval (lower and upper bounds). The pMCMC statistic tests whether the parameter is significantly different from zero and suggests whether the variables contribute to explaining variation in the response variable.

Response Independent variables Posterior mean (95% HDPI) pMCMC

Hatchling coloration Temperature 0.360 (-0.023, 0.768) 0.054

(Full dataset) Female color -2.697 (-5.125, -0.243) 0.019 *

Hatchling coloration Temperature 0.296 (-0.215, 0.785) 0.235

(Paired dataset) Female color -3.145 (-7.292, 0.977) 0.119

Development time Temperature -5.197 (-5.727, -4.675) < 0.001 ***

Hatchling color 1.617 (-1.925, 5.238) 0.376

Hatchling size Temperature 0.010 (-0.180, 0.199) 0.911

Hatchling color -0.305 (-0.161, 1.027) 0.648

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Online Resource 7

Hatchling

Color 100 Unstriped

Striped

ys) a

elopment time (d v e D 60

15 20 Temperature Treatment (°C)

Development time for striped and unstriped Plethodon cinereus hatchlings incubated at 15oC and 20oC. The upper and lower hinges correspond to the 25th and 75th percentiles around the median with the upper and lower whiskers extending no further than 1.5 times the inter- quartile range (IQR). Outlier points (beyond the whiskers) are plotted individually.

8 95

Online Resource 8

22.5 Hatchling Color Unstriped Striped 20.0

17.5 e (mm)

15.0

Hatchling si

12.5

15 20 Temperature Treatment (°C)

Initial hatchling size (snout-tail length, mm) for striped and unstriped Plethodon cinereus individuals incubated at 15oC and 20oC developmental temperatures. The upper and lower hinges correspond to the 25th and 75th percentiles around the median with the upper and lower whiskers extending no further than 1.5 times the inter-quartile range (IQR).

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Online Resource 9 Results for random effects included in each reproductive output MCMCglmm model. HDPI represents the 95% highest density posterior interval (lower and upper bounds). Heterogeneity represents the proportion of the total model variance (i.e. the sum of all variance components in the model) associated with each random variable.

Response Random variable Posterior mean (95% Heterogeneity (%) HDPI) Hatchling coloration Year 0.585 (6.54e-12, 2.375) 12.0%

(Full hatchling Population 0.606 (1.34e-10, 2.434) 13.1%

dataset) Family 2.431 (5.64e-8, 6.804) 46.1%

Hatchling coloration Year 0.859 (8.80e-11, 3.330) 16.7%

(Paired hatchling Population 2.008 (1.48e-9, 2.930) 15.6%

dataset) Family 0.760 (5.56e-9, 5.846) 38.5%

Development time Family 7.487 (1.49e-4, 22.245) 14.7%

Population 13.28 (2.29e-4, 43.606) 14.3%

Block 0.583 (1.40e-4, 49.838) 1.11%

Hatchling size Family 1.015 (2.69e-13, 2.202) 16.5%

Population 0.706 (2.17e-7, 2.342) 10.4%

Block 0.344 (1.18e-10, 1.154) 5.5%

Clutch size Population 0.112 (0.008, 0.330) 92.1%

Eggs yolked Population 0.008 (0.009, 0.381) 92.3%

Mean egg volume Population 0.120 (1.07e-7, 0.367) 11.8%

Mean clutch volume Population 1.334 (0.025, 3.328) 11.1%

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Online Resource 10 Posterior means of MCMC models examining four reproductive output variables for the salamander Plethodon cinereus. HDPI represents the 95% highest density posterior interval (lower and upper bounds). The pMCMC statistic tests whether the parameter is significantly different from zero and suggests whether the variables contribute to explaining variation in the response variable. SVL stands for ‘snout-vent length’ and TL stands for ‘tail length’ of female salamanders. Salamander lengths measured in mm.

Response Independent variables Posterior mean (95% HDPI) pMCMC

Clutch size Female color 0.003 (-0.112 0.128) 0.959

Female SVL 0.013 (-0.007, 0.033) 0.206

Female TL 0.103 (0.004, 0.023) 0.008 **

Eggs yolked Female color -0.049 (-0.118, 0.127) 0.390

Female SVL 0.027 (-0.007, 0.034) < 0.004 **

Female TL 0.013 (0.003, 0.023) < 0.002 **

Mean egg size Female color -0.138 (-0.377, 0.104) 0.257

(volume) Female SVL -0.044 (-0.085, -0.003) 0.030 *

Female TL 0.055 (0.036, 0.075) < 0.001 ***

Eggs laid -0.032 (-0.082, 0.016) 0.199

Mean clutch volume Female color -0.151 (-1.012, 0.676) 0.733

Female SVL 0.010 (-0.137, 0.149) 0.878

Female TL 0.182 (0.111, 0.250) < 0.001 ***

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Online Resource 11

(a) (b)

e 10 z 10

Clutch si 5 Eggs yolked 5

Unstriped Striped Unstriped Striped

Female color Female color )

3 ) 50 (c) (d) 3

300 40

30 200

20 100

10 Mean clutch volume (mm Mean egg size (Volume mm 0 Unstriped Striped Unstriped Striped Female color Female color

Boxplots showing results for four reproductive output measures: (a) clutch size, (b) eggs yolked, (c) mean egg size (volume), and (d) mean clutch volume, recorded for unstriped and striped eastern red-backed salamanders (Plethodon cinereus). The upper and lower hinges correspond to the 25th and 75th percentiles around the median with the upper and lower whiskers extending no further than 1.5 times the inter-quartile range (IQR). Outlier points (beyond the whiskers) are plotted individually.

12 99

Online Resource 12 MCMC output for generalized linear models of proportion of egg mortality under each developmental temperature for each population included in our plasticity analyses. Models were run in the MCMCglmm package assuming a multinomial distribution of errors. We used an inverse Wishart prior for our residual variance and a Chi squared distribution with one degree of freedom as the prior on the family variance components. Both models were run for 500,000 iterations, with a burn-in of 30,000 and thinning interval of 70.

Temperature Independent variables Posterior mean (95% HDPI) pMCMC 15oC Female color -0.659 (-2.054, 0.729) 0.329 Edison Woods (OH) 0.161 (-1.799, 2.252) 0.885 Hinckley Reservation (OH) 0.320 (-1.993, 2.449) 0.785 Moss Tract (CT) 1.050 (-1.050, 3.467) 0.342 Shenipsit State Forest (CT) -0.454 (-2.505, 1.505) 0.677 20oC Female color -0.793 (-2.428, 0.714) 0.309 Edison Woods (OH) -1.205 (-3.439, 1.269) 0.301 Hinckley Reservation, OH -0.216 (-2.928, 2.473) 0.878 Moss Tract (CT) -0.106 (-3.745, 1.684) 0.421 Shenipsit State Forest (CT) -2.119 (-4.892, 0.530) 0.108

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Online Resource 13

(a) Development time

Egg volume

Temperature

0 Mean effect size

(b) Egg volume

Female color

Female SVL

Female TL

Eggs yolked

Mean effect size

Mean effect sizes for post-hoc tests of reproductive output in Plethodon cinereus. Plot (a) shows the effects of initial egg volume and developmental temperature on development time of eggs. Plot (b) shows the effect of female coloration, female snout-vent length (SVL), female tail length (TL) and number of eggs yolked on initial egg size. Circles represent the posterior means from the full hatchling plasticity model (in black) and the paired hatchling plasticity model (in blue). The horizontal lines represent the 50% (thick lines) and 95% (thin lines) credible intervals.

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Chapter 4:

Thermal sensitivity of physiological performances in the

polymorphic salamander, Plethodon cinereus

Abstract

Environmental temperature has a profound impact on the physiological performance of ectotherms, with increasing temperatures often resulting in higher metabolic rates and lower energy assimilation efficiencies. For ectothermic species such as salamanders, physiological performance governs all aspects of ecology and is therefore likely to be under strong natural selection. The sensitivity of physiological traits to temperature is often correlated with the environmental conditions experienced by organisms and hence often differs across spatial scales and between populations. In the forests of northeastern

America, populations of red-backed salamanders (Plethodon cinereus) show differing proportions of two main color morphs, striped and unstriped. Mechanisms underlying the long-term persistence of these alternative color morphs across the species’ range remain uncertain as most ecological differences favor the persistence of striped individuals.

Physiological differences are one mechanism that may maintain unstriped individuals in polymorphic populations, yet morph-specific physiology differences are largely uncertain.

Here, we test whether color morphs vary in their digestive performance and standard metabolic rate across two ecologically relevant temperatures. Our results show that morphs differ in some physiology measures, with striped individuals showing higher levels of energy assimilation efficiency and digestive efficiency. In contrast, we found no 102

evidence that morphs differed in standard metabolic rate. These results suggest unstriped individuals in our populations do not experience physiological benefits that may enable them to outcompete striped individuals at the two temperatures we tested. Our study showed that both color morphs show plastic responses in digestive performance and metabolic rate in response to changes in temperature, and we found evidence for population differences in physiological responses. The thermal plasticity observed in our study suggests these populations may show increased resilience to climate change, although weak correlations between digestive performance and metabolic rate indicate changes in digestive performance alone are unlikely to compensate for increased energetic costs under future climate change.

Introduction

Nutrient availability and temperature exert strong control over the functioning and biological diversity of ecosystems. The interplay between these two factors can alter not only the acquisition and flow of energy through ecosystems but also the behavior, distribution and physiology of taxa at all trophic levels (Cross et al. 2015). Physiological processes such as metabolic rate and energy assimilation are known to be particularly sensitive to changes in temperature, with high temperatures typically elevating metabolic maintenance costs and lowering energy assimilation efficiencies (Fitzpatrick 1973; Bobka et al. 1981). Energy assimilation is an important factor in an organism’s energy balance as it reflects an organism’s capacity to process and absorb nutrients from ingested food

(Benavides et al. 2005). Ectothermic animals often have higher energy conversion

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efficiencies and lower standard metabolic rates compared to endothermic animals, as they use exogenous heat sources to raise their body temperatures (Pough 1983).

Consequently, they can play a vital role in driving the energy flow and dynamics within ecosystems. For example, Burton and Likens (1975) found that a population of the salamander Plethodon cinereus in New Hampshire fixes 4,922 kcal per hectare per year, approximately five-fold higher than the entire local avian community. Because energy assimilation can determine the overall energy budget of an organism and can govern or constrain life history traits such as growth and reproduction, it is expected to be under strong selection (Clay and Gifford 2017). Consequently, geographic variation in environmental conditions, such as temperature, is expected to result in differences in the thermal tolerance and sensitivity of physiological performances among populations

(Gaitán-Espitia et al. 2014).

In the forests of northeastern America, populations of red-backed salamanders (P. cinereus) differ in the proportion of two color morphs, striped and unstriped (Moreno

1989). These color morphs have persisted for millions of years (Fisher-Reid and Wiens

2015), long enough for neutral drift to lead to fixation of one morph, and therefore it is assumed that morphs diverge along one or more niche axes (Moreno 1989; Anthony et al. 2008; Stuczka et al. 2016; Evans et al. 2018). Differences in niche partitioning are important as ecological divergence between morphs can allow selection to vary across small spatial or temporal scales, promoting the long-term maintenance of this color polymorphism. One of these niche axes could be physiology, such as differences in

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metabolic rate and energy assimilation efficiencies. Previous studies investigating physiological differences between P. cinereus color morphs have yielded conflicting results with some evidence of lower standard metabolic rates in unstriped individuals compared to striped individuals in some populations, but not in others (Moreno 1989;

Petruzzi et al. 2006), and physiological differences are not consistently found across temperatures (Petruzzi et al. 2006). Differences in tradeoffs between metabolic rate and energy assimilation efficiency between the two main color morphs of P. cinereus remain untested and might also contribute to the persistence of unstriped individuals. Moreover, differences in energy assimilation efficiency between the two morphs may explain how the morphs can exploit different diets (Benavides et al. 2005) and may explain spatial distributions of morphs by enabling unstriped individuals to cope with reduced foraging time and higher metabolic rates experienced in warmer regions of the species’ range

(Lotter and Scott 1977; Fisher-Reid et al. 2013).

Our study aims to test whether unstriped individuals benefit from higher energy assimilation efficiencies and lower metabolic rates compared to striped individuals, which would enable them to persist at higher frequencies in warmer geographic regions (Lotter and Scott 1977; Gibbs and Karraker 2006) and on lower quality diets (Anthony et al. 2008;

Stuczka et al. 2016). We aim to determine if P. cinereus color morphs from different polymorphic populations differ in their energy assimilation efficiency and metabolic rate across a variety of temperature regimes typically encountered seasonally by wild populations. Our study aimed to test the following three hypotheses: 1) Color morphs

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differ in digestive performance, 2) Color morphs differ in standard metabolic rates, 3)

There is a relationship between metabolic rate and digestive performance that is influenced by both temperature and coloration. Given previous evidence that unstriped individuals survive on lower quality diets compared to striped individuals (Anthony et al.

2008), we predict that unstriped individuals (regardless of source population) will have higher energy assimilation efficiencies than striped individuals. We also predict that unstriped individuals (regardless of source population) will show lower standard metabolic rates than striped individuals as previous studies found evidence for low metabolic rates in unstriped individuals (but only in some populations, see Petruzzi et al. (2006)). Finally, we predict that warmer temperatures will result in a reduced correlation between standard metabolic rate and digestive performance measures (energy assimilation efficiency and digestive efficiency) as temperature is predicted to lower digestive performance yet increase metabolic rate (Fitzpatrick 1973; Bobka et al. 1981). We expect that this correlation will be weaker in unstriped individuals compared to striped individuals, if unstriped individuals show higher digestive performance and lower metabolic rates compared to striped morphs.

Methods

We collected 136 female salamanders over two years (2017 and 2018) from four different polymorphic populations located at similar latitudes across eastern North America, two in

Connecticut (Shenipsit State Forest and Moss Tract) and two in Ohio (Edison Woods and

Hoffman Woods MetroPark) (Table 1). All salamanders in our physiology experiments

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were in a post-reproductive state. We knew the reproductive history of each salamander as all individuals were gravid females collected in May for reproduction experiments

(Evans et al. 2020). Prior to the physiology experiments, salamanders were housed in individual plastic containers lined with damp paper towels and maintained in temperature- controlled incubators at 20oC under a 0L: 24D light cycle. This light cycle mimics the conditions these post-reproductive females would experience in the wild during and following breeding when they retreat inside logs or beneath the surface and tend to their egg clutches (Ng and Wilbur 1995). The animals were fed Drosophila flies ad libitum up until the commencement of the physiological experiments.

Table 1 Number of striped and unstriped Plethodon cinereus from four polymorphic

populations used in our energy assimilation feeding trials and metabolic rate experiments. Number of individuals used in metabolic rate experiments shown in parentheses.

Population Striped morph Unstriped morph 2017 2018 2017 2018 Edison Woods (OH) 12 (12) 10 (8) 12 (8) 8 (8) (41.35127, -82.46744) Hoffman Forest Metropark 8 (7) 7 (7) 9 (9) 8 (8) (OH) (41.31995, -82.52622) Moss Tract (CT) 8 (8) 12 (9) 2 (2) 7 (7) (41.84579, -72.24535) Shenipsit State Forest (CT) 8 (8) 18 (11) 2 (2) 5 (5) (41. 96238, -72.41429)

Energy Assimilation Efficiency

The methods we used to measure energy assimilation efficiencies were modified from

Clay and Gifford (2017). We used feeding trials to quantify energy assimilation (Merchant

1970) and we measured energy assimilation efficiency of each salamander under two

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ecologically relevant temperatures, 15oC and 20oC (Bobka et al. 1981), to test for differences in thermal sensitivity of physiological performance between color morphs and populations. Before starting our experiments, we starved the salamanders for seven days at 20oC to ensure individuals were in a post-absorptive state (Fitzpatrick and Brown 1975;

Homyack et al. 2010; Gifford et al. 2013). The day prior to testing, we weighed and photographed each salamander and placed each animal into a clean plastic housing container lined with damp paper towels. We fed each salamander 10 fruit flies (Drosophila hydei) on day one (Clay and Gifford 2017). Then each day, we recorded the number of flies eaten, counted and removed deceased flies, and added back the number of flies removed or eaten so that each salamander began each day with 10 live fruit flies. After five days of feeding, we recorded and removed all remaining fruit flies and fasted the salamanders until their digestive tract was clear (approximately 1-2 weeks) (Clay and

Gifford 2017). We collected all fecal pellets and combined them with other biological waste materials (e.g., shed skin) produced by each individual during this time. Once the digestive tracts of the salamanders were clear, they were returned to their normal feeding regime for a week before the energy assimilation efficiency trials were repeated at 15oC.

For each salamander fecal pellet, we recorded wet weight and dry weight (after drying samples for 24 – 36 hours at 80oC). We used a Parr 6725 Semi-micro Calorimeter (Parr

Instrument Company, Moline, IL, USA) to determine the energy content of salamander fecal pellets and subsamples of fruit flies. As individual fruit flies and fecal pellets were too small to process on their own, we pooled subsamples of fruit flies (collected from

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different fly cultures over the two years of our experiment) into two groups (n1=50 flies and n2 = 52 flies). We pooled salamander fecal pellets by temperature, color morph and population (i.e. all striped individuals from the Moss Tract population tested at 15oC were pooled to form one fecal sample). This yielded 16 total pooled fecal samples and 2 pooled fly samples, which were then pelletized to minimize the loss of sample during microbomb calorimetry processing. In order to account for potential equipment/measurement error, we divided the larger pooled fecal samples in half for 12 of the 16 fecal samples (Appendix

1) and processed each half in separate calorimetry runs. The results of these paired runs were averaged in order to determine the average energy (kJ) per gram of dry fecal mass at each temperature for each morph in each population. The energy content for the remaining four salamander fecal samples and two fruit fly samples was determined using only one run as the fecal pellets were too small to split into subsamples for separate runs.

To determine the initial energy acquired through feeding (Ea) by each salamander we multiplied the average energy content per fly (calculated to be 0.011kJ) by the number of fruit flies eaten by each salamander during the feeding trials. We determined energy lost through waste products (feces and shed skin (Ef)) for each salamander by multiplying the dry mass of each individual’s fecal pellet by the mean energy content calculated for the pooled fecal pellet from that morph and population. Energy acquired and energy lost were then each divided by the mass of the salamander at the beginning of each trial, and duration of the study to provide a measure of energy gained or lost (in kilojoules) per day

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per gram of salamander. We calculated the daily assimilated energy per gram and overall digestive efficiency using the following equations:

Assimilated energy = Ea – Ef

Digestive efficiency (%) = Ea – Ef / Ea x 100

Standard Metabolic Rate

In order to compare the relationships between various physiological measures, we used a random subset of the same individuals from our energy assimilation efficiency trials to measure standard (minimum) metabolic rates (Table 1). For each temperature treatment

(20oC and 15oC) we measured each salamander’s standard metabolic rate (SMR)

(oxygen consumption) using a Sable Systems FoxBox respirometer (Sable Systems

International Inc., North Las Vegas). We fasted salamanders for one week prior to metabolic measurements to ensure the digestive tract of the salamanders was clear and individuals were in a post-absorptive state during measurement (Fitzpatrick and Brown

1975; Homyack et al. 2010; Gifford et al. 2013). To measure oxygen consumption, we used the manual bolus injection method described by Lighton (2008). We allowed each salamander to acclimate to the metabolic chamber (60 ml syringes) for at least one hour

(Fitzpatrick and Brown 1975) before flushing the metabolic chambers with external air from outside the building. To each metabolic chamber, we also added 5 ml of distilled water and one Kimwipe to ensure that salamanders had sufficient moisture for cutaneous respiration. We used a 3-way luer lock to seal each salamander in 60 ml of air and allowed each animal to respire for five hours, which is sufficient to accurately quantify oxygen

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consumption (David Munoz, pers comm). We staggered our experiment so that each salamander was sealed and tested 5 minutes apart (to allow for processing time). We allowed at least 30 minutes for the respirometer to set the baseline level of oxygen using a flowrate between 340 – 430 ml /min, before taking the first metabolic measurement, and we reset the oxygen baseline before processing each sample. For each metabolic measurement, we injected 30 ml of air from the metabolic chamber into the respirometer, holding the chamber upright so as to prevent water inside the syringe from interfering with the measurement. For each sample, we used Warthog LabAnalyst

(http://www.warthog.ucr.edu) to set the baseline and averaged the five points around the peak percentage oxygen change obtain a mean value for overall oxygen consumption.

For each day in which we measured metabolic rates, we used an empty syringe as a control and subtracted the average change in oxygen recorded in the control sample from each of our salamander oxygen consumption measures. All salamanders were tested at

15oC first before subsequently being tested at 20oC, with at least one week in between experiments to minimize stress and to allow salamanders to acclimate to the new experimental temperature.

We used equation 4.4 in Lighton (2008) to calculate VO2 (Appendix 2), using a respiratory quotient of 0.82 (Greenberg and Ash 1976) to determine VCO2, and we calculated VH2O

(assuming 100% humidity) following the steps outlined in the Static Injection Analysis section in Lighton (2008). We calculated standard temperature and pressure (STP) corrected rates for our VO2 values before correcting our final oxygen consumption values

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for the volume of air sample measured, volume of the metabolic chamber (minus salamander volume), mass (g) of salamander, and duration of confinement (300 minutes).

Salamander volume was calculated as the sum of the volume of a cylinder (volume of salamander body) and volume of a cone (volume of tail). For each salamander we measured snout-vent length (SVL, mm) and tail length (TL, mm) from photographs using

ImageJ, version 1.48. We measured SVL as the distance from the anterior end of the head (snout-tip) to the posterior edge of the rear legs. This measure is highly correlated with the more typical SVL measure (Mott et al. 2010). Tail length was measured from the tip of the tail to the posterior edge of the hindlegs. The diameter of salamander body was measured as the width of the salamander body behind forelegs while diameter of tail was measured as width behind hindlegs.

To test for differences in physiological performance at the different temperatures within and between color morphs and populations we created linear mixed effect models and

ANOVA summary tables via Satterthwaite’s degrees of freedom method using the lme4

(version 1.1-21) (Bates et al. 2018) and lmerTest (version 3.1-1) (Kuznetsova et al. 2017) packages in R (version 1.2.5033) (R Core Team 2013). We modeled five measures of salamander physiology: daily energy assimilation efficiency (kJ/g/day), daily energy ingested (kJ/g/day), daily energy excreted (kJ/g/day), digestive efficiency (%), and standard metabolic rate (ml/g/min). We log-transformed oxygen consumption (ml/g/min) to improve normality. For all models, we included color morph, population, temperature, and the interaction between color morph and temperature as fixed effects, and individual

112

as a random effect. We also included a random variable of day when modeling our metabolic rate data to account for possible daily measurement effects. We modeled changes in salamander weight as a function of the additive and interaction effects of coloration and either daily energy assimilation efficiency or digestive efficiency to determine whether morphs differed in the allocation of assimilated energy towards growth. Finally, to determine whether there is a trade-off in physiological performance measures, we regressed daily energy assimilation and digestive efficiency against standard metabolic rate with population as a fixed effect and included a random effect of individual. In these models we tested for a three-way interaction between metabolic rate, color morph and temperature and sequentially dropped non-significant interaction terms from our models.

Results

Temperature significantly affected the daily energy assimilation efficiency (F(1,134) =

16.165, p < 0.001), energy acquired (Ea) (F(1,134) = 9.840, p = 0.002), and digestive efficiency (F(1,134) = 20.643, p = <0.001) of P. cinereus, regardless of coloration, with warmer temperatures resulting in an increase in all three of these physiological measures, as expected (Figure 1a,b,c). On average, individuals assimilated 0.086 kJ/g/day at 15oC and 0.092 kJ/g/day at 20oC. Neither color morph on its own nor the interaction between coloration and temperature had a significant effect on daily energy assimilation efficiencies (F(1,131) = 0.677, p = 0.412, F(1,134) = 1.470, p = 0.227 respectively). Daily energy assimilation efficiency did have a significant positive effect on changes in

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salamander weight (β = 0.925, SE = 0.272, t = 3.398, p < 0.001, Figure 2), but weight changes were not significantly affected by color morph (β = 0.007, SE = 0.029, t = 0.268, p = 0.789) or the three-way interaction between daily energy assimilation efficiency, temperature, and color morph (β = -0.607, SE = 0.430, t = -1.412, p = 0.160, Figure 2).

Temperature also significantly affect the amount of energy excreted (F(1,134) = 5.028, p =

0.027), with a slight increase in daily energy excreted per gram at 15oC (0.017 kJ excreted) compared to 20oC (0.016 kJ excreted). Energy excreted also differed between the color morphs (F(1,131) = 4.104, p = 0.457), with unstriped individuals losing significantly more energy via excretion than striped individuals (Figure 1c). Both striped and unstriped individuals showed high digestive efficiencies (average 84.6% and 83.3% respectively), and temperature caused a significant (~2%) increase in digestive efficiency for both morphs at warmer temperatures (unstriped: β = -1.634, SE = 0.594, t = -2.750, p = 0.007, striped: β = -1.848, SE = 0.475, t = -3.891, p < 0.001). Overall, striped and unstriped individuals also differed significantly in digestive efficiency (F(1,136) = 11.056, p = 0.001), with striped individuals showing an average increase of 1.26% in daily digestive efficiency per gram of body weight compared to unstriped individuals at both experimental temperatures (Figure 1d). Unstriped individuals had significantly lower digestive efficiencies than striped individuals in both our 15oC treatment (β = -1.255, SE = 0.559, t

= -5.551, p = 0.026) and the 20oC treatment (β = -1.469, SE = 0.559, t = -2.628, p =

0.009).

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(b) b ab 0.16 (a) bc b ab a ac a 0.15 0.12

morph morphColor Morph 0.10Lead UnstripedLead 0.08 Striped SStripedtriped

0.04 0.05 Energy acquired (kJ/g/day)

Energy assimilation efficiency (kJ/g/day) 15 20 15 20 Temperature (°C) Temperature (°C) (d) (c) a b d a a c 0.03 90 a c

morph morph 0.02 Lead Lead Striped 80Striped

0.01 Digestive efficiency (%) Digestive Energy excreted (kJ/g/day) Energy excreted 70

0.00 15 20 15 20 Temperature (°C) Temperature (°C)

Figure 1 Differences in four physiological measures for striped and unstriped individuals (shown as individual data points) of Plethodon cinereus, measured at 15oC and 20oC: (a) Energy assimilation efficiency (kJ/g/day), (b) Energy acquired through feeding; Ea

(kJ/g/day), (c) Energy lost via excretion; Ef (kJ/g/day), and (d) Digestive efficiency (%). Upper and lower hinges correspond to the 25th and 75th percentiles around the median with the upper and lower whiskers extending no further than 1.5 times the inter-quartile range. Boxplots with different letters are significantly different (Tukey’s HSD, p < 0.05). 115

morph Lead Striped

0.16

0.12

0.08

Energy assimiliation (kJ/day/g) 0.04

−0.1 0.0 0.1 0.2 Weight change (g)

Figure 2 Relationship between daily energy assimilation efficiency and changes in weight of striped (triangles) and unstriped (circles) individuals of red-backed salamanders (Plethodon cinereus) after 5 days of feeding trials. Regression lines are surrounded by 95% confidence intervals.

We also observed significant population differences in three of our four physiological measurements, with salamanders from Hoffman Forest Metropark showing lower rates of daily energy assimilation efficiency (β = -0.020, SE = 0.005, t= -3.793, p = <0.001), energy acquired (β = -0.024, SE = 0.006, t = -3.946, p < 0.001), and energy excreted (β

= -0.004, SE = -0.001, t = -4.012, p < 0.001) (Appendix 3). Likewise, there was a significant random effect of individual salamanders on daily energy assimilation (LRT =

59.425, p < 0.001), energy acquired through feeding (LRT = 80.752, p < 0.001), and

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energy excreted (LRT = 18.096 p < 0.001). Temperature had a significant effect on standard metabolic rate (F(1, 4.5) = 47.722, p = 0.001), with higher temperatures inducing

32% greater oxygen consumption (Figure 3). We did not detect any significant differences in metabolic rate between color morphs (F(1, 232.9) = 0.556, p = 0.457) or populations (F(1,

225.4) = 2.435 p = 0.066). Metabolic rate was weakly correlated with daily energy assimilation efficiency (r = 0.3, p < 0.001) but was not correlated with digestive efficiency

(r = 0.032, p = 0.62).

0.3

0.2 b b morphColor Morph Unstripedlead

a a Sstripedtriped

Metabolic rate (ml/min/g) Metabolic rate 0.1

15 20 Temperature (°C)

Figure 3 Standard metabolic rate (oxygen consumption) for striped and

unstriped individuals (shown as individual data points) of Plethodon cinereus, measured at 15oC and 20oC. Upper and lower hinges correspond to the 25th th and 75 percentiles around the median with the upper and lower whiskers extending no further than 1.5 times the inter-quartile range. Boxplots with different letters are significantly different (Tukey’s HSD, p < 0.05). 117

When we regressed standard metabolic rate against energy assimilation efficiency and

digestive efficiency, we found that metabolic rate had no significant effect on energy

assimilation efficiency (F(1, 222.6) = 0.610, p = 0.436, Figure 4a) or digestive efficiency (F(1,

143.9) = 2.806, p = 0.096, Figure 4b). In these models, energy assimilation efficiency was

significantly impacted by temperature and population, while digestive efficiency was

significantly affected by color morph and temperature (Appendix 4).

(a) (b)

R = 0.3 , p = 2.3e−06 R = 0.032 , p = 0.62 0.3 0.3

0.2 0.2

Oxygen consumption (ml/min/g) 0.1 Oxygen consumption (ml/min/g) 0.1 Standard metabolic rate (ml/min/g) metabolicrate Standard

0.1 0.2 70 80 90 Energy assimiliation (kJ/day/g) Digestive efficiency (%)

Figure 4 Regression of standard metabolic rate (oxygen consumption) and (a) energy assimilation efficiency and (b) digestive efficiency of the eastern red-backed salamander, Plethodon cinereus. R value represents the correlation coefficient, regression lines are surrounded by 95% confidence intervals.

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Discussion

We predicted that unstriped individuals would benefit from higher energy assimilation efficiencies and lower metabolic rates compared to striped individuals as these physiological advantages could help explain the higher proportions of unstriped individuals in warmer geographic regions (Lotter and Scott 1977; Gibbs and Karraker

2006) and how unstriped individuals could survive on lower quality diets (Anthony et al.

2008; Stuczka et al. 2016). The results of our study do not support this prediction as we found no significant differences in energy assimilation efficiency, energy acquired through feeding, or standard metabolic rate within each temperature treatment. However, striped and unstriped individuals did show significant differences in energy lost via excretion and overall digestive efficiency, with unstriped individuals excreting more energy via feces and showing lower digestive efficiencies than striped individuals. Coloration also did not significantly impact the relationship between metabolic rate and digestive performance.

Overall, we found that metabolic rate was weakly correlated with energy assimilation efficiency but not digestive efficiency and there was no significant effect of metabolic rate on energy assimilation efficiency.

Coloration and digestive performance

The energy budget of organisms is governed by balancing energy assimilated via feeding against energetic demands of four competing categories: maintenance costs (e.g., metabolic rate), growth, storage, and reproduction (Grant and Porter 1992). The relative distribution of energy amongst these categories is driven by a combination of both

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physiological and behavioral factors, the effects of which can vary both spatially and temporally (Grant and Porter 1992). In many ectotherms, the highest energy assimilation efficiencies are often correlated with temperatures most frequently experienced by an individual (Merchant 1970). In our study we did not find support for our hypothesis that physiological differences are contributing to the persistence of unstriped individuals in warmer regions of the species’ range, where they occur in higher proportions. Instead our work suggests that unstriped individuals may experience physiological limitations associated with lower overall digestive efficiencies and greater amounts of energy excreted across both the experimental temperatures we tested. This implies that unstriped morphs may occur in higher frequencies in warmer regions, not because they are better adapted to warmer temperatures, but rather because lower energy assimilation efficiencies and digestive efficiency might make it more difficult for them to maintain a positive energy balance in colder regions, where foraging time is more limited.

The differences we observed in digestive performance between color morphs contrasts with the results of the only previous study on energy assimilation differences between color morphs of this species (Bobka et al. 1981). This study also experimentally tested digestive performance at 15oC and 20oC yet found no evidence for morph-specific differences in energy assimilation efficiency. This discrepancy could relate to the low statistical power in the previous study (which tested 20 salamanders at each temperature and focused primarily on interspecific difference in physiological performance of plethodontid salamanders). These differences may also relate to experimental design as

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our study measured physiological responses via feeding trials using the same individuals in each temperature treatment, whereas Bobka et al. (1981) sampled energy assimilation destructively, therefore were unable to test physiological responses in the same individuals across different temperatures. In our study the small volumes of biological waste meant we had to pool multiple fecal samples in order to evaluate the amount of energy lost via excretion. While this approach was necessary for improving the accuracy of our bomb calorimeter readings, it may have introduced additional variance into our data that we are unable to account for. Similarly, it is unknown whether the order of experimental temperatures could have impacted the physiological responses of individuals this study compared to previous studies.

The sensitivity of physiological processes to temperature can constrain the geographic area and climatic conditions under which organisms can survive (Gaitán-Espitia et al.

2014). The thermal performance of ectotherms is typically maximized around an individual’s thermal optima, and then declines as temperature continues to increase

(Huey and Kingsolver 1989; Angilletta Jr and Angilletta 2009). Previous studies on more southern populations of P. cinereus found a steady decrease in digestive performance at

10oC and 20oC relative to 15oC, suggesting that the thermal optima of this species lies close to 15oC (Bobka et al. 1981; Fontaine et al. 2018). Our finding that energy assimilation efficiency and digestive efficiency increased from 15oC to 20oC contrasts with these studies and indicates that our more northern populations of this species likely have higher or wider thermal performance optima. This difference is surprising as according to

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the “hotter is better” metabolic theory, individuals from lower latitudes are expected to physiologically outperform individuals from higher latitudes because high latitude populations are adapted to cooler conditions where biochemical rates are constrained

(Bennett 1987; Huey and Kingsolver 1989). Instead, our results appear to support the climate variability hypothesis where individuals from cooler regions have a wider range of thermal performance optima, possibly in response to more variable environmental conditions and this may help individuals capitalize on shorter activity periods (Gaitán-

Espitia et al. 2014).

In line with the metabolic theory of ecology (Brown et al. 2004), we found that experimental temperature had a significant effect on all the metrics of physiological performance we measured. This result is consistent with other physiological studies for both this species (Clay and Gifford 2017; Fontaine et al. 2018) and other ectothermic taxa

(Klepsatel et al. 2019). Given that physiological processes in ectotherms are particularly sensitive to temperature, tradeoffs between different physiological processes are common (Pörtner et al. 2006). For example, energy assimilation efficiency is also influenced by both gut passage time and the activity of digestive enzymes (McConnachie and Alexander 2004). Temperature-dependent digestive processes such as hydrolysis and absorption of nutrients (e.g., sugars and lipids) are often more efficient at warmer temperatures (Pafilis et al. 2007), although this benefit is balanced against the trade-off of increased gut passage rates at warmer temperatures (McConnachie and Alexander

2004). For dispersal-limited species, plasticity is likely to be a crucial response to the

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selective pressure posed by climate change (Seebacher et al. 2015), and our work suggests that, despite morph-specific differences, both striped and unstriped individuals of P. cinereus are able to respond to increased temperatures by plastically increasing their energy assimilation and digestive efficiency. Expanding this work to examine the thermal sensitivity of physiological responses across a wider range of temperatures and in additional populations across the species’ range will provide clearer insight whether the apparent physiological plasticity we observe in our study is present in other populations and if it can confer resilience to climate change.

Coloration and metabolic rate

The metabolic theory of ecology suggests that metabolic rates are constrained by thermodynamics and hence are tightly correlated with temperature (Brown et al. 2004).

Metabolic physiology is also under genetic control and has been linked pleiotropically to the melanocortin system, which is responsible for the production of melanin-pigments in vertebrates (Ducrest et al. 2008). As a result, some metabolic differences between individuals have been linked to coloration, for example under some temperature conditions, barn owl nestlings higher oxygen consumption rates have been correlated with larger dark wing spots (Dreiss et al. 2016), and Iberian newts with higher levels of skin melanin-pigmentation show higher metabolic rates than lighter-pigmented individuals

(Polo-Cavia and Gomez-Mestre 2017).

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The strong population effect on metabolic rate observed in our study and others highlights the importance of testing the physiological responses in multiple populations of this widespread polymorphic salamander as it appears that metabolic rate between striped and unstriped morphs varies geographically. Whether metabolic rate differs between striped and unstriped individuals of P. cinereus has been assessed in only three populations (Moreno 1989; Petruzzi et al. 2006). Moreno (1989) found that morphs diverge in standard metabolic rate at 15oC, with unstriped morphs showing lower metabolic rates than striped morphs. However, this trend appears to be population- specific as later work by Petruzzi et al (2006) on two populations from Ohio only found morph-specific metabolic differences at 10oC in one of two polymorphic populations, with no evidence of metabolic differences at 15oC. In our study, we found little evidence of morph-specific differences in metabolic rate at either 15oC or 20oC, confirming results of

Petruzzi et al. that polymorphic populations at more northern latitudes do not appear to differ in metabolic rate at these temperatures. Further studies expanding this work to include additional populations and experimental temperatures will provide clearer insight into how geographic distribution may impact metabolic rate in this widespread species.

Coloration and the relationship between digestive performance and metabolic rate

Increased metabolic rates at high temperatures can have a profound impact on organism fitness due to the increased self-maintenance costs (Steyermark et al. 2005; Burton et al.

2011). For ectotherms, increases in energetic demand caused by higher metabolic rates at warmer temperatures can drive changes in the rate of other biological processes

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(Lemoine and Burkepile 2012). Our work reveals that while both color morphs show increases in energy assimilation efficiency and digestive efficiency as temperature increase, these changes appear only weakly correlated and are not significantly affected by corresponding changes in metabolic rate. This indicates that while individuals show temperature-induced plasticity in their digestive performance, energy intake may not increase fast enough to compensate for the energetic demands associated with metabolic rate at either of the temperatures we tested. In addition to the effects of metabolic rate and temperature, physiological performance in ectotherms can be impacted by other factors such as circadian rhythm and stress (Novarro et al. 2018; Plasman et al. 2019).

Previous work on P. cinereus has shown that elevated levels of corticosterone stress hormones result in reductions in food conversion efficiency, particularly in individuals from comparatively higher latitude populations similar to the ones we studied here (Novarro et al. 2018). Unstriped individuals have been found to exhibit higher hematological stress levels than striped individuals (Davis and Milanovich 2010), which is one potential mechanism that could cause striped individuals in our study to display higher digestive efficiencies.

If our physiology results scale proportionally with the physiological responses of wild salamanders, then P. cinereus, particularly unstriped individuals, must utilize other mechanisms in order to compensate for the greater energetic costs associated with higher metabolic rates experienced in warm climates. These mechanisms could include increasing volume of food eaten to compensate for lower digestive efficiency, behavioral

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manipulation of physiological processes such as reduced activity to minimize energy expenditure (Huey and Bennett 1990), or the reduction or delay of other energetic costs such growth or reproduction (Angilletta Jr 2001). In our study, we found no evidence for differences in energy intake between morphs, suggesting that increase energy consumption is an unlikely mechanism, at least under laboratory conditions. Previous studies have shown that striped and unstriped individuals differ in their behavior, including surface activity (Lotter and Scott 1977; Anthony et al. 2008), and future studies should test whether these behavioral modifications are associated with optimizing physiological performance measures. Likewise, previous evidence for delayed growth and reproduction in unstriped morphs, particularly in northern populations, suggesting that altering how energy is allocated is a likely mechanism used by unstriped individuals to compensate for potential physiological constraints.

The discrepancy between our temperature-induced physiology results and other studies of thermal sensitivity of physiological performance in this species may relate to the demographics of the individuals in our study. All salamanders in our study were reproductively spent females that had yolked or laid eggs earlier in the year. To determine whether reproductive history impacted the physiological responses of individuals in our study we re-ran our energy assimilation efficiency and digestive efficiency models using the same parameters but with an additional additive effect of either number of eggs laid or number of eggs yolked. These post-hoc tests revealed that the number of eggs laid by females earlier in the season had a significant effect on energy assimilation efficiency (F(1,

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108) = 8.53, p = 0.004, Figure 5) while number of eggs yolked had a marginally significant

impact on energy assimilation efficiency (F(1, 63) = 3.156, p = 0.081, Figure 5). Similarly,

both the number of eggs laid and number of eggs yolked significantly affected digestive

efficiency (eggs laid: F(1, 108) = 6.351, p = 0.013, eggs yolked: F(1, 130) = 4.785, p = 0.030,

Figure 6).

(a) (b) R = -0.22, p = 0.001 R = -0.3, p < 0.001 0.16 0.16

0.12 0.12

0.08 0.08

0.04 0.04 Energy assimiliation (kJ/day/g) Energy assimiliation (kJ/day/g)

5 10 5.0 7.5 10.0 12.5 Eggs laid Eggs yolked Figure 5 Relationship between daily energy assimilation efficiency (kJ/day/g) and (a) the number of eggs laid and (b) the number of eggs yolked by females eastern red-backed salamanders (Plethodon cinereus). R value represents the Pearson’s correlation coefficient. Regression line

surrounded by 95% confidence intervals.

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(b) (a) R = -0.12, p = 0.15 R = -0.13, p = 0.055

90 90

80 80

Digestive efficiency (%) Digestive

70 70

5 10 5.0 7.5 10.0 12.5 Eggs yolked Eggs laid Figure 6 Relationship between digestive efficiency (%) and (a) the number of eggs laid and (b) the number of eggs yolked by females eastern red-backed salamanders (Plethodon cinereus). R value represents the Pearson’s correlation coefficient. Regression line surrounded by 95% confidence intervals.

This suggests that some of the variance in physiological measures we examined that was

explained by individual effects may be associated with reproductive investment earlier in

the year, assuming isometric scaling of weight to body length for these biological traits.

Reproduction in plethodontid salamanders is energetically expensive and females will

forego reproduction if they are not in suitable body condition (Maiorana 1977). Our work

suggests that the physiological costs associated with reproduction may extend well

beyond the egg production and incubation period, potentially explaining why females from

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northern populations of this species often forgo reproduction in consecutive years, breeding biennially or even triennially (Sayler 1966; Leclair et al. 2006).

This work corroborates and extend the growing body of literature on the physiological ecology of the color polymorphic salamander P. cinereus and related taxa. Our study affirms the general and expected trend that temperature induced plastic changes in physiological measures including digestive efficiency, energy assimilation, and metabolic rate. This suggests that, as with many other ectotherms, P. cinereus display thermal plasticity in multiple physiological traits, and this may enable these populations to respond more rapidly to temperature changes in our study region. Our work also provides valuable and novel insight into the differences and similarities in physiological ecology of the two main color morphs of P. cinereus. Contrary to our predictions, we found no evidence that unstriped morphs derive physiological benefits at 15oC or 20oC that may enable them to persist under warmer environmental conditions. Our study highlights the importance of testing physiological responses of even extensively studied organisms across multiple populations as our physiological results contrast against previous work, which found no significant differences in digestive performance or metabolic rate between color morphs.

Examining the extent to which coloration is consistently associated with physiological differences, and the extent to which these relationships vary geographically, will provide a more detailed understanding into the magnitude and extent of physiological divergence between these color morphs. A clearer understanding of these patterns will improve our understanding into if and how physiological plasticity may help populations of this

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ecologically important salamander to respond to changes in the thermal environment caused by climate change.

Acknowledgements

We thank Dr. Morgan Tingley and Austin Spence for supplying the FoxBox metabolic equipment and training, Dr. Alexander Gerson and his lab for access and training for micro-bomb calorimetry, and Dr. Barbara Joos from Sable Systems International Inc. and

Dr. Mark Chappell for assistance and advice for processing metabolic rate readings. We thank David Muñoz and Alex Novarro for methodological and analysis advice early in this project. We also thank Sandy Flittner from Erie MetroParks (Ohio) and Patrick Lorch from

Hinckley Reservation (Ohio) for their assistance with site permissions and access. We thank the numerous people who helped with fieldwork, experimental work, and animal care, particularly Mark R. Smith, Amanda Pastore, Dana Drake, Meera Joshi, Jack

Phillips, Sarah Baker, Ryan Mayer, and Julia Peay. Thank you to Barry and Nancy

Forester as well as Carlton and Lisa Park-Boush for kindly providing assistance and accommodation in Ohio. This project was supported by funding from Sigma Xi GIAR, the

Society for the Study of Evolution (SSE) Rosemary Grant Award, and UConn EEB

Zoology awards to AE. This research was conducted under approvals from the University of Connecticut IACUC (protocol numbers: A15-023, A18-025), a Federal Fish and Wildlife permit (MA93731B-0), Ohio Wild Animal Scientific Collection permits numbers: 17-271,

18-184, and 19-151), and Connecticut Scientific Collection permits (numbers: 0915008 and 0920008).

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Chapter Four Appendices

Appendix 1 Fruit fly samples and fecal pellet samples from striped and unstriped individuals of eastern red-backed salamanders, Plethodon cinereus, processed via micro-bomb calorimetry. Populations sampled include two Connecticut sites: Moss Tract (MT) and Shenipsit State Forest (SSF), and two Ohio sites: Edison Woods (EW) and Hoffman Forest Metropark (HP).

Bomb calorimetry runs Number of Experimental Color Population samples temperature morph Run 1 Run 1 Run 2 Run 2 pooled mass (g) calories/g mass (g) calories/g 15 MT Unstriped 9 0.0331 4542.52 na na 15 MT Striped 20 0.0367 4897.24 0.0413 4644.76 20 MT Unstriped 9 0.0289 5066.11 na na 20 MT Striped 20 0.0305 5026.56 0.0353 4292.69 15 SSF Unstriped 8 0.0288 5112.83 na na 15 SSF Striped 26 0.0488 4672.94 0.0559 5107.74 20 SSF Unstriped 8 0.0284 4289.88 na na 20 SSF Striped 26 0.0471 4442.36 0.0465 4888.94 15 EW Unstriped 22 0.0455 4632.75 0.0437 4876.26 15 EW Striped 22 0.0407 4374.79 0.0441 4777.72 20 EW Unstriped 22 0.0407 4568.22 0.0454 4852.75 20 EW Striped 22 0.0383 4552.69 0.0461 4867.11 15 HP Unstriped 18 0.033 4559.8 0.0338 4592.58 15 HP Striped 15 0.032 4579.09 0.0277 4851.6 20 HP Unstriped 18 0.0323 4686.9 0.0386 4754.61 20 HP Striped 15 0.0287 4193.75 0.0297 4851.32 NA Fly sample Flies1 50 0.0186 5529.04 na na NA Fly sample Flies2 52 0.025 5151.82 na na

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Appendix 2. Equation number 4.4 from Lighton (2008) used to calculate the volume of oxygen consumed (a proxy for metabolic rate) by red-backed salamanders, Plethodon cinereus. See page 26 of Lighton (2008) for additional details about how this equation is derived.

VolO2 = [V(FiO2 – FeO2) – FeO2(VolH2O)]/[1 – FeO2(1 – RQ)]

Where V = volume of air measured by respirometer

FiO2 – FeO2 is the change in oxygen concentration (O2 %/100)

FeO2 = 0.2095 – (O2 %/100)

VolH2O = (Chamber volume – salamander volume)*(Water Vapor Pressure/Barometric Pressure in kPa)

Respirometry Quotient (RQ) = 0.82

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Appendix 3 Tukey HSD population differences for physiological traits of Plethodon cinereus salamanders. Populations include two Connecticut sites: Moss Tract (MT) and Shenipsit State Forest (SSF), and two Ohio sites: Edison Woods (EW) and Hoffman Forest Metropark (HP).

Physiological Population pairwise Estimate Standard error df t-value p-value trait comparisons

EW - HP 0.020 0.005 131 3.793 < 0.001 EW - MT -0.002 0.006 131 -0.314 0.754 Daily energy EW - SSF 0.002 0.005 131 0.295 0.769 assimilation -0.022 < 0.001 efficiency HP - MT 0.006 131 -3.727 HP - SSF -0.019 0.006 131 -3.221 0.002 MT - SSF 0.003 0.006 131 0.574 0.567 EW - HP 0.023 0.005 131 4.337 < 0.001 Daily energy EW - MT 0.002 0.006 131 0.408 0.684 acquired EW - SSF 0.000 0.005 131 0.050 0.960 through HP - MT -0.021 0.006 131 -3.542 0.001 feeding HP - SSF -0.023 0.006 131 -3.950 < 0.001 MT - SSF -0.002 0.006 131 -0.342 0.733 EW - HP 0.004 0.001 131 4.535 < 0.001 EW - MT 0.000 0.001 131 0.419 0.676 Daily energy EW - SSF 0.000 0.001 131 -0.486 0.628 lost in feces HP - MT -0.004 0.001 131 -3.712 < 0.001 HP - SSF -0.004 0.001 131 -4.633 < 0.001 MT - SSF -0.001 0.001 131 -0.851 0.396 EW - HP -0.035 0.528 136 -0.066 0.948 EW - MT -0.020 0.547 136 -0.037 0.971 Digestive EW - SSF 0.547 0.534 136 1.023 0.308 efficiency (%) HP - MT 0.015 0.584 136 0.025 0.980 HP - SSF 0.581 0.573 136 1.014 0.312 MT - SSF 0.567 0.574 136 0.987 0.325 EW - HP 0.243 0.032 235.1 7.473 < 0.001 Standard EW - MT 0.003 0.035 235.7 0.085 0.932 metabolic rate EW - SSF -0.016 0.035 235.8 -0.443 0.658 (oxygen HP - MT 0.214 0.352 235.5 -6.632 < 0.001 consumption) HP - SSF 0.225 0.368 235.5 -7.078 < 0.001 MT - SSF 0.235 0.384 235 -0.494 0.622 138

Appendix 4 ANOVA results for regression of energy assimilation efficiency (kJ/g/day) and digestive efficiency (%) against standard metabolic rate (Oxygen consumption, ml/g/min), for eastern red-backed salamanders, Plethodon cinereus where both metabolic rate and digestive performance were measured.

Physiology DF Fixed effects Sum Sq Mean Sq F value Pr(>F) trait (Num, Den) Daily Metabolic rate 0.0001 0.0001 1, 222.6 0.610 0.436 energy Temperature 0.0007 0.0007 1, 221.8 5.985 0.015 assimilation Color morph 0.0002 0.0002 1, 115.5 1.668 0.199 efficiency Population 0.0022 0.0007 3, 119.1 6.153 0.001 Metabolic rate 25.547 25.547 1, 143.9 2.806 0.096 Digestive Temperature 168.842 168.842 1, 195.0 18.546 < 0.001 efficiency Color morph 114.931 114.931 1, 120.6 12.625 0.001 Population 17.585 5.862 3, 121.06 0.6439 0.588

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