The Influence of Spatiotemporal Variation in Ambient Temperature on the Ecology and Physiology of Birds

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THE INFLUENCE OF SPATIOTEMPORAL VARIATION IN AMBIENT TEMPERATURE ON THE ECOLOGY AND PHYSIOLOGY OF BIRDS

HENRY POLLOCK

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology, Evolution and Conservation Biology in the Graduate College of the University of Illinois at Urbana-Champaign, 2016

Urbana, Illinois

Doctoral Committee:

Professor Jeffrey D. Brawn, Chair, Co-Director of Research Assistant Professor Zachary A. Cheviron, Co-Director of Research, University of Montana Professor James W. Dalling Associate Professor Rebecca C. Fuller Associate Professor Cory D. Suski

ABSTRACT

Ambient temperature has profound effects on the ecology, physiology and distribution of organisms. Ambient temperature regimes vary across spatial and temporal scales and understanding how organisms cope with this variation is a primary goal of ecological and evolutionary physiology. An emerging framework for understanding how geographic variation in ambient temperature is related to thermal physiology is the climatic variability hypothesis

(CVH), which predicts that thermal tolerance breadth should increase with increasing levels of temperature variability. Studies documenting latitudinal increases in the thermal tolerance breadth of ectotherms and thermoneutral zone (TNZ –a proxy for thermal tolerance) breadth in endotherms have provided broad empirical support for the CVH.

However, most previous tests of the CVH have focused on large-scale patterns of variation in thermal tolerance breadth across latitudinal temperature gradients, and little is known about how the CVH applies to small spatial scales (i.e. within a geographic locality) and also to temporal (seasonal) patterns of temperature variability. Furthermore, understanding how ambient temperature regime influences heat tolerances is becoming increasingly important due to climate warming, yet patterns of geographic variation in heat tolerances of endotherms have not been characterized. Finally, few studies have investigated the potential ecological consequences of variation in thermal physiology, particularly in endotherms. My dissertation chapters address each of these knowledge gaps in turn to provide a more complete investigation of the CVH and a clearer picture of how spatiotemporal variation in ambient temperature has influenced the physiology and ecology of birds.

Chapter 2 explores the influence of ambient temperature regime on variation in TNZ breadth at multiple spatial scales. I captured ~140 bird species at three geographic localities

(Illinois and South Carolina, U.S.A. and the Republic of Panama) and used flow-through respirometry to measure TNZ breadth. In phylogenetically informed analyses, I found that temperature variability rather than latitude per se was the best predictor of both interspecific and intraspecific geographic variation in TNZ breadth at large spatial scales (across geographic localities). I also found that migratory tendency was an important predictor of interspecific variation in TNZ breadth, with migratory species having TNZ breadths intermediate between temperate and tropical resident species. In contrast, at a smaller spatial scale (within a geographic locality), ecological traits associated with ambient temperature regime (vertical niche and habitat association) were not significant predictors of variation in TNZ breadth. My results indicate that temperature variability at the micro- or macro-habitat level is not associated with TNZ breadth in birds, and suggest that the CVH may not apply to smaller spatial scales in endotherms.

Chapter 3 extends the CVH from spatial to temporal (seasonal) patterns of temperature variability and tests a secondary prediction of the CVH – that flexibility of thermoregulatory traits should increase with temperature seasonality. To test this prediction, I used flow-through respirometry to measure seasonal flexibility in five thermoregulatory traits [body mass (Mb), mass-adjusted basal metabolic rate (BMR), LCT, UCT and TNZ breadth] in suites of temperate

(high seasonality) and tropical (low seasonality) birds. In phylogenetically-controlled analyses, I found that temperate species exhibited greater seasonal flexibility in LCT, UCT and TNZ breadth than did tropical species. Winter-acclimatized individuals of temperate species exhibited large

iii reductions (up to ~9 °C) in LCT and modest (~2-3 °C) reductions in UCT relative to summer- acclimatized individuals, resulting in an increased winter TNZ breadth. Although some tropical species exhibited flexibility in LCT, UCT and TNZ breadth, patterns of seasonal flexibility were idiosyncratic and the mean amount of seasonal change was close to 0. In contrast, seasonal flexibility in Mb and mass-adjusted BMR did not differ between temperate and tropical species.

My analysis suggests that patterns of geographic flexibility in thermoregulatory traits directly linked to thermal tolerance (e.g. LCT, UCT, TNZ breadth) conform to the CVH, whereas other traits (e.g. BMR, Mb) do not. Furthermore, the patterns I documented contribute to a growing body of evidence suggesting that species from environments with low temperature variability

(e.g. lowland tropical species) may have reduced physiological flexibility relative to their temperate counterparts and may be more sensitive to climate change.

Chapter 4 examines patterns of heat tolerance among tropical and temperate birds in an effort to provide the first analysis of geographic variation in avian heat tolerance. I then coupled heat tolerance data with ambient temperature data to test the prediction that tropical species will be more vulnerable than temperate species to future climate warming, as has been documented in numerous ectothermic taxa. I used flow-through respirometry coupled with temperature-sensitive passive integrated transponder (PIT) tags to measure three traits [UCT, heat-strain coefficient (HScoeff – the slope of the relationship between Tb and Ta above the UCT) and critical thermal maximum (CTmax – the Ta at which birds become hyperthermic and lose the ability to regulate Tb)] that comprise heat tolerance. Contrary to previous studies of ectotherms, I found limited evidence of reduced heat tolerances in tropical species. In phylogenetically-controlled analyses, although temperate species had slightly higher (~2 °C)

CTmax, HScoeff and UCT did not differ between temperate and tropical birds. Importantly, these subtle differences in heat tolerance do not seem to translate into systematic differences in vulnerability to climate warming. Thermal safety margins did not differ significantly between temperate and tropical species, and seem to be large enough to allow for physiological tolerance of projected warming rates. Overall, my data do not indicate that tropical birds will be any more physiologically vulnerable to climate warming than temperate species, in contrast with previous patterns described in ectotherms.

Chapter 5 focuses on the potential ecological consequences of variation in thermal physiology. Understory insectivorous birds of Neotropical forests are a guild that is disproportionately sensitive to disturbance, experiencing population declines and local extirpation in response to forest fragmentation. One hypothesis for their declines is the microclimate hypothesis, which posits that the novel abiotic conditions (i.e. increased temperature and solar radiation, decreased humidity) that habitat fragmentation introduces may physiologically challenge understory insectivores and contribute to their population declines. An important assumption of the microclimate hypothesis is that understory insectivores have narrow physiological tolerances because they inhabit the forest understory, which is an incredibly stable and buffered environment. To test the microclimate hypothesis, I radio-tagged individuals of nine understory insectivore species at three sites along a precipitation gradient in central Panama and compared the microclimates (ambient temperature, relative humidity, solar radiation) that they selected with randomly chosen microclimates to test for the preferential use of particular microclimates (i.e. microclimate selectivity). I found no evidence of selectivity for any of the nine species I sampled on a

v seasonal or spatial basis. Microclimate variation was minimal in the forest understory at all sites, particularly in the wet season. Understory insectivores did not use microhabitats characterized by high light intensity, and may be sensitive to light, though the mechanism remains unclear. The lack of microclimate variation in the understory of tropical forests may have serious fitness consequences for understory insectivores due to climate warming coupled with a lack of thermal refugia.

I dedicate this dissertation to my mother and father, who made it all possible. Dad – glad you

could be around to read this.

vii

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my two co-advisors, Jeff Brawn and Zac

Cheviron, for their mentorship, support, and most of all, friendship over the last 5.5 years. I realize that I am not always the easiest student to work with, and they have both done an admirable job of reigning in my disorganization while also fomenting my enthusiasm and allowing me the flexibility to pursue the avenues of research that have interested me on my own terms. There have been ups, downs and everything in between, but I have come to the end of it all feeling like I have truly accomplished my goals and none of it would have been conceivable without them. Jeff’s creative drive and Zac’s sharp eye for detail and critical thinking have been the foundations of my research, and I am forever indebted to both of them for making my project possible. Also, thanks to my other three Ph.D. committee members – Jim

Dalling, Becky Fuller, and Cory Suski – for their helpful input in the annual committee meetings and for pushing me to improve as a scientist during my prelims.

This project has been built on the shoulders of an army of assistants and field technicians who helped me collect these data, and I am indebted to them for their efforts. In particular, I thank Diego Rincón Guarín, who worked with me at my Panama field site for three years running, and Emily Williams, who led the crews at my South Carolina field site for two years and both of whom have become dear friends. I also thank my friends for life, T.J. Agin and

Zach Welty, both of whom I had known prior to starting my Ph.D. and who (for better or for worse) decided to take a risk and work with me – hopefully it paid off. Finally, I thank John “Big

Johnny” Andrews, Kristina “Krist-in-a” Bartowitz, Yocelin Bello, Fernando Cediel, Jeannille

viii

Hiciano, Jason “J-Husk” Huska, Noah “Cornflake” Horsley, Sean MacDonald, Stephanie “Stephen

E.” Meyers, Simon Nockold, Chris Wagner, Tyler Winter, and Kittie Yang for their hard work.

I would like to thank my lab mates and office mates at the University of Illinois, particularly John Andrews, Tony Celis, Janice Kelly and Bethany Krebs for the many hours and good times I spent together in Turner S-217. Special thanks to my Cheviron lab mates Phred

Benham, Nick Sly, and Maria Stager, who have been instrumental in my formation as a biologist and in my scientific maturation.

For logistical help, I thank Brett DeGregorio, Jinelle Sperry and Tracey Tuberville for facilitating this project in South Carolina, Steve Buck for help with field sites and permits in

Illinois, and Adriana Bilgray, Egbert Leigh, Owen McMillan, Raineldo Urriola, and Joe Wright for their support in Panama. Thanks to Zak and Erica Sutton, Jan Bellington and Spencer Landsman for generously allowing me to capture birds at their feeders.

I am grateful for the funding sources that have allowed me to bring this project to fruition, including the Graduate College, the Program in Ecology, Evolution and Conservation

Biology and the School of Integrative Biology at the University of Illinois, the American Museum of Natural History, the Carolina Bird Club, the Illinois Ornithological Society, the Smithsonian

Institution and the Smithsonian Tropical Research Institute, the U.S. Department of Defense, and the National Science Foundation. Finally, thanks to my family and friends, especially

Michael and Renee Pollock, Elisabeth Pollock, Christian Ray, Gabriel and Tristan Pollock-Ray, and Benita Laird-Hopkins for their continuing love and support throughout it all.

TABLE OF CONTENTS

CHAPTER 1: GENERAL INTRODUCTION…………………………………………………….……………..…………..…....1

CHAPTER 2: THE INFLUENCE OF AMBIENT TEMPERATURE REGIME ON AVIAN THERMONEUTRAL ZONES AT MULTIPLE SPATIAL SCALES……………………………………………………..……………..…….………..….7

CHAPTER 3: TEMPERATURE SEASONALITY DRIVES PATTERNS OF GEOGRAPHIC VARIATION IN FLEXIBLITY OF AVIAN THERMOREGULATORY TRAITS……………………………………..….…………………….28

CHAPTER 4: HEAT TOLERANCES OF TEMPERATE AND TROPICAL BIRDS SUGGEST THAT THEY WILL BE PHYSIOLOGICALLY BUFFERED FROM CLIMATE WARMING……………….…………….……….….43

CHAPTER 5: ABSENCE OF MICROCLIMATE SELECTIVITY IN INSECTIVOROUS BIRDS OF THE NEOTROPICAL FOREST UNDERSTORY……………………………………………………………………………………….62

FIGURES AND TABLES………………………………….………..………………………………………………………………...81

BIBLIOGRAPHY….……………………………………………………………………………………………………………..…...119

CHAPTER 1: GENERAL INTRODUCTION

Ambient temperature is an important selective pressure that has profound effects on the ecology, physiology and distribution of organisms (Angilletta 2009), and understanding how organisms cope with spatial and temporal variation in ambient temperature is a primary goal of ecological and evolutionary physiology (Gaston et al. 2009). On a large spatial scale, ambient temperature variability tends to increase with latitude, a pattern that is primarily driven by lower minimum temperatures at higher latitudes (Ghalambor et al. 2006). Therefore, organisms inhabiting higher latitudes must tolerate a greater range of ambient temperatures at a given time of year than their counterparts from low latitudes. At a smaller spatial scale (i.e. within a geographic locality), ambient temperature regime can also vary substantially and temperature variability tends to increase with increasing habitat openness and exposure to solar radiation.

Temporally, organisms from higher latitudes must tolerate a greater range of seasonal variation in ambient temperatures than their low-latitude counterparts. The primary aim of my dissertation research is to address how this spatiotemporal variation in ambient temperature drives variation in thermal physiological traits of birds and secondarily, to examine the potential ecological consequences of this physiological variation.

An emerging framework for understanding geographic variation in thermal physiology in relation to ambient temperature is the climatic variability hypothesis (CVH), which predicts that thermal tolerance breadth should increase with latitude as a means of tolerating greater levels of temperature variability (Bozinovic et al. 2011). Secondarily, because an organism’s thermal tolerance breadth is related to its capacity for phenotypic flexibility, the CVH also predicts that the flexibility of thermal physiological traits should increase with latitude (Bozinovic et al.

2011). Indeed, recent studies have documented latitudinal increases in thermal tolerance breadth of ectotherms (Addo-Bediako et al. 2000, Sunday et al. 2011, Araújo et al. 2013) and thermoneutral zone (TNZ – a proxy for thermal tolerance; Fig. 1.1) breadth of endotherms

(Araújo et al. 2013, Khaliq et al. 2014). Furthermore, physiological flexibility of metabolic traits has recently been shown to increase with latitude in mammals (Naya et al. 2012) and birds

(Stager et al. 2016). Thus the CVH has received increasing empirical support and is becoming the gold standard for predicting how geographic variation in ambient temperature regime shapes variation in thermal physiological traits and their capacity for flexibility. However, most previous tests of the CVH have focused on large-scale latitudinal patterns of variation in thermal physiology, and several important questions about the generality and applicability of the CVH remain unanswered. First, little is known about how the CVH applies to small spatial scales (i.e. does temperature variability within a geographic locality drive local variation in thermal physiological traits?). Second, most previous studies have focused on spatial patterns of variation in thermal physiology, and as a result, it is unclear whether or not these traits vary temporally (i.e. across seasons) and if so, whether the magnitude of this flexibility also increases with seasonal temperature variability as predicted by the CVH. Third, the TNZ is only a proxy for thermal tolerance, and little is known about how geographic variation in ambient temperature influences heat tolerances of endotherms and their prospects in the face of global climate change. Finally, few studies have investigated the potential ecological consequences of variation in thermal physiology, particularly in endotherms. My dissertation chapters address each of these knowledge gaps in turn to provide a more complete investigation of the CVH and

2 a clearer picture of how spatiotemporal variation in ambient temperature has influenced the physiology and ecology of birds.

Although geographic patterns of variation in thermal tolerance breadth have been established and are consistent with the CVH, it is unclear whether or not the same patterns apply to smaller spatial scales. Just as ambient temperature regime drives patterns of variation in thermal tolerance breadth across large spatial scales, there is substantial variation in ambient temperature on a smaller spatial scale that may drive variation in thermal tolerance breadth

(Suggitt et al. 2011, Scheffers et al. 2014). For example, in ectotherms, thermal tolerance breadth may vary among species that inhabit different thermal microhabitats within a habitat or different habitat types within a geographic locality. To investigate the generality of the CVH to smaller spatial scales, in chapter 2, I collected TNZ data for ~140 species of birds from three geographic localities to examine how species with different micro- and macro-habitat associations within each locality varied with respect to thermal physiology. Because of the large size of the dataset, I was also able to provide the first comprehensive characterization of TNZ breadth in migratory species and the first test of intraspecific variation among populations of the same species sampled at different geographic localities.

In chapter 3, I shifted focus from spatial to temporal patterns of variation in thermal physiology. Specifically, I wanted to test the second prediction of the CVH – that flexibility in thermal physiological traits would increases with seasonal temperature variability. Because temperature seasonality is more pronounced at higher latitudes, I thus predicted that temperate species would exhibit greater flexibility in thermoregulatory traits than their tropical counterparts. I measured seasonal variation in five thermoregulatory traits [body mass (Mb),

3 basal metabolic rate (BMR), lower critical temperature (LCT), upper critical temperature (UCT), and TNZ breadth] and compared the direction and magnitude of seasonal flexibility in each thermoregulatory trait between tropical and temperate species. These data represent the first large-scale geographic comparison of seasonal variation in TNZ breadth and its two component traits (LCT and UCT) and one of the first tests of the impacts of temporal variation in ambient temperature on flexibility of thermal physiological traits.

Understanding the influence of ambient temperature on variation in heat tolerances important step towards predicting the impacts of climate change on endotherms. Tropical ectotherms have lower heat tolerances and live closer to their thermal tolerance limits than their temperate counterparts, and consequently have greater vulnerability to climate warming

(Deutsch et al 2008, Huey et al. 2009, Huey et al. 2012). Tropical birds have narrower TNZs and a greater degree of projected mismatch than temperate species between their TNZ limits and ambient temperatures under future climate warming scenarios (Khaliq et al. 2014). Taken together, these results suggest that tropical birds may be more vulnerable to climate warming than their temperate counterparts, similar to the conclusions drawn from studies of ectotherms. However, although the TNZ provides useful information about the energetic costs of thermoregulation in relation to ambient temperature, it is not a direct measure of heat tolerance. To fully characterize an endotherm’s heat tolerance requires information about thermoregulation above the UCT. Heat tolerance consists of three components – the upper limit of the TNZ (upper critical temperature – UCT), the accumulation of endogenous heat load above the UCT (i.e. the slope of the relationship between body temperature and ambient temperature above the UCT, hereafter termed ‘heat-strain coefficient’; modified from

Weathers 1981) and the absolute thermal limit (critical thermal maximum – CTmax)(Fig. 1.2). In chapter 4, I characterized these three traits in tropical and temperate birds to determine whether tropical birds had lower heat tolerances than their temperate counterparts.

Furthermore, I combined heat tolerance data with ambient temperature data to determine whether tropical birds would be more vulnerable to climate warming than their temperate counterparts. These data represent the first latitudinal comparison of heat tolerances in endotherms and will be important for predicting whether the physiological impacts of climate change on birds will differ across latitude.

In chapter 5, I investigated the potential consequences of variation in thermal physiology on the ecology and behavior of a suite of Neotropical bird species that inhabit the forest understory. Understory insectivores are a guild of birds that is disproportionately sensitive to disturbance, experiencing population declines and local extirpation in response to forest fragmentation (Robinson and Sherry 2012). One hypothesis for their declines is the microclimate hypothesis (Stratford and Robinson 2005, Robinson and Sherry 2012), which posits that the novel abiotic conditions (i.e. increased temperature and solar radiation, decreased humidity) that habitat fragmentation introduces (Didham and Lawton 1999,

Laurance et al. 2002) may physiologically challenge understory insectivores and contribute to their population declines. An important assumption of the microclimate hypothesis is that understory insectivores have narrow physiological tolerances because they inhabit the forest understory, which is an incredibly stable and buffered environment (Ewers and Banks-Leite

2013). To test the microclimate hypothesis, I tagged nine understory insectivorous bird species with radio-transmitters and compared the microclimates that they selected with randomly

5 chosen microclimates to test for the preferential use of particular microclimates (i.e. microclimate selectivity). This study is one of the first to track the microclimate associations of individual birds within their home-ranges and attempt to link variation in thermal physiology with behavior and habitat selection.

CHAPTER 2: THE INFLUENCE OF AMBIENT TEMPERATURE REGIME ON AVIAN THERMONEUTRAL ZONES AT MULTIPLE SPATIAL SCALES

ABSTRACT

Understanding how variation in ambient temperature regime shapes inter- and intraspecific patterns of variation in thermal physiology has been a longstanding goal of ecological and evolutionary physiology. An emerging framework for understanding geographic variation in thermal physiology is the Climatic Variability Hypothesis (CVH), which predicts that thermal tolerance breadth (i.e., the range of ambient temperatures at which an organism can maintain physiological homeostasis) increases with temperature variability. Latitudinal increases in thermal tolerance breadth that are broadly consistent with the CVH have been reported in ectotherms and more recently, in endotherms. However, latitude is only a proxy for temperature variability, and the specific temperature variables underpinning geographic variation in thermal tolerance breadth have not been identified. Furthermore, it is unclear whether or not the CVH applies at smaller spatial scales (i.e. within a geographic locality), where there is substantial temperature variability that may be associated with variation in thermal physiology.

I assessed associations between ambient temperature regime and thermal physiology of birds on broad (across localities) and fine (within localities) spatial scales by estimating breadth of the thermoneutral zone (TNZ; a proxy for thermal tolerance in endotherms) in species from three geographic localities (Illinois, South Carolina, Republic of Panama) with distinct ambient temperature regimes. Across localities, the best predictor of both interspecific and intraspecific variation in TNZ breadth was mean temperature range, a metric of temperature variability.

Species’ migratory tendency was a significant predictor of interspecific variation in TNZ breadth 7

– migratory species had TNZ breadths intermediate between those of tropical and temperate resident species. Within localities, I found no evidence that ecological traits related to ambient temperature regime were associated with TNZ breadth. My data indicate that temperature variability is the most important determinant of geographic variation in avian TNZ breadth.

Furthermore, ecological traits associated with ambient temperature regime were not related to

TNZ breadth, suggesting that unlike thermal tolerances of ectotherms, TNZ breadth may be decoupled from ambient temperature regime within a geographic locality.

INTRODUCTION

Ghalambor et al. 2006, Gaston et al. 2009, Bonebrake 2013). An emerging framework for understanding geographic variation in thermal physiology is the climatic variability hypothesis

(CVH), which predicts that thermal tolerance breadth (the range of ambient temperatures at which an organism can maintain physiological homeostasis) will increase with temperature variability (Janzen 1967, Stevens 1989, Ghalambor et al. 2006). Temperature variability increases with latitude (Ghalambor et al. 2006), and therefore previous tests of the CVH have focused on variation in thermal tolerance breadth across latitudinal temperature gradients.

Indeed, thermal tolerance breadth increases with latitude in terrestrial and marine ectotherms, a pattern consistent with the CVH (Addo-Bediako et al. 2000, Sunday et al. 2011; reviewed in

Huey et al. 2012). Furthermore, reductions to the lower limit of the thermal tolerance in temperate ectotherms are responsible for latitudinal increases in thermal tolerance breadth

(Sunday et al. 2011, Huey et al. 2012, Araújo et al. 2013), which likely reflects the more pronounced latitudinal gradient in minimum temperature relative to maximum temperature

(Ghalambor et al. 2006, Huey et al. 2012). Therefore, the colder minimum temperatures at higher latitudes appear to underlie latitudinal increases in temperature variability and corresponding increases in thermal tolerance breadth of ectotherms (Sunday et al. 2011, Araújo et al. 2013).

Until recently, patterns of latitudinal variation in thermal tolerance breadth had not been described in endotherms (Huey et al. 2012), in part because endotherms exhibit distinct physiological responses to ambient temperature (Angilletta et al. 2010, Buckley et al. 2012).

Whereas metabolic rates and body temperatures of ectotherms vary continuously with ambient temperature, endotherms maintain a relatively stable internal body temperature and metabolic rate across a range of ambient temperatures known as the thermoneutral zone (TNZ;

McNab 2002). Outside the bounds of the TNZ [the Lower Critical Temperature (LCT) and the

Upper Critical Temperature (UCT); see Fig. 1.1], endotherms must expend metabolic resources to maintain constant internal body temperature (McNab 2002). Therefore the TNZ can provide valuable information about the energetic consequences of thermal variation, and the breadth of the TNZ has thus been suggested as a proxy for thermal tolerance in endotherms (Huey et al.

2012). In the first comprehensive analysis of geographic variation in TNZ breadth of endotherms, Khaliq et al. (2014) found latitudinal increases in avian TNZ breadth that were underpinned by reductions to the LCT. Therefore, latitudinal increases in thermal tolerance breadth that are driven by increases in cold tolerance of temperate species may be a common macrophysiological pattern that extends beyond ectotherms to endotherms.

Although latitudinal increases in thermal tolerance breadth are broadly consistent with the CVH, most studies have used latitude (a complex variable that encompasses both current and historical abiotic and biotic selective pressures; Naya et al. 2012) as a proxy for temperature variability rather than explicitly testing the relationship between temperature variability and thermal tolerance breadth predicted by the CVH. Latitude is not necessarily equivalent to temperature variability and other temperature variables that covary with latitude may be important predictors of geographic variation in thermal physiology (Swanson and

Garland 2009, Stager et al. 2016). Consequently, testing the CVH requires examining the effects of different temperature variables on thermal tolerance breadth in a comparative context (e.g.

Stager et al. 2016).

Furthermore, because previous studies have focused on macrophysiological (i.e. latitudinal) patterns of variation in thermal tolerance breadth, it is unclear whether or not the

CVH applies at smaller spatial scales. Just as ambient temperature regime drives patterns of variation in thermal tolerance breadth across large spatial scales, there is substantial variation in ambient temperature on a smaller spatial scale (i.e. within a geographic locality) that may drive variation in thermal tolerance breadth (Suggitt et al. 2011, Scheffers et al. 2014). For example, forest lizards, which experience less temperature variability and are more buffered from temperature extremes than their open-habitat counterparts, have correspondingly narrower thermal tolerances and greater vulnerability to climate warming (Huey et al. 2009,

Muñoz et al. 2014, Brusch et al. 2016). Similarly, within forested habitats, temperature variability increases from the forest floor to the canopy (Allee 1926, Scheffers et al. 2014,

Kaspari et al. 2015), and canopy-dwelling ant species have broader thermal tolerances and

10 higher heat tolerances than terrestrial and subterranean species (Diamond et al. 2012, Baudier et al. 2015, Kaspari et al. 2015). Thus, in ectotherms, ecological or life-history variation (e.g. habitat association, microhabitat choice, etc.) may be correlated with variation in ambient temperature regime and consequently, variation in thermal physiology although it is unclear whether the same is true in endotherms. Elucidating the interplay of ecology/life-history with thermal physiology in endotherms will help to understand the limits of the generality of the

CVH and whether temperature regime influences thermal physiology at a smaller spatial scale.

In this paper, I measured metabolic responses to ambient temperature in birds from three geographic localities (Illinois and South Carolina, U.S.A. and the Republic of Panama) to assess the influence of ambient temperature regime on TNZ breadth of birds at multiple spatial scales. In doing so, I extend the CVH in several ways. First, I used ambient temperature data obtained from weather stations (eight different temperature variables, including latitude and two metrics of temperature variability) to determine the specific temperature variables underpinning geographic variation in avian TNZ breadth. Second, I use my diverse dataset (~140 species) to examine associations between ecological traits (vertical niche, habitat association) and TNZ breadth and, to my knowledge, test the applicability of the CVH at smaller spatial scales (i.e. between different micro- and macro-habitat types within a geographic locality) for the first time in endotherms. Third, it is unclear how the distinct ambient temperature regimes that migratory species experience on their temperate breeding and tropical wintering grounds influence their thermal physiology (Khaliq et al. 2014), and I provide the first test of the influence of a life-history trait (migratory tendency) on TNZ breadth of birds. Finally, characterizing intraspecific geographic variation in physiological traits provides a powerful

11 framework for studying adaptive evolution (Garland and Adolph 1991, Kawecki and Ebert 2004,

Gaston et al. 2009), and I conduct one of the first tests for intraspecific variation in TNZ breadth by comparing TNZs between populations of the same species measured in Illinois and South

Carolina.

MATERIALS AND METHODS

Sampling localities

I sampled birds from 2013-2015 at one tropical (Republic of Panama) and two temperate (South Carolina and Illinois, United States) localities. I restricted sampling to summer months (April–August) at temperate localities to control for potential seasonal variation in TNZ breadth (Maddocks and Geiser 2000, Zheng et al. 2008). Because ambient temperature regime showed little seasonal variation at the tropical site (see Chapter 3) I included data collected throughout the year (February–November). Within each locality, I sampled birds in four different habitat types: open habitat, edge habitat, woodland habitat, and closed-canopy forest

(see ‘Habitat association’ section below).

Bird capture and housing protocols

I captured birds in mist-nets (12 x 2.6 m; 36-mm mesh) between 14:00–18:00 h and banded them with uniquely numbered aluminum leg-bands to facilitate individual identification

(Federal Bird Banding Permit #23942). I also assessed sex when possible based on plumage dimorphism and breeding condition, and I released all individuals exhibiting signs of reproduction such as an active brood patch (Jones 1971) or obvious cloacal protuberance

(Wolfson 1952). I then transported birds to a temperature-controlled lab held at 27 °C and

12 housed them in cloth-covered cages with water provided ad libitum until beginning temperature experiments.

Measuring metabolic responses to ambient temperature

At 19:00 on the day of capture, I weighed focal individuals using a digital scale

(American Weigh Scales model AWS-201, 200 ± 0.01 g) and transferred them from cages to respirometry chambers (see Respirometry system below) inside a PTC-1 temperature cabinet

(Sable Systems, Inc.) controlled by a Peltier device (Pelt-5, Sable Systems, Inc.). Throughout temperature experiments, I precisely regulated ambient temperature in the cabinet and continuously monitored chamber temperatures using thermistor probes (model SEN-TH, Sable

Systems, Inc., ± 0.2 °C accuracy). I let birds acclimate to the chambers at 30 °C (within the TNZ of most focal species; Wiersma et al. 2007) for ≥ 3h to ensure that they were post-absorptive and resting (following McKechnie and Wolf 2004’s criteria for BMR) before initiating the temperature experiment. I used infrared cameras (model WCM-6LNV, Sabrent) to continuously monitor bird behavior and activity levels inside the chambers throughout the experiment.

Because activity level can influence metabolic rate (Aschoff and Pohl 1970) and confound the relationship between ambient temperature (Ta) and metabolic rate, I discarded data for focal birds that that exhibited high levels of activity during the experiment.

After the initial 3-h acclimation period, I used thermal ramping protocols (sensu Mitchell and Hoffmann 2010) to parameterize the relationship between Ta and metabolic rate, and to characterize TNZ breadth. Starting at 30 °C, I either increased (UCT experiments) or decreased

(LCT experiments) Ta in 3 °C increments and held birds at each Ta for one hour while measuring their O2 consumption. I concluded experiments when O2 consumption had increased in

13 successive sampling intervals, indicating that the focal individual had exceeded the limit of the

TNZ. The morning after temperature experiments, I weighed each individual again and used the mean of its initial and final weight measurements as the body mass, after which I released birds at the site of capture.

Respirometry system and gas measurement

I used push-mode flow-through respirometry (Withers 2001, Lighton and Halsey 2011) to measure gas exchange of focal bird species. I pumped incurrent air (PP2 pump; Sable

Systems, Inc.) through a column of Drierite to remove water and into a mass-flow controller

(Flowbar-8; Sable Systems, Inc.) that divided the air stream into four channels, each plumbed through Bevaline IV tubing (Cole-Parmer) to a separate Plexiglas metabolic chamber equipped with a rubber gasket in the lid and sealed with binder clips (ACCO Brands Corporation) to prevent leakage. One empty chamber (baseline) served as a reference to the other three chambers, each of which was designed to hold one bird. During experiments, birds rested in the chambers on perches made of wire mesh. The chamber inlet was situated on the lid of the chamber and the chamber outlet was on the side of the chamber opposite to the inlet to ensure that incurrent air flowed directly across the bird before exiting the chamber. Flow rates

(300-1,500 mL min-1) and chamber sizes (1.97 or 4.53 L) varied depending on the size of the focal species, with higher flow rates and larger chambers used for larger species. Excurrent air from one chamber at a time was subsampled manually at 100-150 mL min-1 through barrel syringes, scrubbed of water vapor (Drierite) and CO2 (Ascarite), and analyzed for %O2 (FoxBox;

Sable Systems, Inc.).

During each experiment, I measured flow rate, Ta, and %O2 in each chamber at one- second intervals using the program Expedata (Sable Systems, Inc.). I used a Catmull-Rom spline correction to correct for drift in the O2 analyzer. I then converted %O2 to V̇O2 (rate of O2

-1 consumption, measured in mL O2 min ) using the following equation:

V̇O2 = FR*(FiO2 – FeO2)/ (1 – FeO2)

-1 where FR is flow rate of the animal chamber (mL min STPD), FiO2 is incurrent fractional oxygen concentration (0.2095), and FeO2 is excurrent fractional oxygen concentration. I calculated BMR as the lowest stable 5-minute average V̇O2 measured during the experiment (Londoño et al.

2015). I used a coefficient of 20.08 Joules/mL O2 to convert V̇O2 to metabolic rate (Schmidt-

Nielsen 1997). To determine the relationship between metabolic rate and Ta, I obtained 3- minute rolling averages of metabolic rate and corresponding Ta throughout the entire experiment, generating a series of paired Ta and metabolic rate measurements that I later used to parameterize the TNZ of each focal individual (see ‘Estimating TNZ breadths’ below).

Estimating TNZ breadths

I estimated TNZ breadth as a proxy for species’ thermal tolerances (Huey et al. 2012,

Khaliq et al. 2014). To determine the limits of the TNZ (LCT and UCT), I identified inflection points in the relationship between Ta and metabolic rate for each individual using piecewise linear regression in the R package segmented (Muggeo 2009). Due to the duration of thermal ramping protocols, I calculated only the UCT or LCT for an individual bird. To obtain species- level estimates of LCT and UCT, I pooled individual values and took the mean as the species’

LCT/UCT. From these values, I calculated species-level TNZ breadth as the difference between a species UCT and LCT (Khaliq et al. 2014).

Determining thermal predictors of interspecific geographic variation in LCT, UCT and TNZ breadth

To determine the influence of ambient temperature on interspecific geographic variation in avian TNZ breadth, I tested for associations between a suite of eight temperature- related variables and LCT, UCT and TNZ breadth, respectively. I obtained daily temperature data for each of the three geographic localities from the National Oceanic and Atmospheric

Administration’s (NOAA) National Centers for Environmental Information’s (NCEI) Climate Data

Online (https://www.ncdc.noaa.gov/cdo-web/). Although these data were derived from weather stations located as close as possible (≤10 miles) to each respective sampling locality, they only approximate the ambient thermal conditions experienced by birds, and do not account for microclimatic variation present within the sampling locality (e.g. Walsberg 1993).

Weather station data were available for 98% of dates within the sampling period at each locality (range = 94–100%). I used these data to extract the following five temperature variables at each locality for the sampling periods in which the birds were measured: absolute minimum temperature (Tmin; the lowest of the daily minimum temperatures), mean minimum temperature (Tμmin; the average of the daily minimum temperatures), absolute maximum temperature (Tmax; the highest of the daily maximum temperatures), mean maximum temperature (Tμmax; the average of the daily maximum temperatures), and mean temperature

(Tmed; the average of the daily mean temperatures). Additionally, I included three metrics of temperature variability for each locality, including latitude (Lat.), absolute temperature range

(TRabs; the difference between the highest Tmax and lowest Tmin recorded during the sampling period) and mean temperature range (TRmed; the mean difference between Tμmax and Tμmin across the sampling period).

Determining the influence of ecological/life-history trait variation on LCT, UCT and TNZ breadth

To investigate how ecological/life-history trait variation was related to variation in TNZ breadth and its component traits and determine the applicability of the CVH at smaller spatial scales, I tested for associations between thermal physiological traits and two ecological traits

(vertical niche, habitat association) and a life-history trait (migratory tendency) that are related to ambient temperature regime:

Vertical niche – I defined ‘vertical niche’ as the vertical stratum of the habitat occupied by a given species (sensu Walther 2002a, 2002b). Ambient temperature regimes vary markedly between a forest’s understory and canopy and higher strata are characterized by higher and more variable ambient temperatures (Allee 1926, Scheffers et al. 2014, Kaspari et al. 2015), so I expected TNZ breadths to increase with increasing vertical niche within a habitat. I characterized vertical niche of bird species as ‘terrestrial’, ‘understory’, ‘midstory’ ‘canopy’, or

‘all’ (for species that were described as using multiple vertical strata with equal frequency) based on descriptions provided in Parker et al. (1996) and the Handbook of the Birds of the

World (HBW) Alive (http://www.hbw.com; del Hoyo et al. 2015).

Habitat association – Open habitats exposed to direct sunlight are characterized by high levels of solar radiation (Chazdon and Fetcher 1984) and as a result, higher and more variable ambient temperatures than shaded habitats (Huey et al. 1989, Kaspari et al. 2015), so I expected TNZ breadth to increase with increasing habitat openness. I characterized habitat associations of bird species as follows: ‘open’ (i.e. species that inhabit open areas and are very frequently exposed to direct sunlight), ‘edge’ (i.e. species that inhabit ecotones and are frequently exposed to direct sunlight) ‘woodland’ (i.e. species that inhabit forest with open

17 canopies and are sometimes exposed to direct sunlight), and ‘forest’ (species that inhabit closed-canopy forest and are rarely exposed to direct sunlight) based on descriptions provided in Parker et al. (1996) and the Handbook of the Birds of the World (HBW) Alive (del Hoyo et al.

2015).

Migratory tendency – Neotropical migrants that breed in the temperate zone and overwinter in the tropics experience distinct climatic regimes at their breeding and overwintering grounds and along their migratory route. I therefore expected migratory species to have intermediate TNZ breadths relative to temperate and tropical resident species because they avoid cold and variable temperate winters but also experience substantially more temperature variability during migration relative to tropical residents (Jetz et al. 2008). I classified species that bred and overwintered in Illinois/South Carolina as ‘temperate residents’, species that bred and overwintered in Panama as ‘tropical residents’, and species that bred and overwintered in disjunct geographic locations as ‘migratory.’ For species that exhibited intraspecific variation in migratory tendency, I assigned categories based on the migratory behavior of the population of interest (e.g. although Northern Mockingbirds are generally considered to be a migratory species, individuals from the South Carolina population were classified as temperate residents because they overwinter there; del Hoyo et al. 2015).

Statistical analysis

Phylogeny construction and phylogenetic generalized least squares (PGLS) regression

I used phylogenetic generalized least squares (PGLS; Grafen 1989) to correct for the covariance expected by phylogenetic relationships among species in regression analyses. I generated a phylogeny (Fig. 2.1) by pruning the maximum likelihood avian phylogenetic tree of

Burleigh et al. (2015) with the drop.tip function in the R package ape (Paradis et al. 2004) to include all focal species. I then further pared down the phylogeny to the species for which I had obtained LCT, UCT or TNZ breadth data. I measured thermal physiology for eight species not included in the Burleigh et al. (2015) phylogeny, so I substituted eight closely related species with adequate sequence data (Table 2.1). I conducted PGLS regressions while scaling branch lengths to a maximum likelihood estimation of Pagel’s λ (Pagel 1999) using the pgls function in the R package caper (Orme et al. 2013).

Because of known allometric associations between physiological traits and body mass

(Mb) (e.g. McKechnie and Wolf 2004), I performed PGLS regressions of Mb on LCT, UCT and TNZ breadth of focal species both i) within each locality and ii) for all localities combined to examine associations between Mb and the three traits. I found significant correlations between Mb and i)

LCT and ii) TNZ breadth within both Illinois and South Carolina and across all localities combined

(Fig. 2.2, Table 2.2). Therefore I chose to include Mb as a covariate in subsequent analyses.

Determining the thermal predictors of interspecific geographic variation in LCT, UCT and TNZ breadth

Traditional phylogenetic comparative methods cannot account for population structure within species, and phylogeographic analysis of gene flow is required instead (Stone et al.

2011). Since I measured 13 species at different geographic locations (South Carolina and

Illinois), and I lack estimates of gene flow in these species between the two sampling localities, I instead used a bootstrapping approach to control for intraspecific variation in physiological traits in the temperature analyses. I ran 20 PGLS regressions with Mb as a covariate to test for pairwise associations between each of the eight temperature variables (Tmed, Tmin, Tμmin, Tmax,

Tμmax, Lat., TRmed, TRabs) and each physiological trait (LCT, UCT, TNZ breadth). In each replicate,

19 the locality for each shared species was assigned randomly and therefore included physiological data from only a single locality per species. I assessed robustness of this bootstrapping approach for each PGLS regression and found essentially identical results across the 20 iterations, and so I present results from one representative iteration below. For each physiological trait, I compared temperature variable models using the Akaike information criterion (AIC; Burnham and Anderson 2004).

Determining the influence of migratory tendency on interspecific variation in LCT, UCT and TNZ breadth

I conducted PGLS regression with Mb as a covariate to test for associations between migratory tendency and each physiological trait (LCT, UCT and TNZ breadth). As with the interspecific temperature analyses, I used a bootstrapping approach and ran 20 PGLS regressions, randomly selecting populations when species were measured at multiple geographic localities. I found essentially identical results across the 20 iterations, and so I present results from one representative iteration below.

Determining the influence of ecological traits on interspecific variation in LCT, UCT and TNZ breadth

I conducted PGLS regression with Mb as a covariate to test for associations between each ecological trait (habitat association, vertical niche) and each physiological trait (LCT, UCT,

TNZ breadth). Because I were interested in how ecological traits were related to thermal physiology at a smaller spatial scale, I ran separate analyses for each ecological trait within each locality, and therefore a bootstrapping approach was not required.

Testing for intraspecific variation in LCT, UCT and TNZ breadth

To test for intraspecific geographic variation in LCT, UCT and TNZ breadth, I ran generalized linear mixed models using the lmer function in the R package lme4 (Bates et al.

2014). Models for each trait included locality as a fixed effect and species as a random effect. I compared models to null models that included just species as a random effect using the anova function to test the importance of locality in predicting variation in the three traits. To quantify the magnitude of intraspecific trait variation, I conducted paired t-tests on each trait for focal species that were measured in both Illinois and South Carolina.

RESULTS

Overall, I sampled 620 individuals of 137 species (Mb range: 2.88–163.0 g) at the three geographic localities. The mean number of individuals sampled per species was 4.03 ± 3.85 individuals (range: 1–21 individuals). I observed phylogenetic signal in my comparative analyses, justifying the use of PGLS regressions. Therefore, I present results from PGLS analyses here (following Freckleton 2009).

Thermal predictors of interspecific variation in LCT, UCT and TNZ breadth

Of the eight temperature variables, after controlling for Mb, mean temperature range

(TRmed; mean difference between the daily minimum and maximum temperatures across the sampling period) was the best predictor of variation and had the lowest AIC scores for LCT (R2 =

0.41, ΔAIC = 3.72), UCT (R2 = 0.06, ΔAIC = 0.14), and TNZ breadth (R2 = 0.39, ΔAIC = 2.70)(Table

2.3). In contrast, latitude was a relatively poor predictor of variation in TNZ breadth and its component traits (Table 2.3). However, no temperature variable explained more than 6% of the variation in UCT and there were several models (TRmed, Tmax, TRabs) with nearly equal AIC values.

This was due to the fact that UCT (range: 32–39 °C, CV = 3.33) was far less geographically

21 variable than LCT (range: 18.5–29.88 °C, CV = 10.30)(Levene’s Test, F1,192 = 32.29, p < 0.0001).

Overall, as TRmed increased, LCT values decreased (t = -7.85, p < 0.0001) and UCT values increased (t = 2.57, p = 0.01) and as a result, TNZ breadths increased with increases in TRmed (t =

6.87, p < 0.0001).

Influence of migratory tendency on interspecific variation in LCT, UCT and TNZ breadth

PGLS regressions indicated that migratory tendency was a significant predictor of variation in species’ LCT, UCT and TNZ breadth (Table 2.4). After controlling for Mb, mean LCT was significantly lower in temperate species than migratory species (1.75 °C difference; p =

0.003) and tropical residents (4.0 °C difference; p < 0.0001) and significantly lower in migratory species than tropical residents (2.25 °C difference; p < 0.0001)(Fig. 2.3a). In contrast, mean UCT was significantly higher in migratory species than both temperate residents (0.94 °C difference; p = 0.02) and tropical residents (1.02 °C difference; p = 0.001) but not differ significantly between temperate and tropical residents (0.08 °C difference; p = 0.81)(Fig. 2.3b). Overall, mean TNZ breadth was significantly greater in temperate residents (3.88 °C difference; p <

0.0001) and migratory species (2.97 °C difference; p < 0.0001) than tropical residents and was slightly greater in temperate residents but not significantly different compared to migratory species (0.91 °C difference; p = 0.25)(Fig. 2.3c).

Influence of ecological traits on interspecific variation in LCT, UCT and TNZ breadth

PGLS regressions with Mb as a covariate revealed that habitat association was not a significant predictor of variation in LCT, UCT or TNZ breadth within any of the three localities. In pairwise comparisons between all possible habitat combinations within a locality, there were no significant differences in LCT, UCT or TNZ breadth (all p > 0.05; Fig. 2.4). Similarly, vertical

22 niche was not a significant predictor of variation in LCT, UCT or TNZ breadth within any of the three geographic localities. In pairwise comparisons between all possible vertical niche combinations within a locality, there were no significant differences in LCT, UCT or TNZ breadth

(all p > 0.05; Fig. 2.5).

Intraspecific geographic variation in LCT, UCT and TNZ breadth

I found clear evidence of intraspecific geographic variation in LCT, UCT, and TNZ breadth

- models that included locality were significantly better at predicting variation in LCT, UCT and

TNZ breadth than null models (Table 2.5). Similar to the interspecific analysis, intraspecific differences in thermal physiology between Illinois and South Carolina populations corresponded with patterns of temperature variability (Fig. 2.6). Although the Illinois locality was at a higher latitiude than the South Carolina locality, mean daily temperature range (TRmed) during the sampling period was slightly greater in South Carolina (12.2 °C) than in Illinois (10.9

°C) (Fig. 2.7). Correspondingly, individuals from South Carolina populations had significantly lower LCTs (t = 2.41, p = 0.04), higher UCTs (t = -2.37, p = 0.04) and broader TNZs (t = -5.80, p =

0.0003) than individuals from Illinois populations (Fig. 2.7; Table 2.6).

DISCUSSION

Thermal predictors of interspecific geographic variation in LCT, UCT and TNZ breadth

Despite high collinearity between temperature variables (Supplementary Table S5), I found that mean temperature range (TRmed, a metric of temperature variability) was the best predictor of interspecific geographic variation in LCT, UCT and TNZ breadth. In contrast, latitude was a relatively poor predictor of variation in LCT, UCT and TNZ breadth. As TRmed increased, mean LCT decreased and mean UCT increased, resulting in increased mean TNZ breadth,

23 consistent with predictions of the CVH (Bozinovic et al. 2011). Variation in TNZ breadth of birds therefore appears to be driven by the average temperature variability that they experience – having a broader TNZ extends the range of ambient temperatures at which an organism is at basal metabolic rate and should reduce the energetic cost of thermoregulation in more thermally variable environments. In my study, increases in TRmed were underpinned by reductions in Tmin, whereas Tmax varied little between localities. Correspondingly, increases to

TNZ breadth were primarily driven by reductions to LCT (R2 = 0.85), whereas UCT varied little between localities and explained much less variation in TNZ breadth (R 2 = 0.22). These data corroborate a clear pattern – changes to the lower limit of the thermal tolerance in response to cold ambient temperatures are largely responsible for increases in thermal tolerance breadth with increasing temperature variability (Sunday et al. 2011, Araújo et al. 2013, Khaliq et al.

2014).

Migratory tendency predicts interspecific geographic variation in LCT, UCT and TNZ breadth

I found that migratory tendency was an important predictor of interspecific variation in avian thermal physiology. Migratory species had TNZ breadths that were intermediate to those of tropical and temperate resident species. Furthermore, patterns of variation in the TNZ breadth of migratory species were driven almost exclusively by variation in LCT – temperate species had the lowest LCTs, those of migratory species were intermediate, and tropical species had the highest LCTs. To my knowledge, this is the first comprehensive assessment of TNZ breadths of migratory birds (n = 32 species) and is consistent with the intermediate levels of temperature variability that they experience during their annual cycle – they avoid cold and variable temperate winters but also experience more temperature variability (and specifically,

24 colder ambient temperatures) during migration than tropical residents (Jetz et al. 2008).

However, I did not measure migratory species during migration but rather on their temperate breeding grounds (n = 28) or on their tropical wintering grounds (n = 4) where they were exposed to the same temperature conditions as temperate or tropical residents, respectively.

McKechnie and Swanson (2010) have suggested that improved cold tolerance in migrants may result as a by-product of selection for flight endurance/capacity, which could explain why migrants have LCTs that are lower than those of tropical residents but higher than those of temperate residents that have to endure cold winter temperatures. However, measurements of

TNZ breadth throughout migratory species’ annual cycle would be necessary to test this hypothesis. Nonetheless, my findings demonstrate how linkages between life-history traits and ambient temperature regime can interact to influence thermal physiology of birds (Jetz et al.

2008).

Ecological traits do not predict interspecific variation in LCT, UCT and TNZ breadth at small spatial scales

At a fine spatial scale (i.e. within each locality), I found that ecological traits (habitat association, vertical niche) associated with ambient temperature regime were poor predictors of variation in TNZ breadth and its component traits. There were no significant differences in mean LCT, UCT or TNZ breadth between species of diverse habitat associations and vertical niches in any of the three localities. Overall, the data that I present here highlight the importance of considering spatial scale and show that the CVH does not seem to apply to different micro- or macro-habitat types within geographic localities in birds. These results contrast with recent studies of ectotherms that have documented strong associations between thermal physiology and ecological traits such as habitat association and vertical niche (e.g. Huey

25 et al. 2009, Muñoz et al. 2014, Baudier et al. 2015, Kaspari et al. 2015). However, endotherms maintain high, stable internal body temperatures (McNab 2002) across a range of ambient temperatures, whereas body temperatures (and, as a result, physiological performance) of ectotherms vary continuously with ambient temperature. My findings suggest that the decoupling of ambient temperature and body temperature in endotherms such as birds may buffer them to some extent from local variation in ambient temperature (Buckley et al. 2012,

Huey et al. 2012). Furthermore, the high vagility of birds may enable them to use behavioral thermoregulation to avoid unsuitable thermal microclimates (Walsberg 1993). Endothermy of birds coupled with their high mobility may therefore explain why ecological traits associated with ambient temperature regime are poor predictors of variation in avian LCT, UCT and TNZ breadth.

Intraspecific geographic variation in thermal physiology

I found clear evidence of intraspecific variation in thermal physiology – locality was a significant predictor of variation in LCT, UCT and TNZ breadth. Although the Illinois locality was located at a higher latitude than the South Carolina locality, TRmed was slightly greater (1.3 °C) at the South Carolina locality. Correspondingly, mean LCT was significantly lower (~1 °C), mean

UCT was significantly higher (1 °C), and mean TNZ breadth was significantly greater (~2 °C) in individuals from the South Carolina population. Although intraspecific variation in the three traits was small, patterns were largely consistent across all of the species included in the intraspecific analysis (Fig. 4). These results strengthen the evidence from interspecific analyses that ambient temperature regime (and temperature variability in particular) is an important selective pressure underlying geographic variation in avian thermal physiology.

Conclusion

Overall, my results corroborate previous analyses that have found latitudinal increases in TNZ breadth on large (i.e. across geographic localities) spatial scales. Furthermore, I found that temperature variability per se was the best predictor of interspecific and intraspecific geographic variation in TNZ breadth, supporting the predictions of the CVH. I present the first comprehensive analysis of the TNZ breadths of migratory bird species – I found that migratory species had TNZ breadths intermediate between those of tropical and temperate resident species, indicating that life-history traits such as migratory tendency can be important predictors of variation in thermal physiology. Finally, I found no significant associations between two ecological traits (vertical niche and habitat association) related to ambient temperature regime and TNZ breadth, indicating that temperature variability at the micro- or macro-habitat level is not associated with TNZ breadth in birds, and suggesting that the CVH may not apply to smaller spatial scales in endotherms.

CHAPTER 3: TEMPERATURE SEASONALITY DRIVES PATTERNS OF GEOGRAPHIC VARIATION IN FLEXIBILITY OF AVIAN THERMOREGULATORY TRAITS

INTRODUCTION

Phenotypic flexibility – the ability to undergo reversible phenotypic adjustments in response to changing environmental conditions (Piersma and van Gils 2010) – is an important component of fitness for organisms that inhabit variable environments (Piersma and Drent

2003). In endotherms, flexibility in thermoregulatory traits is likely to be especially important in seasonal temperate environments where the energetic demands of thermoregulation vary considerably throughout the year. For example, in temperate endotherms, a high thermogenic capacity confers improved tolerance to cold winter temperatures (Swanson 2010). Conversely, these benefits diminish during less thermally challenging periods because of the energetic costs associated with maintaining an elevated thermogenic capacity (Dutenhoffer and Swanson 1996,

Swanson 2010). Thus, a tradeoff exists whereby having a high thermogenic capacity is favorable in cold environments but energetically costly in warm environments. The ability to flexibly adjust thermoregulatory traits such as thermogenic capacity to the current thermal environment thus has clear energetic benefits and implications for organismal fitness (Piersma and Drent 2003, Stager et al. 2015).

An emerging framework for understanding geographic variation in phenotypic flexibility of thermoregulatory traits is the climatic variability hypothesis (CVH), which predicts that species from temperate latitudes will have greater flexibility than those from tropical latitudes, where temperature seasonality is comparatively slight (Chown et al. 2004, Bozinovic et al. 2011,

Naya et al. 2012). Empirical studies of seasonal variation in thermoregulatory traits have yielded mixed support for the CVH and conclusions differ depending on the thermoregulatory 28 trait in question. For example, a recent meta-analysis (McKechnie et al. 2015) found that temperate bird species had greater seasonal flexibility than tropical species in summit metabolic rate (Msum, i.e. maximum cold-induced metabolic rate – a measure of thermogenic capacity), a trait closely tied to cold tolerance (Swanson 2010). Specifically, cold winter temperatures drive seasonal increases in Msum in temperate species (Swanson and Garland

2009, Swanson 2010) that exceed the more modest seasonal adjustments in Msum of tropical species (Wells and Schaeffer 2012, McKechnie et al. 2015, Stager et al. 2016). In contrast, however, McKechnie et al. (2015) found no evidence of latitudinal differences in seasonal flexibility of basal metabolic rate (BMR – minimum maintenance metabolism required for physiological homeostasis). Unlike Msum, which is highly correlated to geographic variation in minimum ambient temperature (Stager et al. 2016), BMR is a complex trait associated with a suite of life history and environmental variables (reviewed in Glazier 2015) including precipitation (White et al. 2007), net primary productivity (Mueller and Diamond 2001), and diet (Naya et al. 2013). These results suggest that traits such as Msum that are directly tied to thermal tolerance may conform to the predictions of the CVH, whereas traits that are less important for coping with temperature seasonality such as BMR may not. However, the generality of this conclusion is unclear, primarily due to few data on seasonal flexibility in thermoregulatory traits (e.g. n = 31 species for BMR, n = 26 species for Msum in birds;

McKechnie et al. 2015).

Another important trait related to thermal tolerance in endotherms is the thermoneutral zone (TNZ) – the range of ambient temperatures at which an organism is at BMR and not expending energy on thermoregulation (McNab 2002)(Fig. 1.1). Outside the lower (lower

29 critical temperature – LCT) and upper (upper critical temperature – UCT) bounds of the TNZ, an endotherm must allocate energetic resources to maintaining internal temperature homeostasis

(McNab 2002). Thus, the breadth of the TNZ influences the energetic costs of thermoregulation. Greater temperature variability should select for increased TNZ breadth to reduce the energetic costs of thermoregulation and allow organisms to persist in thermally variable environments (Khaliq et al. 2014). Indeed, TNZ breadth increases with latitude (Araújo et al. 2013, Khaliq et al. 2014), indicating that it may be important for coping with temperature seasonality. Moreover, latitudinal increases in TNZ breadth are underpinned by reductions to

LCT whereas UCT varies little between temperate and tropical species (Araújo et al. 2013,

Khaliq et al. 2014), suggesting that acclimatization to colder temperate environments is driving patterns of spatial variation in TNZ breadth. Presumably, seasonal variation in TNZ breadth should also be greater in temperate species and should be driven by winter reductions to LCT.

Few studies, however, have characterized seasonal variation in TNZ breadth (but see Bush et al.

2008, Nzama et al. 2010, Zhao et al. 2014, Thompson et al. 2015, Wu et al. 2015), and how seasonal flexibility in TNZ breadth varies geographically is not known.

I quantified seasonal variation in five thermoregulatory traits (body mass, BMR, LCT,

UCT and TNZ breadth) in suites of tropical (Panama) and temperate (Illinois) bird species to test the central prediction of the CVH – that thermoregulatory flexibility should increase with temperature seasonality (Bozinovic et al. 2011). I predicted that:

1) Thermoregulatory traits (i.e. LCT, UCT, TNZ breadth) will exhibit latitudinal increases in flexibility whereas BMR will not.

2) The magnitude of seasonal flexibility in thermoregulatory traits will be greater in temperate species than tropical species.

3) Seasonal flexibility in TNZ breadth will be underpinned by cold-season reductions to LCT.

MATERIALS AND METHODS

Bird capture and handling

I sampled birds from 2014–2015 at one lowland tropical (Gamboa, Panama – 09°07' N,

79°42' W) and one temperate (Urbana, Illinois – 40°06' N, 88°12' W) sampling locality. I sampled temperate species in winter (December-February) and summer (May-August) and tropical species in dry season (February-April) and wet season (June-November). To facilitate seasonal comparison between the temperate and tropical sampling localities, I designated a ‘summer’

(temperate summer, tropical wet season) and ‘winter (temperate winter, tropical dry season) season based on seasonal temperature data obtained from each locality (Fig. 3.1), although I acknowledge that there were only slight differences in ‘summer’ and ‘winter’ temperature regimes at the tropical locality. Diurnal and seasonal temperature variability was greater at the temperate locality (Fig. 3.1). Although sampling dates were not temporally synchronized between localities, my approach allowed us to examine seasonal differences in thermal physiology across the annual spectrum of ambient temperature conditions experienced by birds at each locality.

I captured birds in mist-nets (12 x 2.6 m; 36-mm mesh) between 14:00–18:00 h and transported them back to the laboratory in cloth bags, where I banded each individual with a uniquely numbered aluminum leg-band to facilitate future identification. I then held birds at 27

°C (within the TNZ of most focal species; Wiersma et al. 2007) in cloth-covered cages with water provided ad libitum until beginning temperature experiments.

Measuring metabolic responses to ambient temperature

At 19:00 on the day of capture, I weighed focal individuals using a digital scale

(American Weigh Scales model AWS-201, 200±0.01 g) and transferred them to respirometry respirometry chambers (see Respirometry system and gas measurement below) inside a PTC-1 temperature cabinet (Sable Systems, Inc.) controlled by a Peltier device (Pelt-5, Sable Systems,

Inc.). Throughout temperature experiments, I precisely regulated ambient temperature in the cabinet and continuously monitored chamber temperatures using thermistor probes (model

SEN-TH, Sable Systems, Inc., ± 0.2 °C accuracy). I allowed birds to acclimate to metabolic chambers for at least 3 hours at 27 °C, which ensured that focal birds had been post-absorptive for ≥ 5 hours and had reached BMR (following the criteria required for BMR as outlined in

McKechnie and Wolf 2004). I used infrared cameras (model WCM-6LNV, Sabrent) to continuously monitor bird behavior and activity levels inside the chambers throughout the experiment. Because activity level can influence metabolic rate (Aschoff and Pohl 1970) and confound the relationship between ambient temperature (Ta) and metabolic rate, I discarded data for focal birds that that exhibited high levels of activity during the experiment. Following the 3-h acclimation period, I either increased (to determine UCT) or decreased (to determine

LCT) Ta in 3 °C increments and held birds at each Ta for one hour while measuring their O2 consumption. I concluded experiments when O2 consumption had increased in successive sampling intervals, indicating that the focal individual had exceeded its TNZ. Immediately after concluding temperature experiments, I re-weighed birds and released them at the site of capture.

Respirometry system and gas measurement

I used push-mode flow-through respirometry (Withers 2001, Lighton and Halsey 2011) to measure gas exchange of focal bird species. During each experiment, I pumped incurrent air

(PP2 pump; Sable Systems, Inc.) through a column of Drierite to remove water and then pumped dried air into a mass-flow controller (Flowbar-8; Sable Systems, Inc.) that divided the air stream into four channels, each plumbed through Bevaline IV tubing (Cole-Parmer) to a separate Plexiglas metabolic chamber equipped with a rubber gasket in the lid and sealed with binder clips (ACCO Brands Corporation) to prevent leakage. One empty chamber (baseline) served as a reference to the other three chambers, each of which was designed to hold one bird. During experiments, birds rested in the chambers on perches made of wire mesh. The chamber inlet was situated on the lid of the chamber and the chamber outlet was on the side of the chamber opposite to the inlet to ensure that incurrent air flowed directly across the bird before exiting the chamber. Flow rates (300-1,500 mL min-1) and chamber sizes (1.97 or 4.53 L) varied depending on the size of the focal species, with higher flow rates and larger chambers used for larger species. Excurrent air from one chamber at a time was subsampled manually at

100-150 mL min-1 through barrel syringes, scrubbed of water vapor (Drierite) and CO2

(Ascarite), and analyzed for %O2 (FoxBox; Sable Systems, Inc.).

-1 consumption, measured in mL O2 min ) using the following equation:

V̇O2 = FR*(FiO2 – FeO2)/ (1 – FeO2)

-1 where FR was the flow rate of the animal chamber (mL min STPD), FiO2 was the incurrent fractional oxygen concentration (0.2095), and FeO2 was the excurrent fractional oxygen concentration.

I calculated BMR as the lowest stable 5-minute average V̇O2 measured during the experiment (Londoño et al. 2015) and used a coefficient of 20.08 Joules/mL O2 (Schmidt-

Nielsen 1997) to convert V̇O2 to metabolic rate (watts). I calculated Mb as the mean of the pre- and post-experiment weights. Due to the well-known allometric relationship between Mb and

BMR (Lasiewski and Dawson 1967, McKechnie and Wolf 2004), I log10-transformed Mb and BMR to examine metabolic scaling relationships in my dataset. As expected, BMR scaled positively

2 2 with Mb during both winter (R = 0.77, F1,45 = 154.2, p < 0.0001; Fig. 3.2a) and summer (R =

0.83, F1,45 = 224.1, p < 0.0001; Fig. 3.2b). A common practice to control for the influence of Mb on BMR is to use the residuals of the Mb–BMR regression as data in subsequent analyses, yet this methodology can lead to biased parameter estimates if Mb is correlated with other variables of interest in subsequent analyses (Freckleton 2009). Therefore, to account for the effects of Mb on metabolic rate, I empirically calculated the allometric scaling relationship

b between Mb and BMR (following Stager et al. 2016) using the equation BMR = aMb , where a is the y-intercept and b is the scaling exponent. Typical values of b reported in the literature range from 0.65-0.75 for BMR (McKechnie and Swanson 2010). I obtained slightly lower values of b =

0.49 (winter) and b = 0.51 (summer), probably because my dataset was comprised almost exclusively of species from one order (Passeriformes) with a narrow range of Mb (3.94–119.85

b g). I then calculated mass-adjusted BMR by dividing each BMR measurement by Mb using the

34 above scaling exponent for the season of interest (e.g., cold-season mass-specific BMR =

0.49 BMR/Mb ).

To estimate the limits of the TNZ (LCT and UCT) of focal individuals, I obtained 3-minute averages of V̇O2 and corresponding Ta for each individual throughout the experiment, generating a series of paired Ta and V̇O2 measurements; because of the duration of thermal ramping protocols, I measured only LCT or UCT for a given individual. I then identified inflection points in the relationship between Ta and V̇O2 using piecewise linear regression (R package

‘segmented’; Muggeo 2009). TNZ breadth of each species was calculated as the difference between the mean UCT and mean LCT (TNZ = UCT – LCT; Khaliq et al. 2014). Due to the aforementioned relationship between Mb and BMR, I also tested for associations between Mb and a) LCT, b) UCT, and c) TNZ breadth in birds measured in both winter and summer. However,

I did not detect any significant relationships between Mb and any of the three traits in either season (all p > 0.05; Table 3.1), so I did not control for the influence of Mb on LCT, UCT or TNZ breadth in subsequent analyses.

Estimating seasonal flexibility in thermoregulatory traits

Phenotypic flexibility is defined as reversible, within-individual phenotypic changes across a range of environmental conditions (Piersma and Drent 2003). Due to the logistical challenges associated with recapturing individual birds across seasons, I were unable to obtain within-individual measurements across seasons for most of the individuals within my dataset.

Instead, I compared differences in species-level trait values among individuals sampled across seasons. I were able to measure 15 individuals of 9 species (6 tropical, 3 temperate) in both winter and summer, however, and I found qualitatively similar patterns of variation in the

35 thermoregulatory traits, suggesting that species-specific trends are representative of patterns of within-individual flexibility. Therefore I refer to the species-level seasonal changes in thermoregulatory traits as seasonal flexibility throughout the rest of the paper.

To estimate seasonal flexibility in thermoregulatory traits, I first calculated species- specific winter/summer (W/S) ratios for each trait (i.e. I divided the mean winter trait value by the mean summer trait value; McKechnie 2008). W/S ratios may a) be equal to 1 (indicates that the trait does not change seasonally), b) > 1 (indicates that the trait value increases in winter), or c) < 1 (indicates that the trait value decreases in winter).

In total, I estimated seasonal flexibility in one or more of the five thermoregulatory traits described above [body mass (Mb), basal metabolic rate (BMR), lower critical temperature

(LCT), upper critical temperature (UCT) and thermoneutral zone (TNZ) breadth] in 353 individuals of 47 species (6 temperate, 41 tropical). Because I sampled far fewer temperate species, I used Levene’s tests (R package ‘car’; Fox and Weisberg 2011) to test for heteroscedasticity in trait variances of tropical and temperate species within each season. I found no evidence of heteroscedasticity in either the cold or warm season in any of the five thermoregulatory traits (Table 3.2), indicating that the sample size disparity between temperate and tropical species was not influencing the validity of my inferences about seasonal flexibility.

Statistical analyses

Phylogenetic generalized least squares regressions

To account for the influence of phylogeny on thermoregulatory traits, I generated a phylogeny (Fig. 3.3) by pruning a maximum likelihood avian phylogenetic tree (Burleigh et al.

2015) with the drop.tip function in the R package ape (Paradis et al. 2004) to include all focal species. I then employed my phylogeny to conduct phylogenetic generalized least squares

(PGLS) regressions. To assess phylogenetic signal in my dataset, I employed Pagel’s λ (Pagel

1999) – a metric of phylogenetic signal ranging from 0 (no phylogenetic signal) to 1 (species’ traits covary in direct proportion to their shared evolutionary history)(Freckleton et al. 2002). I estimated Pagel’s λ (Pagel 1999) in the residual error of each regression model while simultaneously estimating regression parameters (Revell 2010) and then scaled regression models using estimates of λ.

The maximum-likelihood estimates of λ were close to 0 for many of the traits that I measured, indicating that these traits are not strongly influenced by phylogenetic relationships among the focal species (Pagel 1999). Accordingly, the results of OLS and PGLS regressions were qualitatively similar for all analyses. I did detect significant phylogenetic signal in some of my analyses, however, justifying my use of PGLS regressions, and I therefore report results from

PGLS analyses in the paper (following Freckleton 2009).

Characterizing geographic variation in seasonal flexibility of thermoregulatory traits

To test for differences between temperate and tropical species in the direction and magnitude of seasonal flexibility in thermoregulatory traits, I compared W/S ratios using PGLS regressions with locality as the independent variable.

Relationships between seasonal flexibility in LCT, UCT and TNZ breadth

To determine the relative influence of LCT and UCT on seasonal flexibility in TNZ breadth, I performed bivariate PGLS regressions of ΔLCT and ΔUCT on ΔTNZ breadth. I then compared the two candidate models (ΔLCT ~ ΔTNZ; ΔUCT ~ ΔTNZ) using a corrected Akaike

37 information criterion (AICc) for small sample sizes (Hurvich and Tsai 1989). Finally, I conducted a bivariate PGLS regression between ΔLCT and ΔUCT to test for functional linkages between the two traits.

RESULTS

Geographic variation in seasonal flexibility of thermoregulatory traits

I found that temperate species exhibited a greater magnitude of seasonal flexibility in

LCT (Fig. 3.4a), UCT (Fig. 3.4b) and TNZ breadth (Fig. 3.4c) than tropical species (Table 1). The direction of seasonal flexibility in these traits was also much more consistent in temperate species than tropical species. For example, 5 of 6 temperate species exhibited large reductions

(6.3 ± 2.87 °C) to LCT (Fig. 3.5a) and moderate reductions (1.95 ± 1.81 °C) to UCT in winter (Fig.

3.5b), resulting in large increases (4.77± 1.57 °C) in winter TNZ breadth (Fig. 3.5c). In contrast, the direction of seasonal flexibility in TNZ breadth was more variable in tropical species.

Although several tropical species showed substantial seasonal flexibility in TNZ breadth, with some increasing (e.g. Euphonia laniirostris – W/S ratio = 0.58) and others decreasing

(Ramphocelus dimidiatus – W/S ratio = 1.43) winter TNZ breadth, most species made only modest (~1-3 °C) seasonal adjustments to LCT and UCT, resulting in low mean seasonal flexibility in TNZ breadth (mean ΔTNZ = 0.08 °C).

With respect to the other two thermoregulatory traits, 5 of 6 temperate species exhibited modest cold-winter increases in Mb and mass-adjusted BMR; tropical species exhibited a more idiosyncratic pattern of seasonal variation in each trait. Nevertheless, the magnitude of seasonal flexibility in Mb (Fig. 3.4d) and mass-adjusted BMR (Fig. 3.4e) was low

(i.e. W/S ratios close to 1) and did not differ between temperate and tropical species (Table 1).

Relationship between seasonal flexibility in LCT, UCT and TNZ breadth

Seasonal flexibility in TNZ breadth was underpinned by seasonal flexibility in LCT, whereas UCT was less flexible in both temperate and tropical species. Temperate species in particular exhibited large cold-season reductions to LCT that outpaced more modest reductions to UCT and resulted in a net increase in winter TNZ breadth. I therefore found a highly significant linear relationship between ΔLCT and ΔTNZ (p < 0.0001, R2 = 0.79; Fig. 3.6a) but not between ΔUCT and ΔTNZ (p = 0.06, R2 = 0.13; Fig. 3.6b). As a result, ΔLCT was a far superior predictor of ΔTNZ compared to ΔUCT (ΔAICc = 21.64; Table 3.3). I also found a moderately strong positive correlation between ΔLCT and ΔUCT (λ = 0.00, R2 = 0.51, t = 3.66, p = 0.003) – therefore, species that exhibited larger ΔLCT also tended to exhibit larger ΔUCT.

DISCUSSION

I found that the magnitude of seasonal flexibility in three of the five thermoregulatory traits (LCT, UCT, and TNZ breadth) was greater in temperate species than tropical species.

Furthermore, the direction of seasonal change in these traits was inconsistent among tropical species, whereas temperate species consistently underwent cold season increases in TNZ breadth that were underpinned by large reductions to LCT. In contrast, I found no differences between tropical and temperate species in seasonal flexibility of Mb or mass-adjusted BMR, corroborating my prediction that CVH may only apply to traits that are closely linked to thermal tolerance.

Geographic variation in seasonal flexibility of thermoregulatory traits

Temperate species exhibited large (up to 9 °C) reductions in LCT and moderate (~2-3 °C) reductions in UCT in winter, resulting in a net increase in winter TNZ breadth (~2-7 °C). In

39 contrast, tropical species exhibited idiosyncratic patterns of seasonal variation in LCT, UCT and

TNZ breadth. Consequently, the magnitude of seasonal flexibility (as indicated by W/S ratio) in

TNZ breadth and its component traits was significantly greater in temperate species. Previous studies investigating seasonal change in TNZ breadth or its component traits have yielded mixed results. For example, several studies have found seasonal flexibility in TNZ breadth of certain bird species (e.g. Maddocks and Geiser 2000, Bush et al. 2008), including cold season reductions to LCT (Bush et al. 2008, Thompson et al. 2015, Wu et al. 2015), while other studies have found little seasonal variation in TNZ breadth (Nzama et al. 2010). My data represent the first temperate-tropical comparison of seasonal variation in avian TNZ breadth and clearly demonstrate that the avian TNZ is a flexible trait that varies seasonally. Furthermore, I show that the magnitude of seasonal flexibility is correlated with the magnitude of temperature seasonality that birds experience as predicted by the CVH (Bozinovic et al. 2011).

Increases in TNZ breadth should reduce the energetic costs of thermoregulation by extending the range of ambient temperatures at which an organism does not need to expend energetic resources to maintain temperature homeostasis (McNab 2002). I thus interpret cold season increases in TNZ breadth of temperate species as an energy-savings mechanism that functions to reduce the energetic costs of thermoregulation during winter. This interpretation is bolstered by the fact that reductions to LCT were largely responsible for winter increases in TNZ breadth in temperate species. Consequently, ΔLCT was a far superior predictor of variation in

ΔTNZ breadth compared to ΔUCT (ΔAICc= 24.04). My results suggest that LCT is a key trait related to cold tolerance that drives seasonal flexibility in TNZ breadth of temperate birds.

Although there were also moderate winter reductions to UCT in temperate species, they were

40 far less pronounced that seasonal reductions to LCT. I propose that there is a functional linkage between ΔLCT and ΔUCT, which is supported by the significant (R2 = 0.51) correlation that I found between ΔUCT and ΔLCT, and posit that winter reductions to UCT are therefore a byproduct of corresponding reductions to LCT. The mechanism for this functional linkage may be related to thermal conductance, which decreases during the winter as a function of increased feather insulation and serves to lower the LCT and reduce heat loss to the environment (Scholander et al. 1950). As a result, it would be more difficult for a bird to dissipate heat, explaining the corresponding (albeit more moderate) reductions to UCT.

In contrast to the other TNZ breadth and its component traits, I found no differences between temperate and tropical species in seasonal flexibility of either Mb or mass-adjusted

BMR. Most temperate species exhibited modest (5-10%) cold-season increases in Mb, consistent with previous studies (McKechnie 2008). Tropical species exhibited less consistent responses, with some increasing and others decreasing Mb in the cold season, but the overall magnitude of seasonal flexibility in Mb was similar to that of temperate species. On average, both temperate (11.6 %) and tropical species (5.0 %) exhibited moderate increases in winter

BMR, although there was substantial variation in seasonal flexibility among both temperate

(W/S ratio range: 0.89–1.31) and tropical (W/S ratio range: 0.71–1.33) species. My results corroborate a recent meta-analysis (McKechnie et al. 2015) that found that W/S ratios for mass-adjusted BMR did not differ between temperate and tropical bird species, contrary to previous analyses that suggested that temperate species had greater seasonal flexibility in BMR

(Smit and McKechnie 2010). Overall, my data indicate that a) tropical bird species are capable of substantial metabolic flexibility (McKechnie et al. 2015) and b) not all temperate species

41 exhibit seasonal acclimatization in BMR (Dawson and O’Connor 1996, McKechnie 2008).

Instead, increases in BMR of temperate species in winter are likely a byproduct of the enhanced thermogenic machinery required to maintain a higher Msum, a thermoregulatory trait that is directly linked to cold tolerance (Swanson 2010, Stager et al. 2016). My results support the prediction that flexibility in traits directly related to thermal tolerance, such as Msum and TNZ breadth, is especially important for temperate endotherms coping with high levels of temperature seasonality.

My results indicate that tropical birds have reduced seasonal flexibility in key thermoregulatory traits and may be less tolerant of environmental change than their temperate counterparts. In general, species with narrow thermal tolerances (Deutsch et al. 2008, Huey et al. 2009, Huey et al. 2012) and low physiological flexibility (Calosi et al. 2008, Somero 2010) are predicted to be most sensitive to climate change, and my study indicates that both of these characteristics apply to tropical birds. Furthermore, both tropical and temperate species exhibited relatively low flexibility in UCT (e.g. compared to LCT), suggesting that birds in general may have limited acclimatization capacity with respect to UCT (Araújo et al. 2013). However, because I were unable to control the environmental conditions to which focal species were exposed, acclimation and common garden experiments will be necessary to directly measure the acclimatization capacity of thermoregulatory traits and accurately predict whether the impacts of climate change on birds will be more severe in the tropics (Şekercioğlu et al. 2012).

CHAPTER 4: HEAT TOLERANCES OF TEMPERATE AND TROPICAL BIRDS SUGGEST THAT THEY WILL BE PHYSIOLOGICALLY BUFFERED FROM CLIMATE WARMING

ABSTRACT

In light of rising global temperatures, characterizing organismal heat tolerances is critical for predicting which species will be most vulnerable to climate change. The impacts of climate warming are expected to vary geographically, and recent studies of ectotherms have found that vulnerability to climate warming is greatest in the tropics. Tropical ectotherms tend to have lower heat tolerances than temperate counterparts and seem to be at a greater risk of exposure to ambient temperatures that are beyond their tolerance limits. In contrast, direct measurements of heat tolerance are largely lacking for endotherms. Therefore, it is unclear whether tropical endotherms will have lower heat tolerances and greater vulnerability to climate warming than their temperate counterparts.

To address this empirical gap, I measured three physiological traits related to heat tolerance (upper critical temperature – UCT, heat-strain coefficient – HScoeff, and critical thermal maximum – CTmax) in 81 bird species (23 temperate, 58 tropical) to determine if tropical species had lower heat tolerances than temperate species. I then coupled heat tolerance data with ambient temperature data to assess vulnerability to climate warming across latitude. Contrary to previous studies of ectotherms, I found limited evidence of reduced heat tolerances in tropical species. Although temperate species had slightly higher (~2 °C) CTmax, HScoeff and UCT did not differ between temperate and tropical birds. Importantly, these subtle differences in heat tolerance do not seem to translate into systematic differences in vulnerability to climate warming. Thermal safety margins did not differ significantly between temperate and tropical species, and seem to be large enough to allow for physiological tolerance of projected warming

43 rates. Overall, my data do not indicate that tropical birds will be any more physiologically vulnerable to climate warming than temperate species, in contrast with previous patterns described in ectotherms.

INTRODUCTION

Recent increases in global temperatures have had profound ecological and evolutionary impacts on biodiversity and ecosystem integrity (Walther et al. 2002; Bradshaw and Holzapfel

2006; Parmesan 2006; Bellard et al. 2012). However, these impacts are not uniform across communities – species differ in their relative vulnerability to climate warming. A better understanding of geographic variation in warming tolerance will therefore be critical for predicting which species and communities will be most vulnerable to climate warming (Deutsch et al. 2008; Dillon et al. 2010; Somero 2010; Huey et al. 2012). Previous studies have identified clear patterns of latitudinal variation in the heat tolerances of ectotherms. Tropical lizards, amphibians, and insects tend to have lower heat tolerances and narrower thermal safety margins (i.e. they are currently experiencing ambient temperatures closer to the limits of their heat tolerances) than their temperate counterparts (Deutsch et al. 2008; Huey et al. 2009; Huey et al. 2012; Diamond et al. 2012; Duarte et al. 2012). Therefore, tropical ectotherms may be disproportionately vulnerable to climate warming even under the relatively modest temperature increases projected for tropical environments (Dillon et al. 2010; IPCC 2014).

Despite the widespread evidence that the impacts of climate warming will be most harmful for tropical ectotherms, similar comparative studies of endotherms are lacking. The historical focus on ectotherms stems, in part, from the expectation that physiological and biochemical processes in endotherms are more buffered from fluctuations in ambient

44 temperature than those in ectothermic species (Buckley et al. 2012). Body temperature varies continuously with ambient temperature in ectotherms, and as a result physiological performance declines rapidly at high ambient temperatures (Angilletta 2009). In contrast, endotherms actively defend body temperature as ambient temperature changes, which helps to maintain optimal physiological function across a range of ambient temperatures. For example, endotherms exhibit increased metabolic rates in cold environments (Wiersma et al.

2007; Swanson 2010), allowing them to maintain higher activity levels and broader geographic distributions than ectotherms (Buckley et al. 2012). At high ambient temperatures, however, endotherms must actively dissipate heat, which also requires an increase in metabolic rate that consequently increases heat load and counteracts heat dissipation (Weathers 1981). Therefore, heat tolerance in endotherms may be more constrained than cold tolerance (Araújo et al. 2013) and the expectation that endotherms are more resilient to warming is also an open question.

Although I currently lack an understanding of geographic variation in endothermic heat tolerances, a recent meta-analysis suggested that tropical endotherms may also be particularly vulnerable to climate warming. Khaliq et al. (2014) compiled a global database of thermoneutral zones (TNZ) – the range of ambient temperatures within which an endotherm is able to maintain a basal metabolic rate and not expend metabolic resources on thermoregulation (McNab 2002)(Fig. 1.1). They then used the upper limit of the TNZ (upper critical temperature – UCT) to extrapolate the potential energetic costs of thermoregulation under future climate warming scenarios. They found that tropical endotherms were currently experiencing maximum temperatures closer to their UCT than their temperate counterparts and were therefore projected to be at greater risk from climate warming (Khaliq et al. 2014).

These patterns mirror those in ectotherms and suggest that tropical ecosystems, which contain a disproportionate proportion of the world’s biodiversity, may be the most imperiled in the face of rising global temperatures.

Although the UCT provides useful information about the energetic costs of thermoregulation at high ambient temperatures, it is not a complete measure of heat tolerance. To fully characterize the heat tolerance of endotherms, information about thermoregulation above the UCT is needed. Heat tolerance in endotherms is a function of three components: UCT, the heat-strain coefficient (HScoeff) and the critical thermal maximum

(CTmax)(Fig. 1.2). The HScoeff is the slope of the relationship between body temperature and ambient temperature above the UCT (modified from Weathers 1981), which approximates the accumulation of endogenous heat load. The CTmax represents the upper temperature extreme at which an organism can no longer maintain physiological function (Lutterschmidt and

Hutchinson 1997a; Lutterschmidt and Hutchinson 1997b; Terblanche et al. 2007). For endotherms, CTmax is estimated as the ambient temperature at which the ability to regulate body temperature is lost (i.e. the temperature at which uncontrolled hyperthermia occurs).

Although endotherms incur an energetic cost of thermoregulation above the UCT, variation in

HScoeff and CTmax are more direct measures of heat tolerance. For example, an endotherm with a low UCT could enhance heat tolerance by having a lower HScoeff, effectively slowing the rate of endogenous heat accumulation. Conversely, an endotherm with a high UCT but also a high

HScoeff would be more sensitive to heat above the UCT. In principle, HScoeff should also be functionally linked to CTmax – organisms with higher HScoeff should accumulate heat faster and reach CTmax at lower ambient temperatures than organisms with low HScoeff (Fig. 1.2).

Understanding the relationships among these three traits is needed to provide a more complete picture of heat tolerance and vulnerability to climate warming in endotherms.

Here, I measured three fundamental components of heat tolerance (UCT, HScoeff, and

CTmax) in temperate (South Carolina, United States) and tropical (Republic of Panama) bird species. The aims of my study were twofold. First, I characterized patterns of latitudinal variation in avian heat tolerances to determine whether tropical species have lower heat tolerances than their temperate counterparts. Second, I combined heat tolerance data with ambient temperature data to determine whether tropical species are currently experiencing temperatures closer to the limits of their heat tolerances and thus, are more immediately vulnerable to climate warming than temperate species.

MATERIALS AND METHODS

Sampling localities

I conducted field work in 2014 at one tropical (Gamboa, Republic of Panama – 09° 07' N,

79° 42' W) and one temperate (Aiken, South Carolina, United States – 33° 32′ N, 81° 43′ W) locality. I restricted sampling to the months of April-August (tropical wet season, temperate summer) for two reasons: 1) to control for the effects of seasonal variation in ambient temperature (Ta) on thermal physiology (Swanson 2010; Noakes et al. 2016) and 2) because species experience the highest ambient temperatures at both localities during this time period

(Fig. 4.1).Within both the temperate and tropical localities, I sampled birds in three different habitat types to encompass the available range of local variation in ambient temperature regimes: open habitats, closed-canopy forest, and edge habitats (i.e. ecotones between open and forest habitats).

Bird capture and housing protocols

I captured birds in mist-nets between 06:00–10:00 h and banded them with uniquely numbered aluminum leg-bands to facilitate individual identification (Federal Bird Banding

Permit #23942). I also determined sex when possible based on plumage dimorphism and the presence of brood patches or cloacal protuberances and immediately released individuals exhibiting external signs of being in reproductive condition. I then transported birds to a temperature-controlled lab held at 27 °C (within the TNZ of all focal species; Wiersma et al.

2007) and housed them in cloth-covered cages with water provided ad libitum for 1-3 hours.

Quantifying thermoregulatory responses to acute heat stress

Before beginning each heat stress experiment, I weighed each focal individual using a digital scale (American Weigh Scales model AWS-201, 200 ± 0.01 g) and inserted a temperature- sensitive passive integrative transponder (PIT) tag (model Biothermo13: 13 mm x 2.2 mm,

Biomark, Inc.) into its cloaca to measure the bird’s internal body temperature (Tb) during the experiment. I calibrated PIT tags before experiments using a thermometer certified by the

National Bureau of Standards, and determined that they were accurate across a range of Ta to within 0.2 ± 0.7 °C (mean ± sd). Following PIT tag insertion, birds were placed in respirometry chambers inside a PTC-1 temperature cabinet (Sable Systems, Inc.) controlled by a Peltier device (Pelt-5, Sable Systems, Inc.). Throughout experiments, I precisely regulated cabinet temperature and continuously monitored the chamber Ta using thermistor probes (model SEN-

TH, Sable Systems, Inc., ± 0.2 °C accuracy). I used infrared cameras (model WCM-6LNV, Sabrent) to monitor bird behavior and activity levels inside the chambers throughout the experiment.

Because activity level can influence metabolic rate (Aschoff and Pohl 1970) and confound the

48 relationship between Ta and metabolic rate, I discarded data for focal birds that that exhibited high levels of activity during the experiment.

I started each experiment at 30 °C and allowed birds to attain a stable, resting metabolic rate (RMR) and become normothermic (38–42 °C; Prinzinger et al. 1991) before beginning to increase the chamber Ta. Once birds became normothermic, I then began to increase chamber

Ta in 3 °C increments and held birds at each successive Ta for a minimum of 15 minutes. I allowed birds to acclimate to each Ta until I were able to obtain at least 5 minutes of stable gas traces at a given Ta. I continued increasing Ta until focal individuals became hyperthermic (i.e.

-1 exhibited increases in Tb of ≥ 1.0 °C min ), and the maximum Tb that I allowed birds to reach before concluding heat-stress experiments was 45.5 °C (lower than the published lower lethal limit recorded for passerine birds of 46 °C; Dawson 1954). Because humidity can influence the thermoregulatory responses of birds to high ambient temperature (Powers 1992; Gerson et al.

2014), I maintained chamber humidity below 30 % RH in all heat-stress experiments. I removed birds from the respirometry chambers immediately following onset of hyperthermia or alternatively, if they exhibited prolonged signs of distress behavior (i.e. flight attempts, pecking the chamber walls, loss of coordination, etc.). Following heat stress experiments, I immediately removed the PIT tags, reweighed each individual, placed them in front of a fan and applied alcohol to their legs with a cotton ball to promote heat loss. I then provided water to help birds restore water balance and upon resuming normal behavior, they were transported to the site of capture in cloth bags and released.

Respirometry system and gas measurements

I used push-mode flow-through respirometry (Withers 2001; Lighton and Halsey 2011) to measure gas exchange of each individual during the heat-stress experiments. I pulled incurrent air (PP2 pump; Sable Systems, Inc.) through a column of Drierite to remove water and then pumped dried air into a mass-flow controller (Flowbar-8; Sable Systems, Inc.) that divided the air stream into two channels, each plumbed through Bevaline IV tubing (Cole-Parmer) to a separate respirometry chamber. One empty chamber (baseline) served as a reference to the animal chamber. During experiments, birds rested in the chamber on a perch made of wire mesh. The chamber inlet was situated on the lid of the chamber and the chamber outlet was on the side of the chamber opposite to the inlet to ensure that incurrent air flowed directly across the bird before exiting the chamber. Flow rates (500–3,000 mL min-1) and chamber sizes (1.97 or 4.53 L) varied depending on the size of the focal species, with higher flow rates and larger chambers used for larger species. Excurrent air from one chamber at a time was subsampled manually at 100–150 mL min-1 through barrel syringes, scrubbed of water vapor (Drierite) and analyzed for %CO2 (FoxBox; Sable Systems, Inc.).

During each experiment, I recorded Tb at 10-second intervals using a PIT tag reader

(HPRPlus Reader, Biomark, Inc.) and the program Bioterm (Biomark, Inc.) and flow rate, Ta, and

%CO2 in each chamber at one-second intervals using the program Expedata (Sable Systems,

Inc.). I used a Catmull-Rom spline correction to correct for drift and then converted %CO2 to

-1 V̇CO2 (rate of CO2 production, measured in mL CO2 min ) using equation (10.5) from Lighton

(2008) and assuming an RQ of 0.71 (Walsberg and Wolf 1995).

Estimating heat tolerances

Upper critical temperature: I defined UCT as the upper limit of the TNZ (Fig. 1.2; McNab

2002). To estimate the UCT of focal individuals, I obtained 3-minute averages of V̇CO2 and corresponding Ta for each individual throughout the experiment, generating a series of paired

Ta and V̇CO2 measurements. I then identified inflection points in the relationship between Ta and V̇CO2 using piecewise linear regression (R package ‘segmented’; Muggeo 2009).

Coefficient of heat strain: Above the UCT, an endotherm must expend metabolic energy on evaporative and convective cooling to maintain a stable internal Tb and reduce its heat load

(McNab 2002). To estimate the accumulation of heat load within birds during heat stress experiments, previous studies have used a metric termed the “coefficient of heat strain” (HScoeff hereafter), defined as the slope of the relationship between metabolic heat production and Ta

(Weathers 1981; Weathers 1997; Whitfield et al. 2015). One potential problem with the HScoeff is that birds exhibit substantial variation in their ability to evaporate heat (Dawson 1982), and therefore increases in metabolic heat production do not necessarily translate directly into increases in heat load. To control for interspecific variation in evaporative heat loss capacity, I instead defined HScoeff as the slope of the relationship between Tb and Ta, because I were interested in quantifying the accumulation of internal body heat in relation to increasing ambient temperature.

Critical thermal maximum: I deemed birds to have reached CTmax if they met one of two criteria. First, I defined CTmax as the Ta at which onset of uncontrolled hyperthermia (i.e. loss of

Tb regulation) occurred. Certain bird species exhibit controlled hyperthermia above the UCT

(e.g. Weathers 1981; Schleucher 2001) and these facultative increases in Tb do not necessarily

51 indicate that a bird has lost the ability to regulate Tb. Therefore, to distinguish between controlled and uncontrolled hyperthermia, I deemed birds to have reached CTmax if they

-1 exhibited increases in Tb of ≥ 1.0 °C min . Second, some focal individuals never exhibited uncontrolled hyperthermia. I deemed these individuals to have reached CTmax if they achieved a

Tb of ≥ 45.5 °C (slightly below lethal Tb of 46-48 °C for birds; Randall 1943; Dawson 1954; Brush

1965; Arad and Marder 1982).

Thermal safety margin: I combined heat tolerance data with habitat-specific temperature data to estimate species’ thermal safety margins. I used iButtons (Hygrochron model DS 1923, Embedded Data Systems, Inc.) to record ambient temperature (accuracy ±

.0625° C) at 10-minute intervals on a daily basis from April-August in three different habitat types: open habitats, edge habitats, and forest habitats. I then averaged daily maximum temperatures throughout the sampling period to estimate mean daily maximum ambient Ta

(Tmax) for each habitat type. I classified species’ habitat types based on personal observations coupled with descriptions provided in Parker et al. (1996) and the Handbook of the Birds of the

World (HBW) Alive (del Hoyo et al. 2015).

I defined a species’ thermal safety margin (TSM) as the difference between habitat- specific Tmax at the respective sampling locality and the species’ mean UCT (Tmax – UCT; following Khaliq et al. 2014). I chose to take the average of the daily Tmax across the sampling period during which the species were measured to account for the fact that most physiological traits are flexible and vary depending on the environmental conditions to which the organism is recently exposed (Piersma and Drent 2003; Piersma and van Gils 2010).

Warming tolerance: I defined a species’ warming tolerance (WT) as the difference between habitat-specific Tmax at the sampling locality and the species’ mean CTmax (WT = Tmax –

CTmax; see Duarte et al. 2012).

Statistical analyses

Controlling for the influence of phylogeny on heat tolerances

To control for the influence of phylogeny on heat tolerances of the focal species, I derived a phylogeny (Fig. 4.2) by pruning the maximum likelihood avian phylogenetic tree of Burleigh et al. (2015) with the drop.tip function in the R package ape (Paradis et al. 2004) to include all focal species. Because three of my focal species (Cyphorhinus phaeocephalus, Malacoptila panamensis, Melanerpes rubricapillus) were not included in the Burleigh et al. (2015) phylogeny, I substituted three congeneric species (Cyphorhinus arada, Malacoptila semicincta,

Melanerpes aurifrons, respectively) to approximate their position in the tree. I then employed my phylogenetic tree to conduct phylogenetic generalized least-squares regression (PGLS;

Grafen 1989). To assess phylogenetic signal in my dataset, I employed Pagel’s λ (Pagel 1999) – a metric of phylogenetic signal ranging from 0 (no phylogenetic signal) to 1 (species’ traits covary in direct proportion to their shared evolutionary history)(Freckleton et al. 2002). I estimated

Pagel’s λ in the residual error of each regression model while simultaneously estimating regression parameters (Revell 2010) and then scaled regression models using the estimates of

λ. I detected significant phylogenetic signal in the residuals of all physiological traits (Table 4.1) and therefore I only present results from PGLS regressions (following Freckleton 2009).

Controlling for the influence of Mb on heat tolerances

Given the established allometric relationship between Mb and metabolic rate in birds

(Lasiewski and Dawson 1967; McKechnie and Wolf 2004), I tested for associations between Mb and the three physiological traits that comprise heat tolerance (UCT, HScoeff, CTmax). I performed these analyses in two ways: 1) linear regressions of log-transformed data (e.g. logUCT vs. logMb) and 2) linear regressions between using raw trait data (e.g UCT vs. Mb). I found that linear regressions of raw trait data explained more variance (i.e. had higher R2) in all three of the traits than the log-transformed regressions. This result may stem from the fact that I sampled primarily passerine species with a narrow range of Mb (7.3–161.5 g), and I therefore opted to use raw trait values rather than log-transforming for subsequent analyses.

Across all focal species, I did not find any significant associations between Mb and UCT (λ

2 2 = 0.53, R = 0.05, F1,60 = 3.25, p = 0.08), HScoeff (λ = 0.76, R = 0.02, F1,60 = 1.16, p = 0.28) or CTmax

2 (λ = 0.70, R = 0.02, F1,60 = 1.10, p = 0.30). However, when I included latitude as a grouping variable to look for potential latitudinal differences in the relationship between Mb and the three traits, I found significant interactions between latitude and Mb for all three traits (Fig.

4.3). In tropical species, Mb was not significantly associated with UCT (t =1.48, p = 0.15), HScoeff

(t = -0.60, p = 0.55) or CTmax (t = 0.23, p = 0.82), whereas in temperate species, Mb scaled

2 positively with all three measures [UCT (t = 3.49, p = 0.003, R = 0.42), HScoeff (t = -4.37, p =

2 2 0.0004, R = 0.53), and CTmax (t = 2.95, p = 0.009, R = 0.34)]. Therefore, I chose to include Mb and its interaction with latitude in subsequent latitudinal analyses involving UCT, HScoeff or

CTmax.

Characterizing latitudinal variation in avian heat tolerance

To characterize latitudinal variation in heat tolerances and test if tropical bird species had lower heat tolerances than temperate species, I conducted PGLS regressions on 1) UCT, 2)

HScoeff, and 3) CTmax with latitude, Mb and their interaction as predictor variables.

Characterizing latitudinal variation in thermal safety margins and warming tolerances

To test if tropical bird species had narrower TSM and WT than temperate species and consequently, greater vulnerability to climate warming, I also conducted PGLS regressions on 1)

WT and 2) TSM with latitude, Mb and their interaction as predictor variables.

In total, I measured thermoregulatory responses to acute heat-stress in 254 individuals of 81 species (23 temperate, 58 tropical). Because I had small sample sizes (n < 3 individuals) for nearly half (39/81) of my focal species, I conducted analyses in two ways: 1) on the entire complement of focal species and 2) on focal species that had sample size of n ≥ 3 individuals. I found quantitatively similar results for both analyses for all traits except HScoeff, which was significantly different between tropical and temperate species overall (p = 0.05) but only marginally different (p =0.08) in the analysis where species with n <3 were excluded (Table 4.2).

Therefore, I report data from the analysis of the entire complement of focal species here.

RESULTS

Patterns of latitudinal variation in avian heat tolerance

I found that only one of the three thermoregulatory traits comprising heat tolerance varied significantly between temperate and tropical species (Fig. 4.4). After controlling for the interaction between latitude and Mb, there were no significant differences between temperate and tropical species in mean UCT (p = 0.99; Table 4.1) or mean HScoeff (p = 0.98; Table 4.1)

55 although mean CTmax was still significantly higher (1.76 °C) in temperate species (p = 0.01; Table

4.1). Overall, temperate species had a slightly enhanced ability to tolerate high ambient temperatures relative to tropical species due to their higher mean CTmax.

Patterns of latitudinal variation in thermal safety margins and warming tolerances

Despite their significant increases in CTmax, I found no evidence that temperate bird species had higher thermal safety margins (TSM) or warming tolerances (WT) than their tropical counterparts (Fig. 4.5). Indeed, after controlling for the interaction between latitude and Mb, I found that tropical species (mean TSM = 8.6 °C) had a slightly but significantly higher mean TSM than temperate species (mean TSM = 8.3 °C)(p = 0.05; Table 4.1). Mean WT did not differ significantly between tropical (14.0 °C) and temperate (14.8 °C) species (p =0.65; Table 4.1).

DISCUSSION

To my knowledge, this study is the first comparative analysis of latitudinal variation in the heat tolerances of endotherms. In contrast to the strong latitudinal clines in heat tolerance that have been documented in ectotherms, I found only subtle differences in heat tolerance between temperate and tropical bird species. Importantly, these differences do not seem to translate into systematic differences in vulnerability to climate warming between temperate and tropical birds; both tropical and temperate species had equally large thermal safety margins and warming tolerances. Thus, across a broad latitudinal range, many birds are currently experiencing temperatures that are well within their thermoneutral zones. Together, these results suggest that, from a purely thermal physiology perspective, many birds should be able to tolerate projected levels of climate warming.

Limited evidence of latitudinal variation in avian heat tolerances

I found only limited evidence that tropical birds had lower heat tolerances than their temperate counterparts. After correcting for Mb and controlling for phylogenetic relationships among the focal taxa, temperate species did have significantly higher (~2 °C) mean CTmax (the ambient temperature at which uncontrolled hyperthermia occurs) than tropical species. In contrast, however, neither UCT (the ambient temperature above which energetic resources must be used for thermoregulation) nor HScoeff (the rate of accumulation of endogenous heat load above the UCT) varied significantly between temperate and tropical species. In summary, temperate bird species were able to tolerate slightly higher ambient temperatures, but other aspects of heat tolerance did not differ between temperate and tropical species.

The subtle differences in heat tolerance between tropical and temperate species that I report here starkly contrast with patterns documented in other avian thermoregulatory traits.

Recent studies have found that tropical birds have lower basal metabolic rates (BMR) and summit metabolic rates (Msum)(Wiersma et al. 2007), reduced metabolic scope (Msum–BMR; a measure of metabolic flexibility) (Stager et al. 2016) and narrower TNZs (Araújo et al. 2013;

Khaliq et al. 2014) than temperate birds. As a result, tropical birds are often thought to be fundamentally physiologically constrained in their ability to cope with ambient temperature

(Stratford and Robinson 2005; Şekercioğlu, Primack and Wormworth 2012). A growing body of evidence suggests that tropical birds are indeed constrained, but primarily in response to cold temperatures. For example, latitudinal differences in thermoregulatory traits such as TNZ breadth and metabolic scope are driven by enhanced cold tolerance (i.e. lower LCT, higher

Msum) in temperate birds (Khaliq et al. 2014; Stager et al. 2016). In contrast, previous analyses

57 have shown that UCT in endotherms is largely conserved across lineages and latitude (Araújo et al. 2013; Khaliq et al. 2014), consistent with the results I present here. The lack of latitudinal variation in avian heat tolerances may be attributed to a) acclimatization to similar environmental conditions (i.e. mean daily Tmax was only slightly higher (~ 1.5 °C) at the temperate sampling locality; Supplementary Fig. S1), or b) physiological constraints on the evolution of heat tolerance (Araújo et al. 2013). Unfortunately, my measurements represent only snapshots of birds’ physiological responses to high ambient temperatures. To differentiate between these two alternative hypotheses would require common garden experiments to estimate the heritability of heat tolerance and/or controlled acclimation experiments to determine its potential for acclimatization or evolutionarily adaptation.

No evidence of reduced thermal safety margins and warming tolerances in tropical bird species

I found no evidence indicating that tropical bird species had lower thermal safety margins or warming tolerances than temperate species. Thermal safety margins (the difference between maximum Ta and UCT) were actually slightly higher in tropical species (mean = 8.6 °C) than temperate species (mean = 8.3 °C) and warming tolerances did not differ significantly between tropical (mean = 14.0 °C) and temperate (mean = 14.8 °C) species. In contrast to my analysis, Khaliq et al. (2014) found that tropical endotherms had narrower thermal safety margins than temperate species and a greater projected degree of ‘thermal mismatch’ across their geographic distributions. There are two possible explanations for these opposing results.

First, Khaliq et al. (2014) used historical climate data ranging from 1961-1990, which were not necessarily temporally associated with UCT data. In contrast, I used weather station data derived from within 10 miles of field sites during the sampling period when UCT data were

58 collected. Second, the data used in Khaliq et al. (2014) were obtained from literature sources that used widely different methodologies to estimate UCT. For example, their dataset had a 23

°C range in UCT values (22–45 °C) compared to a ~ 8 °C range (34.7–43°C) in my dataset compiled using a standardized methodology. As a result, Khaliq et al. (2014) found larger variance in thermal safety margins (range = 35 °C: -15–20 °C) than I did (range = 7 °C: 5.60–

12.60 °C). Some of this increased variance is almost certainly biological – their dataset contained nearly twice as many species (161 vs. 81) and encompassed a greater geographic extent. However, many of their UCT data were surprisingly low, resulting in thermal safety margins as low as -15 °C and suggesting that some species included in their dataset are already experiencing substantial energetic costs and physiological stress in response to current temperature conditions. While I agree with these authors that most bird species should be able to physiologically tolerate projected rates of climate warming, I did not find the same latitudinal trends in thermal safety margins and increased vulnerability in tropical bird species.

Implications for climate change

The broad thermal safety margins and high warming tolerances that I found suggest that neither tropical nor temperate birds are regularly experiencing ambient temperatures near their UCT. In fact, even under a climate warming scenario of a 4 °C increase in global mean Ta

(general circulation model RCP8.5; IPCC 2014) mean ambient temperatures would not exceed the thermal safety margins of any of the bird species I measured. Therefore, from a purely physiological standpoint, tropical birds do not appear to be systematically more vulnerable to climate warming than their temperate counterparts as has been suggested in other studies

(Jetz et al. 2007; La Sorte and Jetz 2010; Şekercioğlu et al. 2012; Khaliq et al. 2014).

However, there are several factors that I did not take into account that may influence physiological responses of birds to climate warming. First, I did not measure thermoregulatory responses to operative temperatures (sensu Bakken 1976), which incorporate humidity, ambient temperature, and solar radiation and can often far exceed ambient temperatures

(Walsberg 1993; Wolf and Walsberg 1996). High humidity can interact with temperature to lower CTmax (Gerson et al. 2014) and could exacerbate the physiological impacts of future climate warming. Second, operative temperatures that exceed birds’ thermal safety margins could still have sublethal impacts on energy expenditure and resource allocation that impact fitness (du Plessis et al. 2012). Third, daily temperature extremes or climatic events such as heat waves may result in ambient temperatures that exceed birds’ warming tolerances and may exert strong selective pressures on bird populations even if mean temperatures do not pose an immediate threat (McKechnie and Wolf 2010). These pressures may be particularly strong for desert bird species, which are regularly exposed to extremely high Ta and have far greater CTmax than those of the mesic species described in my analysis (Smith et al. 2015; Whitfield et al.

2015; McKechnie et al. 2016a,b). These desert species may have reduced thermal safety margins and warming tolerances relative to mesic species and consequently may be much more vulnerable to future climate warming. Future studies that incorporate more species from different biomes and that take into account the influence of other environmental factors (e.g. humidity, solar radiation, wind, etc.) on thermoregulation will be necessary to evaluate the generality of the conclusions derived from this analysis.

In conclusion, using relevant physiological traits, I provide the first direct analysis of geographic variation of heat tolerance in endothermic taxa. My results demonstrate that a)

60 avian heat tolerance does not differ markedly between temperate and tropical species and b) both tropical and temperate birds have similarly broad thermal safety margins and high warming tolerances. Therefore, compared to ectotherms, birds may be more physiologically buffered from climate warming. However, similar studies in mammals are needed to determine the applicability of these conclusions for endotherms in general. Compared to current physiologically and evolutionarily naïve niche models, future studies incorporating heat tolerances into mechanistic niche models and accounting for acclimatization capacity will add much greater precision to estimates of the biological impacts of climate warming.

CHAPTER 5: ABSENCE OF MICROCLIMATE SELECTIVITY IN INSECTIVOROUS BIRDS OF THE NEOTROPICAL FOREST UNDERSTORY1

ABSTRACT

Local abiotic conditions (microclimates) vary spatially and selection of favorable microclimates within a habitat can influence an animal’s energy budgets, behavior, and ultimately, fitness. Insectivorous birds that inhabit the understory of tropical forests may be especially sensitive to environmental variation and may select habitat based on microclimatic

(e.g. temperature, humidity, light) conditions. Sensitivity to microclimate could contribute to the population declines of understory insectivores in response to forest fragmentation or degradation, which changes the physical structure of the forest, thereby increasing light intensity and temperature and decreasing humidity. To understand the role of microclimates in the habitat selection of understory insectivores, I characterized the microclimatic associations of nine species of understory insectivores at three sites along a precipitation gradient and across seasons in central Panama. I compared the distributions of microclimates selected by birds with microclimates at randomly chosen points within their home ranges to test for microclimate selectivity. I predicted that: 1) birds would select microclimates that are more humid, cooler, and less bright than random microclimates 2) selectivity would be greater in hotter, drier habitats and 3) selectivity would be greatest in the dry season. I found no evidence of selectivity for the nine species I sampled on a seasonal or spatial basis. Microclimate

1 This chapter appeared in its entirety in the journal Biological Conservation – Pollock, H. S., Cheviron, Z. A., Agin, T. J., and Brawn, J. D. 2015. Absence of microclimate selectivity in insectivorous birds of the Neotropical forest understory. Biological Conservation 188: 116-125. This article is reprinted with the permission of the publisher and is available from http://www.sciencedirect.com/science/article/pii/S0006320714004388 using DOI: 10.1016/j.biocon.2014.11.013.

62 variation was minimal in the forest understory at all sites, particularly in the wet season.

Understory insectivores did not use microhabitats characterized by high light intensity, and may be sensitive to light, though the mechanism remains unclear. The lack of microclimate variation in the understory of tropical forests may have serious fitness consequences for understory insectivores due to increasing temperatures associated with climate change coupled with a lack of thermal refugia.

INTRODUCTION

Maintaining energy balance is a primary challenge for all organisms (Piersma and van

Gils 2010) and the fitness consequences of persisting in energetically demanding habitats can be substantial (Bakken 1976, Huey 1991). The energetic costs associated with unsuitable environmental conditions can govern macroecological patterns such as species’ geographic range limits (Root 1988, Brown et al. 1996) and can also have profound effects on behavior and habitat use on a local scale (Adolph 1990, Hertz 1992, Huey et al. 2012). Within a habitat, spatial variation in solar radiation, wind speed, air temperature and humidity creates a mosaic of local abiotic conditions (hereafter microclimates, sensu Angilletta 2009) that can influence behavior, energy budgets and ultimately, fitness (Huey 1991). For example, selection of favorable microclimates can enhance an organism’s ability to escape from predators (Hertz et al. 1983, Carrascal et al. 1992), improve foraging efficiency (du Plessis et al. 2012), reduce costs of thermoregulation (Buttemer 1985, Jenni 1991, Wiersma and Piersma 1994, Cooper 1999) and even increase survival (Huey et al. 1989, Dawson et al. 2005) and reproductive success

(Martin 1998, Jones and Reichert 2008). Previous research on the physiological consequences of habitat selection, however, has focused largely on ectotherms (Huey 1991) because they are

63 predominantly ‘thermoconformers’ and are therefore more directly dependent on ambient temperature (Angilletta 2009, Somero 2010).

The role of microclimates in the habitat selection of endotherms such as birds remains relatively unexplored. Microclimatic conditions can influence energy budgets in endotherms, but most sampling has been limited to fixed locations such as roost sites (e.g. Buttemer 1985) and nest sites (e.g. Gloutney and Clark 1997, Martin 1998). Daily patterns of microclimate selection, particularly in birds, have received less attention but are equally important to understand given that habitat selection is a dynamic process that also occurs during an animal’s active phase (Walsberg 1993). For example, in arid regions of the southwest U.S., birds avoided foraging in microclimates characterized by high ambient temperatures and light intensity

(Walsberg 1993), resulting in substantial energy savings (Wolf and Walsberg 1996). Similarly,

Karr and Freemark (1983) suggested that tropical forest bird species moved seasonally to track microclimatic optima within their home ranges. In a changing world, understanding how birds respond to microclimate variation within their habitats is emerging as an important conservation issue because microclimatic heterogeneity may provide important thermal refugia and mitigate the negative impacts of climate change (Bonebrake and Deutsch 2012).

Understory insectivorous birds of Neotropical forests are characterized by low dispersal capabilities (Moore et al. 2008, Tarwater 2012, Woltmann et al. 2012a), specialized foraging habits (Sherry 1984, Marra and Remsen 1997, Şekercioğlu et al. 2002, Walther 2002a, 2002b) and narrow niche breadth (Marra and Remsen 1997, Stratford and Stouffer 2013). Understory insectivores are also especially sensitive to anthropogenic disturbance, experiencing population declines and local extirpation in response to habitat loss and fragmentation (Bierregaard and

Lovejoy 1989, Stouffer and Bierregaard 1995, Canaday 1997, Şekercioğlu et al. 2002, Sigel et al.

2006, Sigel et al. 2010, Cordeiro et al. 2015). The mechanistic underpinnings of these declines, however, are unclear (Powell et al. 2015b). One possibility is that understory insectivores are particularly sensitive to the altered environmental conditions that result from forest fragmentation (Stratford and Robinson 2005, Robinson and Sherry 2012, Stratford and Stouffer

2015). The understory of tropical forests is characterized by relatively low environmental variability on both ecological (Didham and Lawton 1999) and evolutionary time scales (Janzen

1967). Constancy in environmental conditions is hypothesized to promote physiological specialization (Janzen 1967), including in understory insectivores (Robinson and Sherry 2012).

The microclimate hypothesis (Stratford and Robinson 2005, Robinson and Sherry 2012) posits that by altering the distribution of microclimates within a forest (Didham and Lawton 1999,

Laurance et al. 2002) habitat fragmentation introduces novel abiotic conditions that may physiologically challenge understory insectivores and contribute to their population declines.

A tenet of the microclimate hypothesis is that understory insectivores are sensitive to local abiotic environmental variation, and there is evidence supporting this idea. Activity and local abundances of certain understory insectivorous species in central Panama declined in xeric areas within individual home ranges during the tropical dry season (Karr and Freemark

1983), suggesting that habitat selection is at least partially a function of microclimatic conditions. Similarly, a study across a precipitation gradient in central Panama found that Song

Wren (Cyphorhinus phaeocephalus) individuals from drier forests had poorer mean body condition and abnormally low hematocrit values (Busch et al. 2011). Along this gradient, the species richness and abundance of understory insectivores declines with decreasing

65 precipitation (Rompre et al. 2007). The limited evidence suggests that understory insectivores are sensitive to low humidity and high temperature, but plausible alternatives (e.g. responses to variation in food resources) exist (Robinson and Sherry 2012). More detailed studies of the microclimatic associations of understory insectivores are needed to determine the role of microclimate variation in their habitat selection.

Light intensity is another microclimatic variable that may influence the habitat selection of understory insectivores and their sensitivity to fragmentation. For example, species from

Neotropical forests that occupied low-light environments (e.g. understory insectivores) exhibited the greatest negative population trends and propensity for local extirpation (Patten and Smith-Patten 2012). Similarly, high sensitivity to light may restrict movements of understory insectivores throughout a landscape matrix (Develey and Stouffer 2001, Laurance et al. 2004, Stratford and Robinson 2005) and could explain their low dispersal capabilities (Moore et al. 2008, Burney and Brumfield 2009, Salisbury et al. 2012, Tarwater 2012, Woltmann et al.

2012a) relative to other guilds. Habitat loss and fragmentation reduces connectivity (Andren

1994) and may impede understory insectivores from recolonizing fragments (Powell et al.

2013), turning them into population sinks (Robinson et al. 1995). Sensitivity to light may therefore contribute to the population declines of understory insectivores (Stratford and

Robinson 2005, Robinson and Sherry 2012) and could also be an important factor in their selection of microclimates.

To understand how understory insectivorous birds respond to variation in abiotic conditions within their home ranges, I assessed microclimate selectivity in a suite of nine understory insectivorous species in central Panama. Previous studies of avian microclimatic

66 associations have relied on indirect sampling methods such as mist-nets (Karr and Freemark

1983, Champlin et al. 2009) or point-counts (Patten and Smith-Patten 2012), which do not allow for direct observation of the microhabitats used by birds. I adopted a novel approach by intensively sampling radio-tagged individuals of focal species within their own home ranges to characterize their microclimatic associations (light, temperature and humidity). I then compared distributions of bird microhabitat points with randomly selected points within the bird’s home range to test for selectivity (i.e. to determine if birds were selecting microclimates within their home range that differed from microclimates at random points). Selectivity should be greater where environmental conditions are more challenging (e.g. Walsberg 1993).

Therefore, I sampled along a precipitation gradient, where intensity of the dry season decreases and annual rainfall increases with distance from the Pacific coast of Panama (Condit et al. 2000,

Van Bael et al. 2004), to examine microclimate selectivity across differing environmental regimes. I predicted that understory insectivores would: 1) exhibit microclimate selectivity (i.e. select microclimates with significantly different humidity, temperature, and light intensity distributions than random), 2) exhibit greater microclimate selectivity in hotter, drier environments compared to cooler and more humid habitats, and 3) exhibit greater microclimate selectivity within localities during the dry season when humidity is lower and more variable than in the wet season.

MATERIALS AND METHODS Study sites

I sampled microclimates at three sites along the Isthmus of Panama (Fig. 5.1) between the months of February-July from 2012-2013. The three sites (Table 5.1) differ substantially in annual precipitation and degree of seasonality (Fig. 5.2). The driest site, Metropolitano Natural

Park (Metropolitano hereafter), is a 232-ha fragment of semi-deciduous secondary tropical dry forest on the Pacific coast located within Panama City that receives 1800 mm annual rainfall and has a pronounced dry season (Van Bael et al. 2004). Metropolitano is surrounded on all sides by urban areas and is one of the only remaining tracts of dry forest left along the Pacific coast of Panama. The wet site, “Limbo”, is a 104-ha study plot of old secondary and some primary (300-400 years old) tropical moist forest located within the 22,000-ha Soberanía

National Park that receives 2600 mm annual rainfall and has a moderate dry season (Robinson et al. 2000). Limbo is deep within contiguous forest and is situated at least 3.5 km from the nearest forest edge. A narrow (~5 m) gravel road runs through the center of the plot, but the canopy is closed over most of its length and it does not create enough edge to attract second- growth species (Robinson et al. 2000). The mesic site, the Gamboa Woodlot (Gamboa hereafter), is a 22-ha lowland secondary moist forest fragment located 6.9 km southwest of

Limbo that receives 2100 mm of annual rainfall and also has a moderate dry season (Robinson et al. 2000). Gamboa is separated from the closest contiguous forest (Soberanía National Park) by a 100-m wide grassy field. I did not consider degree of fragmentation as a confounding factor since I sampled a variety of species (see Focal species) in both edge and interior habitats at all sites.

Focal species

I sampled nine understory insectivorous species (Table 5.2) representing six different avian families, with varying habitat preferences and sensitivities to anthropogenic disturbance

(i.e. propensity to decline in abundance or disappear from disturbed habitats).

Radio-tracking understory insectivores

I sampled the microclimatic associations of individual birds within their home ranges using radio-telemetry. I sampled during the dry season (February-April) and the wet season

(May-August) to investigate potential seasonal differences in the degree of microclimate selectivity. I captured birds in mist-nets (12 X 2.6 m; 36-mm mesh) set at ground level. Upon capture, I placed birds in cloth bags and weighed them to the nearest 0.5 grams. I then attached radio-transmitters (0.5-0.9g: BD-2 model, Holohil Systems, Inc.) to birds with 1 mm beading cord using the leg harness method (Rappole and Tipton 1991). Transmitters always weighed <5% of the bird’s body mass to minimize adverse effects on behavior and fitness. I observed focal individuals for 5 minutes to ensure that the bird was able to move normally. All focal birds were given ≥ 24-h to acclimate to the radio-transmitters before observation took place.

I followed radio-tagged individuals of focal species using handheld VHF radio-telemetry to localize focal individuals. I recorded an average of 21.4 microhabitat points per individual

(n=58 individuals, sd=6.90, range=9-50 microhabitat points) and followed focal birds for an average of 3.78 days (n=58 individuals, sd=3.23, range=1-14 days). Once a bird was visually located, I recorded behavior, time of day and amount of time spent at the microhabitat point.

Once the bird vacated the point, I marked the precise location where the bird was observed with flagging tape and georeferenced the position using a handheld GPS unit (Garmin 60CSx).

Successive microhabitat points were separated by ≥ 15 minutes and/or 30 meters, allowing birds to re-locate to an independent microhabitat point in an effort to control for spatial and temporal autocorrelation in microclimate (Swihart and Slade 1985). I only investigated

69 microclimates at microhabitat points where focal individuals spent ≥ 5 minutes to ensure that birds were not transiently passing through the microhabitat. I conducted preliminary trials to determine the appropriate distances (between 5-15 meters, depending on the species) between observer and birds to minimize the effect of human disturbance on birds and maintained these distances throughout sampling. Any observations with abnormal behavior

(e.g. rapid movements, alarm vocalizations, etc.) induced by human proximity were discarded.

Temperature, relative humidity and light measurements

I measured three environmental variables: temperature, relative humidity (RH hereafter), and light intensity. At each microhabitat point, I deployed stainless steel Hygrochron iButtons (Embedded Data Systems, Inc.), which recorded temperature (°C) and RH (%) at 10- minute intervals (temperature precision: ± .0625° C, RH precision: ±.04% RH) for 2-5 days. All iButtons were calibrated against a mercury thermometer (model T6000, Miller and Weber, Inc.) with standards traceable to the U.S. National Bureau of Standards prior to use. I deployed iButtons with a protective plastic cover painted flat white to prevent solar radiation and precipitation from biasing temperature and humidity measurements (iButtons exposed to direct solar radiation and precipitation can yield spurious data). I measured light intensity (kLux) at each point after the bird departed using an Extech® EasyView EA30 digital light meter. The light meter reading was allowed to stabilize for one minute and then light intensity was recorded. I measured light within one minute of the bird’s departure from the microhabitat point.

Selection of random points

At each microhabitat point, I designated a corresponding random point using a random- number generator to choose a cardinal direction (N, E, S, W) and distance (15-30 m) from the microhabitat point. I deployed and recovered iButtons simultaneously at microhabitat points and random points, allowing for direct comparisons between the two point types on the home range level. I placed iButtons at the same height at the random point at which the bird was observed at the microhabitat point. I measured light at each random point within 5 minutes of the light measurement at the corresponding microhabitat point.

Statistical analyses

I measured temperature and humidity at 10-minute intervals for 2-5 days at microhabitat points within each individual bird’s home range, but I restricted analyses to the temperature and humidity values that corresponded to when birds were observed at a given microhabitat point. Similarly, for random points, I used only the temperature and humidity values recorded at the same time of day when the bird was present at its corresponding microhabitat point to detect whether or not the bird was selecting a non-random subset of microclimates at that particular time of day. I first compared microhabitat points with random points on the level of individual home range. For each individual, I used the ks.test function in R (R Development Core

Team 2013) to compare the distribution of temperature, humidity and light intensity values of microhabitat points to the corresponding distribution of random points using nonparametric

Kolmogorov-Smirnov (KS) two-sample tests with the null hypothesis that the microhabitat and random distributions are similar. To assess microclimate selectivity for understory insectivores as a guild, I took two approaches for each of the three microclimatic variables: 1) I pooled the

71 results of all individual KS tests and performed a Fisher’s combined probability test using the function combined.test from the package survcomp in R (Schröder et al. 2011), which combined the results from the independent KS tests to generate a χ2 distribution to test the null hypothesis that all of the individual null hypotheses were true; 2) I pooled all data and conducted two-sample permutation tests with 10,000 permutations. Each permutation combined the values of microhabitat and random points and then generated two distributions from the pooled data. The p-value was calculated as the proportion of sampled permutations where the difference in means was greater than or equal to the original mean difference between microhabitat and random points. To investigate whether selectivity was occurring on a seasonal basis, I pooled data from all species into either the dry season (February 1 – May 15) or wet season (May 16 – August 30), and conducted separate two-sample permutation tests with 10,000 permutations for each season for temperature, humidity and light intensity values.

Although the exact date of the transition between dry and wet season varies annually (Fu and Li

2004), and simplifying a continuous variable (day of year) into a categorical variable (wet vs. dry season) may be problematic in some cases, only six individuals were not able to be placed unambiguously in either season. Additionally, I conducted all analyses on this subset of six individuals and obtained similar results and therefore deemed my seasonal classification to be adequate. To investigate whether level of selectivity varied across sites, I pooled data from all species and conducted separate two-sample permutation tests with 10,000 permutations for each site for temperature, humidity and light intensity values.

RESULTS

I found little evidence of microclimate selectivity for any species. After pooling all individuals of each species across sites, temperature (Fig. 5.3), humidity (Fig. 5.4) and light intensity (Fig. 5.5) distributions were similar between microhabitat points and their paired random points. Only 1 of 58 comparisons at the individual level was significant (D=1, p=.029; C. phaeocephalus, dry season 2013) for humidity and none was significant for temperature or light intensity. Overall, understory insectivores as a whole showed no evidence of microclimate selectivity for temperature (Fisher’s combined probability test, χ2=11.46, df=116, p=1), humidity

(Fisher’s combined probability test, χ2=25.73, df=116, p=1), or light intensity (Fisher’s combined probability test, χ2=78.09, df=116, p=0.997). The two-sample permutation test indicated no significant differences between random points and microhabitat points with respect to temperature (Z = 0.01, p = 0.99), humidity (Z = -0.30, p = 0.76), or light intensity (Z = -0.27, p =

0.79).

I also did not observe variation in microclimate selectivity on a seasonal basis, despite the fact that environmental conditions varied seasonally at all study sites (Fig. 2). Temperature did not differ between microhabitat and random points in the wet season (Z = -0.40, p = 0.69) or dry season (Z = -1.38, p = 0.17). Similarly, humidity did not differ between microhabitat and random points in the wet season (Z = 0.39, p = 0.70) or dry season (Z = 0.76, p = 0.45). Lastly, light intensity did not differ between microhabitat and random points in the wet season (Z = -

0.81, p = 0.42) or dry season (Z = -1.31, p = 0.20).

Despite the observed variation in temperature and humidity regimes among my sampling sites (Fig. 5.2), I did not observe microclimate selectivity at the site level.

Temperatures did not differ between microhabitat and random points at Limbo (Z = 0.34, p =

0.72), Metropolitano, (Z = -0.69, p = 0.49) or Gamboa (Z = 0.36, p = 0.72). Similarly, humidity did not differ between microhabitat and random points at Limbo (Z = -0.28, p = 0.78),

Metropolitano (Z = 0.32, p = 0.75) or Gamboa (Z = -0.19, p = 0.85). Lastly, light intensity did not differ between microhabitat and random points at Limbo (Z = 0.18, p = 0.86), Metropolitano (Z

= -0.57, p = 0.57) or Gamboa (Z = 0.41, p = 0.68).

DISCUSSION

My study is the first to document microclimatic associations of understory insectivores using focal observations of individual birds. Despite their putative sensitivity to humidity (Karr and Freemark 1983) and light (Patten and Smith-Patten 2012), I found no evidence of microclimate selectivity on the home range level across seasons or among sites distributed across a pronounced precipitation gradient in Panama. These results were consistent across nine species that show considerable variation in habitat preferences and sensitivity to anthropogenic disturbance, suggesting that lack of microclimate selectivity on the level of the home range is a general pattern in this guild.

A central assumption of the microclimates hypothesis (Stratford and Robinson 2005,

Robinson and Sherry 2012) is that understory insectivores are sensitive to local environmental variation, which may underlie their population declines in response to the altered environmental conditions associated with forest fragmentation (Laurance et al. 2002). I tested this assumption in both contiguous forest and forest fragments and found no evidence of sensitivity to environmental variation, at least in terms of microclimate use. However, I only sampled birds at three sites, all of which were older secondary forests. The microclimatic effects of fragmentation on understory insectivores may only occur early on during forest

74 regeneration, when edge effects are most severe (Laurance and Yensen 1991). Alternatively, the degree of microclimate selectivity may depend on fragment size (e.g. understory insectivores only exhibit sensitivity to local environmental variation in very small fragments).

Future research should compare the microclimate associations of understory insectivores in forest fragments of different size and age in contiguous forest to test the generalizability of my results.

Perhaps the most unexpected result was the lack of microclimate selectivity across three sites with substantially different precipitation regimes and across seasons. I originally predicted that selectivity would be greater (i.e. distributions of bird microhabitat and random points would be more dissimilar) at hotter, drier sites and during the dry season, when environmental conditions are more challenging (Williams and Middleton 2008). The apparent lack of microclimate selectivity at drier sites and in the dry season ostensibly suggests that individuals of my focal species are insensitive to microclimate variation within their home ranges, at least within the levels of variation to which they were exposed at my field sites in

Panama.

Within each sampling site at a given time of year, there was little variation in temperature and humidity in the forest understory. Uniformity in the understory environment was especially apparent at my contiguous wet forest site (Limbo), where in the wet season

(May-August), relative humidity remained close to 100% and daily temperature varied by as little as 3°C over a 24-h time period. Low variability in environmental conditions in the forest understory corroborates previous studies of forest microclimate (Ewers and Banks-Leite 2013) and differs from conclusions drawn by Karr and Freemark (1983), who reported substantial

75 microclimate variation at locations sampled within Limbo. The lack of microclimate selectivity, therefore, might simply be attributed to a general lack of microclimate variation for understory insectivores to select.

Similar to temperature and humidity, light intensity was relatively uniform throughout the forest understory at all sites. The low (0-2 KLux) mean light intensity and low light variability that I found in the understories of all of my forest sites corroborate previous results from other tropical forest sites. Only a small fraction (0.5-5%) of incident light reaches the understory of closed-canopy tropical forests (Chazdon and Pearcy 1991) and diurnal and seasonal variability of light intensity is low in forest understory relative to other environments (Chazdon and

Fetcher 1984).

In my focal observations, I noticed that understory insectivores appeared to avoid microhabitats such as canopy gaps. Light intensity in gaps can reach 120 kLux on sunny days

(H.S. Pollock, unpublished data), two orders of magnitude greater than the light intensity encountered in shaded forest understory. These microhabitats were rare within individual territories, but were actively avoided when encountered by a variety of understory insectivore species (H.S. Pollock, personal observation). Many tropical forest bird species exhibit a marked preference for either closed-canopy forest or gap habitats (Schemske and Brokaw 1981), and the avoidance of microhabitats with high light intensity corroborates patterns recently reported for other understory insectivores (e.g. Patten and Smith-Patten 2012). Inability or unwillingness to use areas with high light intensity may be due to physiological sensitivity to the light itself

(e.g. understory insectivores may have evolved eyes specialized to low-light environments to detect their cryptic arthropod prey), to the higher temperature and lower humidity associated

76 with high light intensity, or to another proximate factor (e.g. reduced predation risk, increased prey availability, etc.). The high light intensity environments associated with fragmentation could make habitats physiologically unsuitable for understory insectivores (e.g. Robinson and

Sherry 2012), or could limit movements between habitats (e.g. Gillies and St. Clair 2010, Powell et al. 2015a), both of which could contribute to the population declines of understory insectivores in response to forest fragmentation. Given the increasing rates of deforestation and fragmentation in the Neotropics (Achard et al. 2002), understanding the mechanism of sensitivity to light in understory insectivores should be made a research priority in Neotropical avian conservation.

Despite the lack of observed microclimate selectivity across species, sites, and seasons, there were several limitations to my sampling design that could be improved upon in future research efforts. One drawback of my study is that I were only able to sample larger-bodied insectivore species (range: 17-46 g) due to time constraints and equipment limitations. Many understory insectivore species (e.g. Dot-winged Antwren (Microrhopias quixensis; 9 g), White- flanked Antwren (Myrmotherula axillaris; 8 g), Golden-crowned Spadebill (Playtrinchus coronatus; 9 g), Ruddy-tailed Flycatcher (Terenotriccus erythrurus; 7 g) were too small to attach radio-transmitters without affecting behavior and mobility (H.S. Pollock, personal observation).

These smaller-bodied species are especially vulnerable to forest fragmentation (Sigel et al.

2006, Sigel et al. 2010), possibly due to increased sensitivity to local environmental variation, and may exhibit microclimate selectivity, in contrast to their larger-bodied counterparts. Future studies should expand sampling to include smaller-bodied species to account for the possibility of microclimate selectivity occurring in this subset of understory insectivores.

The spatial and temporal extent of my sampling efforts was also quite limited.

Temporally, my data were collected in only two sampling years (2012-2013), both of which were characterized by mild dry seasons (Panama Canal Authority, unpublished data), and it is possible that I did not observe microclimate selectivity because it is only manifested during periods of severe environmental stress. The abundance of cool, dark, humid microclimates in the understory of closed-canopy forest may act as a buffer for understory insectivores and preclude their need for microclimate selectivity except under extreme environmental conditions. Intensity and length of the dry season varies substantially from year to year and can have strong effects on population dynamics (Sillett et al. 2000, Williams and Middleton 2008) and behavior (W.A. Boyle, personal communication) of tropical birds. It would be informative to examine microclimate use on a longer temporal scale to determine whether or not understory insectivores exhibit selectivity in drier years.

My spatial scale was also restricted to comparing bird microclimate points with randomly selected points within each bird’s home-range, and therefore, I did not quantify all of the microclimates “available” to each bird (i.e. microclimates outside of the bird’s home-range, or even microclimates within the bird’s home-range that it does not use, such as gaps).

Therefore, I were unable to compare microclimates used by birds with all microclimates

“available” to birds (sensu Jones 2001), and birds may actually be exhibiting microclimate selectivity on a larger spatial scale (i.e. between home-ranges) or even within their home- ranges by avoiding gaps. Future research should attempt to quantify all microclimates available to birds to determine whether understory insectivorous birds exhibit selectivity on a larger spatial scale or via gap avoidance.

Alternatively, understory insectivores may exhibit sensitivity to environmental variation on a geographic scale, coping with ambient environmental conditions until an environmental threshold is reached, which limits species’ distributions. Although within-site microclimate variation was low at any given time, variation among sites was substantial. For example, the driest site, Metropolitano, routinely experienced temperature ranges 4 °C greater and humidity ranges 20% greater than the wettest site, Limbo. Many species present at Limbo and Gamboa were either present in very low abundances or completely absent from the dry forest site,

Metropolitano (e.g. C. phaeocephalus, H. perspicillatus, S. guatemalensis, H. naevioides, M. exsul). Sensitivity to microclimate variation could explain the declines of these species across the precipitation gradient and their absence from the drier and hotter Metropolitano. Thus, despite the lack of microclimate selectivity within habitats, environmental variation on a larger spatial scale may limit the distribution of understory insectivores (Rompre et al. 2007), though the mechanism (e.g. food limitation, direct sensitivity to climate) remains unclear (Williams and

Middleton 2008, Busch et al. 2011, Robinson and Sherry 2012).

The lack of environmental variation in tropical forest understory is troubling in light of recent climate change. If environmental conditions exceed the physiological tolerances of understory insectivores, they will not be able to rely on local microclimatic heterogeneity to escape physiological stress (e.g. Walsberg 1993) and may experience severe fitness costs.

Nonetheless, vagile animals such as birds can also take advantage of large-scale spatial heterogeneity (i.e. between habitat patches) in environmental conditions to mitigate the effects of climate change (Bonebrake and Deutsch 2012). However, the lowland tropics are characterized by shallow latitudinal temperature gradients, meaning that many understory

79 insectivorous birds may have to travel large distances to find suitable habitat in response to increasing temperatures (Wright et al. 2009). Finding thermal refugia will be exacerbated by increasing habitat fragmentation (Achard et al. 2002) coupled with the low dispersal abilities of understory insectivores (Moore et al. 2008, Woltmann et al. 2012a) and their avoidance of habitats characterized by high light intensity (Schemske and Brokaw 1981, Patten and Smith-

Patten 2012). The potential synergistic effects of climate change with habitat fragmentation have received little attention in the literature and will be an important aspect of predicting the impacts of anthropogenic change on tropical bird populations. In addition, tropical organisms in general are predicted to have narrow physiological tolerances (Janzen 1967) and low physiological flexibility (Bozinovic et al. 2011), yet the physiological tolerances of tropical understory insectivores are not known (Robinson and Sherry 2012). Direct measurements of the physiological tolerances to temperature and humidity of understory insectivores and further investigation of the mechanism of light avoidance will be a crucial first step towards predicting responses to climate change and forest fragmentation in these species.

FIGURES AND TABLES

BMR

Metabolic rate (watts) rate Metabolic TNZ breadth

LCT UCT

Ambient temperature (°C)

Fig. 1.1. Schematic of the endothermic thermoneutral zone (TNZ). Within the TNZ, an endotherm is able to maintain a basal metabolic rate and the metabolic costs of thermoregulation are minimal. At ambient temperatures below the lower critical temperature (LCT) or above the upper critical temperature (UCT), an endotherm must expend energy to maintain internal temperature homeostasis, thereby increasing metabolic rate at a linear rate.

Tbmax

BMR

Body temperature (°C) temperature Body Metabolic rate (watts) rate Metabolic TNZ breadth

LCT UCT CT max

Ambient temperature (°C)

Fig. 1.2. Schematic of an endothermic thermal performance curve. Within the thermoneutral zone (TNZ), an endotherm is able to maintain a basal metabolic rate (BMR) and the metabolic costs of thermoregulation are minimal. However, at ambient temperatures below the lower critical temperature (LCT) or above the upper critical temperature (UCT), an endotherm must expend energetic resources to maintain internal temperature homeostasis. The solid and dashed black lines represent a high and low heat-strain coefficient (HScoeff, i.e. the slope of the relationship between body temperature and ambient temperature; modified from Weathers

1981), respectively. Theoretically, organisms with a high HScoeff (solid line) should accumulate endogenous heat load more rapidly and reach their critical thermal maximum (CTmax) at a lower ambient temperature than organisms with a low HScoeff (dashed line).

Fig. 2.1. A phylogeny for all species sampled in this study, pruned from a maximum-likelihood tree of all avian species with genetic data (Burleigh et al. 2015). This tree was pruned further to include the species sets for the separate LCT, UCT, and TNZ analyses.

a) b)

Fig. 2.2. Bivariate linear regressions of body mass (Mb) on a) lower critical temperature (LCT), b) upper critical temperature (UCT) and c) thermoneutral zone (TNZ) breadth of focal species from Illinois (open circles), South Carolina (closed triangles), and Panama (open squares).

a) b)

Fig. 2.3. Box plots of a) LCT, b) UCT and c) TNZ breadth of tropical resident, migratory, and temperate resident focal species. *p < 0.05; **p < 0.01; ***p < 0.0001.

a) b)

Fig. 2.4. Box plots of a) LCT, b) UCT and c) TNZ breadth based on habitat association of focal species measured within each geographic locality.

a) b)

Fig. 2.5. Box plots of a) LCT, b) UCT and c) TNZ breadth based on vertical niche of focal species measured within each geographic locality.

Fig. 2.6. Daily ambient temperature range during the 2014 sampling period in Illinois (blue) and South Carolina (gray). Dashed lines indicate mean temperature range for each locality.

Fig. 2.7. Intraspecific variation in thermoneutral zone breadths of species sampled in both Illinois and South Carolina.

Focal species (Scientific name) Substitute species (Scientific name) Song Wren (Cyphorhinus phaeocephalus) Musician Wren (Cyphorhinus arada) Broad-billed Motmot (Electron platyrhynchum) Tody Motmot (Hylomanes momotula) Streak-chested Antpitta (Hylopezus White-lored Antpitta (Hylopezus perspicillatus) fulviventris) White-whiskered Puffbird (Malacoptila Semicollared Puffbird (Malacoptila panamensis) semicincta) Red-crowned Woodpecker (Melanerpes Golden-fronted Woodpecker (Melanerpes rubricapillus) aurifrons) Southern Bentbill (Oncostoma olivaceum) Northern Bentbill (Oncostoma cinereiregulare) Golden-fronted Greenlet (Pachysylvia Tawny-crowned Greenlet (Tunchiornis aurantiifrons) ochraceiceps) Long-billed Gnatwren (Ramphocaenus Tawny-faced Gnatwren (Microbates melanurus) cinereiventris)

Table 2.1. Eight focal species lacking genetic sequence data and thus missing from the phylogeny in Burleigh et al. (2015), and closely related substitute species used to place them in a phylogenetic tree for PGLS analysis.

a) Lower critical temperature 2 Locality λ F-value (df1,df2) R p-value Illinois 0.99 19.24 (1,25) 0.43 0.0002 Panama 0.00 2.85 (1,69) 0.04 0.10 South Carolina 0.64 4.30 (1,23) 0.12 0.05 All 0.49 9.12 (1,110) 0.08 0.003

b) Upper critical temperature 2 Locality λ F-value (df1,df2) R p-value Illinois 0.00 1.04 (1,24) 0.04 0.32 Panama 0.36 0.001 (1,66) <0.0001 0.98 South Carolina 0.00 0.24 (1,26) 0.01 0.63 All 0.00 0.57 (1,107) 0.01 0.45

c) Thermoneutral zone breadth 2 Locality λ F-value (df1,df2) R p-value Illinois 0.77 6.58 (1,22) 0.23 0.02 Panama 0.33 0.24 (1,51) 0.004 0.62 South Carolina 0.96 8.88 (1,19) 0.32 0.008 All 0.55 4.83 (1,86) 0.05 0.03

Table 2.2. Results of PGLS regressions between Mb and a) LCT, b) UCT and c) TNZ breadth within each locality and for all localities combined. Bold font indicates significant p-values (α = 0.05).

LCT UCT TNZ Var. λ AIC Δ R2 λ AIC Δ R2 λ AIC Δ R2 TRmed 0.42 449.85 0.00 0.41 0.30 348.84 0.00 0.06 0.65 386.59 0.00 0.39 TRabs 0.41 453.57 3.72 0.39 0.29 348.98 0.14 0.06 0.61 389.29 2.70 0.37 Lat. 0.41 457.14 7.29 0.37 0.28 349.23 0.39 0.06 0.59 391.99 5.40 0.35 Tmax 0.38 455.93 6.08 0.38 0.29 350.03 1.19 0.05 0.59 393.01 6.42 0.34 Tmin 0.40 459.11 9.26 0.36 0.27 349.39 0.55 0.06 0.57 393.48 6.89 0.34 Tμmin 0.40 462.77 12.92 0.34 0.27 349.72 0.88 0.05 0.55 396.23 9.64 0.32 Tmed 0.41 482.98 33.13 0.21 0.00 353.00 4.16 0.03 0.52 411.17 24.58 0.19 Tμmax 0.49 499.86 50.04 0.08 0.00 355.95 7.14 0.01 0.55 424.96 38.37 0.06

Table 2.3. Results of a representative iteration of PGLS regression testing for associations between temperature variables and lower critical temperature (LCT), upper critical temperature (UCT) and thermoneutral zone (TNZ) of focal species. The phylogenetic signal (λ), AIC score (AIC), delta AIC (Δ), model R2 are listed for each temperature variable. Top models indicated in bold.

Model λ F-value (df1,df2) p-value LCT ~ migratory tendency + Mb 0.44 28.61 (3,108) <0.0001 UCT ~ migratory tendency + Mb 0.25 4.11 (3,105) 0.008 TNZ breadth ~ migratory tendency + Mb 0.59 16.21 (3,84) <0.0001

Table 2.4. Results of a representative iteration of PGLS regression testing for associations between migratory tendency and lower critical temperature (LCT), upper critical temperature (UCT) and thermoneutral zone (TNZ) breadth of focal species. The model structure, phylogenetic signal (λ), F-value, and p-value are listed for each model. Bold font indicates significant models (α = 0.05).

Table 2.5. Results of mixed model analysis testing for intraspecific variation in each trait (lower critical temperature (LCT), upper critical temperature (UCT), thermoneutral zone (TNZ) breadth) by comparing full models with locality included as a fixed effect to null models including only species as a random effect. Model structure, degrees of freedom (df), AIC score (AIC), ΔAIC score (ΔAIC) and p-value (p) are listed. Bold font indicates a significant difference between the full model and the null model (α = 0.05).

Trait Locality Trait value (°C) (°C) n p-value Illinois 23.41 LCT 1.08 10 0.04 South Carolina 22.33 Illinois 35.71 UCT 0.70 13 0.04 South Carolina 36.41 Illinois 12.42 TNZ 1.93 10 0.0003 South Carolina 14.35

Table 2.6. Intraspecific variation in lower critical temperature (LCT), upper critical temperature (UCT) and breadth of the thermoneutral zone (TNZ) between populations of birds measured in Illinois and South Carolina. represents the mean difference in trait value between localities and n is the sample size (number of species). Bold font indicates significant p-values (α = 0.05).

Fig. 3.1. Seasonal variation in daily ambient temperature regime during the sampling period in the a) cold season (December-February) and b) warm season (May-August) at the temperate (blue) and tropical (red) sampling localities. Ambient temperature data derived from weather stations (National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information’s (NCEI) Climate Data Online (https://www.ncdc.noaa.gov/cdo- web/). Tmax (maximum ambient temperature) represented by filled circles. Tmin (minimum ambient temperature) represented by open circles.

a) b)

Fig. 3.2. Metabolic scaling of tropical (black circles) and temperate (white squares) bird species in the a) summer and b) winter.

Fig. 3.3. A phylogeny for all species sampled in this study, pruned from a maximum-likelihood tree of all avian species with genetic data (Burleigh et al. 2015). This tree was pruned further to include the species sets for each of the five separate thermoregulatory traits.

W/S ratio W/S

Fig. 3.4. Box plots of winter/summer (W/S) ratios of a) Mb, b) mass-adjusted BMR, c) LCT, d) UCT and e) TNZ breadth in temperate (white squares) and tropical (black circles) species. Dashed line indicates a W/S ratio of 1, i.e. no seasonal change. * p < 0.05. *** p < 0.0001.

Fig. 3.5. Seasonal flexibility in a) LCT (n = 30 species), b) UCT (n = 18 species) and c) TNZ breadth (n = 15 species) of temperate (white squares) and tropical (black circles) species.

100

a) b)

Fig. 3.6. Linear regressions of a) ΔLCT and b) ΔUCT on ΔTNZ breadth in temperate (white squares; n = 6) and tropical (black circles; n = 9) species.

101

Trait LCT UCT TNZ breadth

Season Warm Cold Warm Cold Warm Cold

p-value 0.29 0.99 0.1 0.48 0.06 0.59

Table 3.1. Associations between Mb and LCT, UCT and TNZ breadth across seasons. Bold font indicates significant p-values (α = 0.05).

102

Thermoregulatory Mb Mass-adjusted LCT UCT TNZ breadth trait BMR

Season Warm Cold Warm Cold Warm Cold Warm Cold Warm Cold

p-value 0.38 0.43 0.82 0.41 0.97 0.91 0.09 0.43 0.23 0.18

Table 3.2. Results of Levene’s tests for homogeneity of variances between temperate and tropical species for each of the five thermoregulatory traits within each season. Bold font indicates significant p-values (α = 0.05).

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Trait λ AICc ΔAICc Wt. LL

ΔLCT 0.00 44.70 0.00 1.0 -19.81

ΔUCT 0.00 68.74 24.04 0.0 -31.83

Table 3.3. Associations between ΔTNZ breadth and a) ΔLCT and b) ΔUCT as determined by PGLS regression using the Akaike information criterion corrected for small sample sizes (AICc) to rank each model. The phylogenetic signal (λ), corrected AIC scores (AICc), ΔAICc, model weights (Wt.) and log-likelihood scores (LL) are listed for each model. Top model fit indicated in bold.

104

Fig. 4.1. Mean daily maximum ambient temperature (Tmax) during the sampling period at temperate (gray) and tropical (red) sampling localities. Ambient temperature data were derived from weather stations (National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information’s (NCEI) Climate Data Online (https://www.ncdc.noaa.gov/cdo-web/).

105

Fig. 4.2. A phylogeny for all species sampled in this study, pruned from a maximum-likelihood tree of all avian species with genetic data (Burleigh et al. 2015). This tree was pruned further to include the species sets for each of the three traits (UCT, heat-strain coefficient, CTmax) comprising thermal sensitivity.

106

a) b)

Fig. 4.3. Least-squares linear regressions of Mb on a) UCT, b) HScoeff, and c) CTmax for temperate (white squares, dotted line) and tropical (black circles, solid line) focal species.

107

a) b)

Fig. 4.4. Box plots of a) upper critical temperature (UCT), b) critical thermal maximum (CTmax) and c) heat-strain coefficient of tropical (black circles) and temperate (white squares) focal species. * indicates a significant p-value (α = 0.05).

108

a) b)

Fig. 4.5. a) Thermal safety margins (TSM) and b) warming tolerances (WT) of tropical (black circles) and temperate (white squares) focal species. * indicates a significant p-value (α = 0.05).

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t-value p-value Model λ latitude Mb latitude:Mb latitude Mb latitude:Mb

UCT ~ latitude*Mb 0.52 -0.003 0.70 2.33 0.99 0.48 0.02

HScoeff ~ latitude*Mb 0.77 0.02 -0.52 -1.20 0.98 0.61 0.24

CTmax ~ latitude*Mb 0.83 2.64 0.57 1.39 0.01 0.57 0.17

TSM ~ latitude*Mb 0.52 -2.05 0.70 2.33 0.05 0.48 0.02

WT ~ latitude*Mb 0.83 2.69 0.57 1.39 0.65 0.57 0.17

Table 4.1. PGLS regression model outputs for upper critical temperature (UCT), heat-strain coefficient (HScoeff), critical thermal maximum (CTmax), thermal safety margin (TSM), and warming tolerance (WT). Model terms include latitude, Mb and their interaction. Bold font indicates a significant p-value (α = 0.05).

110

t-value p-value Model λ latitude Mb latitude:Mb latitude Mb latitude:Mb

UCT ~ latitude*Mb 0.52 0.14 0.79 1.57 0.89 0.44 0.12 HScoeff ~ latitude*Mb 1.0 1.15 2.07 -2.87 0.26 0.05 0.01 CTmax ~ latitude*Mb 1.0 2.05 -1.45 1.99 0.05 0.16 0.06

TSM ~ latitude*Mb 0.52 -1.78 0.79 1.57 0.08 0.44 0.12 WT ~ latitude*Mb 1.0 -0.39 -1.45 1.99 0.70 0.16 0.06

Table 4.2. PGLS regression model outputs for upper critical temperature (UCT), heat-strain coefficient (HScoeff), critical thermal maximum (CTmax), thermal safety margin (TSM), and warming tolerance (WT) for all species with n ≥ 3 individuals. Model terms include latitude, Mb and their interaction. Bold font indicates a significant p-value (α = 0.05).

111

Fig. 5.1. Map of the study sites across the isthmus of Panama; numbers indicate annual rainfall isohyets in millimeters (adapted with permission from www.hidromet.com.pa).

112

Dry Wet 32 32

30 30

C) 28 28

26 26

24 24

Temperature ( Temperature

Temperature ( Temperature 22 22

20 20

6:00 7:00 8:00 9:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Location Time of Day Time of Day Limbo Plot

Gamboa Woodlot Dry Wet Metropolitano 100 100

90 90

80 80

70 70

RelativeHumidity(%)

60 60

6:00 7:00 8:00 9:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time of Day Time of Day

Fig. 5.2. Hourly diurnal variation in temperature and humidity by season collected at the study sites between 2012-2013 (mean±SE). Data include bird microhabitat points and random microhabitat points and represent a characterization of the understory abiotic environment at each site. No wet season data were collected in Metropolitano.

113

A. aurantiirostris C. phaeocephalus H. fuscicauda

0.3

0.2

0.1

0.0 H. naevioides H. perspicillatus M. exsul

0.3

Point Type 0.2 Microhabitat Random 0.1

ProbabilityDensity 0.0 M. longipes S. guatemalensis T. rufalbus

0.3

0.2

0.1

0.0 20 25 30 35 40 20 25 30 35 40 20 25 30 35 40 Temperature ( C)

Fig. 5.3. Probability density functions of ambient temperature at microhabitat and random points of focal species (data for each species pooled across sites).

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A. aurantiirostris C. phaeocephalus H. fuscicauda 0.4

0.3

0.2

0.1

0.0 H. naevioides H. perspicillatus M. exsul 0.4

0.3 Point Type

0.2 Microhabitat Random 0.1

ProbabilityDensity 0.0 M. longipes S. guatemalensis T. rufalbus 0.4

0.3

0.2

0.1

0.0 60 70 80 90 100 60 70 80 90 100 60 70 80 90 100 Relative Humidity (%)

Fig. 5.4. Probability density functions of relative humidity at microhabitat and random points of focal species (data for each species pooled across sites).

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A. aurantiirostris C. phaeocephalus H. fuscicauda 1.00

0.75

0.50

0.25

0.00 H. naevioides H. perspicillatus M. exsul 1.00

0.75 Point Type

0.50 Microhabitat Random 0.25

ProbabilityDensity 0.00 M. longipes S. guatemalensis T. rufalbus 1.00

0.75

0.50

0.25

0.00 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 Light Intensity (Klux)

Fig. 5.5. Probability density functions of light intensity at microhabitat and random points of focal species (data for each species pooled across sites).

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Site GPS coordinates Annual rainfall Forest age Canopy (mm) (years) height (m) Wet (Limbo) 9° 9’ N, 79° 44’ W 2600 60-120 30-40

Mesic (Gamboa) 9° 7’ N, 79° 41’ W 2100 60-70 20-30

Dry 8° 59’ N, 79° 33’ W 1800 90 20-35 (Metropolitano)

Table 5.1. Characteristics of the three study sites (data from Robinson et al. 2000, Van Bael et al. 2004).

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Species Family Mass Habitat Sensitivity to nWet nMesic nDry (g) disturbance Sclerurus Furnariidae 34 Primary High 5 - - guatemalensis forest Myrmeciza Thamnophilidae 28 Primary Low 4 2 1 exsul forest Myrmeciza Thamnophilidae 28 Secondary Low - 8 2 longipes forest Hylophylax Thamnophilidae 17 Primary Moderate 4 - - naevioides forest Hylopezus Grallariidae 46 Primary High 5 - - perspicillatus forest Thryophilus Troglodytidae 28 Secondary Low - 2 5 rufalbus forest Cyphorhinus Troglodytidae 25 Primary High 8 6 - phaeocephalus forest Habia Cardinalidae 42 Secondary Low - 2 2 fuscicauda forest Arremon Emberizidae 34 Secondary Low - 1 1 aurantiirostris forest Total 26 21 11

Table 5.2. Characteristics of study species and samples sizes of radio-tracked individuals at the wet (Limbo Plot), mesic (Gamboa Woodlot) and dry (Metropolitano) sites (data from Parker et al. 1996, Woltmann et al. 2012b).

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